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Influence of mould design on the Eco-efficiency of the Injection Moulding Industry
João Luís de Matos Eliseu
Thesis to obtain the Master of Science Degree in
Mechanical Engineering
Supervisors: Prof. Inês Esteves Ribeiro
Prof. Paulo Miguel Nogueira Peças
Examination committee
Chairperson: Prof. Rui Manuel dos Santos Oliveira Baptista
Supervisor: Prof. Paulo Miguel Nogueira Peças
Members of the Committee: Prof. Elsa Maria Pires Henriques
Eng. Eduardo João de Almeida e Silva
November 2015
i
Resumo
A Sustentabilidade é um tema que se está a tornar cada vez mais popular entre a sociedade, empresas
e a comunidade científica. Entre as várias ferramentas e conceitos que foram desenvolvidos com a
finalidade de implementar estratégias sustentáveis, o conceito de Eco-eficiência emergiu recentemente.
Ao relacionar o valor de um produto/serviço com a respectiva influência ambiental, este novo conceito
tem o objectivo de criar mais valor, diminuindo os impactos ambientais gerados.
Este conceito já foi aplicado na indústria de injecção de plástico através da criação de perfis de eco-
eficiência que incluem longas listas de indicadores, com o objectivo de auxiliar a indústria nos processos
de decisão.
Tendo como base as normas recomendadas, esta tese utiliza estes perfis com o objectivo de
desenvolver rácios de eco-eficiência que possam ser utilizados para comparar diferentes tipos de
designs de moldes de injecção de plástico em certas áreas de interesse para o processo de produção
de moldes e para a sua respectiva fase de uso.
Um caso de estudo representativo da indústria da injecção de plástico será aplicado a estes rácios de
eco-eficiência para inferir acerca da sua utilidade na comparação de diferentes moldes nestas áreas
de interesse.
Finalmente, após a selecção dos rácios cuja sua utilidade para a indústria foi comprovada, serão
propostas redes de rácios de eco-eficiência com o intuito de auxiliar as empresas de produção de
moldes em processos de decisão do foro interno e na comunicação externa para possíveis utilizadores
de moldes (empresas de injecção de plástico).
Palavras-chave: Sustentabilidade; Eco-eficiência; Indústria de injecção de plástico; Tomada de
decisão.
ii
Abstract
Sustainability is a subject that is becoming more and more popular nowadays among the society,
companies and the research community. Among the various tools and concepts that were developed to
apply sustainable strategies, the concept of Eco-efficiency recently emerged. By relating a
product/service value to the environmental influence that it generated, this relatively new concept has
the scope of creating more value while generating lower environmental impact.
This concept was already applied to the injection moulding industry where eco-efficiency profiles
featuring long lists of indicators were proposed, in order to aid decision making in the injection moulding
business.
By following the recommended norms and guidelines, this thesis will use these suggested profiles, with
the objective of developing eco-efficiency ratios that can be used to compare different injection mould
designs in certain areas of interest that relate to the mould production and mould use phases.
A case study representative of the injection moulding industry will be applied to these eco-efficiency
ratios in order to infer if they are in fact useful to compare the different moulds in these areas of interest.
Finally, with the useful ratios selected from this analysis frameworks of eco-efficiency ratios will be
proposed to aid mould producing companies in internal decision making and external communication
for possible mould users (injection moulding companies).
Key-words: Sustainability; Eco-efficiency; Injection moulding industry; Decision making.
iii
Acknowledgments
It is strange to realize only a few steps remain to finish my academic life and start new challenges. For
sure I would never arrive at this stage on my own and there are so many people that I am thankful for
in the past few years.
I would like to start of course, by thanking the great guidance and fundamental help provided by my two
supervisors, Prof. Inês and Prof. Paulo, who always kept me on my toes and focused on this thesis.
This thesis would not be possible to do without the support of my parents, who apart from all the normal
boring parenting moments, know what they are doing. They deserve the best and I owe them everything.
Same goes to my grandparents who helped me to raise me the way I am.
To my big sister Ana who was far this last year, but always very present and to my little brother António
who is turning into a man (or not) every day that goes by! Still on the subject of family, of course my
cousin Pedro (or should I call him brother) deserves to be mentioned.
Also, I would like to thank so many friends that I do not know where to start… Maybe I should begin by
my Alentejo related ones. I thank my all-time friends Miguel Albano, Paula, “Padrinho & Madrinha”,
Tiago, Sarah, Graça, Carrilho, Pisco, Daniel, Esteves, Cruz and Gonçalo.
A special mention to the best couple in the world “Sapo” and “Di” and to Rodrigo! Looking forward to
add a new member to the Benfica family!
To the awesome people I have met in university and during Erasmus, namely Marçal, Miguel, Agostinho,
Paulo, Leonardo, Sandro, Raquel and Teresa, who are the best friends I could ever ask for.
Now and because friends have no borders, I would like to thank my favourite sisters Lilit and Maria
Tonoyan and my great friends Anna and Marco, for your friendship and the awesome moments spent
together! On the same subject and because it is impossible not to connect these people, I want to thank
Jérôme Foulon and his fiancée Nataly! Cannot wait to return to Moscow next year to celebrate!
A last and very important mention to my brothers and sisters in arms Rita, Sebastião, Mariana, Maria
and Ana!
It is finally done!
iv
Table of contents Resumo .................................................................................................................................................... ii
Abstract.................................................................................................................................................... iii
Acknowledgments ................................................................................................................................... iv
List of Figures ......................................................................................................................................... vii
List of Tables ............................................................................................................................................ ix
Nomenclature .......................................................................................................................................... xi
1. Introduction ...................................................................................................................................... 1
2. Eco-efficiency: the philosophy, the principles and its challenges .................................................... 3
2.1. Historical facts ....................................................................................................................... 3
2.2. The philosophy behind eco-efficiency ................................................................................... 3
2.3. The seven principles of eco-efficiency .................................................................................. 5
2.4. Types of indicators and their utility ........................................................................................ 6
2.5. Methodologies of application of eco-efficiency ...................................................................... 8 2.5.1. Goal & Scope Definition.......................................................................................... 10 2.5.2. Environmental Assessment (LCIA) ......................................................................... 10 2.5.3. Product System Value Assessment ....................................................................... 12
2.6. Areas and studies of eco-efficiency’s applicability .............................................................. 13
2.7. Motivation and present challenges faced by eco-efficiency ................................................ 14
3. Plastic injection moulding process: from the mould to the part ..................................................... 14
3.1. The process of plastic injection moulding ............................................................................ 14
3.2. The injection mould ............................................................................................................. 17 3.2.1. The components of a mould and its design variations ........................................... 17
3.3. Phases of a typical mould production process .................................................................... 20
3.4. Studies on environmental and economic performance of injection moulding ..................... 20
4. Methodology .................................................................................................................................. 23
5. Case study: Connector for electronic industry ............................................................................... 25
5.1. Description of the case study .............................................................................................. 25
5.2. Development of the process based model (PBM) ............................................................... 27 5.2.1. Mould production process....................................................................................... 27 5.2.2. Mould use phase .................................................................................................... 29
5.3. Development of the process based cost model (PBCM) .................................................... 31
v
5.3.1. Mould production process....................................................................................... 31 5.3.2. Mould use phase .................................................................................................... 33
5.4. Mould reliability .................................................................................................................... 34
5.5. End of Life (EoL) .................................................................................................................. 36
5.6. Environmental impact evaluation ........................................................................................ 37 5.6.1. Mould production process....................................................................................... 37 5.6.2. Mould use phase .................................................................................................... 38
5.7. Results for the mould production process ........................................................................... 39
5.8. Results for mould use phase ............................................................................................... 43
5.9. Conclusions ......................................................................................................................... 49
6. Eco-efficiency assessment of the case study ................................................................................ 49
6.1. Development of ratios and relations for mould production phase ....................................... 52 6.1.1. Value added ............................................................................................................ 52 6.1.2. Productivity ............................................................................................................. 59 6.1.3. Waste generated .................................................................................................... 62
6.2. Development of ratios and relations for the mould use phase ............................................ 63 6.2.1. Value added ............................................................................................................ 63 6.2.2. Productivity ............................................................................................................. 66 6.2.3. Waste generated .................................................................................................... 69
7. Frameworks of eco-efficiency ratios and relations to aid in decision making ................................ 70
7.1. Internal decision making for mould producing companies .................................................. 70
7.2. External communication for potential clients ....................................................................... 72
8. Conclusions ................................................................................................................................... 73
9. Future work .................................................................................................................................... 76
10. References ..................................................................................................................................... 77
Annexes ................................................................................................................................................... A
A1 – Exogenous Variables ............................................................................................................... A
A2 – Material parameters ................................................................................................................ A
A3 – Maintenance and Downtime data ............................................................................................ B
A4 – Process Variables .................................................................................................................... C
A5 – Mould reliability (Failure data) ................................................................................................. D
A6 – Mould Data .............................................................................................................................. E
vi
List of Figures
Figure 1 – Phases of an eco-efficiency assessment recommended by ISO 14045:2012 [3, 5]. ............ 9
Figure 2 - Relationship between LCI parameters (left), midpoint indicator (middle) and endpoint
categories (right), in ReCiPe [15]. .......................................................................................................... 11
Figure 3 – Flowchart of a PBCM [21]. ................................................................................................... 13
Figure 4 – Components needed for the injection moulding process [34]. ............................................. 16
Figure 5 – Example of a mould used in the injection moulding industry [36]. ....................................... 18
Figure 6 – Mould with cold runner system (left) vs Mould with hot runner system (right) [37]. ............. 19
Figure 7 – Methodology followed throughout this thesis. ...................................................................... 24
Figure 8 – Connector for electronic industry with runners (4 examples in different positions) [23]. ..... 27
Figure 9 – Line utilization for a working day [17]. .................................................................................. 28
Figure 10 - Steel consumption (left), energy consumption (right) and steel scrap generated (waste)
(bottom), for mould production. ............................................................................................................. 40
Figure 11 – Total costs for mould production. ........................................................................................ 41
Figure 12 – Steel consumption EI (top left) with respective detail graphic (top right), energy consumption
EI (bottom left) and steel scrap (recycled) EI (bottom right), for mould production. .............................. 42
Figure 13 – Total EI generated by the mould production process. ........................................................ 43
Figure 14 – Plastic used per part (left), energy consumption per part (right) and plastic waste generated
per part (bottom), for mould use phase. ................................................................................................ 44
Figure 15 – Total costs for the three scenario conditions. ..................................................................... 45
Figure 16 – Cost per part for the three production scenarios, for the mould use phase. ...................... 47
Figure 17 – Plastic used EI per part (left), energy consumption EI per part (right) and plastic waste
generated EI per part (bottom), for mould use phase. .......................................................................... 48
Figure 18 - Total EI per part, for mould use phase. ............................................................................... 49
Figure 19 – Graphic for eco-efficiency ratio Mould EBITDA/Mould production EI. ............................... 54
Figure 20 - Graphic for eco-efficiency ratio Mould sale potential/Mould production EI. ........................ 59
Figure 21 – Graphic for eco-efficiency ratio Rate of steel removed/Mould production EI. .................... 60
Figure 22 – Graphic for relation Total mould production time/Mould production EI. ............................. 61
Figure 23 – Graphic for relation Quantity of waste (steel)/Mould production EI. .................................. 62
Figure 24 – Graphic for eco-efficiency ratio Part EBITDA/Part production EI. ...................................... 65
Figure 25 – Graphic for ratio Quantity of useful plastic per cycle/Part production EI. .......................... 67
vii
Figure 26 – Graphic for relation Cycle time/Part production EI. ............................................................ 68
Figure 27 – Graphic for relation Plastic waste generated/Part production EI. ...................................... 69
viii
List of Tables
Table 1 – The seven principles of Eco-efficiency [9]. .............................................................................. 5
Table 2 – Generally applicable indicators [9]. .......................................................................................... 6
Table 3 – The categories and respective aspects featured in WBCSD methodology [9]. ....................... 8
Table 4 – Weights for categories and perspectives for Eco-Indicator 99 [10, 17]. ................................ 12
Table 5 – Hot runners system vs. Cold runner system. ......................................................................... 19
Table 6 - Mould alternatives used in the case study, based on [23]. ..................................................... 26
Table 7 - Properties of the part used in the case study [23]. ................................................................. 26
Table 8 – Inputs and outputs of the PBM for the mould production process. ........................................ 27
Table 9 - Inputs and outputs of the PBM for the mould use phase. ...................................................... 29
Table 10 – Variable costs for mould production. .................................................................................... 31
Table 11 - Fixed costs for mould production. ......................................................................................... 32
Table 12 – Financial model for injection moulding. ............................................................................... 33
Table 13 – ReCiPe mPt/unit of resource for mould production. ............................................................ 38
Table 14 - ReCiPe mPt/unit of resource for mould use phase. ............................................................. 39
Table 15 – Production scenarios tested for mould use phase. .............................................................. 45
Table 16 – Eco-efficiency ratio Mould GVA/Mould production EI. ......................................................... 52
Table 17 - Eco-efficiency ratio Mould EBITDA/Mould production EI. .................................................... 53
Table 18 - Eco-efficiency ratio Mould EBITDA/EI milling. ...................................................................... 55
Table 19 - Eco-efficiency ratio Mould EBITDA/EI EDM. ........................................................................ 55
Table 20 – Weighing process for definition of the value added indicator “Mould sale value” ............... 57
Table 21 - Rating process for definition of the value added indicator “Mould sale value” ..................... 57
Table 22 - Indexing process for definition of the value added indicator “Mould sale value” .................. 58
Table 23 - Eco-efficiency ratio Mould sale potential/Mould production EI. ............................................ 58
Table 24 – Eco-efficiency ratio Rate of steel removed/Mould production EI. ........................................ 60
Table 25 - Eco-efficiency ratio Part EBITDA/Part production EI. ........................................................... 64
Table 26 - Eco-efficiency ratio Part EBITDA/Mould production EI. ....................................................... 66
Table 27 – Ratio for Quantity of useful plastic per cycle/Part production EI. ........................................ 67
Table 28 - Framework of eco-efficiency ratios and relations for internal decision making. ................... 71
Table 29 - Framework of eco-efficiency ratios and relations for external communication..................... 72
ix
Table 30 – A1, Exogenous Variables ....................................................................................................... A
Table 31 – A2, Material parameters ......................................................................................................... A
Table 32 – A3, Maintenance data ............................................................................................................ B
Table 33 – A3, Downtime data ................................................................................................................. B
Table 34 – A4, Process Variables ............................................................................................................ C
Table 35 – A5, Weibull parameters .......................................................................................................... D
Table 36 – A5, Cost per element ............................................................................................................. D
Table 37 – A6, Mould production subcontracts ....................................................................................... E
Table 38 – A6, Mould production bought components ............................................................................ E
Table 39 – A6, Mould dimensions and volumes ...................................................................................... F
x
Nomenclature
EE Eco-efficiency
CFM Carbon Footprint Method
GHG Greenhouse Gas
ODS Ozone Depleting Substance
GVA Gross Value Added
EBITDA Earnings Before Interest Taxes Depreciation and Amortization
KEPI Key Environmental Performance Indicator
ER Eco-efficiency Ratio
EPI Eco-efficiency Performance Indicator
EI Environmental Impact
TCO Total Cost of Ownership
LCC Life Cycle Cost
LCIA Life Cycle Impact Assessment
LCA Life Cycle Assessment
LCI Life Cycle Inventory
PMB Process Based Model
PBCM Process Based Cost Model
LCE Life Cycle Engineering
CAD Computer Assisted Design
CNC Computer Numerical Control
EDM Electrical Discharge Machining
CR Cold Runners
HR Hot Runners
SC Separate cavities
MB Cavities machined in block
xi
1. Introduction
Sustainability is a subject that is becoming more and more popular nowadays among the society,
companies and the research community. This increasing interest in this theme has led to the
development of tools and concepts that can be used to apply sustainable strategies to the most various
types of industries and sectors.
Among these developed ideas, in recent years emerged the concept of Eco-efficiency. In short, eco-
efficiency is concerned with creating more value while generating less environmental impact. This is a
new idea in environmental management, integrating it with economic analysis to improve products,
systems and technologies. The application of this concept is increasingly spreading to different sectors
and industries, enabling overall economic and environmental improvements.
However, very few studies have been made regarding the application of eco-efficiency to the injection
moulding industry. Being this a very important industry worldwide, the motivation of this thesis will be
focused in giving continuation to the few work that has been developed in order to contribute to reduce
the knowledge gaps in this area.
One of the studies done in this sector concerned identifying the main eco-efficiency indicators that can
help to apply eco-efficiency to the injection moulding industry, namely the mould production and mould
use phases. These indicators were compiled into eco-efficiency profiles that can be used as a supporting
tool for eco-efficiency analysis in this sector, aiding in performing faster assessments. However, these
developed profiles feature very extensive lists of indicators, which still makes it hard to perform an
analysis in this area.
By following the recommended norms and guidelines to correctly perform an eco-efficiency analysis,
this thesis will use these suggested eco-efficiency profiles, with the objective of developing eco-
efficiency ratios that can be used to compare different injection mould designs in certain areas of interest
that relate to the mould production and mould use phases.
These developed eco-efficiency ratios will be applied to a case study that features five mould
alternatives with different designs that are used to inject the same part. Because the mould alternatives
presented in this case study include moulds with higher and lower levels of design complexity, the
conclusions that can be taken from the analysis of this can be assumed representative of the injection
moulding industry.
That said, according to the results obtained for the case study, the objective of this thesis will be to
evaluate if the developed eco-efficiency ratios are in fact useful to effectively compare different mould
designs in different areas of interest for the injection moulding industry.
The first two chapters of this thesis (chapter 2 and 3) are used to present the state of the art. Chapter 2
will present the philosophy behind eco-efficiency, the principles that rule it, the methodologies used and
the challenges that are faced nowadays. Chapter 3 will concern the injection moulding process. Here a
brief explanation is made concerning the steps of an injection cycle. Then are addressed two main
1
components needed for this process: the injection moulding machine and the mould (types of moulds
and steps in the mould production process). A literature survey is also done the topic of environmental
and economic performance of injection moulding.
Chapter 4 will focus in detail the methodology used in this thesis.
Chapter 5 will present the case study used in this thesis. In this chapter will be developed the models
(PBM, PBCM and EI evaluation) that will be used to get the results needed to perform the eco-efficiency
analysis.
The eco-efficiency analysis will be performed in chapter 6. Here the case study results be applied to the
eco-efficiency ratios developed by having as a base the recommended profiles with indicators to perform
an eco-efficiency analysis in the injection moulding industry. Conclusion will be made regarding these
eco-efficiency ratios are in fact useful for mould design comparison.
After the selection of the useful eco-efficiency ratios that overcome this analysis, in chapter 7
frameworks of eco-efficiency ratios are proposed to aid mould producing companies in internal decision
making and external communication for possible mould users (injection moulding companies). These
frameworks are useful for companies in the injection moulding industry to apply eco-efficiency to their
businesses, in order to aid them in decision making, helping to continue the trend of applying sustainable
strategies in the most various industries and sectors.
Finally, chapters 8 and 9 will be used to present the overall conclusions obtained from the work done
and the future studies that can overcome from this thesis.
2
2. Eco-efficiency: the philosophy, the principles and its challenges
This chapter addresses the philosophy behind eco-efficiency, the seven principles that rule it, as well as
its historical origins and its objectives.
Following this, a reference is made to the types of indicators used on the methodologies of application,
their classification and utility for eco-efficiency implementation.
The methodologies and the main challenges faced by the strategies of eco-efficiency are also
addressed.
In the last sub-section, some examples of application of eco-efficient strategies in various sectors of the
industry of plastic injection moulding are given.
2.1. Historical facts
Worries about environmental issues began in the early 60s with the publication of Rachel Carson’s
“Silent Spring”. This book highlighted important questions about mankind’s impact on the environment
providing a lunch pad for the beginning of the environmental movement [1].
In 1972 took place the first UN Conference on the Human Environment that joined the leaders from
industrialized and developing countries in order to delineate the “rights” to a healthy and productive
environment. The next decade was marked by the implementation various legislations with the goal of
protecting the environment.
The term eco-efficiency, that first established the link between environmental performance and
economic benefits was first introduced in 1990 by two Swiss researchers, Schaltegger and Sturm [2]
and was adopted and presented by the World Business Council for Sustainable Development (WBCSD)
in 1991 [1].
The First International Conference on Eco-efficiency took place in Leiden, Netherlands, in 2004. This
conference has been focused on identifying operational methods for quantified eco-efficiency analysis
that can guide decision making towards community eco-efficiency, contributing to sustainability. More
conferences followed this event, in 2006, 2010 and 2014.
2012 marked the implementation of the norm ISO 14045:2012, which describes the principles,
requirements and guidelines for an eco-efficiency assessment [3].
2.2. The philosophy behind eco-efficiency
Nowadays, companies worry about economic growth, environmental impact, their social and ethically
behaviour, among others. In other words, sustainability is one of the main goals that is being pursued.
Lehni et al. [4], said that the way to go to sustainability is through decoupling the resources used and
pollutant emissions from economic development. In other words, the decoupling process can be
described as the separation made between environmental impacts and the production of wealth.
3
These topics embrace a wide range of aspects that are hard to evaluate. Following this need, the
concept of eco-efficiency emerged playing an important role on the rising of sustainable developments
[5]. Eco-efficiency is then a relatively new concept in environmental management which integrates it
with economic analysis to improve products, systems and technologies.
This concept is increasingly becoming used for different applications. Consequently, the use of this idea
has been shifting from specific, to larger and more embracing systems, enabling the overall economic
and environmental improvements [1, 3].
The World Business Council for Sustainable Development (WBCSD) defines eco-efficiency as “the
delivery of competitively priced goods and services that satisfy human needs and bring quality of life,
while progressively reducing ecological impacts and resource intensity throughout the life cycle, to a
level at least in line with the earth’s carrying capacity” [4]. Another definition, according to ISO
14045:2012 [3], says “eco-efficiency is a quantitative management tool that enables the consideration
of life cycle environmental impacts of a product system alongside its product system value to a
stakeholder” [1, 3]. On the other hand, the European Environment Agency (EEA) defines eco-efficiency
as “a concept and strategy that enables the separation of the “use of nature” from economic activity
needed to meet human needs (welfare) to allow it to remain within carrying capacities and to permit
equitable access and use of the environment by current and future generations” [7].
However, the EEA definition differs from the other two since for this organization, products are compared
to each other and not from the point of view of resources available. There are also no normative
elements in this definition therefore, for the EEA, a production method is not good or bad for the
environment or economy, only better or worse [1].
Eco-efficiency is generally described as the ratio between economic value and environmental influence,
as represented by equation 1 [8].
𝐸𝐸𝐸𝐸𝐸𝐸 − 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝐸𝐸𝑒𝑒 =𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃 𝐸𝐸𝑃𝑃 𝑆𝑆𝑒𝑒𝑃𝑃𝑆𝑆𝑒𝑒𝐸𝐸𝑒𝑒 𝑉𝑉𝑉𝑉𝑉𝑉𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒𝑆𝑆𝑒𝑒𝑃𝑃𝐸𝐸𝑒𝑒𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑉𝑉 𝐼𝐼𝑒𝑒𝑒𝑒𝑉𝑉𝑃𝑃𝑒𝑒𝑒𝑒𝐸𝐸𝑒𝑒
(1)
In short, eco-efficiency is concerned with creating more value with the less environmental impact.
Analysing equation 1, one tool that can be used to assess the environmental influence is ReCiPe, or for
example the Carbon Footprint Method (CFM). For the product or service value the WBCSD recommends
the use of monetary indicators that can give clear information and are easy to analyse [6], like for
example gross value added (GVA) or earnings before interest, taxes, depreciation and amortization
(EBITDA). On the other hand, ISO 14045:2012 recommends the use of functional values [3]. An example
of this can be the use of the production capacity, or the lifetime of a product [5].
According to WBCSD, eco-efficiency should have the following characteristics [9]:
• Be relevant and meaningful with respect to protecting the environment and human health and/or
improving the quality of life;
• Inform decision making to improve the performance of the organization;
4
• Recognize the inherent diversity of business;
• Support benchmarking and monitoring over time;
• Be clearly defined, measurable, transparent and verifiable;
• Be understandable and meaningful to identified stakeholders;
• Be based on an overall evaluation of a company’s operations, products and services, especially
focusing on all those areas that are of direct management control;
• Recognize relevant and meaningful issues related to upstream (e.g. suppliers) and downstream
(e.g. product use) aspects of a company’s activities.
In a short conclusion, eco-efficiency is a strategic tool that helps companies in their decision-making in
a way that improvement policies are developed and applied. This will help companies’ to set priorities
and goals that improve their environmental and economical performances.
2.3. The seven principles of eco-efficiency
To achieve a successful eco-efficiency policy, the following three objectives are defined by the WBCSD
[9]:
• Optimizing the use of resources;
• Reduce environmental impact;
• Increase product or service value.
To achieve these objectives, the WBCSD identified the seven principles of eco-efficiency [5]. These are
enumerated in table 1, grouped with the respective objectives where they belong.
Table 1 – The seven principles of Eco-efficiency [9].
Objectives Eco-efficiency principles
Optimize the use of resources
Reduce the material intensity
Reduce energy intensity
Enhance recyclability
Reduce environmental impact Reduce dispersion of toxic substances
Maximize the use of renewable resources
Increase product or service value
Extend product durability
Increase service intensity
The results obtained from an eco-efficiency assessment will help and assist on the outcome of issues
such as:
• Identifying and evaluating the units and/or processes that have a low eco-efficiency results;
• Defining eco-efficiency strategies;
• Detect and quantify the variables or aspects that affect eco-efficiency’s performance;
5
• Quantifying eco-efficiency performance variations due to the introducing of new equipments or
technology replacements;
• Finding the significant environmental aspects and quantifying indicators relating the seven eco-
efficiency principles with the appropriate environmental aspect.
2.4. Types of indicators and their utility
According to WBCSD eco-efficiency indicators can be divided into two main categories: generally
applicable indicators and business specific indicators [9].
Like the name suggests, the first category focuses on the type of indicators that generally can be applied
to all businesses and companies, even if their importance may vary considerably depending in the
business. For an indicator to be considered generally applicable, it has to tick the following boxes [9]:
• The indicator should be related to a global environmental concern or business value;
• It is relevant and meaningful to virtually all businesses;
• The methods for measurement should be established and its definitions accepted globally.
WBCSD defined the indicators which are considered to be generally applicable. These are currently
divided into two different sections: product/service value and environmental influence in product/service
creation. Another three categories are being discussed to join the generally applicable indicators. These
are: additional financial value indicators, acidification emissions to the air and total waste [9].
Table 2 – Generally applicable indicators [9].
Indicators Units
Product/service value Quantity of goods/services produced or
provided to customers Number or mass
(kg, ton, etc.)
Net sales €, £, $, etc.
Environmental influence in product/service creation
Energy consumption GJ, kW.h, etc.
Material consumption kg, ton, etc.
Water consumption m3
Greenhouse gas (GHG) emissions CFC11 equivalent/ton
Ozone depleting substance (ODS) emissions CO2/ton
If an indicator does not meet these specifications it is declared a business specific indicator. These are
the ones that vary according to the business and type of company where they are implemented.
Between the business specific indicators can figure key environmental performance indicators (KEPI).
These indicators are used to show the performance of a system from the point of view of the
environment. KEPI’s have to be developed according to three main principles [10]:
• Quantification;
• Relevance;
6
• Comparability.
According to ISO 14031:2005 [11], to fulfil these principles these indicators have to be related to a
functional unit that relates them with each other. This unit allow the comparability of data from different
aspects by relating them to a common metric. In other words, KEPI’s show physical quantities related
to a functional unit. For example for the injection moulding industry there are KEPI’s that can be defined
like: kW.h/Mould, kg of steel/kg of mould, kg of waste/Part, etc.
The generally applicable indicators can work as a matter of comparison between similar businesses,
while business specific care more about what the company thinks that matters to itself. This allows every
business to use the same small set of indicators, getting the rest of the important information from
business specific indicators, which give information that is useful and meaningful to an individual
company. Generally applicable indicators alone do not represent the eco-efficiency performance of a
company. They must be combined with the appropriate business specific indicators to have the full
picture of information available [9].
WBCSD recommends that on top of these eco-efficiency indicators, companies can use this data to
estimate eco-efficiency through metrics that are ratios between sets of eco-efficiency indicators that they
regard as most relevant and meaningful for their business. These metrics can be used to evaluate the
eco-efficiency performance by comparing a key set of eco-efficiency indicators along time [5]. These are
used to evaluate eco-efficiency performances of systems, processes or products. These metrics are
developed by respecting equation 1, which means that these metrics are built by dividing a value
indicator by an environmental influence indicator. The higher the value of these metrics are, the better
the eco-efficiency performance is [8]. These metrics are called eco-efficiency ratios by the WBCSD [9].
What is understood by the literature survey made is that there is not a defined nomenclature to call
these metrics. If WBCSD calls them eco-efficiency ratios, Baptista et al. [5] calls them eco-efficiency
indicators. This nomenclature however might create misunderstandings because for the WBCSD eco-
efficiency indicators are the values defined in the beginning of this sub-chapter. This thesis will follow
the nomenclature recommended by WBCSD.
Numerous combinations of these metrics can be made, depending on the indicators that are chosen.
However, these metrics have to respect some rules. Baptista et al. [5] divides these metrics into two
categories:
• Eco-efficiency ratios (ER) – These relate to equation 1 by having a value eco-efficiency
indicator as numerator and an environmental influence indicator as denominator. These
denominators are calculated by using eco-indicators like Eco-indicator 99 or ReCiPe, to
translate this metric into impact points (Pt).
• Eco-efficiency performance indicators (EPI) – Composed by a value indicator as numerator
and an environmental aspect indicator as denominator. These aspects can be energy
consumptions (kWh), material consumptions (kg), etc.
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Esteves et al. [8], applies these concepts to the injection moulding industry by developing eco-efficiency
three types that are focused in the injection mould:
• Life Cycle Oriented – These ratios are formed by a value indicator in the numerator and an
environmental impact indicator in the denominator, where a ratio should be made by using an
environmental impact indicator representing each life cycle phase of the mould;
• Functional – These are associated with the functional value of the mould. These use as value
entries for the ratio an indicator that is based on specific functional characteristics of the mould,
related to an environmental impact indicator;
• Specific Performance - These aim to assess the performance of the mould’s manufacturing
process and of the injection moulding process. The indicators used in this ratio regard to the
resources consumption related to the environmental influence.
2.5. Methodologies of application of eco-efficiency
According to Baptista et al. [5] an eco-efficiency assessment should be:
• Understandable, to make it easy and useful for decision making.
• Relevant, so the data obtained is pointed towards the problems that need to be solved,
evaluating past, present and future events.
• Reliable, so that decision making is not compromised by errors and faults.
• Comparable, because this type of analysis should be carried out over time, in order to be
consistent.
Approaches to implement eco-efficiency have some variations, however WBCSD defined a general and
flexible framework that is easy to apply to all business sectors [9].
This methodology has three degrees or levels for the eco-efficiency data: categories, aspects and
indicators. The categories are divided into areas related to environmental influence or value, having
each one of them the respective and corresponding specific aspects. Then, there are indicators that
measure the impact of each of these aspects.
Table 3 – The categories and respective aspects featured in WBCSD methodology [9].
Categories Aspects
Product / service value
Volume/ mass
Monetary
Function
Environmental influence in product/ service creation
Energy consumption
Materials consumption
Natural resources consumption
Non-product output
Unintended events
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Environmental influence in product/ service use
Product/service characteristics
Packaging waste
Energy consumption
Emissions during use/ disposal
With the information obtained from the indicators profiles can be created. These profiles give complete
information and can be used to easily track changes in the behaviour of the company. WBCSD
recommends the use of three types of profiles [9]:
• Organization profile – This profile has the typical information about the matters like the number
of employees, business segments and primary products, system boundary conditions and
contacts. The organization profile should take into account major changes in the structure of the
company as well;
• Value profile – Provides information about the business specific and generally applicable
indicators that relate specially to evaluating value. In other words, indicators related to financial
information, the amount of products sold, or functional indicators for specific products should be
referred;
• Environmental profile – Here should figure generally applicable environmental influenced
indicators, as well as business specific indicators relating to product/service creation.
In addition to the profiles recommended by the WBCSD, Baptista et al. [5] recommends that should be
introduced an eco-efficiency profile as well. This profile is where all the eco-efficiency ratios (ER) that
are meaningful to the company’s business should figure. This profile can be used to better understand
the development of eco-efficiency in a certain area. It is then the company’s job to define which ratios
are the most important and relevant to itself, in order to develop the eco-efficiency ratios that give the
wanted information.
According to ISO 14045:2012 [3], an eco-efficiency assessment is divided into several phases. These
phases are represented in figure 1.
Figure 1 – Phases of an eco-efficiency assessment recommended by ISO 14045:2012 [3, 5].
Several adjustments to data and methodology have to be made in order to achieve a certain consistency
between goals and results obtained. These adjustments should be done by sensitivity analysis covering
different choices of data and methodology to see how they influence the study being performed. Since
all the phases are linked together, the resulting new data obtained from a certain phase affects the
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neighbouring phase. This means that each step has to be revised to check if a coherent eco-efficiency
analysis/study is being executed [5].
This framework has three major areas that are used to collect the data needed for an eco-efficiency
assessment. These are: Goal & Scope Definition, Environmental Assessment and Product System
Value Assessment.
2.5.1. Goal & Scope Definition
The main and first step to start an eco-efficiency analysis is to define its goal and scope. When defining
the goal it should figure [3]:
• The intended application;
• The reasons for doing the assessment;
• The intended audience;
• Whether the results are intended to be used in comparative studies or to disclose to the public.
In the definition of the scope ISO 14045:2012 recommends the inclusion of [3]:
• The product system to be studied;
• The functions of the product system(s);
• The functional unit;
• The system borders;
• Allocation procedures;
• Impact categories selected and methodology of impact assessment;
• Data requirements, assumptions and limitations.
2.5.2. Environmental Assessment (LCIA)
The Environmental Assessment can be also called Life Cycle Impact Assessment (LCIA). The LCIA is
based on ISO 14040 and ISO 14044 [5] and is part of the Life Cycle Assessment (LCA) method, that is
vastly described in several books and papers and it has with the purpose to evaluate the magnitude of
potential environmental impacts of a product system via inventory analysis, throughout its life cycle.
The first step of this module is called the classification stage. This begins by allocating each inventory
result to an environmental impact category according to the types of environmental impact they
generate. By doing so, this process creates an indicator for each inventory result [12]. These categories
can be resources, materials, energy, or processes used.
The next step is called characterisation. In this stage inventory parameters are multiplied by
equivalence factors depending on the amount of damage they represent, creating the category indicator
results. For characterizing these indicators, eco-indicators like ReCiPe can be used. The data resulting
from this phase is represented by a representative environmental impact unit (Pt, CO2eq, etc.). After this
sometimes the results are normalized for them to be easier to interpret. This is done by calculating the
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magnitude of the categories indicator results relative to a reference value [12]. This step is crucial for
the next phase, called weighting.
The weighting phase is subjective, since it depends on factors like the target audience, product system,
or the goal of the assessment, where each person can have a different point of view. This phase is done
by adding factors to the normalized results, according to their importance [13].
2.5.2.1. Standard Eco-indicators
Standard Eco-indicators are basically numeric values that express the total environmental impact of a
product or process [14]. These can be used to convert the inventory defined in the LCIA (also called life
cycle inventory (LCI)) into impact points. The objective of using this tool is then to give higher scores or
points to the LCI results that have a bigger environmental impact and vice-versa [15].
The Eco-Indicators suffer alterations from time to time in order to adapt to the newest trends, when it
comes to environmental assessments. After the creation of the Eco-Indicator 95, this was updated to
the Eco-Indicator 99, which was later followed ReCiPe. This is the up to date version most and it is the
one most commonly used nowadays. The Eco-Indicator used in this thesis will then be ReCiPe.
There are two levels of indicators to go through the characterization of ReCiPe: midpoint and endpoint
indicators, in which the number of indicators for each level is 18 and 3 respectively [15]. The large
number of midpoint indicators and the fact that they are more abstract make them difficult interpret, so
endpoint level indicators were created to facilitate this interpretation [16]. The three Endpoint impact
categories are [15]:
• Damage to Human health;
• Damage to Ecosystems;
• Damage to Resource availability.
Figure 2 - Relationship between LCI parameters (left), midpoint indicator (middle) and endpoint categories (right), in ReCiPe [15].
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Similarly to Eco-Indicator 99, ReCiPe can be evaluated through three different human attitude
perspectives: individualistic, equalitarian and hierarchic. These three perspectives give different weights
to the three endpoint impact indicators [14, 17]. Depending on the perspective chosen, different results
are obtained. Generally the scientific community uses the hierarchic perspective, by the fact that this is
a more balanced perspective [18]. The weights between the different perspectives are presented in table
4 for Eco-Indicator 99.
Table 4 – Weights for categories and perspectives for Eco-Indicator 99 [10, 17].
Perspectives
Endpoint impact categories Individualistic Equalitarian Hierarchic
Ecosystems 25% 50% 40%
Human Health 55% 30% 30%
Resources 20% 20% 30%
2.5.3. Product System Value Assessment
The value of a product or service can be obtained by various methods. The WBCSD recommends the
utilization of monetary indicators like for example, net sales, GVA, EBITDA, etc. By the fact that such
indicators are used, it is necessary to know the costs involved in the production of a product,
implementation of a service, or technology, etc. (depending on the goal of the assessment).
There are some methods to determine the cost like for example the Total Cost of Ownership (TCO) [19]
or the Life Cycle Cost (LCC) [20].
LCC helps to choose the alternative with the most cost effective approach from a series of choices
available, making it a very useful tool. This method sums up the cost estimates for the product(s) or
service(s) in evaluation starting from “cradle to grave”, analysing then the total costs verified during their
entire life cycle.
Process Based Models (PBM) are effective tools to calculate the resources used by a process or
product. A PBM is divided into two sub-models. They are [21]:
• Process model – The objective of this sub-model is to describe the product (part material and
geometry), as well as the process(es) that are required for its production and the variables that
relate to them (the materials and energy consumption, cycle time, equipments and labour
required, etc.). This data is obtained thought theoretical and empirical relations that relate the
product to the manufacturing processes;
• Operations model –This sub-model is created by introducing to the process model, the
operating conditions and inputs of the factory where the product is going to be manufactured.
These can be the number of tools, machines, operators, etc.;
By adding a third sub-model (financial model) to the process and operations models of the PBM, a
Process Based Cost Model (PBCM) can be developed. These are useful to calculate the costs involved
in a process or product. With them it is also possible to evaluate different production scenarios since it
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is known that these affect the product’s performance as well as the production costs [22]. PBCM’s help
engineers to relate design parameters and production scenarios with manufacturing cost techniques
[21]. Some authors use it as a complementary tool to LCC by the fact that its application makes possible
to know the resources used while studying different production scenarios as well as their respective cost
[23].
To define the financial model needed to define the PBCM, factor prices are applied to the PBM process
and operational models. After this the product cost can be computed [16, 21].
Figure 3 – Flowchart of a PBCM [21].
Alternatively to the recommendation of the WBCSD to use of monetary indicators as value creators, ISO
14045:2012, treats the product or service value as a functional value (measurable benefit to the product
or service user) [2, 5]. In other words, according to this normative, the value of a product or service is
measured by functional characteristics that are considered to benefit the performance of a product or
service. The higher these functional characteristic are, the higher the value created. An example given
at Baptista et al. [5] states that the light bulb brightness (luminous flux) and durability (warranty lifetime)
can be functional characteristics of this product and are considered to create value.
2.6. Areas and studies of eco-efficiency’s applicability
The number of papers where the use of eco-efficiency is studied is increasing each day showing that it
is a concept that is highly adaptable to numerous areas.
Papers can be found in areas like the petroleum and petrochemical industry [24], iron rod industry [25],
forest industry [26], electronic industry [27], agriculture [28], waste management [29], pulp and paper
industry [30], etc. Another example is the study by Czaplicka-Kolarz et al. [2] that applies an eco-
efficiency methodology to a case study about the polyolefins production.
Regarding the injection moulding industry, after some research concerning the application of eco-
efficiency, it was concluded that almost no work has been done, proving that this is an area that should
be studied. One of the few examples found was a study done by Esteves et al. [8]. This paper assesses
the injection mould performance though the selection of energy efficiency, eco-efficiency and
environmental impact indicators applied to a case study, helping in decision making. In his Master thesis,
Roda [10] recommends a set of value and environmental profiles developed according to the accepted
norms, that are applied to the injection moulding industry and organized by applicability, being ready to
provide assistance on future eco-efficiency evaluations.
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2.7. Motivation and present challenges faced by eco-efficiency
Roda (2015) concluded that, even if they are extensive, his developed eco-efficiency profiles might
prove useful in the reduction of work and time needed to assess the eco-efficiency of the injection
moulding industry. Although, because there are lots of different combinations of indicators that give the
most various information, the main challenge in this process is then to have the know-how to choose
the eco-efficiency indicators that apply to the problem that is to be studied. This will be the main
motivation of this thesis.
Looking at this concept’s other challenges, WBCSD states that when presenting the results obtained by
the eco-efficiency indicators, companies should take into account the scope and limitations of their
indicators, because if the data is not collected correctly it could lead to precision errors. In order to
minimize this problem, some authors suggest that margins of error should be applied to the results [9].
Another big challenge is the ability to read and interpret the results obtained [9].
Additional issues are the fact that this concept is still relatively new making it more difficult to find cases
in which it is applied nowadays, even if the interest for eco-efficiency is increasing.
3. Plastic injection moulding process: from the mould to the part
In Portugal, the injection moulding industry is internationally regarded as a high quality sector.
With injection moulding, plastic parts, even with complex geometries, can be mass produced at very
high speeds. Because of this big advantage, injection moulding is by far the most commonly used
method in the plastic industry.
In this chapter it will be explained how the process of injection moulding works, including the relevant
parameters that affect the quality and efficiency of this process. It is also explained the phases that an
injection mould goes through, since the beginning of the project, until it is ready to be used for injection
moulding.
A literature survey developed on the subject of economic and environmental impact of injection moulding
is also addressed.
3.1. The process of plastic injection moulding
Injection moulding is a cyclic and repetitive process in which melted plastic (most commonly
thermoplastic and thermosetting polymers) is injected into a mould cavity or cavities, where it is held
under pressure while it cools, until it is ejected in a solid state. The mould may consist of a single cavity
or a number of equal or unequal cavities, each connected to flow channels, commonly called runners,
which direct the flow of the melted material to the individual cavities. Injection moulding can be used
with thermoplastics, thermosetting and elastomeric polymers [31].
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The injection moulding process has the following advantages [32]:
• Producing part in different sizes and complexity;
• Good for big batches (mass production);
• Making parts with different surface finishing;
• It is a repeatable process;
• Parts can be made with few finishing operations and tight tolerances.
The two main components of the injection moulding process are the mould and the injection machine,
where this machine is divided into two different sections: the injection unit and the clamping unit, being
the two linked to each other [32].
The Injection moulding process starts at the injection unit with feeding of the desired polymeric material
into the feed hopper (generally plastics are fed into the form of solid grains). The material is then
conducted into the barrel. Into this barrel a reciprocating screw turns with the help of an external electric
or hydraulic motor, mixing and warming the plastic. Additional heaters help the temperature to rise until
the material melts and arrives into a desired temperature. At the end of the barrel there is a nozzle from
where the plastic is injected into the mould, aided by the screw that acts as a trigger to inject the plastic
at certain pressure. At this point the material enters the clamping unit which holds the mould into place,
in order to receive the melted material. A hydraulic cylinder clamps the parts of the mould exerting a
certain force that closes the mould. After the material is injected it is cooled with the aid of a refrigeration
system built into the mould, bringing the part and mould temperatures down into the desired standards.
The cycle is completed with the opening of the mould and the respective ejection of the part from the
mould with the help of the ejector pins that exist in the mould. The whole process is then repeated for
producing the desired number of production parts [31].
Cooling should be performed as evenly as possible around the whole area of the part. A uniform cooling
reduces the appearance of defects.
After this we can say injection moulding is divided into three main stages: heating, injecting and
moulding. Even so, the moulding category, correspondent to the last stage of the cycle is usually divided
into two sub-stages: packing and cooling [31, 33].
When a part is injected, it is a normal procedure vary the injection pressure when the cavity is almost
filled. This happens because the cooling stage corresponds to the longest and more time consuming
stage of the whole process, where the material experiences shrinkage due to its change of stage from
liquid to solid. To avoid this from happening, variable or constant pressure profiles are used. This
counters the shrinking and warping effects of cooling, maintaining the part with the according quality
standards. This process is called “packing” [31–33].
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Figure 4 – Components needed for the injection moulding process [34].
Various types of injection machines are available on the market, each one has different properties that
vary in characteristics like the volume capacity of material (size), clamping force exerted, screw
diameter, injection pressure, injection rate, cost, energy consumption, etc. [31–33].
Knowing the part to be produced, a study should be done in order to use the correct injection machine
that suits the needs of production. For example, if the mould used to produce the part(s) has a very low
volume capacity in its cavities, it makes no sense to use a big capacity injection machine. This would
result in the deterioration of the material in the barrel due to staying too long under high temperatures.
The mould could be damaged as well if the material is injected with too much pressure of if the clamping
force is too high. Energy consumption would increase as well. In other words, this process would not be
efficient.
There are some important parameters to take into account when analysing the injection moulding
process [31]:
• Cycle time – This is one of the most important parameters of injection moulding and it
corresponds to the time that a part begins to be injected until it is extracted from the mould. This
time corresponds to the sum of the injection time with the cooling time. The objective here is to
reduce the cycle as much as possible, reducing the waste and damaged parts. The design of
the part plays an important role in defining the cycle time. Thicker areas of a part take longer to
solidify, increasing the cooling cycle time and consequently the total cycle time.
• Working temperatures – These are very important for the quality of the process. These
determine whether the polymer is in the recommended working conditions. Damage to the
injection machine and mould can appear if the polymer does not reach the melting temperature.
On the other hand, if this temperature is too high the material can degrade and the part will not
have the desired finishing quality. The ejection temperature has to be taken into consideration
as well. If the temperature is too high the part could not be solid enough for extraction and
damages can appear.
• Material (plastic) – These materials have different properties, like the density, viscosity or the
melting temperature, making them have behaviours that differ from each other. The mould and
the injection machine need to suit the properties of the material in order to the process to be
16
more efficient and to have the desired quality. The different viscosity and working temperatures
of each plastic make the need for a mould and machine to be subjected to different values of
stress and temperature gradients that, in the limit, could cause damage. Parts with defects like
welding lines, shrinkage or bubbles will be more frequent, enhancing the waste of material, time,
energy, etc.
3.2. The injection mould
3.2.1. The components of a mould and its design variations
The mould is one of the most important component of the injection moulding process. It determines the
size, shape, dimensions, finish, and often the physical properties of the final product. Even if the basic
function of a mould is to replicate the part being produced, this tool holds a lot of different functions that
are essential for the process of injection moulding, making it a very complex piece of engineering [32].
A mould should produce quality parts using the less time and less maintenance as possible. To do so,
this component has to properly execute the following tasks [35]:
• To define the volume(s) or cavities with the shape of the part(s) to produce, assuring the
dimensional reproducibility, from cycle to cycle;
• To allow the filling of the referred volume(s) with the desired polymer;
• To favour the cooling of the injected polymer;
• To promote the extraction of the injected part(s).
A mould is a structure composed by a set of boards or plates, where several functional systems are
assembled or machined. These functional systems include [35]:
• Printing – This system includes the cavities that compose the shape of the part(s) to be
produced;
• Guiding and centring – Mainly needed to keep the mould aligned and centred, so that all the
parts have the same exact shape and tolerances;
• Runner(s) – This system is composed by the channels (sprues, runners and gates) that guide
the melted polymer to the cavities of the mould. It covers all the feeding system of the mould.
• Exhaust – Used to extract the gases accumulated from the injection moulding process;
• Cooling – This structure is one of the most important in the mould and is composed by a set
of channels situated around the cavities, that cool the part and take heat from the mould,
helping to reduce the injection cycle time;
• Extraction – This system is composed by the pins, springs and extractors that push the
finished part out of the mould, for it to be ready for the next cycle.
An example an injection mould is presented in figure 5.
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Figure 5 – Example of a mould used in the injection moulding industry [36].
In general, a mould can be divided into two basic halves: the cavities and cores. They are often called
the male and female halves. The term “half” symbolises just the division between the two parts. This
means both sections may not be equally dimensioned. The line separating the two mould halves is
called “parting line” [32].
Analysing figure 5, apart from the already mentioned core and cavity halves, a highlight can be given to
the sprue. This important channel is located in the sprue bushing and it corresponds to the channel that
links the injection machine’s nozzle to the mould. In single cavity moulds, the sprue usually feeds the
polymer directly into the mould cavity, whereas in multi cavity moulds it feeds the polymer to the runner
system (cold or hot), which leads into each mould cavity through a gate. Different gate locations should
be study in order to reach the better spot where the material should be injected [32].
Cooling channels control the temperature of the mould surfaces, helping to cool the plastic until it
reaches a desired temperature, in a solid state, while the vents allow the escape of trapped air and
gases accumulated during the process. Generally, the core of the mould will require the removal of
approximately 67% of the heat generated in the injection moulding process [32].
The ejection or extraction mechanism is composed by pins, blades and a stripper plate. Those force the
solid part out of the mould. The ejector return the ejector pins to a retracted position as the mould closed
to the next cycle [32].
The dimensions and weight of the parts being produced limits the number of cavities in the mould and
also determine the machinery capacity required. This means the mould’s size, weight and number of
parts produced in each cycle depend on the part itself. Also, if a part has a complex shape, this increases
the complexity of the mould, increasing its cost [32].
When designing a mould, it is important to think that it has to safely absorb the forces of clamping,
injection, and ejection. Furthermore, the flow conditions of the melted plastic in the runner system must
be adequately proportioned in order to obtain a uniform part with less defects, like bubbles or welding
18
lines and to ensure the same quality cycle after cycle. Effective heat absorption (cooling system) is also
required to avoid such defects [32].
There are two main types of mould designs. These designs change according to the feeding systems
used. These feeding system can be cold runner or hot runner systems. The main difference between
the two types of feeding systems is the fact that in a cold runner system there is the need of waiting for
the runners and throughput to arrive to the ejection temperature, whether in a hot runner system the
runners do not solidify because the plastic is heated to stay in a molten state. Each one of these feeding
systems presents different compositions. These are represented in figure 6.
Figure 6 – Mould with cold runner system (left) vs Mould with hot runner system (right) [37].
Both these feeding systems have advantages and disadvantages. Table 5 represents the pros and cons
or each design and the differences between them [32, 38].
Table 5 – Hot runners system vs. Cold runner system.
Cold runner system
Pros Cons
It is cheaper to maintain and use; Involves the creation of solid runners;
Allows a faster changing of polymer colours during injection cycles;
It has higher cycle times (due to the waiting of the runner and throughput to arrive to the ejection temperature);
If runner cutting robots are used, the cycle time can be faster;
Much bigger waste generation and environmental impacts, specially if the plastic cannot be recycled (due to the creation of solid runners);
Lower maintenance costs. Higher consumption of energy;
- Lower quality parts.
Hot runner system
Pros Cons
Faster cycle times; It is more expensive to buy and run;
Lower consumption of resources (no solidification of runners);
It is not easy to change colours during injection cycles;
There is no need to invest in runner cutting robots, as no solid runners are created; The maintenance cost is higher;
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It is ideal for making large parts; There is a possibility of higher downtimes (higher levels of maintenance due to bigger complexity);
Parts with higher quality standards; Not the best feeding system for polymers that are heat sensitive.
Reduced injection pressure; -
Operator has more process control and ability to fine-tuning. -
3.3. Phases of a typical mould production process
A typical mould production process has the following phases (in order) [35, 39]:
1. Select the construction material. When choosing the materials to be used in the building of
the mould it is fundamental to know the properties of the resources in order to select the one
that is suitable to the job. A wide range of steels and alloys are available for selection.
2. Design the mould using computer assisted design (CAD);
3. Cut the raw materials blocks into approximate sizes. This is done with the help power sawing
machines.
4. Mill the blocks with the help of CNC milling machines;
5. The final size of the blocks is reached using grinding methods, since the required tolerances
and dimensions can’t be obtained using cutting technologies such as milling, or turning. Cavities
are usually machined using electrical discharge machining (EDM) and wire EDM. Technologies
like laser machining or ultra-sound equipment can also be utilized in order to provide extra
precision.
6. Install the screw holes and retention heels, as well as the mould’s cooling, ejection and exhaust
systems.
3.4. Studies on environmental and economic performance of injection moulding
A literature survey was done in order to know the degree of knowledge and study that has been made
regarding environmental and economic performances in the injection moulding industry.
Several studies have been made during the last years regarding the mould production and injection
moulding processes and their industrial and technological developments. However, in recent years, with
the increasing interest about sustainable strategies, environmental impact control, as well as economic
concerns, studies have been made regarding these areas.
What was verified this survey was that the majority of these studies were made towards the injection
moulding process itself, more specifically about energy consumption and environmental impacts of the
process. Some examples of these are presented on the following paragraphs.
• Park et al. [40] optimizes the injection moulding process of a car fender by resolving the problem
between energy consumption and product quality. In this paper, a non-denominated genetic
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algorithm is applied to a car fender case study, in order to resolve multiple part optimization
problems [40].
• Madan et al. [41], proposes guidelines for energy prediction, benchmarking and performance
evaluation and improvement for unit manufacturing processes, applying then these methods to
an injection moulding case study. Considering factors like part geometry and material properties
and the injection machine, energy consumption can be predicted and the efficiency of the
process can be improved.
• Müller et al. [42] presents two methods for separating time and energy consumption in the
injection moulding process, giving it a potential for improvements in these two areas.
• Nee et al. [43], presents a semi-empirical model for calculating the energy consumption of an
injection moulding machine based on the energy profile of the injection machine. Using a
theoretical analysis of the characteristics of the polymers in question, it is possible to determine
the energy needed for the process [43].
• Sellés et al. [44] does a small analysis on five computer aided software’s that calculate the costs
of the injection moulding process. This paper uses a cost per part approach in or order to reduce
the costs of the injection moulding machine.
• A paper by Lucchetta et al. [45], says that when using life cycle engineering methodologies
(LCE), companies often have the aim of minimizing the material intensity mainly by decreasing
the part volume and increasing the use of recycled materials, while fulfilling structural and
manufacturability requirements, causing environmental impacts that are often not taken into
account. This problem is studied in this paper and applied to a specific case study in injection
moulding.
• Pun et al. [46] proposes a methodology for assessing the environmental impacts in the injection
moulding process, and applies this methodology to a case study conducted in Hong Kong. By
identifying various indicators of environmental impact assessment a multiple-criteria matrix is
built. This matrix is analysed by a pilot program that discusses importance of each indicator.
• Chin et al. [47] uses knowledge-based systems on the development of plastic parts, especially
on the design part. With this approach, the efficiency and effectiveness of the process of
injecting a plastic part can be studied. The respective framework to do this kind of evaluation is
described in this paper, and a knowledge-based system is introduced. This system can select
the appropriate material and the design features of the mould used to inject the respective part.
• A paper from Spiering et al. [48] analyses the energy efficiency in the injection moulding of
automotive parts, emphasising more the impact of the mould. In this paper, comparative
evaluations and analysis are made regarding the use of energy, material and process time.
Furthermore, analysing the energy efficiency of single processes allow to find improvements
that contribute for a better product design. This framework can be used to know energy
consumption of production plants as well.
• Peças et al. [49] analyses the different mould alternatives using the LCE method for analysing
the environmental impacts during the development process of a plastic part. The life cycle
phases are identified throughout the analysis while giving special attention on addressing the
21
environmental impact of the part production and the energy spent on the process. Conclusions
are taken regarding the design steps that should be followed on producing a plastic part,
showing that the injection moulding process has a much lower environmental impact than the
mould production process.
• Ribeiro et al. [50], performs a life cycle assessment regarding the process of manufacturing an
injection mould with the objective of finding the combination of technologies, machine tools,
process parameters, cutting fluids and materials that have less environmental impact. Because
the analysis is done to the life cycle of the mould, the cycle time, material wasted, energy
required, among other parameters are evaluated as well, during the injection phase.
• Ribeiro et al. [51] This paper presents a model to estimate in an early design phase the energy
consumption in the production of injection moulding parts. The proposed model is sensitive to
different part designs, different machines and process conditions. An energy balance is
proposed, comprising two components, a thermodynamic model and an empirical machine
model, integrating coefficients sensitive to both part design and machine characteristics.
All these studies focus more on the injection moulding process itself, but do not focus on studying the
influences of mould designs on the injection moulding process. Still, some studies can be found
addressing this subject. These are presented on the following paragraphs.
• Gantar et al. [52] uses LCA and LCC supported by numerical calculations to study injection
moulding designs in their early design stages. This approach is applied to a case study where
three different mould designs used to produce the same part are compared with each other to
see which one is more efficient. The following conclusions were taken from the study:
o The potential of the mould design to influence the environmental impact of the injection
moulding phase is high;
o In mass production, better production process usually consumes fewer resources
(energy and material);
• Peças et al. [53] uses a LCE model to study different mould designs. This model uses easy to
read ternary diagrams to show the best characteristics of each alternative. This diagrams show
three different domains for each option that globally compare each one: environmental
performance, technical performance and economic performance. This approach is then applied
to a case study involving two different technologies. This case study aims the production of
small volumes of parts.
• Ribeiro et al. [54] refers that nowadays companies do not tend to use life cycle approaches
during the designing of injection moulds. To fight this tendency LCC and LCA methods have
great capabilities to influence mould designers to implement life cycle strategies on the design
phase of injection moulds, specially because the big majority of the moulds are built using very
similar technologies and materials. The model required to execute a life cycle assessment on
an injection mould using LCC and LCA is presented in this paper.
• Ribeiro et al. [23] proposes a Comprehensive Life Cycle Cost (C-LCC) framework, combining
the life cycles of both part and tool, linking all the process based cost models (PBCM) involved
22
and showing all the dependencies and impacts between them. This framework is applied to a
case study that involves the production of certain plastic part and the designing of its injection
mould. In this case study different mould type alternatives are compared with each other and
the advantages of each design are shown, giving special attention to tool reliability, expected
downtime, energy and material consumption. This tool allows the estimation of future costs and
impacts without the need of big investments and it is useful for decision making regarding the
tool design phase before the part is produced [23].
Even if some studies have been done regarding the environmental impact of injection moulds, very little
work has been developed concerning the use of eco-efficiency to assess the impact of injection mould’s
design. The only example of this approach was studied by Esteves et al. [8], where a set of eco-efficiency
indicators are proposed to help in the decision making process of mould design and manufacturing. The
framework used to implement this approach is described and presented through a case study about the
life cycle of an injection mould. This paper was already referred in chapter 2.
After this research, the conclusion is that there are still a lot of developments that can be made regarding
the use of eco-efficiency strategies applied on the injection moulding process, more specifically on
studying the differences in mould designs in the mould production and injection moulding processes.
This thesis focuses on this matter by studying this this process, using a case study in Ribeiro et al. [23],
which drifts from the PhD Thesis developed by Ribeiro [17].
4. Methodology
This thesis basic methodology involves applying eco-efficiency to the injection moulding industry, more
specifically to the injection moulds used by companies to inject specific plastic parts. The Master Thesis
developed by Roda et al. [10] has already worked in this area, namely by developing eco-efficiency
profiles for the mould production process and mould use phase.
A case study was selected involving five different moulds with different designs and specifications, where
each one of them is used to inject the same part. From this case study are going to be obtained results
regarding the resources consumptions, environmental impact (EI) generated and costs involved. After
this, it is possible to study how the different designs used in these moulds influence the mould production
process and their use phase. Having as a base the recommended eco-efficiency profiles, guidelines
and norms [3, 9, 10], eco-efficiency ratios can be developed. These ratios can be used to study the eco-
efficiency behaviour of the different mould designs in various areas of interest. The eco-efficiency
analysis will be focused on testing these ratios and conclude about their applicability and if they are in
fact useful or not to compare different mould designs.
After this analysis, with the ratios that are concluded to be useful in the referred areas of interest,
frameworks of eco-efficiency metrics are going to be developed. These frameworks have the intention
of helping companies in certain areas of the injection moulding industry.
23
After this short summary, the detailed methodology of this thesis can be seen in the flowchart present
in figure 7.
Figure 7 – Methodology followed throughout this thesis.
The methodology of this thesis is divided into three different main phases that need to be processed in
a certain order (figure 7). These phases are the following.
1. Case study’s results collection and analysis;
2. Development and analysis of eco-efficiency ratios;
3. Proposition of eco-efficiency metrics to aid in decision making.
The case study (5 mould designs; 1 part) that will be evaluated is of special interest to this thesis
because it features information data not only about the different moulds use phase, but contains data
about their production process as well. This will allow to develop a PBM that gives information about
both processes, marking the beginning of the first phase of the methodology of this thesis.
This PBM will be used to obtain the results regarding the resources consumption involved in mould
production and use phase of the five moulds.
After this, these results will be used to obtain the costs and EI generated by both processes. To obtain
the costs involved, a PBCM will be developed by applying cost relations to the resources consumed,
while to get the EI generated an EI evaluation will be developed. This evaluation will be performed using
the eco indicator ReCiPe to translate to impact points the amount of resources consumed, in order to
know the EI generated by the case study’s mould production and mould use phase processes.
24
The second phase of this thesis methodology starts developing eco-efficiency ratios having from base
the indicators available in the recommended eco-efficiency profiles (environmental and value profiles)
[10]. These ratios will be used to evaluate the mould production and mould use phase processes in
certain areas of interest, namely value added, productivity and waste generated. Then, by applying the
results obtained from the case study an analysis will be perform to infer if the developed eco-efficiency
ratios give useful information about the injection moulding industry and can be used to aid mould
producing companies and possible mould users. After this analysis the useful eco-efficiency ratios will
be selected.
In the third and last phase of this methodology, frameworks of eco-efficiency metrics to aid in companies’
decision making will be proposed. These frameworks will be composed by the useful eco-efficiency
ratios developed before and by adding relevant indicators from the suggested eco-efficiency profiles.
These frameworks developed for mould producing companies’ internal decision making and for external
communication by these companies, to present to possible clients and might be interested in buying an
injection mould.
5. Case study: Connector for electronic industry
In this chapter a case study is introduced in order to apply the methodology described before, in chapter
4. This case study is representative of the injection moulding industry and features a set of five moulds
with different designs and specifications that will be used to inject the same part, serving as a host to do
an eco-efficiency assessment by comparing the different moulds behaviours in their mould production
and mould use phase.
For this point to be fulfilled a PBM, PBCM and a EI evaluation were developed and in order to obtain
the resources consumptions, costs and EI generated by both processes.
5.1. Description of the case study
This case study is based in a study developed by Ribeiro et al. [23]. All the data used to obtain the
results needed for this case study was gathered onsite in a Portuguese company called Celoplás [23].
This company specializes in producing parts of high complexity and the respective moulds for its own
production. The case study used in this paper features four moulds with different designs used to
produce the same part. The only difference between the case study from Ribeiro et al. [23] and the one
used in this thesis has to do with the inclusion of an additional mould featuring 10 cavities and 5 hot
runner nozzles allowing this case study to have five mould alternatives. Since the other four alternatives
have 8 cavities, the inclusion of this extra mould will allow to see how the differences in the number of
cavities affect the eco-efficiency behaviour.
The five mould alternatives in question were selected according to their different characteristics and
specifications. These different mould designs will then include variations in the feeding system (cold or
hot runners), on the block (cavities machined in block or machined separately) and in the number of
25
cavities (8 moulding cavities or 10 moulding cavities). These different designs greatly influence mould
production and mould use processes, with variations in the resources consumption, costs and
environmental impacts generated. The five different mould’s alternatives, with the respective
specifications are present in table 6.
Table 6 - Mould alternatives used in the case study, based on [23].
Mould alternatives Characteristics
Number of elements
Ejector pins Nozzles Manifold Moulding
cavity/core Core pins
A1-8SC; 4HR 8 Separate cavities, 4 hot runners 136 4 1 8 488
A2-8MB; 4HR Machined in block, 4 hot runners 136 4 1 1 488
A3-8SC; CR 8 Separate cavities, cold runners 136 1 0 8 488
A4-8SC; 2HR 8 Separate cavities, 2 hot runners 136 2 1 8 488
A5-10SC; 5HR 10 Separate
cavities, 5 hot runners
170 5 1 10 610
Based in the case study of Ribeiro et al. [23], the part to be produced by the different mould designs is
a connector for the electronic industry that has the following characteristics (table 7).
Table 7 - Properties of the part used in the case study [23].
Part designation Connector for electronic industry
Material Polybutylene terephthalate (PBT)
Part Volume (cm3) 3.73
Part height (mm) 55
Projected area (cm2) 279
Max. Thickness (mm) 3
Runner diameter (mm) 6
Material recycle rate 30%
Complexity High
Figure 8 shows the part described in table 7.
26
Figure 8 – Connector for electronic industry with runners (4 examples in different positions) [23].
5.2. Development of the process based model (PBM)
As stated in sub-chapter 2.5.3, PBM’s are useful to obtain the resources consumed by a certain product
or process.
By using empirical relations and equations, a PBM was developed for this case study in order to obtain
its consumptions for mould production and mould use phases.
In order to better organize the equations used to develop the PBM, these were divided into two sub-
chapters. One concerning the mould production process and another concerning the mould use phase.
5.2.1. Mould production process
Concerning the mould production process, the PBM was used to obtain the consumptions of the
following resources and wastes generated:
• Material consumption (steel);
• Steel scrap (waste);
• Energy consumption;
• Consumables consumption.
Table 8 displays the input parameters that make part of the process and operational models of the PBM
for the mould production phase. These inputs are used to get the output variables, to obtain the needed
resources consumptions and waste generated.
Table 8 – Inputs and outputs of the PBM for the mould production process.
Parameters (inputs) Unit Variables (outputs) Eq.
no.
Part height m 𝑴𝑴𝑴𝑴𝑴𝑴𝒆𝒆𝒆𝒆𝒆𝒆𝑴𝑴𝒆𝒆 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄𝒄𝒄 (𝒄𝒄𝑴𝑴𝒆𝒆𝒆𝒆𝒆𝒆)(𝒌𝒌𝒌𝒌) =
𝑀𝑀𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑉𝑉𝑉𝑉 𝑃𝑃𝑒𝑒𝑒𝑒𝑑𝑑𝑒𝑒𝑃𝑃𝑒𝑒 (𝑑𝑑𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉) �𝑘𝑘𝑘𝑘 𝐸𝐸3� � × 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑆𝑆𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒(𝐸𝐸3) (2) Mould length m
Mould width m
27
No. of cavities -
𝑺𝑺𝑴𝑴𝒆𝒆𝒆𝒆𝒆𝒆 𝒄𝒄𝒄𝒄𝒆𝒆𝑴𝑴𝒄𝒄 (𝒘𝒘𝑴𝑴𝒄𝒄𝑴𝑴𝒆𝒆)(𝒌𝒌𝒌𝒌) = 𝑀𝑀𝑉𝑉𝑃𝑃.𝑃𝑃𝑒𝑒𝑒𝑒𝑑𝑑𝑒𝑒𝑃𝑃𝑒𝑒(𝑑𝑑𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉) �𝑘𝑘𝑘𝑘 𝐸𝐸3� �×𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑉𝑉𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒(𝐸𝐸3) × 𝑁𝑁𝐸𝐸. 𝐸𝐸𝑒𝑒 𝐸𝐸𝑉𝑉𝑆𝑆.
(3) Mould volume m3
Part volume m3
Material density (steel) kg/m3
Setup time s/Cav
𝑬𝑬𝒄𝒄𝒆𝒆𝒆𝒆𝒌𝒌𝒓𝒓 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄𝒄𝒄(𝒌𝒌𝒌𝒌.𝒉𝒉) = 𝑀𝑀𝑉𝑉𝐸𝐸ℎ𝑒𝑒𝑒𝑒𝑒𝑒 𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝑃𝑃(𝑘𝑘𝑘𝑘) ×(𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑇𝑇𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑 𝐸𝐸𝑉𝑉𝑆𝑆.⁄ ) × 𝑁𝑁𝐸𝐸. 𝐸𝐸𝑒𝑒 𝐸𝐸𝑉𝑉𝑆𝑆. +𝑆𝑆𝑒𝑒𝑃𝑃𝑃𝑃𝑆𝑆 𝑇𝑇𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑))/3600 (4)
Production time s/Cav
Machine power kW
Consumables dm3/s
Downtime per day h/day
𝑪𝑪𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴𝒂𝒂𝒆𝒆𝒆𝒆𝒄𝒄 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄𝒄𝒄(𝒅𝒅𝒄𝒄𝟑𝟑) = 𝐶𝐶𝐸𝐸𝑒𝑒𝑑𝑑𝑃𝑃𝐸𝐸𝑉𝑉𝐶𝐶𝑉𝑉𝑒𝑒𝑑𝑑 �𝑃𝑃𝐸𝐸3𝑑𝑑� � ×
𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑇𝑇𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑 𝐸𝐸𝑉𝑉𝑆𝑆.⁄ ) × 𝑁𝑁𝐸𝐸.𝐸𝐸𝑒𝑒 𝐸𝐸𝑉𝑉𝑆𝑆. (5)
Uptime per year h/year
Number of operators allocated -
Number of shifts -
In this table, the parameters Setup Time, Production time per cavity, Machine power have independent
values for each production technology. The first two mentioned parameters were considered as inputs
since they were gathered onsite, being featured in the case study data.
The intermediate variable “Mould volume”, used to obtain the material consumption is given by equation
6.
𝑴𝑴𝒄𝒄𝒄𝒄𝒆𝒆𝒅𝒅 𝑽𝑽𝒄𝒄𝒆𝒆𝒄𝒄𝒄𝒄𝒆𝒆 (𝒄𝒄𝟑𝟑) = 𝑆𝑆𝑃𝑃𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝑃𝑃𝑒𝑒 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑑𝑑 𝑉𝑉𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒(𝐸𝐸3) +𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘 𝑑𝑑ℎ𝑒𝑒𝑒𝑒𝑃𝑃 𝑉𝑉𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒(𝐸𝐸3) (6)
In equation 6 the structure components volume was taken from the case study data and the moulding
sheet volume is given by the equation 7 [17].
𝑴𝑴𝒄𝒄𝒄𝒄𝒆𝒆𝒅𝒅𝒆𝒆𝒄𝒄𝒌𝒌 𝒄𝒄𝒉𝒉𝒆𝒆𝒆𝒆𝑴𝑴 𝑽𝑽𝒄𝒄𝒆𝒆𝒄𝒄𝒄𝒄𝒆𝒆(𝒄𝒄𝟑𝟑) = �𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 ℎ𝑒𝑒𝑒𝑒𝑘𝑘ℎ𝑃𝑃(𝐸𝐸) + 0.03(𝐸𝐸)�× 2 × �𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑉𝑉𝑒𝑒𝑒𝑒𝑘𝑘ℎ𝑃𝑃(𝐸𝐸) − 0.1(𝐸𝐸)�×
�𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑃𝑃𝑒𝑒𝑃𝑃𝑃𝑃ℎ(𝐸𝐸) − 0.1(𝐸𝐸)� (7)
Also the operational times, namely the downtime and uptime have to be defined. The downtime
corresponds to the non-productive time (breaks, no shifts periods, maintenance and idle), while the
uptime corresponds to the time in which the parts or products are being produced [17]. Figure 9 helps
to understand this subject.
Figure 9 – Line utilization for a working day [17].
28
The operational parameter uptime per year (table 8), can be obtained from figure 9. This parameter is
given by equation 8.
𝑼𝑼𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄𝒆𝒆 𝒄𝒄𝒆𝒆𝒆𝒆 𝒓𝒓𝒆𝒆𝑴𝑴𝒆𝒆 �𝒉𝒉 𝒓𝒓� � = 𝑘𝑘𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑒𝑒𝑘𝑘 𝑃𝑃𝑉𝑉𝑒𝑒𝑑𝑑�𝑃𝑃 𝑒𝑒� � × �24�ℎ 𝑃𝑃� � − 𝐷𝐷𝐸𝐸𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑆𝑆𝑒𝑒𝑃𝑃 𝑃𝑃𝑉𝑉𝑒𝑒�ℎ 𝑃𝑃� �� (8)
Information for every downtime parameters, can be consulted in annex A3 – Maintenance and Downtime
data.
5.2.2. Mould use phase
Regarding the mould use phase, the inputs and outputs of the PBM concern the injection moulding
process of the part. The outputs obtained are the following:
• Material consumption (plastic);
• Plastic waste generated;
• Energy consumption.
Table 9 contains the input parameters used to obtain the output variables for mould use phase.
Table 9 - Inputs and outputs of the PBM for the mould use phase.
Parameters (inputs) Unit Variables (outputs) Eq.
no.
Annual production Parts
𝑴𝑴𝑴𝑴𝑴𝑴𝒆𝒆𝒆𝒆𝒆𝒆𝑴𝑴𝒆𝒆 𝑪𝑪𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄𝒄𝒄 �𝒌𝒌𝒌𝒌 𝒓𝒓� � = 𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝑆𝑆𝑒𝑒 𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃.𝑉𝑉𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒 ×
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑘𝑘𝑒𝑒𝑒𝑒𝑘𝑘ℎ𝑃𝑃(𝑘𝑘𝑘𝑘) + 𝑆𝑆𝑒𝑒𝑃𝑃𝑃𝑃𝑆𝑆 𝑑𝑑𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 �𝑘𝑘𝑘𝑘 𝑒𝑒� � +
𝐸𝐸𝑒𝑒𝑘𝑘𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃 𝑑𝑑𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 �𝑘𝑘𝑘𝑘 𝑒𝑒� � – 𝑅𝑅𝑒𝑒𝑃𝑃𝑑𝑑𝑒𝑒𝑃𝑃 𝑆𝑆𝑉𝑉𝑉𝑉𝑑𝑑𝑃𝑃𝑒𝑒𝐸𝐸 �𝑘𝑘𝑘𝑘 𝑒𝑒� � (9) Part life Year
Reject rate %
Setup time s
Cycle time s
𝑷𝑷𝒆𝒆𝑴𝑴𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄 𝒘𝒘𝑴𝑴𝒄𝒄𝑴𝑴𝒆𝒆�𝒌𝒌𝒌𝒌 𝒓𝒓� � = 𝐴𝐴𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑉𝑉 𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑉𝑉𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒 ×
𝑅𝑅𝑒𝑒𝑅𝑅𝑒𝑒𝐸𝐸𝑃𝑃 𝑅𝑅𝑉𝑉𝑃𝑃𝑒𝑒(%) × 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑘𝑘𝑒𝑒𝑒𝑒𝑘𝑘ℎ𝑃𝑃(𝑘𝑘𝑘𝑘) + 𝑆𝑆𝑒𝑒𝑃𝑃𝑃𝑃𝑆𝑆 𝑑𝑑𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 �𝑘𝑘𝑘𝑘 𝑒𝑒� � +
𝐸𝐸𝑒𝑒𝑘𝑘𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃 𝑑𝑑𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 �𝑘𝑘𝑘𝑘 𝑒𝑒� � –𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝑆𝑆𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 �𝑘𝑘𝑘𝑘 𝑒𝑒� �
(10)
Power required kW
Batch Parts
Scrap per setup kg/setup
Downtime per day h/day
Uptime per year h/year
𝑬𝑬𝒄𝒄𝒆𝒆𝒆𝒆𝒌𝒌𝒓𝒓 𝑪𝑪𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄𝒄𝒄(𝒌𝒌𝒌𝒌.𝒉𝒉 𝒓𝒓� ) = 𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝑃𝑃 𝑃𝑃𝑒𝑒𝑟𝑟𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃(𝑘𝑘𝑘𝑘) ×
𝐴𝐴𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑉𝑉 𝑃𝑃𝑒𝑒𝑟𝑟𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒�ℎ 𝑒𝑒� � (11) Workers per line -
Maintenance level
Cycles/maintenance op.
Some intermediate variables present in table 9 need to be defined.
The first of them is the part weight which is given by equation 12.
29
𝑷𝑷𝑴𝑴𝒆𝒆𝑴𝑴 𝒘𝒘𝒆𝒆𝒆𝒆𝒌𝒌𝒉𝒉𝑴𝑴(𝒌𝒌𝒌𝒌) = 𝑀𝑀𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑉𝑉𝑉𝑉 𝑃𝑃𝑒𝑒𝑒𝑒𝑑𝑑𝑒𝑒𝑃𝑃𝑒𝑒 �𝑘𝑘𝑘𝑘 𝐸𝐸3� � × 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑆𝑆𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒(𝐸𝐸3) (12)
The annual required time is obtained by applying equation 13.
𝑨𝑨𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴𝒆𝒆 𝒆𝒆𝒆𝒆𝒓𝒓.𝑻𝑻𝒆𝒆𝒄𝒄𝒆𝒆 �𝒉𝒉 𝒓𝒓� � =𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝑆𝑆𝑒𝑒 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃. 𝑉𝑉𝐸𝐸𝑉𝑉.× 𝐶𝐶𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 𝑇𝑇𝑒𝑒𝐸𝐸𝑒𝑒
𝑆𝑆𝑉𝑉𝑃𝑃𝑃𝑃�
3600+𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝑆𝑆𝑒𝑒 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃.𝑉𝑉𝐸𝐸𝑉𝑉.𝑁𝑁𝐸𝐸.𝐸𝐸𝑒𝑒 𝐶𝐶𝑉𝑉𝑆𝑆.× 𝐵𝐵𝑉𝑉𝑃𝑃𝐸𝐸ℎ
×𝑆𝑆𝑒𝑒𝑃𝑃.𝑇𝑇𝑒𝑒𝐸𝐸𝑒𝑒(𝐸𝐸𝑒𝑒𝑒𝑒)
60 (13)
Where the effective production volume is given by equation 14.
𝑬𝑬𝑬𝑬𝑬𝑬𝒆𝒆𝒄𝒄𝑴𝑴𝒆𝒆𝑬𝑬𝒆𝒆 𝒄𝒄𝒆𝒆𝒄𝒄𝒅𝒅𝒄𝒄𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄𝒄𝒄 𝑬𝑬𝒄𝒄𝒆𝒆𝒄𝒄𝒄𝒄𝒆𝒆 =𝐴𝐴𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑉𝑉 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑆𝑆𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒
1 − 𝑅𝑅𝑒𝑒𝑅𝑅𝑒𝑒𝐸𝐸𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒(%) (14)
The intermediate variables concerning the scrap produced are given by equations starting from 15 to
19.
𝑺𝑺𝒄𝒄𝒆𝒆𝑴𝑴𝒄𝒄 𝒄𝒄𝒆𝒆𝒆𝒆 𝒄𝒄𝒆𝒆𝑴𝑴𝒄𝒄𝒄𝒄(𝒌𝒌𝒌𝒌) = 5 × 𝑁𝑁𝐸𝐸. 𝐸𝐸𝑒𝑒 𝐸𝐸𝑉𝑉𝑆𝑆𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑑𝑑 × (𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘ℎ𝑃𝑃(𝑘𝑘𝑘𝑘) + 𝐸𝐸𝑒𝑒𝑘𝑘𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃 𝑑𝑑𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 𝑆𝑆𝑒𝑒𝑃𝑃 𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 �𝑘𝑘𝑘𝑘 𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒� � (15)
𝑺𝑺𝒆𝒆𝑴𝑴𝒄𝒄𝒄𝒄 𝒄𝒄𝒄𝒄𝒆𝒆𝑴𝑴𝒄𝒄 𝒄𝒄𝒆𝒆𝒆𝒆 𝒓𝒓𝒆𝒆𝑴𝑴𝒆𝒆�𝒌𝒌𝒌𝒌 𝒓𝒓� � = 𝑆𝑆𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 𝑆𝑆𝑒𝑒𝑃𝑃 𝑑𝑑𝑒𝑒𝑃𝑃𝑃𝑃𝑆𝑆(𝑘𝑘𝑘𝑘) ×𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝑆𝑆𝑒𝑒 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃.𝑆𝑆𝐸𝐸𝑉𝑉.
𝐵𝐵𝑉𝑉𝑃𝑃𝐸𝐸ℎ (16)
𝑬𝑬𝒄𝒄𝒌𝒌𝒆𝒆𝒄𝒄𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒆𝒅𝒅 𝒄𝒄𝒄𝒄𝒆𝒆𝑴𝑴𝒄𝒄 𝒄𝒄𝒆𝒆𝒆𝒆 𝒓𝒓𝒆𝒆𝑴𝑴𝒆𝒆�𝒌𝒌𝒌𝒌 𝒓𝒓� � = 𝐸𝐸𝑒𝑒𝑘𝑘𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃 𝑑𝑑𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 𝑆𝑆𝑒𝑒𝑃𝑃 𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 �𝑘𝑘𝑘𝑘 𝐶𝐶𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒� � ×𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝑆𝑆𝑒𝑒 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃. 𝑆𝑆𝐸𝐸𝑉𝑉.𝑁𝑁𝐸𝐸. 𝐸𝐸𝑒𝑒 𝐸𝐸𝑉𝑉𝑆𝑆𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑑𝑑
(17)
𝑺𝑺𝒉𝒉𝒄𝒄𝑴𝑴 𝒘𝒘𝒆𝒆𝒆𝒆𝒌𝒌𝒉𝒉𝑴𝑴 (𝒌𝒌𝒌𝒌) = 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘ℎ𝑃𝑃(𝑘𝑘𝑘𝑘) + 𝐸𝐸𝑒𝑒𝑘𝑘𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃 𝑑𝑑𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 𝑆𝑆𝑒𝑒𝑃𝑃 𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 �𝑘𝑘𝑘𝑘 𝐶𝐶𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒� � (18)
𝑹𝑹𝒆𝒆𝒄𝒄𝒄𝒄𝒆𝒆𝒅𝒅 𝒄𝒄𝒆𝒆𝑴𝑴𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄 𝒄𝒄𝒆𝒆𝒆𝒆 𝒓𝒓𝒆𝒆𝑴𝑴𝒆𝒆 �𝒌𝒌𝒌𝒌 𝒓𝒓� � =𝐸𝐸𝑒𝑒𝑒𝑒. 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃.𝑉𝑉.𝑁𝑁𝐸𝐸. 𝐸𝐸𝑒𝑒 𝐸𝐸𝑉𝑉𝑆𝑆𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑑𝑑
× 𝑅𝑅𝑒𝑒𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒(%) × 𝑆𝑆ℎ𝐸𝐸𝑃𝑃 𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘ℎ𝑃𝑃(𝑘𝑘𝑘𝑘) + 𝑆𝑆𝑒𝑒𝑃𝑃𝑃𝑃𝑆𝑆 𝑑𝑑𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 �𝑘𝑘𝑘𝑘 𝑒𝑒� � (19)
Similarly to the mould production phase, the uptime per year will respond to equation 8, with the
respective data for the parameters coming from annex A3 – Maintenance and Downtime data.
However, unlike from what happens for the mould production process, in the mould use phase the
parameter unplanned breakdowns (figure 9) will vary according to the maintenance level (table 9)
defined by the company. In annex A3 – Maintenance and Downtime data there is information relating
the cycles between the on shift planned maintenance operations with the unplanned breakdowns that
possibly will occur. An equation defines the data present in this table can be obtained. The path created
by this information is given by equation 20 [17].
𝒓𝒓 = 0.0043 × 𝑥𝑥−0.752 (20)
Where the variable x present in equation 20 is given by equation 21.
𝒙𝒙 = 1 𝑀𝑀𝑉𝑉𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝑆𝑆𝑒𝑒𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 �𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒𝑑𝑑 𝑀𝑀𝑉𝑉𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝑆𝑆.� �� (21)
30
The unplanned breakdowns will be given by the value of y parameter of equation 20 divided by the
number of working hours per day (21h/day).
𝑼𝑼𝒄𝒄𝒄𝒄𝒆𝒆𝑴𝑴𝒄𝒄𝒄𝒄𝒆𝒆𝒅𝒅 𝒂𝒂𝒆𝒆𝒆𝒆𝑴𝑴𝒌𝒌𝒅𝒅𝒄𝒄𝒘𝒘𝒄𝒄𝒄𝒄�𝒉𝒉 𝒅𝒅� � =𝑒𝑒
21�ℎ 𝑃𝑃� � (22)
5.3. Development of the process based cost model (PBCM)
The PBCM is obtained by applying empirical cost relations to the PBM. This model is used to obtain the
variable and fixed costs involved in the mould production and mould use phases. The PBCM developed
in this thesis, namely the variable, fixed costs and their empirical relations were based on the PhD thesis
by Ribeiro [17].
Similarly to the development of the PBM, this model will be divided into sections (mould production and
mould use phase) for easier comprehension.
5.3.1. Mould production process
The variable costs involved in the mould production process and their respective cost relations are
defined in table 10.
Table 10 – Variable costs for mould production.
Cost scope Cost (€) Cost relations Eq. no.
Production technology
Labour 𝑀𝑀𝑉𝑉𝑒𝑒 ℎ𝐸𝐸𝑃𝑃𝑃𝑃𝑑𝑑�€
ℎ� � × (𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑) + 𝑆𝑆𝑒𝑒𝑃𝑃𝑃𝑃𝑆𝑆 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑)) × 𝑁𝑁𝑃𝑃𝐸𝐸𝐶𝐶𝑒𝑒𝑃𝑃 𝐸𝐸𝑒𝑒 𝐸𝐸𝑆𝑆𝑒𝑒𝑃𝑃𝑉𝑉𝑃𝑃𝐸𝐸𝑃𝑃𝑑𝑑 𝑉𝑉𝑉𝑉𝑉𝑉𝐸𝐸𝐸𝐸𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃3600
(23)
Energy 𝑀𝑀𝑉𝑉𝐸𝐸ℎ𝑒𝑒𝑒𝑒𝑒𝑒 𝑆𝑆𝐸𝐸𝑃𝑃𝑒𝑒𝑃𝑃(𝑘𝑘𝑘𝑘) × (𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑) + 𝑆𝑆𝑒𝑒𝑃𝑃𝑃𝑃𝑆𝑆 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑))
3600 (24)
Process Material
(Machining)
𝐶𝐶𝑃𝑃𝑃𝑃𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘 𝑒𝑒𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃 𝐸𝐸𝐸𝐸𝑒𝑒𝑑𝑑𝑃𝑃𝐸𝐸𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 �𝑃𝑃𝐸𝐸3𝑑𝑑� � × 𝐶𝐶𝑃𝑃𝑃𝑃𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘 𝑒𝑒𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃 �€
𝑃𝑃𝐸𝐸3� �
× 𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑) (25)
Process Material
(EDM tech.)
𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑) × 𝐶𝐶𝐸𝐸𝑒𝑒𝑑𝑑𝑃𝑃𝐸𝐸𝑉𝑉𝐶𝐶𝑉𝑉𝑒𝑒𝑑𝑑 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃�€ℎ� �
3600 (26)
Project Project 𝑇𝑇𝑒𝑒𝐸𝐸𝑒𝑒 𝑑𝑑𝑆𝑆𝑒𝑒𝑒𝑒𝑃𝑃(ℎ) × 𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒�€ℎ� � (27)
Bought components Material 𝑇𝑇𝐸𝐸𝑃𝑃𝑉𝑉𝑉𝑉 𝐸𝐸𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑉𝑉𝑉𝑉 𝑃𝑃𝑑𝑑𝑒𝑒𝑃𝑃 (𝑑𝑑𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉)(𝑘𝑘𝑘𝑘) × 𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 �€
𝑘𝑘𝑘𝑘� � (28)
On the other hand, the fixed costs and their cost relations are presented in table 11.
31
Table 11 - Fixed costs for mould production.
Cost scope Cost (€) Cost relation Eq. no.
Production technologies
Equipment 𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑) × 𝑀𝑀𝑉𝑉𝐸𝐸ℎ𝑒𝑒𝑒𝑒𝑒𝑒 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃�€
ℎ� �3600
(29)
Tooling 𝑇𝑇𝐸𝐸𝐸𝐸𝑉𝑉𝑑𝑑 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃�€
ℎ� � × 𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑑𝑑)3600
(30)
Fixed overhead
𝐸𝐸𝑟𝑟. 23 ×𝐼𝐼𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝐸𝐸𝑃𝑃 𝑃𝑃𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑃𝑃𝑑𝑑𝐷𝐷𝑒𝑒𝑃𝑃𝑒𝑒𝐸𝐸𝑃𝑃 𝑃𝑃𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑃𝑃𝑑𝑑
(31)
Building 𝐵𝐵𝑃𝑃𝑒𝑒𝑉𝑉𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘 𝑒𝑒𝑒𝑒𝑆𝑆𝑒𝑒𝑑𝑑𝑃𝑃𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃(€) ×(1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙
(1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙 − 1 (32)
Maintenance 𝐼𝐼𝑒𝑒𝑆𝑆𝑒𝑒𝑑𝑑𝑃𝑃𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃 𝐸𝐸𝑉𝑉𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(%) × (𝐸𝐸𝑟𝑟. 29 + 𝐸𝐸𝑟𝑟. 30 + 𝐸𝐸𝑟𝑟. 32) (33)
Bought components
Structure
The cost of these components depend on the market price and the overall quality of the components bought. -
Standard parts
Hot runners
Others
Subcontracts Heat
treatments - -
Testing 𝑇𝑇𝑒𝑒𝐸𝐸𝑒𝑒 𝑑𝑑𝑆𝑆𝑒𝑒𝑒𝑒𝑃𝑃(ℎ) × 𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒�€ℎ� � (34)
The origin of the intermediate variables from table 11, used at the cost equations are directly obtained
from the case study data, however there are some that need to be calculated by their own equation.
This is the case with the following variables: machine cost per hour, tools cost per hour and building
investment.
The machine cost per hour needed to calculate the equipment cost is given by equation 35.
𝑴𝑴𝑴𝑴𝒄𝒄𝒉𝒉𝒆𝒆𝒄𝒄𝒆𝒆 𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴�€𝒉𝒉� � =
𝐸𝐸𝑟𝑟𝑃𝑃𝑒𝑒𝑆𝑆𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) × (1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸𝐸𝐸𝑙𝑙𝐵𝐵𝐸𝐸 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙
𝑈𝑈𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑆𝑆𝑒𝑒𝑃𝑃 𝑒𝑒𝑒𝑒𝑉𝑉𝑃𝑃�ℎ 𝑒𝑒� � × ((1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸𝐸𝐸𝑙𝑙𝐵𝐵𝐸𝐸 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙 − 1) (35)
To be able to calculate the tools cost per hour a simplification was done by considering that this cost is
given by a percentage value of the machine cost per hour (equation 35). This percentage is given by
variable k which in the case of this case study corresponds to 8% of the machine cost per hour. The
tools cost per hour variable is given by equation 36.
32
𝑻𝑻𝒄𝒄𝒄𝒄𝒆𝒆𝒄𝒄 𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴�€𝒉𝒉� � = 𝑘𝑘 × 𝑀𝑀𝑉𝑉𝐸𝐸ℎ𝑒𝑒𝑒𝑒𝑒𝑒 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€
ℎ� ) (36)
The building investment used to obtain the building cost is given by equation 37.
𝑩𝑩𝒄𝒄𝒆𝒆𝒆𝒆𝒅𝒅𝒆𝒆𝒄𝒄𝒌𝒌 𝑰𝑰𝒄𝒄𝑬𝑬𝒆𝒆𝒄𝒄𝑴𝑴. (€) =�𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃.𝑇𝑇. (𝑑𝑑) + 𝑆𝑆𝑒𝑒𝑃𝑃𝑃𝑃𝑆𝑆 𝑇𝑇. (𝑑𝑑)�
𝑈𝑈𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒�ℎ 𝑒𝑒� � × 3600× 𝑉𝑉𝑃𝑃𝑒𝑒𝑉𝑉(𝐸𝐸2) × �1 + 𝐼𝐼𝑃𝑃𝑉𝑉𝑒𝑒 𝑑𝑑𝑆𝑆𝑉𝑉𝐸𝐸𝑒𝑒(%)�× 𝐵𝐵𝑃𝑃𝑒𝑒𝑉𝑉𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘 𝑈𝑈𝑒𝑒𝑒𝑒𝑃𝑃 𝐶𝐶. �€
𝐸𝐸2� � (37)
Data about the machine area is available in annex A4 – Process Variables, while the variables idle
space and building unit cost can be found annex A1 – Exogenous Variables.
5.3.2. Mould use phase
The variable and fixed costs involved in the mould use phase are present in table 12.
Table 12 – Financial model for injection moulding.
Cost type Cost (€) Cost relation Eq.
no.
Variable costs
Labour cost 𝐷𝐷𝑒𝑒𝑃𝑃𝑒𝑒𝐸𝐸𝑃𝑃 𝑃𝑃𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑃𝑃𝑑𝑑(%) × 𝐴𝐴𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑉𝑉 𝑆𝑆𝑉𝑉𝑒𝑒𝑃𝑃 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒�€ 𝑒𝑒� �× 𝑘𝑘𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑃𝑃𝑑𝑑 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃�€ℎ� � (38)
Energy cost 𝑈𝑈𝑒𝑒𝑒𝑒𝑃𝑃 𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑘𝑘𝑒𝑒 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃�€𝑘𝑘𝑘𝑘. ℎ� � × 𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃𝑘𝑘𝑒𝑒 𝐸𝐸𝐸𝐸𝑒𝑒𝑑𝑑𝑃𝑃𝐸𝐸𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒(𝑘𝑘𝑘𝑘. ℎ) (39)
Material cost 𝐸𝐸𝑟𝑟. 9 × 𝑀𝑀𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑉𝑉𝑉𝑉 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃�€ℎ� � (40)
Fixed costs
Main machine cost
𝐴𝐴𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑉𝑉 𝑃𝑃𝑒𝑒𝑟𝑟𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒�ℎ 𝑒𝑒� � × 𝐼𝐼𝑒𝑒𝑅𝑅𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝑉𝑉𝐸𝐸ℎ𝑒𝑒𝑒𝑒𝑒𝑒 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃�€ℎ� � (41)
Tooling cost 𝑇𝑇𝐸𝐸𝐸𝐸𝑉𝑉 𝑒𝑒𝑒𝑒𝑆𝑆𝑒𝑒𝑑𝑑𝑃𝑃. (€)(1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝑃𝑃𝑃𝑃𝑃𝑃𝐸𝐸 𝐸𝐸𝑃𝑃𝑝𝑝𝐵𝐵𝐵𝐵𝑝𝑝𝐸𝐸𝐵𝐵𝑝𝑝𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙
(1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝑃𝑃𝑃𝑃𝑃𝑃𝐸𝐸 𝐸𝐸𝑃𝑃𝑝𝑝𝐵𝐵𝐵𝐵𝑝𝑝𝐸𝐸𝐵𝐵𝑝𝑝𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙 − 1−𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝐸𝐸𝐸𝐸𝐸𝐸 𝑉𝑉𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒(€) (42)
Fixed overhead cost
𝐸𝐸𝑟𝑟. 38 ×𝐼𝐼𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝐸𝐸𝑃𝑃 𝑃𝑃𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑃𝑃𝑑𝑑(%)𝐷𝐷𝑒𝑒𝑃𝑃𝑒𝑒𝐸𝐸𝑃𝑃 𝑃𝑃𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑃𝑃𝑑𝑑(%)
(43)
Building cost 𝐵𝐵𝑃𝑃𝑒𝑒𝑉𝑉𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘 𝑒𝑒𝑒𝑒𝑆𝑆𝑒𝑒𝑑𝑑𝑃𝑃𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃 𝑉𝑉𝑉𝑉𝑉𝑉𝐸𝐸𝐸𝐸𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃(€)(1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙
(1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙 − 1 (44)
Maintenance cost
𝐴𝐴𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑉𝑉 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑆𝑆𝐸𝐸𝑉𝑉𝑃𝑃𝐸𝐸𝑒𝑒 �𝑆𝑆𝑉𝑉𝑃𝑃𝑃𝑃 ℎ� �
𝑀𝑀𝑉𝑉𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉𝑒𝑒𝐸𝐸𝑒𝑒 𝑉𝑉𝑒𝑒𝑆𝑆𝑒𝑒𝑉𝑉 �𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒𝑑𝑑 𝑀𝑀𝑉𝑉𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝑆𝑆.� �×
51.67(€𝐸𝐸𝑆𝑆. ) �
𝑁𝑁𝐸𝐸.𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑆𝑆𝑒𝑒𝑃𝑃𝑒𝑒𝑒𝑒𝑑𝑑
(45)
Several intermediate variables used in the cost relations of table 12 need to be defined.
The next section of intermediate variables relates to data regarding the labour cost (line shutdown data
is present in annex A3 – Maintenance and Downtime data).
𝑨𝑨𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴𝒆𝒆 𝒄𝒄𝑴𝑴𝒆𝒆𝒅𝒅 𝑴𝑴𝒆𝒆𝒄𝒄𝒆𝒆�𝒉𝒉 𝒓𝒓� � = 𝑘𝑘𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑒𝑒𝑘𝑘 𝑃𝑃𝑉𝑉𝑒𝑒𝑑𝑑 × (24�ℎ 𝑃𝑃� � −𝑘𝑘𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑃𝑃 𝑃𝑃𝑒𝑒𝑆𝑆𝑉𝑉𝑒𝑒𝑃𝑃 𝐶𝐶𝑃𝑃𝑒𝑒𝑉𝑉𝑘𝑘𝑑𝑑�ℎ 𝑃𝑃� � − 𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒 𝑑𝑑ℎ𝑃𝑃𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒�ℎ 𝑃𝑃� � (46)
𝑫𝑫𝒆𝒆𝒆𝒆𝒆𝒆𝒄𝒄𝑴𝑴 𝒘𝒘𝒄𝒄𝒆𝒆𝒌𝒌𝒆𝒆𝒆𝒆𝒄𝒄(%) = 𝑘𝑘𝐸𝐸𝑃𝑃𝑘𝑘𝑒𝑒𝑃𝑃𝑑𝑑𝑉𝑉𝑒𝑒𝑒𝑒𝑒𝑒� × 𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒 𝑃𝑃𝑒𝑒𝑟𝑟𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃(%) (47)
33
𝑳𝑳𝒆𝒆𝒄𝒄𝒆𝒆 𝒆𝒆𝒆𝒆𝒓𝒓𝒄𝒄𝒆𝒆𝒆𝒆𝒆𝒆𝒅𝒅(%) =𝐴𝐴𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑉𝑉 𝑃𝑃𝑒𝑒𝑟𝑟𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒�ℎ 𝑒𝑒� �
𝑈𝑈𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑆𝑆𝑒𝑒𝑃𝑃 𝑒𝑒𝑒𝑒𝑉𝑉𝑃𝑃�ℎ 𝑒𝑒� � (48)
𝒌𝒌𝒄𝒄𝒆𝒆𝒌𝒌𝒆𝒆𝒆𝒆𝒄𝒄 𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴�€𝒉𝒉� � =
𝑘𝑘𝑉𝑉𝑘𝑘𝑒𝑒(€) × 14(𝐸𝐸𝐸𝐸𝑒𝑒𝑃𝑃ℎ𝑑𝑑) × 1.23(𝑃𝑃𝑉𝑉𝑥𝑥)
𝑈𝑈𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑆𝑆𝑒𝑒𝑃𝑃 𝑒𝑒𝑒𝑒𝑉𝑉𝑃𝑃�ℎ 𝑒𝑒� � (49)
The following set of equations are used to obtain the economic related intermediate variables.
𝑰𝑰𝒄𝒄𝑰𝑰𝒆𝒆𝒄𝒄𝑴𝑴𝒆𝒆𝒄𝒄𝒄𝒄 𝒄𝒄𝑴𝑴𝒄𝒄𝒉𝒉𝒆𝒆𝒄𝒄𝒆𝒆 𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴(€𝒉𝒉� ) =
𝐸𝐸𝑟𝑟𝑃𝑃𝑒𝑒𝑆𝑆𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) × (1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸𝐸𝐸𝑙𝑙𝐵𝐵𝐸𝐸 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙
𝑈𝑈𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑆𝑆𝑒𝑒𝑃𝑃 𝑒𝑒𝑒𝑒𝑉𝑉𝑃𝑃�ℎ 𝑒𝑒� � × ((1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝐸𝐸𝐸𝐸𝐵𝐵𝐵𝐵𝐸𝐸𝐸𝐸𝑙𝑙𝐵𝐵𝐸𝐸 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙 − 1) (50)
𝑩𝑩𝒄𝒄𝒆𝒆𝒆𝒆𝒅𝒅𝒆𝒆𝒄𝒄𝒌𝒌 𝒆𝒆𝒄𝒄𝑬𝑬𝒆𝒆𝒄𝒄𝑴𝑴𝒄𝒄𝒆𝒆𝒄𝒄𝑴𝑴 𝑴𝑴𝒆𝒆𝒆𝒆𝒄𝒄𝒄𝒄𝑴𝑴𝑴𝑴𝒆𝒆𝒅𝒅(€) = 𝐴𝐴𝑃𝑃𝑒𝑒𝑉𝑉(𝐸𝐸2) × 𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒 𝑃𝑃𝑒𝑒𝑟𝑟𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃(%) × 𝐵𝐵𝑃𝑃𝑒𝑒𝑉𝑉𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘 𝑃𝑃𝑒𝑒𝑒𝑒𝑃𝑃 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃 �€𝐸𝐸2� � (51)
𝑻𝑻𝒄𝒄𝑴𝑴𝑴𝑴𝒆𝒆 𝑴𝑴𝒄𝒄𝒄𝒄𝒆𝒆 𝒆𝒆𝒄𝒄𝑬𝑬𝒆𝒆𝒄𝒄𝑴𝑴𝒄𝒄𝒆𝒆𝒄𝒄𝑴𝑴(€) = 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑃𝑃𝐸𝐸𝑃𝑃𝑉𝑉𝑉𝑉 𝑒𝑒𝑒𝑒𝑆𝑆𝑒𝑒𝑑𝑑𝑃𝑃𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃(€) +𝑁𝑁𝑒𝑒𝑃𝑃 𝑆𝑆𝑃𝑃𝑒𝑒𝑑𝑑𝑒𝑒𝑒𝑒𝑃𝑃 𝑆𝑆𝑉𝑉𝑉𝑉𝑃𝑃𝑒𝑒 𝑅𝑅𝑒𝑒𝑆𝑆𝑉𝑉𝑒𝑒𝑃𝑃𝑑𝑑(€) (52)
Where in equation 52, the variable Net present value repairs will depend on the mould’s reliability. This
topic is described in the sub-chapter 5.4.
On the other hand, the variable Mould EoL annuity, present in equation 42 will depend on the injection
mould end of life (EoL). This topic is addressed in sub-chapter 5.5.
5.4. Mould reliability
The mould reliability needs to be considered since the failure of this injection moulding tool or one of its
components will origin additional breaks in the injection moulding process, causing unplanned
breakdowns. Apart from creating delays in the production process, mould failures will origin additional
repair costs by replacing the broken component(s), contributing to the increase of the injection moulding
tooling cost [23].
The Weibull parameters can be used to calculate the probability of failure of each mould component
[23]. Three parameters are needed for defining this distribution. These are: b (shape), T (scale) and t0
(location). These parameters can be calculated by using three different equations: one that defines the
mode of the components failure (tmod) (equation 53), another to define the failure density (equation 54)
and its maximum, which is given by the last equation (equation 55) [23].
𝑃𝑃𝐸𝐸𝑝𝑝𝐵𝐵 = �𝐶𝐶 − 1𝐶𝐶
𝑏𝑏(𝑃𝑃 − 𝑃𝑃0) + 𝑃𝑃0 (53)
𝑒𝑒(𝑃𝑃) =𝐶𝐶
𝑇𝑇 − 𝑃𝑃0�𝑃𝑃 − 𝑃𝑃0𝑇𝑇 − 𝑃𝑃0
�𝑏𝑏−1
𝑒𝑒−�𝐸𝐸−𝐸𝐸0𝑇𝑇−𝐸𝐸0
�, 0 ≤ 𝑃𝑃0 ≤ 𝑃𝑃 (54)
𝑃𝑃𝑒𝑒(𝑃𝑃)𝑃𝑃𝑃𝑃
= 𝐶𝐶 − 1 − �𝑃𝑃 − 𝑃𝑃0𝑇𝑇 − 𝑃𝑃0
�𝑏𝑏
. 𝐶𝐶 = 0 (55)
34
By having the three parameters the Weibull distribution can be defined (equation 56) [23].
𝐹𝐹(𝑃𝑃) = 1 − 𝑒𝑒−�𝐸𝐸−𝐸𝐸0𝑇𝑇−𝐸𝐸0
�𝑏𝑏
(56)
This distribution determines the failure probability by displaying the longest and shortest observed
periods of failure (tmax and tmin). These periods can be studied by using the equation for Bernard medium
rank relation (equation 57 and 58), where the parameter nf means the number of failures observed in a
component type [23].
𝐹𝐹(𝑃𝑃𝐸𝐸𝑃𝑃𝑚𝑚) =𝑒𝑒𝑙𝑙 − 0.3𝑒𝑒𝑙𝑙 + 0.4
(57)
𝐹𝐹(𝑃𝑃𝐸𝐸𝐵𝐵𝐵𝐵) =1 − 0.3𝑒𝑒𝑙𝑙 + 0.4
(58)
After defining all the parameters, it is useful to determine the mean time between fails (MTBF). The
MTBF is calculated by the following equation [23].
𝑴𝑴𝑻𝑻𝑩𝑩𝑴𝑴 = (𝑇𝑇 − 𝑃𝑃0) × Γ �1b
+ 1� + t0 (59)
For the present case study, the parameters for the calculation of the MTBF, the number of elements for
each mould component, as well as their cost were gathered on sight.
Using this information, the number of failures that are probable to happen in a determined number of
production years can be calculated through the total number of cycles in the defined period of time, per
the respective MTBF.
𝑵𝑵𝒄𝒄𝒄𝒄𝒂𝒂𝒆𝒆𝒆𝒆 𝒄𝒄𝑬𝑬 𝑬𝑬𝑴𝑴𝒆𝒆𝒆𝒆𝒄𝒄𝒆𝒆𝒆𝒆𝒄𝒄 𝒆𝒆𝒄𝒄 𝑵𝑵 𝒓𝒓𝒆𝒆𝑴𝑴𝒆𝒆𝒄𝒄 =𝑇𝑇𝐸𝐸𝑃𝑃𝑉𝑉𝑉𝑉 𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒𝑑𝑑 𝑒𝑒𝑒𝑒 𝑁𝑁 𝑒𝑒𝑒𝑒𝑉𝑉𝑃𝑃𝑑𝑑
𝑀𝑀𝑇𝑇𝐵𝐵𝐹𝐹 (60)
By having the number of failures in N years, the expected cost per element for this specific period of
time is given by multiplying this information by the cost of the respective element and by its probability
of failure in MTBF. To have the probability of failure in MTBF, equation 56 was applied.
𝑬𝑬𝒙𝒙𝒄𝒄𝒆𝒆𝒄𝒄𝑴𝑴𝒆𝒆𝒅𝒅 𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴 𝒄𝒄𝒆𝒆𝒆𝒆 𝒆𝒆𝒆𝒆𝒆𝒆𝒄𝒄𝒆𝒆𝒄𝒄𝑴𝑴 𝒆𝒆𝒄𝒄 𝑵𝑵 𝒓𝒓𝒆𝒆𝑴𝑴𝒆𝒆𝒄𝒄�€ 𝒓𝒓� � = 𝐸𝐸𝑟𝑟. 60 × 𝐸𝐸𝑉𝑉𝑒𝑒𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) × 𝑃𝑃𝑃𝑃𝐸𝐸𝐶𝐶. 𝑒𝑒𝑒𝑒 𝑀𝑀𝑇𝑇𝐵𝐵𝐹𝐹 (61)
By dividing equation 61 per the period of time in analysis, the expected cost for the number of failing
elements per year can be calculated (equation 62).
𝑬𝑬𝒙𝒙𝒄𝒄𝒆𝒆𝒄𝒄𝑴𝑴𝒆𝒆𝒅𝒅 𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴 𝑬𝑬𝒄𝒄𝒆𝒆 𝑵𝑵 𝒆𝒆𝒆𝒆𝒆𝒆𝒄𝒄𝒆𝒆𝒄𝒄𝑴𝑴𝒄𝒄 𝒄𝒄𝒆𝒆𝒆𝒆 𝒓𝒓𝒆𝒆𝑴𝑴𝒆𝒆�€ 𝒓𝒓� � =𝐸𝐸𝑟𝑟. 52𝑁𝑁 𝑒𝑒𝑒𝑒𝑉𝑉𝑃𝑃𝑑𝑑
(62)
35
After having this information, by applying equation 62 for every mould component type and by adding
all these values, the expected cost of failure per year is defined.
𝑬𝑬𝒙𝒙𝒄𝒄𝒆𝒆𝒄𝒄𝑴𝑴𝒆𝒆𝒅𝒅 𝒄𝒄𝒄𝒄𝒄𝒄𝑴𝑴 𝒄𝒄𝑬𝑬 𝑬𝑬𝑴𝑴𝒆𝒆𝒆𝒆𝒄𝒄𝒆𝒆𝒆𝒆�€ 𝒓𝒓� � = � 𝐸𝐸𝑥𝑥𝑆𝑆𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝑃𝑃 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃 𝑒𝑒𝐸𝐸𝑃𝑃 𝑁𝑁 𝑒𝑒𝑉𝑉𝑒𝑒𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃𝑑𝑑 𝑆𝑆𝑒𝑒𝑃𝑃 𝑒𝑒𝑒𝑒𝑉𝑉𝑃𝑃�€ 𝒓𝒓� �𝑪𝑪
𝒆𝒆=𝟏𝟏
(63)
Where C corresponds to the component type.
With this cost, the net present value of the repairs cost (Net present value of repairs) can be calculated
for the number of years of part production (equation 64).
𝑵𝑵𝒆𝒆𝑴𝑴 𝒄𝒄𝒆𝒆𝒆𝒆𝒄𝒄𝒆𝒆𝒄𝒄𝑴𝑴 𝑬𝑬𝑴𝑴𝒆𝒆𝒄𝒄𝒆𝒆 𝑹𝑹𝒆𝒆𝒄𝒄𝑴𝑴𝒆𝒆𝒆𝒆𝒄𝒄 (€) = �𝐸𝐸𝑥𝑥𝑆𝑆𝑒𝑒𝐸𝐸𝑃𝑃𝑒𝑒𝑃𝑃 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃 𝐸𝐸𝑒𝑒 𝑒𝑒𝑉𝑉𝑒𝑒𝑉𝑉𝑃𝑃𝑃𝑃𝑒𝑒�€ 𝑒𝑒� �
1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒�𝑃𝑃𝑃𝑃𝑃𝑃𝐸𝐸 𝐸𝐸𝑃𝑃𝑝𝑝𝐵𝐵𝐵𝐵𝑝𝑝𝐸𝐸𝐵𝐵𝑝𝑝𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙
(64)
All data related to the failure of each mould is available in annex A5 – Mould reliability (failure data).
5.5. End of Life (EoL)
When the injection mould is projected, one of the topics that engineers try to apply in its design is the
easiness to dismantle. This is important when the mould reaches its End of Life (EoL).
The ideal case is to dismantle and recycle the mould, meaning that its structure and components are
turned into pallets or granulates, being ready to be used in another function. What happens in reality will
depend in the country in which the mould operated [23]. In this case it was considered that the mould is
recycled.
Even if the cost value of the mould EoL is low when compared to the overall injection moulding process
it will decrease the injection moulding’s tooling cost (equation 42), because the Mould EoL annuity need
to be discounted from the tool investment annuity.
The first step to calculate the Mould EoL annuity is to calculate its profit. This is done by multiplying the
mould’s weight by its material (steel) scrap price (equation 65).
𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝐸𝐸𝐸𝐸𝐸𝐸 𝑆𝑆𝑃𝑃𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃 (€) = 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑃𝑃𝑒𝑒𝑒𝑒𝑘𝑘ℎ𝑃𝑃(𝑘𝑘𝑘𝑘) × 𝑆𝑆𝐸𝐸𝑃𝑃𝑉𝑉𝑆𝑆 𝑆𝑆𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 (𝑑𝑑𝑃𝑃𝑒𝑒𝑒𝑒𝑉𝑉) �€𝑘𝑘𝑘𝑘� � (65)
Because the Mould EoL annuity requires the present value of the profit considered in equation 65, this
is calculated by the following equation.
𝑃𝑃𝑉𝑉 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝐸𝐸𝐸𝐸𝐸𝐸 𝑆𝑆𝑃𝑃𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃(€) =𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝐸𝐸𝐸𝐸𝐸𝐸 𝑆𝑆𝑃𝑃𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃(€)
(1 + 𝑃𝑃)𝐵𝐵 (66)
Now that all the fields are defined, the Mould EoL annuity is given by a derivate of equation 66, only with
the value of the investment being the PV of Mould EoL profit.
36
𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝐸𝐸𝐸𝐸𝐸𝐸 𝑉𝑉𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒(€) = 𝑃𝑃𝑉𝑉 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝐸𝐸𝐸𝐸𝐸𝐸 𝑆𝑆𝑃𝑃𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃(€)(1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝑃𝑃𝑃𝑃𝑃𝑃𝐸𝐸 𝐸𝐸𝑃𝑃𝑝𝑝𝐵𝐵𝐵𝐵𝑝𝑝𝐸𝐸𝐵𝐵𝑝𝑝𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙
(1 + 𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑑𝑑𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒)𝑃𝑃𝑃𝑃𝑃𝑃𝐸𝐸 𝐸𝐸𝑃𝑃𝑝𝑝𝐵𝐵𝐵𝐵𝑝𝑝𝐸𝐸𝐵𝐵𝑝𝑝𝐵𝐵 𝐵𝐵𝐵𝐵𝑙𝑙𝑙𝑙 − 1 (67)
It has to be stated that the part EoL annuity was not considered by the fact that this value does not
concern directly the injection moulding company, but the recycling or landfill company that collects the
waste.
For this case study it was considered that 30% of the plastic waste is reused to inject new parts (recycle
rate used to define equation 19), while the rest of the plastic scrap is recycled.
5.6. Environmental impact evaluation
The environmental impact (EI) evaluation for this case study will be done through the application of the
ReCiPe method. This evaluation will use the resources consumptions obtained from the PBCM to see
how they translate in EI.
The first step in the EI evaluation points out the kind and quantity of resources consumed and waste
generated in both processes. Then, with the aid of database Eco-Invent [16], from software SimaPro,
these resources and wastes have to be selected from the databases. These databases use ReCiPe
method to give information about the conversion of one generic unit of a specific resource or waste into
points (Pt).
After selecting all the resources from the database and checking how many Pt they produce per unit,
they need to be multiplied by the respective quantity of resources consumed. This will give the total EI
Pt that a certain resource produces.
It has to be addressed that for this case study, it was decided that the waste generated by both
processes will be recycled, being this the best case scenario.
For easier comprehension, similarly to what was done with the development of the PBM and PBCM, the
EI evaluation will be presented into two separated sub-chapters, one for mould production and another
for the mould use phase.
5.6.1. Mould production process
The resources consumed in the mould production phase are the following:
• Material used for mould construction (steel for mould construction) [55];
• Waste generated by steel used for mould construction (steel recycled);
• Energy consumed by the mould production technologies.
Table 13 presents the ReCiPe results taken from SimaPro’s database for the resources pointed above.
The Pt for the respective resources are divided into the three ReCiPe categories of impact.
37
Table 13 – ReCiPe mPt/unit of resource for mould production.
Steel for mould construction Recycling of steel Energy consumed
ReCiPe categories mPt/kg ReCiPe categories mPt/kg ReCiPe categories mPt/kW.h
Human Health 357.3 Human Health -55.1 Human Health 22.2
Ecosystems 59.1 Ecosystems -15.6 Ecosystems 11.7
Resources 407.5 Resources -79.6 Resources 17.7
Total 823.9 Total -150.3 Total 51.6
Because it was considered that the steel waste is recycled, it generated negative EI.
It can be seen that depending on the resource that is analysed, that the ReCiPe categories have different
proportions, meaning that they are harmful for the environment in different ways and different scales.
To obtain the value results for the EI of the mould use phase, the values contained in table 13 have to
be multiplied by the respective results obtained by the outputs of table 8.
5.6.2. Mould use phase
The resources consumed and waste generated in the mould use phase are the following:
• Material used for part injection (PBT plastic);
• Waste generated by the plastic used in part injection (PBT plastic recycled);
• Energy consumed by the injection moulding process.
Similarly to what was done in sub-chapter 5.6.1, table 14 displays the amount of Pt per unit of resource
consumed or waste generated. However it has to be addressed that it was not possible to find data
regarding the PBT plastic on the software’s database, both for its use and for its recycling process.
Because of this, what was done was do find on the database the plastic that had the closest
characteristics to the PBT plastic, in order to perform an approximation.
By the fact that the PBT plastic is from the family of the polyesters, it was decided to select the PET
plastic (same family of polymers) [56, 57] for the plastic used in the injection moulding process due to
their similarities.
For the recycling of the plastic the SimaPro software database only had two plastic types to choose
from: PS (amorphous thermoplastic polymer) and PP (semi crystalline thermoplastic polymer). Because
the PBT plastic used in the part of this case study is a semi crystalline thermoplastic, it was decided to
select the PP as an approximation for the recycling process due to its bigger similarities in its
composition when compared to the PS plastic [57].
Like what was done for table 14, the Pt are distributed by the three ReCiPe categories.
38
Table 14 - ReCiPe mPt/unit of resource for mould use phase.
Plastic for injection (PET) Recycling of plastic (PP) Energy consumed
ReCiPe categories mPt/kg ReCiPe categories mPt/kg ReCiPe categories mPt/kW.h
Human Health 116 Human Health -77.4 Human Health 22.2
Ecosystems 56.9 Ecosystems -30 Ecosystems 11.7
Resources 179 Resources -164 Resources 17.7
Total 351.9 Total -271.4 Total 51.6
To obtain the value results for the EI of the mould use phase, the values contained in table 14 have to
be multiplied by the respective results obtained by the outputs of table 9.
5.7. Results for the mould production process
In this sub-chapter are presented the results obtained by the PBM, PBCM, as well as the EI evaluation
for the mould production process. These results will be used to study the eco-efficiency performance of
the five mould alternatives in their mould production phase in the next chapter.
Analysing the resources consumptions, the graphic on the left of figure 10 shows that mould alternatives
A2-8MB; 4HR and the mould with cold runners are the moulds that use less steel in their construction.
This happens because both moulds have the smallest dimensions of the five alternatives. Moulds A1-
8SC; 4HR and A4-8SC; 2HR have a bigger dimension than the two alternatives mentioned before,
therefore they use more steel in their production. The mould with 10 cavities is the biggest mould since
it features two more cavities than the other alternatives, needing a bigger volume and consequently
more material consumption in its production.
The energy consumption results are present in figure 10 display the total energy consumed by all the
production technologies used in the production of each mould alternative. The mould with 10 cavities
will need more energy consumption in its production due to the need to machine two extra cavities when
compared to the other alternatives. The other four moulds with 8 cavities will consume similar amounts
of energy. Even so, the mould with cold runners is the one that needs lower energy consumptions. This
happens due to the lower complexity needed in its channels due to its feeding system used.
From the lot of the moulds with 8 cavities and hot runner systems mould A2-8MB; 4HR is the one that
consumes lower amounts of energy. This happens because its cavities are machined in block, instead
of separately. Because moulds A4-8SC; 2HR and A1-8SC; 4HR have the same amount of cavities and
they are machined separately, they will require higher production times in technologies like the wire
EDM, when compared to mould A2-8MB; 4HR.
The steel scrap results are easily explained by the number of cavities that need to be machined. Moulds
with 8 cavities will generate the exact same waste and the mould with 10 cavities will be the one that
39
generated more waste in its production due to the necessity of producing two extra cavities. In any case,
the waste generated by the five moulds is very small when compared to the steel used in mould
construction.
Figure 10 - Steel consumption (left), energy consumption (right) and steel scrap generated (waste) (bottom), for mould production.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
The cost results for the mould production process (figure 11) shows that moulds the hot runner moulds
with 4 hot runners nozzles have similar costs. However, the most expensive of the three is alternative
A1-8SC; 4HR, since it has cavities machined separately, resulting in a slight increase in the labour cost
and the subcontracts and bought components, when compared to alternative A2-8MB; 4HR. Alternative
A4-8SC; 2HR costs because it features 2 less hot runner nozzles in comparison.
Analysing the remaining two alternatives it can be observed that mould alternative with cold runners is
the one that costs less to produce and on the opposite side, with the highest cost is the alternative with
10 cavities. This is explained by the differences in the engineering level and complexity between these
two moulds, being the mould with cold runners the least complex of the five alternatives and the mould
with 10 cavities the most complex.
40
It can be seen that the labour, subcontracts/bought components and equipment cost have more
influence in the total production cost than the rest of the cost types. With the exception of the
subcontract/bought components cost, these are the cost types that depend on the production times
involved. Because the workers cost per hour (man hours) is higher than the machine cost per hour
allowing the labour costs to be higher than the equipment cost.
The subcontracts/bought components do not depend on the production technologies. The same
happens with the project cost. Even so, the subcontracts/bought components have the second biggest
cost due to the high cost of the bought components, like the structure, or the hot runner system.
Figure 11 – Total costs for mould production.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
The results for the EI generated shown in figure 12 shows that even if there is a very significant EI
generated by the steel consumption, the energy is the resource that generated bigger environmental
impact, by a large margin. This is explained by the long hours that the machines need to be working to
produce a mould. Also, like it was expected, the recycling of steel produces almost no impact due to the
very small amounts of steel waste generated.
Figure 12 shows that the impact generated by each ReCiPe category does not change their percentage
on the total EI generated by each mould alternative, only changing their magnitude according to the
amount of resources consumed.
41
Figure 12 – Steel consumption EI (top left) with respective detail graphic (top right), energy consumption EI (bottom left) and steel scrap (recycled) EI (bottom right), for mould production.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
The total EI generated results present in figure 13 show that the mould with 10 cavities will obviously be
the mould that generated more EI due to the bigger use of resources. The other four moulds with 8
cavities will have similar EI due to similar resources consumption with small variations related to their
design complexity. The mould with cold runners being the mould with lower complexity will be that
generates less EI.
42
Figure 13 – Total EI generated by the mould production process.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
5.8. Results for mould use phase
This sub-chapter shows the results obtained by the PBM, PBCM and EI evaluation for the mould use
phase.
Looking at the results from figure 14, the alternative with cold runners by a large margin is the one with
the higher resources consumption, when compared to the other alternatives. This is explained by the
use of cold runners, since these generate a higher amount of scrap material, boosting the material and
waste generation.
Since the energy consumption is function of the injection cycle time, this resource consumption is higher
for the mould with cold runners. Due to the fact that for moulds using this feeding system there is the
need for the throughput to reach the ejection temperature, the cycle times obtained are much higher
when compared to the other mould alternatives with hot runners.
The moulds with 5 and 4 hot runners, being more complex than the other alternatives, are the ones that
uses less resources in their use phase. However, because the mould with 10 cavities has two more
cavities than the rest, allows it to have a lower cycle time per part, decreasing its energy consumption.
The alternatives with 8 cavities and hot runners will consume the same energy due to the fact that they
have the same cycle times.
Alternative A4-8SC; 2HR will use consume more plastic and generate more waste because it uses less
hot runner nozzles than the other alternatives with similar feeding systems. This allows this alternative
to generate bigger amounts of scrap per cycle due to the need of using bigger channels to reach all the
cavities, generating more waste due to plastic leftovers in these channels after each cycle.
43
Figure 14 – Plastic used per part (left), energy consumption per part (right) and plastic waste generated per part (bottom), for mould use phase.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
Since the costs vary according to the production conditions, it can be interesting to test the mould
alternatives in different scenarios. Table 15 show three production scenarios that will be tested. The first
situation includes a base scenario whose conditions were taken from Ribeiro 2013 [23], based in data
collected at the Celoplás company that originated the case study used in this thesis. The other
production scenarios where developed to test extreme and opposite conditions. Scenario 1 includes a
large annual production volume, part production life and low maintenance level and scenario 2 has the
exact opposite conditions.
44
Table 15 – Production scenarios tested for mould use phase.
Base scenario Scenario 1 Scenario 2
Annual production volume (part/year) 6,500,000 8,000,000 350,000
Part production life (year) 8 10 2
Maintenance level (cycles/maintenance op.) 25,000 1,000,000 2,000
The total costs presented in figure 15 show the increase of overall costs according to the increase of
the annual production. These results are obtained because the variable costs increase proportionally to
the increase of the annual production volume.
Figure 15 – Total costs for the three scenario conditions.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
What is verified from the results of figure 16 is that because the variable costs vary according to the
annual production volumes, the fixed costs will play a major part in the results because they do not
depend on the production conditions. That said, what is verified is that the fixed costs are simply shared
by the number of parts produced, implicating that the higher the annual production volume is, the lower
the impact of the fixed costs is, by the fact that these costs can be divided by a higher number of parts
produced.
This point is clearly observed by analysing the results for the material (variable cost) and tooling cost
(fixed cost), because they are the cost types that dominate the overall results by a large margin, defining
the overall costs almost by themselves.
By comparing the results for both the base scenario and scenario 1 with the results of scenario 2, it can
be seen that the tooling cost is the big differentiator in the overall results, drastically changing its
relevance in the cost results according to the production conditions.
Figure 16 shows that for scenario 2, the tooling cost represents more than half of the overall costs per
part. This happens because the production volume used is low, so there are less parts to share the
45
burden of this cost. In such scenario, the advantage is taken by the mould with cheaper cost in its
production process. The mould with cold runners, being the cheaper to produce will be the one that
costs less to run. On the other hand, moulds with more complex designs like the mould with 10 cavities
and 5 hot runner nozzles are the most expensive in their production phases, boosting the tooling cost.
This means they are more expensive in scenarios with low annual productions.
In the other two scenarios with high production volumes the advantage in the overall costs is taken by
the moulds with higher complexity, namely the mould with 10 cavities and the moulds with 4 hot runner
nozzles, because they are more efficient in their resources consumptions, decreasing the variable costs
like the material and energy costs.
The maintenance cost will depend on the number of cavities used in the mould and on the maintenance
level defined by the company. The increase in the number of cavities of a mould in inversely proportional
to less the maintenance cost. This explains why the mould with 10 cavities, has the lowest maintenance
cost. The other four moulds have two less cavities in their composition increasing their maintenance
cost.
Also because the maintenance level used in scenario 2 is the highest, this will increase the number of
maintenance operations per cycle when compared to the other two scenarios, explaining why this is the
third biggest cost for this scenario. On the other hand, for scenario 1 it is used the lowest level of
maintenance, allowing this cost to be the lowest.
Being a variable cost, the labour cost will be affected by the annual production volume. On top of this,
this cost type will be also indirectly affected by the maintenance level, because this influences the
unplanned breakdowns that are function of the direct workers intermediate variable (equation 38) that
defines this cost type. This explains why for scenario 1, having a low maintenance level, allows the
labour cost to be higher in this scenario than for other production conditions.
These differences in the maintenance and labour cost explain why the cost per part is slightly higher in
for scenario 1, when compared to the results for the base scenario.
46
Figure 16 – Cost per part for the three production scenarios, for the mould use phase.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
Because the results for the EI generated (figure 17) are proportional to the resources consumed, the
reasons why a mould generated more EI than other are the same.
Analysing these results, it can be verified even if the energy consumption generates significant amounts
of EI, their magnitude is much lower than the one verified for the plastic consumption.
Also, because it was considered that the plastic waste generated was recycled, this will help to decrease
the EI, especially in a mould that produces a lot of waste like the mould with cold runners.
47
Figure 17 – Plastic used EI per part (left), energy consumption EI per part (right) and plastic waste generated EI per part (bottom), for mould use phase.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
The total EI generated results are presented in figure 18. The values shown above the results bars for
each mould alternative correspond to the total EI generated per part.
These results show that the plastic consumption is by far the resource that generates more EI. This is
the exact opposite behaviour than the one verified for the mould production phase, where the energy
was the resource that generates more EI. This explains why a mould with a more complex design like
the alternative with 10 cavities, generates more EI during its production phase and uses much less
resources in its use phase, generating much less EI in consequence. The opposite is verified for the
mould with cold runners.
In other words, it can be seen that as the complexity of the mould increases, the EI generated decreases
in the mould use phase.
48
Figure 18 - Total EI per part, for mould use phase.
(SC – Separate Cav; MB – Machined block; HR – Hot runners; CR – Cold runners)
5.9. Conclusions
With the development of the PBM, PBCM and EI evaluation was possible to get the resources
consumed, cost involved and EI generated by the mould production and mould use phases of the five
different mould alternatives. Because the results obtained were significant and easy to read and interpret
they are suitable to apply to a possible eco-efficiency analysis, suiting the purpose of this thesis.
6. Eco-efficiency assessment of the case study
After characterizing the case study in terms of the consumption of resources, costs and EI generated,
the next step is to use this case study has a host to study the influence of mould design on the eco-
efficiency of the mould production and mould use phases. Because this case study contains a wide
range of mould designs that are commonly used, it has the required conditions to be regarded as
representative to the injection moulding industry, being suitable to be used in this analysis (as stated in
chapter 2, the nomenclature used for this eco-efficiency analysis will be the one suggested by the
WBSCD [9]).
The selection of eco-efficiency indicators to do an evaluation can be hard due to the fact that it is possible
to include numerous different indicators in the assessment. Following the existing standards and
guidelines to correctly apply eco-efficiency, Roda’s Master Thesis [10] proposes very complete profiles,
containing indicators for both mould production and the injection moulding processes. Even if these
profiles are already summarized and synthetized they still have very extensive lists of indicators. These
indicators can be used to develop eco-efficiency ratios that can be useful to compare different mould
designs, in order to aid in decision making.
Following the recommendations of ISO 14045:2012 for an eco-efficiency assessment, the functional
units used in an eco-efficiency analysis need to be defined. These units have to be carefully selected in
order to adapt correctly to the needs/aims of the assessment in evaluation [3]. Transporting this idea to
49
the present case study, the functional units must be adapted to the mould production and mould use
phase. It was decided that these units should be as simple as possible in order to the obtained results
to be easy to read. Roda (2015) proposed the use of the following functional units for an assessment in
these areas:
• Functional unit “Mould”;
• Functional unit “Mould mass”;
• Functional unit “Mould lifetime”;
• Functional unit “Part”;
• Functional unit “Part mass”. Between the recommended functional units that can be chosen, it was decided to select the units
“Mould” and “Part”. The unit “Mould” will be used to compare aspects that have to do with mould
production. On the other hand the unit “part” will be used in indicators that relate to the injection moulding
process. These units were selected because of their simplicity, in order to create eco-efficiency ratios
that are easy to read and interpret.
The developed eco-efficiency ratios can be useful to compare de differences in mould designs in
different areas of interest. These areas of interest were selected based on the case study that will be
used to perform this study. These areas are:
• Value added – This area is useful to infer about the influence of the differences in mould design
in the economic value created in the mould use and mould production phase, being useful for
mould producing companies and injection moulding companies (mould users); • Productivity – One of the main goals pursued by mould producing companies and the clients
that buy this product to inject a specific part is to have a mould that can achieve good levels of
productivity, allowing the production of more parts in the shortest possible time. The productivity
assessment is also important from the point of view of the mould production process, in order
to infer about how the different mould designs affect the production times of this process.
• Waste generated– The quantity of waste produced is fundamental to evaluate efficient use of
materials, especially during the use phase where the quantity of resources used is much higher
than in the mould production process [50]. It would be interesting to integrate in this analysis areas of interest like the availability, reliability or
durability, but the case study used in this thesis has limitations in these areas (data not available) not
allowing to infer about the influence on the different mould designs in the eco-efficiency of the injection
moulding industry. The data from this case study is featured in the PhD Thesis developed by Ribeiro
[23], [58], where with the data obtained, the expected unplanned downtime of the mould is correlated
with the preventive maintenance level, according to the injection of parts with different characteristics.
Because the part used in this case study is always the same, these characteristics do not change, not
being able to infer about the influence of differences in mould designs in the referred areas of interest.
The objective of this analysis will be then to assess the utility of the eco-efficiency indicators featured in
the recommended profiles, norms and guidelines [3, 9, 10], to help the development of eco-efficiency
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ratios that can be useful to show and compare how the differences in mould designs will influence the
eco-efficiency in the defined areas of interest. The eco-efficiency in these areas will be analysed for both
mould production and mould use phase by using the functional units “Mould” and “Part”, respectively.
Before assessing the eco-efficiency in this case study it is important to define “Gross value added” (GVA)
and “Earnings before interest, taxes, depreciation and amortization” (EBITDA) that appear in the
suggested eco-efficiency profiles and are suggested by the norms and guidelines [3, 9, 10]. The OECD
defines GVA as “the value of output less the value of intermediate consumption” [59]. Furthermore, it is
“a measure of the contribution to the Gross domestic product (GDP) made by an individual producer,
industry or sector and it is the source from which the primary incomes of the financial account are
generated” [59]. In the case of this case study the GVA will be given by equation 68.
𝐺𝐺𝑉𝑉𝐴𝐴(€) = 𝑆𝑆𝑉𝑉𝑉𝑉𝑒𝑒𝑑𝑑(€) − 𝐶𝐶𝐸𝐸𝐸𝐸𝑆𝑆𝐸𝐸𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑑𝑑 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) − 𝑆𝑆𝑃𝑃𝐶𝐶𝐸𝐸𝐸𝐸𝑒𝑒𝑃𝑃𝑃𝑃𝑉𝑉𝐸𝐸𝑃𝑃𝐸𝐸𝑃𝑃𝑑𝑑 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) −𝑀𝑀𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑉𝑉𝑉𝑉 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) − 𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃𝑘𝑘𝑒𝑒 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) (68)
NASDAQ announces EBITDA as “a financial measure defined as revenues less cost of goods sold and
selling, general, and administrative expenses.” Additionally, it is the “operating and non-operating profit
before the deduction of interest and income taxes, where depreciation and amortization expenses are
not included in the costs” [60]. This measure might prove useful when comparing for example products
built in different countries, where financial policies and conditions are different, allowing comparisons of
similar products or businesses in equal terms. The EBITDA will be given by equation 69.
𝐸𝐸𝐵𝐵𝐼𝐼𝑇𝑇𝐷𝐷𝐴𝐴(€) = 𝑆𝑆𝑉𝑉𝑉𝑉𝑒𝑒𝑑𝑑(€) −𝑀𝑀𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒𝑉𝑉𝑉𝑉 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) − 𝐸𝐸𝑒𝑒𝑒𝑒𝑃𝑃𝑘𝑘𝑒𝑒 𝐸𝐸𝐸𝐸𝑑𝑑𝑃𝑃(€) −𝑘𝑘𝑉𝑉𝑘𝑘𝑒𝑒𝑑𝑑(€) (69)
The ratios results will be also displayed by graphics when necessary. These graphics consist in matching
the results of eco-efficiency indicator present in the numerator part of the ratio with the respective
environmental impact that sits in the denominator position. The analysis of the eco-efficiency
performance is done by measuring the position of the ratio result, relatively to the origin of the graphic.
There are some other metrics that might be interesting to evaluate the eco-efficiency as well. These
cannot be called eco-efficiency ratios because they do not respect equation 1 [9], in which in the
numerator should figure a value indicator. For example, indicators like the quantity of waste generated
by a mould are not considered as value to the company, but they are important indicators of eco-
efficiency. By this fact it is important to see how they vary according to the respective environmental
impact by forming metrics similar to eco-efficiency ratios. It was decided to define these metrics “eco-
efficiency relations”. These are going to be evaluated by similar graphics as the ones used for eco-
efficiency ratios.
The results presented in the tables featuring the eco-efficiency ratios’ results are organised by the value
indicator (numerator) increasing, for easy understanding of the evolution of the results.
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6.1. Development of ratios and relations for mould production phase
The eco-efficiency ratios and relations analysed in this section relate to the mould production process
by using the functional unit “Mould”. These will be grouped into the three different areas of interest
previously mentioned.
6.1.1. Value added
According to the recommended eco-efficiency profiles [10], GVA and EBITDA indicators should be used
as value added eco-efficiency indicators. The eco-efficiency ratios featured in this area of study will have
in their constitution these recommended indicators related to EI indicators related to the mould
production process.
One of the difficulties of this thesis was to obtain information regarding the sale price of the moulds used
in the case study because these are function of combinations of design parameters that companies find
hard to give access to. By this fact, to get these sales prices it was decided to use a fixed profit margin
(15% above the mould production cost) for all the mould the alternatives that is representative of the
margins used nowadays in the injection moulding industry. This margin was obtained from a reliable
source.
The first ratio to be presented relates the mould production GVA with the EI generated by mould
production. This eco-efficiency ratio results are present in table 16.
Table 16 – Eco-efficiency ratio Mould GVA/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Mould alternatives A3-8SC;CR A2-8MB;4HR A4-8SC;2HR A1-8SC;4HR A5-10SC;5HR
Mould GVA (€/Mould) 48,312 € 49,645 € 50,013 € 50,328 € 61,675 €
Mould production EI (mPt/Mould) 869,742 870,517 881,354 881,354 1,045,614
Eco-efficiency Ratio 0.056 0.057 0.057 0.057 0.059
Normalization 0.942 0.967 0.962 0.968 1.000
Observing table 16, it is verified that the results obtained through this ratio show almost no variation in
their behaviour when applied to the different mould alternatives of the case study. This can be explained
because the two eco-efficiency indicators that form this ratio increase proportionally to the complexity of
the mould.
There are three factors that are used to calculate the mould GVA that are function of the complexity of
the mould. These factors are the following:
• Fixed profit margin;
• The resources consumption and consequently their cost;
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• The subcontracts and bought components cost.
Following the tendency of the mould GVA, the mould production EI increases according to the
complexity of the mould to the increase according to the resources consumption. These facts allow the
variations shown between the two indicators too increase almost in the same proportion causing the
ratios results to be almost constant according to the different mould designs featured in the case study.
Verdict - Considering that the mould alternatives used in this case study are representative of typical
moulds used in the injection moulding industry show that this eco-efficiency ratio is not very useful to
decision making, since the differences between the mould alternatives’ eco-efficiency performance are
barely noticeable, being hard to take conclusions.
Eventually by using more accurate profit margins to calculate the GVA, bigger differences will appear
in this ratio’s results. Even so, these differences would never be very significant.
The next eco-efficiency ratio to be analysed relates the different mould alternatives EBITDA with the EI
generated during their production. These results are shown in table 17.
Table 17 - Eco-efficiency ratio Mould EBITDA/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
It can be seen by comparing the results from the eco-efficiency ratios using GVA and EBITDA results,
that even if the EBITDA results share same aspects with the GVA like its proportional increase relatively
to the sale price and the resources consumption cost, this indicator shows bigger differences in the eco-
efficiency results. The introduction of a graphic (figure 19) will help to better analyse the results.
Mould alternatives A3-8SC;CR A4-8SC;2HR A2-8MB;4HR A5-10SC;5HR A1-8SC;4HR
Mould EBITDA (€/Mould) 39,223 € 46,571 € 47,522 € 59,051 € 66,194 €
Mould production EI (mPt/Mould) 869,742 881,354 870,517 1,045,614 881,354
Eco-efficiency Ratio 0.045 0.053 0.055 0.056 0.075
Normalization 0.600 0.704 0.727 0.752 1.000
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Figure 19 – Graphic for eco-efficiency ratio Mould EBITDA/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Even if the mould with 10 cavities has a higher sale price, which could point to a higher EBITDA, this
mould has a lower EBITDA relatively to mould A1-8SC; 4HR. This is explained by differences in the
resources consumption cost between the two alternatives. The bigger resource consumption for the
mould with 10 cavities explains the bigger EI generated in its production phase.
This fact causes the mould with 10 cavities to have a lower eco-efficiency in this ratio relatively to mould
A1-8CS; 4HR due to the much higher EI generated.
The mould with cold runners has the lower sale price and similar resources consumptions and costs,
explaining the lowest ratio result among the alternatives.
Verdict – With the GVA results important conclusions can be taken, namely regarding the overall
impact of the value created by the subcontractors and bought components cost, which is an
advantage. However, this value indicator has a handicap when compared to the EBITDA which is
“camouflaging the raw value” produced by a company when producing an injection mould. The
EBITDA results only count with factors directly pointed to the company, not counting with external
value created. By this fact, with its pros and cons taken into consideration, from now on, in the value
added areas of interest it was chosen to use EBITDA indicators instead of GVA.
As opposed to the ratio using GVA, this ratio shows significant changes in the its behaviour according
to the mould designs, being a useful eco-efficiency ratio to show and compare the value added of
moulds in their production phase.
Other type of indicators present in the suggested profiles [10], intend to evaluate the EI generated by
the mould production technologies. To see if these indicators are useful to compare the value added in
the mould production phase, two ratios were analysed featuring the EI’s from two of the main mould
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production technologies. One featuring the mould EBITDA related to EI generated by milling (table 18)
and a similar ratio featuring the EI generated by EDM (table 19).
Table 18 - Eco-efficiency ratio Mould EBITDA/EI milling.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Mould alternatives A3-8SC;CR A4-8SC;2HR A2-8MB;4HR A5-10SC;5HR A1-8SC;4HR
Mould EBITDA (€/Mould) 39,223 € 46,571 € 47,522 € 59,051 € 66,194 €
EI milling (mPt/Mould) 105,190 115,526 119,803 144,347 115,526
Eco-efficiency Ratio 0.373 0.403 0.397 0.409 0.573
Normalization 0.651 0.704 0.692 0.714 1.000
Table 19 - Eco-efficiency ratio Mould EBITDA/EI EDM.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Mould alternatives A3-8SC;CR A4-8SC;2HR A2-8MB;4HR A5-10SC;5HR A1-8SC;4HR
Mould EBITDA (€/Mould) 39,223 € 46,571 € 47,522 € 59,051 € 66,194 €
EI EDM (mPt/Mould) 190,116 190,116 190,116 236,599 190,116
Eco-efficiency Ratio 0.206 0.245 0.250 0.250 0.348
Normalization 0.593 0.704 0.718 0.717 1.000
Analysing the results for both ratios regarding the moulds with 8 cavities it can be seen that the EBITDA
results are the big differentiators in the ratios’ results, since the EI indicators show very similar results
between the mould alternatives. In any case, mould A1-8SC; 4HR can be highlighted for presenting the
highest eco-efficiency in both eco-efficiency ratios.
It can be seen as well that the EDM technology generated bigger amounts of EI than the milling
technology. This can be explained by the bigger energy consumptions verified in the EDM technology
when compared to milling. This means that mould alternatives that need more EDM intervention in their
production will suffer more in their eco-efficiency performance and vice-versa.
Verdict – Even if there are significant changes in the ratio results present in both tables, for the
purpose of an eco-efficiency assessment, these two ratios seem rather pointless and do not seem to
bring useful information regarding decision making apart from the variations in the EI generated.
Because of this, the same information can be obtained just by comparing the EI generated by the
55
production technologies, proving that not every eco-efficiency indicators combinations make useful
eco-efficiency ratios.
The next eco-efficiency ratio to be analysed is not present in the recommended eco-efficiency profiles
developed by Roda (2015). Even so, if all the eco-efficiency norms and guidelines are respected, new
eco-efficiency indicators and ratios can be proposed. If it is proved that these indicators give useful
information regarding the injection mould’s eco-efficiency, they might even integrate and improve the
suggested eco-efficiency profiles already defined.
A trend that is verified nowadays in the injection moulding industry is that due to low budgets, companies
usually choose the mould that is cheaper to buy, perhaps not giving the needed attention to other
important areas of interest. It would be interesting to have a value added indicator that would analyse
this trend. The next eco-efficiency indicator was born from this idea, analysing the mould sale potential. This indicator analyses the sensitivity of the client to the complexity of the design of each mould.
6.1.1.1. Algorithm for defining eco-efficiency indicator “Mould sale potential”
Three main steps are needed to define this indicator:
• Weighting – Processed by defining the percentages for the relevance of the areas of the
interest according to the client’s needs and sensibility. This process is therefore, subjective.
• Rating – Processed by giving scores the mould alternatives in the defined areas of interest,
where the higher marks are given to the moulds which have better performance in that area and
vice-versa.
• Indexing – Processed by multiplying the rating scores by the weight given by the categories,
creating indexes for every category. Following this, these indexes are summed up to get the
final scores for the eco-efficiency indicator.
When applied to this case study, four areas of interest were defined accordingly (more areas can be
added depending on the assessment).
• Sale price – Rates are given according to the complexity of the mould (the cheaper the better
the rating);
• Maintenance (mould use phase) – Rating is made according to two criteria, in this specific
order:
1. Type of block – cavities machined in block have worst rating due to more expensive
maintenance and higher reparation time. Following this, the ratings are given according
to the number of cavities, having better rating moulds with less cavities (less
components do maintain);
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2. Feeding system – Cold runners are less complex than hot runners, having less
components (less maintenance, lower rating). Ratings after this are comparing the
number of hot runner nozzles (less nozzles, lower rating).
• Productivity (mould use phase) – Rates are given according to the performance results
obtained from the case study;
• Waste generated (mould use phase) – Similar to the productivity area.
Areas of interest like reliability or durability were not added to the definition of this indicator due to the
reasons already presented in chapter 6.
Applied to the case study analysed in this thesis the weighing of the categories could be given by the
table 20. It has to be taken into consideration that since the weighting process is subjective, this is just
one of the many examples of application that can be made for this case study. However, this example
tries to emulate the trend that is verified nowadays in the injection moulding industry.
Table 20 – Weighing process for definition of the value added indicator “Mould sale value”
Weighting of areas of interest
Category Relevance to the client (%)
Cost 70
Maintenance 5
Productivity 20
Waste generated 5
The rating process is shown in the table 21.
Table 21 - Rating process for definition of the value added indicator “Mould sale value”
Mould alternatives Rating of areas of interest (1-5)
Sale price Maintenance Productivity Waste
generated
A3-8SC;CR 5 5 1 1
A4-8SC;2HR 4 4 3 3
A2-8MB;4HR 3 1 4 4
A1-8SC;4HR 2 3 4 4
A5-10SC;5HR 1 2 5 5
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The indexing phase is presented in table 22.
Table 22 - Indexing process for definition of the value added indicator “Mould sale value”
Mould alternatives
Value index score (mould Pt) Total indicator
score (mould Pt)
Cost Index
Maintenance index
Productivity index
Waste generated
index
A3-8SC;CR 3.50 0.20 0.20 0.05 3.95
A4-8SC;2HR 2.80 0.15 0.60 0.15 3.70
A2-8MB;4HR 2.10 0.25 0.80 0.20 3.35
A1-8SC;4HR 1.40 0.10 0.80 0.20 2.50
A5-10SC;5HR 0.70 0.05 1.00 0.25 2.00
6.1.1.2. Analysis of the eco-efficiency ratio featuring the indicator “Mould sale potential” related to the mould production EI
An eco-efficiency was then developed featuring the mould sale potential as a value entry of the ratio,
related to the mould production EI. This ratio is present in table 23.
Table 23 - Eco-efficiency ratio Mould sale potential/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Mould alternatives A3-8SC;CR A2-8MB;4HR A4-8SC;2HR A1-8SC;4HR A5-10SC;5HR
Mould sale potential (mould Pt x 105 /
Mould) 3.95E+05 3.70E+05 3.35E+05 2.50E+05 2.00E+05
Mould production EI (mPt/Mould) 869,742 870,517 881,354 881,354 1,045,614
Eco-efficiency Ratio 0.450 0.430 0.380 0.280 0.190
Normalization 1.000 0.936 0.837 0.625 0.421
Table 23 shows that the ratio result values decrease with the complexity of the mould. This is explained
by:
• The decrease of the mould sale potential with the increase of the complexity of the mould. This
is caused due to bigger weight given to the sale price area of interest, benefiting the moulds
that have lower sale prices, due to their lower complexity (see Appendix). This scenario goes
accordingly to the trend that is verified nowadays.
• The increase of the mould production EI with the complexity of the mould designs, due to the
bigger consumption of resources.
The introduction of a graphic showing table 23 results is useful to compare the behaviour of the ratio
according to the different mould designs. This graphic is present in figure 20.
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Figure 20 - Graphic for eco-efficiency ratio Mould sale potential/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Figure 20 shows clearly that the moulds with lower sale price and lower complexity have higher eco-
efficiency performances when compared to the moulds with higher complexity. It can be seen in this
graphic that the mould with 10 cavities and 5 hot runner nozzles, being the most expensive and complex
is clearly penalized in this ratio’s results. The opposite happens with the mould with cold runners.
Verdict – These ratio results penalize the moulds that have bigger complexity and better performance
in their use phase (studied in the next sub-chapter).
By this fact, according to the trend that is verified nowadays, it can be concluded that the use of eco-
efficiency to evaluate the moulds in their production phase has to be done very carefully. That said,
the use of this eco-efficiency ratio can be useful to invert this tendency, by investing in moulds with
better quality standards, having better performances in their use phase. This is important because
the mould use phase produces by far value and EI results of a much bigger magnitude, being much
more significant than the mould production phase [50].
6.1.2. Productivity
By looking at the suggested eco-efficiency profiles, one way to measure the productivity of the mould
production process can be by determining the quantity of steel removed in the production of each mould.
This determines which type of mould is produced faster, giving an idea of its productivity in its production
phase.
By relating this indicator to the EI generated in mould production a ratio can be developed. This ratio is
shown in table 24 and aided by figure 21.
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Table 24 – Eco-efficiency ratio Rate of steel removed/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Mould alternatives A1-8SC;4HR A4-8SC;2HR A2-8MB;4HR A3-8SC;CR A5-10SC;5HR
Rate of steel removed (g/s) 0.124 0.124 0.125 0.126 0.154
Mould production EI (mPt/Mould) 881,354 881,354 870,517 869,742 1,045,614
Eco-efficiency Ratio 1.41E-07 1.41E-07 1.44E-07 1.44E-07 1.47E-07
Normalization 0.957 0.957 0.980 0.982 1.000
Considering that the mould alternatives present in this case study have different mould designs the
differences in the ratios results are not very significant. The graphic of figure 21 features the results of
table 24 and can help to take better conclusions.
Figure 21 – Graphic for eco-efficiency ratio Rate of steel removed/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
What is verified in these results can be unexpected since even if the mould with 10 cavities is the one
with bigger number of cavities and higher EI generated in the production process (which could point to
the mould with lower eco-efficiency), it is in fact the alternative with better ratio’s results.
These results have to do with the setup times of the machines used in the various production
technologies. Because the same setup times are shared by all the mould alternatives allows the mould
with 10 cavities, having two extra cavities when compared to the other alternatives, to have better rate
of removal.
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Verdict – The goal of this ratio is to show and compare which mould is produced faster, therefore
having better productivity in the mould production. However, the information given by this ratio does
not allow to know which mould is produced faster in overall terms. More specifically, a mould that has
a better rate of steel removal does not mean that it is produced faster.
Also, the changes verified by the results of this ratio are not significant differences according to the
alternatives in analysis.
These reasons lead to the conclusion that this ratio does not give useful information for the purpose
for which it was developed.
Since an indicator is featured in the recommended profiles [10], an effective way to solve this problem
can be by developing an eco-efficiency relation featuring the mould production time indicators present
in the profiles, related to the EI generated by mould production. The graphic for this relation is present
in figure 22.
Figure 22 – Graphic for relation Total mould production time/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Analysing the eco-efficiency relation results from figure 22 it can be verified that the mould production
times increase with the complexity of the mould design.
By this fact the mould with cold runners has the highest eco-efficiency because it needs less production
time and generates lowest EI. The opposite is verified for the mould with 10 cavities because it is the
biggest of the five, therefore having the highest production time and EI.
Verdict – This relation points to the intended idea of productivity for the mould production
process, when assessing a mould’s productivity performance using eco-efficiency. It is then
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recommended to use this eco-efficiency relation instead of the eco-efficiency ratio shown in
table 24.
6.1.3. Waste generated
Roda (2015) features in his developed profiles indicators that measure the waste generated by the
mould production process. To check if these indicators produce useful eco-efficiency results, an eco-
efficiency relation was developed where the quantity of waste generated per mould is related to the EI
generated in mould production. This relation is represented in figure 23.
Figure 23 – Graphic for relation Quantity of waste (steel)/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
In the results of this eco-efficiency relation, the considered steel waste generated from the production
of the moulds of this case study corresponds to the volume of cavities of each mould. This means that
the quantity of steel waste generated in the mould production process is function of the number of
cavities. This fact linked with the higher EI generated in its production explains why the mould with 10
cavities has a lowest eco-efficiency of the lot.
However, from these results (and from the analysis of chapter 5) it is observed that levels of steel waste
generated per mould are so low when compared to the total amount of steel used to build an injection
mould that it shows that this relation does not add much to the analysis.
Roda (2015) proposes in his profiles indicators to analyse the waste generated by the consumables
involved in mould production. This means that on top of the relation of figure 24, it is recommended that
ratios that analyse the waste generated by these consumables are developed. However, due to the fact
that the amount of consumables used in mould production is extremely low and because their nature is
not particularly harmful to the environmental it can be concluded even without developing a ratio or
relation that the information that would overcome from these metrics would be totally negligible and
would not add useful information to an eco-efficiency assessment of the mould production process.
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Verdict – It can be concluded that according to this case study results, that due to the negligible
waste generated in mould production that the eco-efficiency ratios and relations that can be
developed through the suggested eco-efficiency profiles [10] do not show relevance to an eco-
efficiency assessment.
6.2. Development of ratios and relations for the mould use phase
This sub-chapter will look into the recommended eco-efficiency profiles developed [10] to develop eco-
efficiency ratios and relations that can be useful to show and compare different mould designs in their
use phase. To develop ratios and relations to the use phase of the mould, instead of using functional
unit “mould” was used the unit “part”, which is more focused to the injection moulding performance. The
areas of interest to be tested will be the same as the mould production phase.
Because the results from the ratios of this sub-chapter are presented “per part”, they are not affected by
the production conditions used.
Also, in the big majority of the eco-efficiency ratios and relations developed, the EI indicator corresponds
to the “Part production EI”, whose results were already presented in sub-chapter 5.8.
6.2.1. Value added
The recommended eco-efficiency profiles that were developed based on the norms suggested GVA and
EBITDA indicators to test the value added in the mould use phase. To develop ratios that can be useful
to compare the differences in value added shown by moulds with different designs it was decided not to
use GVA indicators, not only because of the reasons already mentioned in 6.1.1, but because in the
injection moulding process of this case study there are no subcontracts or externally bought components
(equation 68), making the results between GVA and EBITDA indicators almost the same in their result
values. The only difference between the two types of indicators is the introduction of the wages cost in
the EBITDA (equation 69), which does not play a very important role in the overall results.
The first eco-efficiency ratio to be tested will then relate the part EBITDA with the total EI generated in
part production. To obtain the EBITDA results, a fixed sale price for the part was used (0.19€). This sale
price was given by a reliable source (company). Table 25 presents the results of this eco-efficiency ratio.
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Table 25 - Eco-efficiency ratio Part EBITDA/Part production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Mould alternatives A3-8SC;CR A4-8SC;2HR A2-8MB;4HR A1-8SC;4HR A5-10SC;5HR
Part EBITDA (€/Part) 0.1567 0.1694 0.1722 0.1722 0.1725
Part production EI (mPt/Part) 2.38 2.00 1.93 1.93 1.88
Eco-efficiency Ratio 0.0658 0.0849 0.0893 0.0893 0.0918
Normalization 0.72 0.93 0.97 0.97 1.00
Analysing the part EBITDA for the different moulds it can be seen that its results increase with the
complexity of the mould design. This is can be explained by:
• The resources consumption costs decrease with complexity of the mould, since moulds with
bigger complexity use less resources;
• The wages cost are function of the time used to produce the parts. More complex moulds need
less time to produce the same amount of parts than a less complex mould, reducing the wages
costs.
• The used part sale price used for the EBITDA does not change according to the mould designs.
By this fact the EBITDA variations for the alternatives are controlled by the resources and wages
costs.
These EBITDA results, aided by the fact that the part production EI generated decreases with the
complexity of the mould, contributes to the increase of the ratio results according to the complexity of
the alternatives.
The graphic of figure 24 helps to better analyse the ratio results of the five mould alternatives.
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Figure 24 – Graphic for eco-efficiency ratio Part EBITDA/Part production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Figure 24 clearly shows that the mould with cold runners has the lowest eco-efficiency performance.
The moulds with hot runners present much better results. It can be seen that the EBITDA results
increase with the increase of the number of hot runner nozzles. The mould with 10 cavities, producing
lower amounts of EI will be the mould with higher eco-efficiency using this ratio.
Verdict – The results for this ratio for mould use benefit the moulds with bigger complexity and better
qualities in their use phase. By comparing this ratio results with the equivalent ratios for the mould
production phase it can be seen that the mould use phase contains levels of EI and EBITDA
incomparably high to the mould production phase. This means that although the ratios for the mould
production phase contain useful information, when buying a mould it is of great interest for the clients
to consider moulds with better performances in their use phase. This ratio can be then be useful to
compare mould designs influence in the use phase and can be used to show to clients in order to
invert the tendency that is verified nowadays, that consists of investing the cheapest mould due to
low budgets.
This eco-efficiency ratio is especially interesting for an injection moulding company, where the mould
is externally bought and used for part injection. Such company will not have any particular interest in
the mould production eco-efficiency performance, but only in their use phase eco-efficiency
performance.
Since it is featured in the recommended profiles [10], it can be interesting to develop ratios that conciliate
the part EBITDA with the EI generated by the mould production process (EI indicator already used in
sub-chapter 6.1) and by part production, whose eco-efficiency ratio was already analysed (table 25 and
figure 24).
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Table 26 presents the results featuring EI for mould production.
Table 26 - Eco-efficiency ratio Part EBITDA/Mould production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Mould alternatives A3-8SC;CR A4-8SC;2HR A2-8MB;4HR A1-8SC;4HR A5-10SC;5HR
Part EBITDA (€/Part) 0.1567 0.1694 0.1722 0.1722 0.1725
Mould production EI (mPt/Mould) 869,742 881,354 870,517 881.354 1,045,614
Eco-efficiency Ratio 1.80E-07 1.92E-07 1.98E-07 1.95E-07 1.65E-07
Normalization 0.91 0.97 1.00 0.99 0.83
The analysis of both tables (table 25 and 26) shows very different results. The most obvious example
can be given by the mould with 10 cavities, where the ratio results that feature the part production EI
(table 25) show that this is the mould with highest eco-efficiency and the ratio results using the mould
production EI (table 26) show the exact opposite.
From the results of these ratios it can be established that:
• The ratio that features the injection moulding EI benefit the moulds with higher complexity;
• The ratio that features the part production EI punish the moulds with higher complexity;
• The EI generated in the injection moulding process is much higher than in the mould production
phase, confirming what was already verified by Peças et al. [50].
Verdict – The comparison between these ratios can be useful in the context proving that a mould
eco-efficiency performance in the mould production phase is totally the opposite of the one verified in
the mould use phase.
That said, the analysis of both these eco-efficiency ratios is useful to seed the idea that the use of
eco-efficiency for the mould production process has to be done very carefully.
6.2.2. Productivity
The suggested eco-efficiency profiles recommend the use of an indicator that illustrates the quantity of
useful plastic injected per cycle (being the useful plastic the material used except the waste). An
eventual ratio using this indicator could be used to compare the different mould designs influence on
the productivity of the mould use phase. An eco-efficiency ratio can then be developed by relating this
indicator to the EI generated in part production. This ratio’s results can be seen in table 27.
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Table 27 – Ratio for Quantity of useful plastic per cycle/Part production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Mould alternatives A5-10SC;5HR A2-8MB;4HR A1-8SC;4HR A4-8SC;2HR A3-8SC;CR
Quantity of useful plastic (g/Cycle) 1.343 1.075 1.075 1.075 0.882
Part production EI (mPt/Part) 1.88 1.93 1.93 2.00 2.38
Eco-efficiency Ratio 0.71 0.56 0.56 0.54 0.37
Normalization 1.000 0.782 0.782 0.755 0.521
The results from table 27 show that the quantity of useful plastic increases according to two factors:
• The feeding system – A cold runner system has a higher injection cycle time than a mould with
hot runner system, due to the fact that the throughput has to arrive to the ejection temperature,
increasing the cooling time. By this fact the useful plastic per cycle is higher in a hot runner
system when compared to a cold runner system;
• The number of cavities – The increase in the number of cavities of a mould allows more parts
to be produced per cycle, increasing the quality of useful plastic per cycle.
Both these reasons linked with the fact that the part production EI decreases according the engineering
level of the mould designs, allows the moulds with more complex design to have better results by using
this eco-efficiency ratio.
Figure 25 shows table 27 results in a graphical form.
Figure 25 – Graphic for ratio Quantity of useful plastic per cycle/Part production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
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Figure 25 shows undoubtedly the differences in the eco-efficiency results by using this ratio. That said,
the mould with cold runners has by far the lowest eco-efficiency in this ratio. On the other hand the
mould with 10 cavities has the highest eco-efficiency. The moulds with hot runners and 8 cavities have
similar performances. However, the differences in the EI generated determine that the mould A4-8SC;
2HR has a lower eco-efficiency between these three moulds.
Verdict – The results of this ratio confirm the fact that the productivity of the injection moulding
process improves with the increase in the engineering level of mould design of the moulds, especially
by adding hot runners in its constitution.
This eco-efficiency ratio proves then to be useful in successfully showing and comparing the mould’s
productivity in the mould use phase.
Roda’s (2015) recommended eco-efficiency profiles feature an indicator for the injection cycle time of
the moulds use phase. Because the cycle time was so determinant in the analysis of the ratio featuring
the quantity of useful plastic per cycle, an eco-efficiency relation was developed by relating the cycle
time with the part production EI. This relation is present in figure 26.
Figure 26 – Graphic for relation Cycle time/Part production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Results from figure 26 confirm that the mould with cold runners has by far the higher cycle time and EI
generated having the lowest eco-efficiency. On the other hand, the mould with 10 cavities has the lowest
cycle time and EI having the highest eco-efficiency performance.
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Verdict – The use of this eco-efficiency relation to compare how the different mould designs affect
the productivity of the mould use phase is recommended because its results are easy to read and
understand, being useful to explain the results from table 27 and figure 25.
By this fact this relation can be used to complement this eco-efficiency ratio.
6.2.3. Waste generated
Roda (2015) features in his developed profiles indicators that measure the waste generated by the
mould use phase. To check if these indicators produce useful eco-efficiency results, an eco-efficiency
relation was developed where the quantity of plastic waste generated is related to the EI generated in
mould production. This relation is represented in figure 27.
Figure 27 – Graphic for relation Plastic waste generated/Part production EI.
(SC – Separate Cavities; MB – Machined block; HR – Hot Runners; CR – Cold Runners)
Results from this eco-efficiency relation show that the amount of waste generated is proportional to the
engineering level of the mould design.
These results are greatly influenced by the feeding system of a mould, namely the use of cold or hot
runners. The reasons that explain this fact are:
• A cold runner system will spend material in the solidification of the throughput and the plastic
leftovers in the mould channels, adding to a lot amount of waste generated when compared to
a hot runner system.
• Hot runner systems will generate very low amounts of plastic waste due to the nonexistence of
a throughput, contributing to very low waste of material only originated by leftovers of injection
cycles.
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Following this it can be seen that the mould with cold runners is by far the mould with lowest eco-
efficiency in this relation, having the highest waste and EI generated.
Mould A4-8SC; 2HR will generate more waste than the rest of the moulds with hot runners by the fact
that the existence of just 2 hot runners adds to the need of having bigger channels that reach all the 8
cavities. This will enhance the waste generated due to plastic leftovers in the end of injection cycles.
The mould with 10 cavities has the highest eco-efficiency in these relation’s results due to the lower EI
generated in its use phase.
Verdict – As opposed to what happened for the analysis for the mould production process, where the
results from the developed relations were negligible, in the mould use phase the use of eco-efficiency
ratios and relations has to be considered due to the magnitude of the plastic waste generated in the
injection moulding process.
This relation presents then very useful information for the company and possible clients and gives
the advantage to the moulds that have the best efficiency in the use of resources.
7. Frameworks of eco-efficiency ratios and relations to aid in decision making
Based on the eco-efficiency analysis of the case study and the conclusions taken regarding which eco-
efficiency ratios and relations can be give useful information regarding the comparison of mould designs
for the mould production and mould use phase, it was decided to develop frameworks of eco-efficiency
ratios and relations with the intention of helping in the decision making of companies (company that sells
the mould and company that buys it) in specific situations.
The frameworks proposed aid decision making into two different areas:
• Internal decision making for mould producing companies;
• External communication for potential clients. It has to be advised that because these proposed frameworks of eco-efficiency metrics are done based
on the analysis performed in chapter 6, they have the handicap of not including important areas of
interest to mould producing companies and possible clients, like the availability, durability or reliability.
7.1. Internal decision making for mould producing companies
The proposed framework of eco-efficiency ratios and relations for internal decision making is intended
for mould producing companies to analyse how the different mould designs affect the eco-efficiency of
the mould production and the mould use phase.
This proposed framework is present in table 28.
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Table 28 - Framework of eco-efficiency ratios and relations for internal decision making.
Ratio/relation no. Recommended eco-efficiency ratios and relations Area of
interest
1 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝐸𝐸𝐵𝐵𝐼𝐼𝑇𝑇𝐷𝐷𝐴𝐴 �€
𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃� �
𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃� �
Value added 2 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑑𝑑𝑉𝑉𝑉𝑉𝑒𝑒 𝑆𝑆𝐸𝐸𝑃𝑃𝑒𝑒𝑒𝑒𝑃𝑃𝑒𝑒𝑉𝑉𝑉𝑉 �𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑃𝑃𝑃𝑃
𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃� �
𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃� �
3 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝐸𝐸𝐵𝐵𝐼𝐼𝑇𝑇𝐷𝐷𝐴𝐴 �€
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
4 𝑇𝑇𝐸𝐸𝑃𝑃𝑉𝑉𝑉𝑉 𝐸𝐸𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 �ℎ 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃� �
𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃� �
Productivity 5 𝑄𝑄𝑃𝑃𝑉𝑉𝑒𝑒𝑃𝑃𝑒𝑒𝑃𝑃𝑒𝑒 𝐸𝐸𝑒𝑒 𝑃𝑃𝑑𝑑𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉 𝑆𝑆𝑉𝑉𝑉𝑉𝑑𝑑𝑃𝑃𝑒𝑒𝐸𝐸 𝑆𝑆𝑒𝑒𝑃𝑃 𝐸𝐸𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 �𝑘𝑘 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
6 𝐶𝐶𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 �𝑑𝑑 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
7 𝑃𝑃𝑉𝑉𝑉𝑉𝑑𝑑𝑃𝑃𝑒𝑒𝐸𝐸 𝑃𝑃𝑉𝑉𝑑𝑑𝑃𝑃𝑒𝑒 𝑘𝑘𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃 �𝑘𝑘 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� � Waste
generated
The choices for the ratios and relations for this framework are justified by:
• Ratio no. 1 – This ratio proved to be useful for comparing the value added produced by the
manufacturing of different mould designs. This ratio is also useful to compare similar moulds
produced in different countries. This is then a ratio that mould producing companies should use
internally.
• Ratio no. 2 – This ratio is useful for the mould producing company to see the tendencies of the
market and to see what do the potential client most valorise in an injection mould. This ratio is
flexible and can with the future trends of the market.
• Ratio no. 3 – This is effective for mould producing companies to analyse the value added by
their products in the mould use phase.
• Relation no. 4 – This relation is useful to the mould producing company by the fact that with it,
it is possible see how the different mould designs influence the productivity of the mould
production process.
• Ratio no. 5 and relation no. 6 – By using both this ratio and relation, the mould producing
company can see and compare the productivity capabilities of their moulds in their use phase.
• Relation no. 7 – This relation is useful for mould producing companies to see how different
mould designs compare in terms of waste generated, being useful to control the use of
resources consumed.
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As already concluded in chapter 6, the introduction of a ratio or relation to study the waste generated in
the mould production process is negligible.
7.2. External communication for potential clients
Another framework of eco-efficiency ratios and relation was developed to aid mould producing
companies in external communication with possible clients that buy the moulds to inject a part (injection
moulding companies). By this fact, ratios and relations used in this proposed framework were selected
with the client view in mind.
This framework is represented in table 29.
Table 29 - Framework of eco-efficiency ratios and relations for external communication.
Ratio/relation no. Recommended eco-efficiency ratios and relations Area of
interest
1 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝐸𝐸𝐵𝐵𝐼𝐼𝑇𝑇𝐷𝐷𝐴𝐴 �€
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
Value added
2 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝐸𝐸𝐵𝐵𝐼𝐼𝑇𝑇𝐷𝐷𝐴𝐴 �€
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑀𝑀𝐸𝐸𝑃𝑃𝑉𝑉𝑃𝑃� �
3 𝐶𝐶𝑒𝑒𝐸𝐸𝑉𝑉𝑒𝑒 𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 �𝑑𝑑 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� � Productivity
4 𝑃𝑃𝑉𝑉𝑉𝑉𝑑𝑑𝑃𝑃𝑒𝑒𝐸𝐸 𝑃𝑃𝑉𝑉𝑑𝑑𝑃𝑃𝑒𝑒 𝑘𝑘𝑒𝑒𝑒𝑒𝑒𝑒𝑃𝑃𝑉𝑉𝑃𝑃𝑒𝑒𝑃𝑃 �𝑘𝑘 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� �
𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃 𝑆𝑆𝑃𝑃𝐸𝐸𝑃𝑃𝑃𝑃𝐸𝐸𝑃𝑃𝑒𝑒𝐸𝐸𝑒𝑒 𝐸𝐸𝐼𝐼 �𝐸𝐸𝑃𝑃𝑃𝑃 𝑃𝑃𝑉𝑉𝑃𝑃𝑃𝑃� � Waste
generated
The ratios and relations for this framework were chosen by the following reasons:
• Ratio no. 1 – This ratio is useful to show to potential clients the value added created with the
use of their moulds in the injection moulding phase.
• Ratio no. 2 – This indicator should be used together with ratio 1 to compare the value added
by mould designs in their mould production and mould use phase, in order to show to clients
that even if moulds with higher complexity are less eco-efficient in their production process, they
have by far a better eco-efficiency in their use phase, where the magnitudes of value and EI
created are much higher.
• Relation no. 3 – This ratio is useful to show to potential clients the productivity a certain mould
design during its use phase. Since one of the objectives of this framework is to be for a potential
client to understand, it was decided to include just this relation in the productivity area, instead
of including the ratio containing the quantity of useful plastic per cycle, which is a concept harder
to understand.
• Relation no. 4 – This relation allows to show the potential client the performance of the mould
in the use of resources.
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Ratios and relations that concern the productivity of the mould production process and value added
ratios that feature the mould EBITDA were not included in this framework, due to the fact that this
information is not useful to potential clients.
8. Conclusions
The research developed showed that even if it this concept is relatively recent, eco-efficiency is already
being applying in some areas of study with very interesting results.
Also, the literature survey done throughout the development of this thesis allow the conclusion that there
is a gap regarding studies that apply eco-efficiency methodologies to the injection moulding industry,
since very few research has been done.
To be able to apply the case study data to the eco-efficiency analysis performed in this thesis, a
methodology was developed where the first step concerned collecting the results regarding the
resources consumed, the respective costs and EI generated in the mould production and mould use
phases of the five mould featured in this case study. To get these referred results was decided to develop
a PBM and a PBCM to obtain the results for the resources and costs respectively. These tools revealed
to be very useful in this matter, giving clear results that can be subjected to various production conditions,
being ideal to get the results to apply to the eco-efficiency ratios developed. Furthermore, to obtain the
last set of results that concern the EI generated by the mould production and mould use phases, an EI
evaluation was performed by applying ReCiPe to the resources results obtained from the PBM.
From the resources consumptions results it was concluded that even if the steel consumed to produce
an injection mould has significant results, the resource that has higher consumption in the mould
production phase is the energy. Another conclusion has do with the very low amounts of steel scrap
(waste) generated by the mould production process, which the amounts generated are incomparably
lower when compared to the resources consumptions.
Regarding the resources consumptions for the mould use phase it was concluded that the plastic
material consumption is by far the resource that has higher consumption rates. Oppositely to the results
for mould production, the waste generated in this case is very significant.
From the point of view of the costs obtained by the PBCM results, it was concluded that the labour and
equipment cost are one of the highest costs for mould production due to their dependence on the
production times because of their higher costs per hour. The subcontracts/bought components cost
plays a big role in the mould production results, since essentially the feeding systems are expensive
(especially hot runners).
The costs results for the mould use phase showed that since the variable cost depend on the production
scenarios, namely the annual production volume, the fixed costs will play a major part in the results
since they do not depend on the production conditions, meaning that their cost is shared by the amount
of parts produced. It was also concluded that the material (variable cost) and the tooling (fixed cost)
73
costs dominate the cost results for the mould use phase, defining the overall results almost by
themselves.
The EI generated results for the mould production phase showed that even if the steel producing a
higher EI when compared to the EI generated by the energy, because these last cited resource has a
much higher consumption than the one verified for steel, means that similarly to the results for the
resources consumptions the energy is the resource that generates a higher EI by a large margin. Also,
because the waste scrap generated is so low when compared to the resources consumed, it will
generate a negligible EI.
For the mould use phase the material is the resource generating a higher EI by a large margin, due to
its much higher consumptions when compared to the other resources. The waste generated in this case
will generate a significant EI due to the levels of plastic scrap generated by the mould production phase,
especially for the mould with cold runner feeding system.
Like expected from the three sets of results (resources, costs and EI) obtained from the case study, it
was concluded for the mould production phase that the moulds who have a higher engineering level
and complexity need a higher resources consumption, namely more use of material and energy, than
the moulds with lower complexity. These conclusions were furthermore supported by the higher costs
and EI generated, following the same tendency of the resources consumed.
For the mould use phase what is verified is exactly the opposite, where the moulds with higher
engineering level have much better resource efficiency when compared to the moulds with lower
complexity.
The case study results obtained support the conclusion that the PBM, PBCM and EI evaluation were
successfully developed and implemented.
Following the work done by Roda (2015) (one of the few studies in this area), who developed extensive
profiles to apply eco-efficiency to the injection moulding industry, the main objective of this thesis was
to develop eco-efficiency ratios that can be used to compare the performance of different mould designs,
in certain areas of interest (value added, productivity and waste generated) for the mould production
and mould use phases. The development of these ratios was done based on these recommended eco-
efficiency profiles, norms and guidelines to correctly apply the concept of eco-efficiency.
The collected case study results (five different mould designs used to produce the same part) were then
applied to the developed eco-efficiency ratios with the objective of testing their behaviour and response
according to the five mould alternatives to infer if these in fact can be useful to compare different mould
designs.
For the mould production phase, starting by the eco-efficiency ratios to compare the value added, it was
concluded that the ratio featuring the mould GVA is not very useful to decision making, since the
differences between the mould alternatives’ eco-efficiency performance are barely noticeable.
74
On the other hand, the ratio using EBITDA shows significant changes in its behaviour according to the
mould designs, being a useful eco-efficiency ratio to show and compare the value added of moulds in
their production phase.
Regarding the same topic, it was concluded that it is not useful to use eco-efficiency ratios that feature
the EI generated by production technologies by the fact that the same conclusions can be obtained just
by comparing the indicators for the EI generated, proving that not every eco-efficiency indicators
combinations make useful eco-efficiency ratios.
The eco-efficiency ratio featuring the developed indicator to assess the mould sale potential is useful to
study the tendencies of the market. Because the priority nowadays is to buy the mould is cheaper,
instead of the mould that has better performances in the use phase, this eco-efficiency ratio can be
useful to invert this tendency.
Regarding the productivity ratios for the mould production phase it was concluded that the ratio featuring
the rate of steel removed does not give useful information for the purpose for which it was developed,
which is to evaluate which mould designs have faster production times.
On the other hand, the eco-efficiency relation featuring the mould production time fulfils this goal,
therefore it is then recommended to use this eco-efficiency relation.
Because the waste generated for the mould production phase is negligible, the use of eco-efficiency
ratios to compare the differences in mould designs regarding this matter is pointless.
Regarding the eco-efficiency ratios for the mould use phase, in the value added section it was concluded
that the use the ratio featuring the part EBITDA is useful to compare mould designs influence in the use
phase and can be used to show to clients in order to invert the tendency that is verified nowadays, that
consists of investing the cheapest mould due to low budgets. This eco-efficiency ratio is especially
interesting for an injection moulding company because it does not take into consideration the mould
production phase.
By comparing this ratio with the one featuring the EI generated for the mould production phase it can be
concluded a mould’s eco-efficiency performance in the mould production phase is totally the opposite
of the one verified in the mould use phase. The conclusion is that the analysis of both these eco-
efficiency ratios is useful to seed the idea that the use of eco-efficiency for the mould production process
has to be done very carefully because it can penalize the mould designs that have better performance
in the mould use phase.
For the productivity in the mould use phase, it was concluded that although the eco-efficiency ratio
featuring the quantity of useful plastic is useful for the comparison of different mould designs, the use of
the eco-efficiency relation featuring the cycle time is recommended because its results are easy to read
and understand.
With the eco-efficiency relation used to compare the waste generated in the mould use phase it was
concluded that, as opposed to what happened for the analysis for the mould production process, in the
75
mould use phase the waste generated has to be considered due to the magnitude of the plastic waste
generated. The eco-efficiency relation used to compare the waste generated in the mould use phase is
useful to the company and possible clients and gives the advantage to the moulds that have the best
efficiency in the use of resources.
With the conclusion taken from the eco-efficiency analysis, the useful eco-efficiency ratios and relations
were gathered to propose frameworks of eco-efficiency ratios and relations to aid in decision making.
This thesis results show that eco-efficiency helps to implement sustainable policies by evaluating the
environmental performance of a product, service or process, while taking into consideration its economic
performance as well. It can be concluded that eco-efficiency is a tool that can effectively help in decision
making.
9. Future work
Because of the limitations of the case study used, as future work it would be interesting to analyse the
eco-efficiency ratios that can overcome from areas of interest like the availability, reliability or durability,
because along with the areas of interest analysed in this thesis they are very important to the injection
moulding industry.
Also it would be interesting to ask among the injection moulding industry the applicability of the proposed
frameworks of eco-efficiency ratios and relations developed for decision making, as well as applying
them in a real industry situation.
For future work it is proposed to develop eco-efficiency assessments to another type of industries,
products or services, in order to continue the trend of implementing sustainable strategies and to prove
the wide applicability of eco-efficiency.
76
10. References
[1] TRUST IN - European Training Partnership on Sustainable Innovation, “History and Definitions
of Eco-Efficiency,” 2010.
[2] K. Czaplicka-Kolarz, D. Burchart-Korol, and P. Krawczyk, “Eco-efficiency analysis methodology
on the example of the chosen polyolefins production,” J. Achiev. Mater. Manuf. Eng., vol. 43, no.
1, pp. 469–475, 2010.
[3] International Organization for Standardization, “ISO 14045:2012 Environmental management -
Eco-efficiency assessment of product systems - Principles, requirements and guidelines,”
Geneva.
[4] M. Lehni, “Eco-efficiency. Creating more value with less impact,” 2000.
[5] A. J. Baptista, E. J. Lourenço, J. P. Pereira, F. Cunha, B. Marques, E. J. Silva, and P. Peças,
“Eco-efficiency Framework as a Decision Support Tool to Enhance Economic and Environmental
Performance of Production Systems,” 2014.
[6] O. Michelsen, “Eco-Efficiency Assessments as a Tool for Revealing the Environmental
Improvement Potential of New Regulations,” Sustainability, vol. 2, no. 1, pp. 117–126, 2010.
[7] EEA, “European Environmental Agency,” European Environmental Agency, 2008. .
[8] S. Esteves, E. J. Lourenço, N. Moita, P. Peças, I. Ribeiro, E. Henriques, and J. P. Pereira,
“Injection Moulding Process Indicators to Foster a More Sustainable Production of Plastic Parts,”
2014.
[9] H. Verfaillie and R. Bidwell, “Measuring eco-efficiency: a guide to reporting company
performance,” 2000.
[10] C. Roda, “Identificação de Indicadores de Ecoeficiência para a Indústria dos Moldes de Injeção
de Plástico,” 2015.
[11] International Organization For Standardization, “Environmental management The ISO 14000
family of International Standards ISO in brief ISO and the environment,” Environ. Manage., pp.
1–12, 2009.
[12] ISO 14040:2006 - Environmental management: life cycle assessment-principles and framework.
2009.
[13] J. G. Vogtländer, A practical guide to LCA for students designers and business managers. 2010.
[14] P. Consultants, “Eco-indicator 99 Manual for Designers,” Minist. Housing, Spat. Plan. Environ.,
no. October, 2000.
[15] M. Goedkoop, R. Heijungs, A. De Schryver, J. Struijs, and R. van Zelm, “ReCiPe 2008. A LCIA
method which comprises harmonised category indicators at the midpoint and the endpoint level.
Characterisation.,” A life cycle impact …, p. 133, 2013.
77
[16] PRe, “Simapro Database Manual - Methods library,” pp. 3–48, 2015.
[17] I. E. Ribeiro, “Comprehensive Life Cycle Framework Integrating Part and Tool Design,” 2012.
[18] N. S. Ermolaeva, M. B. G. Castro, and P. V. Kandachar, “Materials selection for an automotive
structure by integrating structural optimization with environmental impact assessment,” Mater.
Des., vol. 25, no. 8, pp. 689–698, 2004.
[19] L. M. Ellram and S. P. Siferd, “Total Cost of Ownership: A Key Concept in Strategic Cost
Management Decisions,” J. Bus. Logist., vol. 19, no. 1, pp. 55–84, 1998.
[20] H. P. Barringer, “A Life Cycle Cost Summary,” in International Conference of Maintenance
Societies (ICOMS®-2003, 2003, pp. 1–10.
[21] F. Field, R. Kirchain, and R. Roth, “Process cost modeling: Strategic engineering and economic
evaluation of Materials technologies,” Jom, vol. 59, no. 10, pp. 21–32, 2007.
[22] R. Kirchain and F. R. Field, “Process-based cost modelling: Understanding the economics of
technical decisions.,” Encycl. Mater. Sci. Eng., vol. 2, pp. 1718–1727, 2001.
[23] I. Ribeiro, P. Peças, and E. Henriques, “Incorporating tool design into a comprehensive life cycle
cost framework using the case of injection molding,” J. Clean. Prod., vol. 53, pp. 297–309, 2013.
[24] K. Charmondusit and K. Keartpakpraek, “Eco-efficiency evaluation of the petroleum and
petrochemical group in the map Ta Phut Industrial Estate, Thailand,” J. Clean. Prod., vol. 19, no.
2–3, pp. 241–252, 2011.
[25] G. P. Kharel and K. Charmondusit, “Eco-efficiency evaluation of iron rod industry in Nepal,” J.
Clean. Prod., vol. 16, no. 13, pp. 1379–1387, 2008.
[26] M. Koskela, “Measuring eco-efficiency in the Finnish forest industry using public data,” J. Clean.
Prod., vol. 98, pp. 316–327, 2014.
[27] S. Lozano, B. Adenso-Díaz, and Y. Barba-Gutiérrez, “Russell non-radial eco-efficiency measure
and scale elasticity of a sample of electric/electronic products,” J. Franklin Inst., vol. 348, no. 7,
pp. 1605–1614, 2011.
[28] K. Müller, A. Holmes, M. Deurer, and B. E. Clothier, “Eco-efficiency as a sustainability measure
for kiwifruit production in New Zealand,” J. Clean. Prod., pp. 1–10, 2014.
[29] Z. Yang, X. Zhou, and L. Xu, “Eco-efficiency optimization for municipal solid waste management,”
J. Clean. Prod., vol. 104, pp. 242–249, 2015.
[30] Y. Wang, J. Liu, L. Hansson, K. Zhang, and R. Wang, “Implementing stricter environmental
regulation to enhance eco-efficiency and sustainability: A case study of Shandong Province’s
pulp and paper industry, China,” J. Clean. Prod., vol. 19, no. 4, pp. 303–310, 2011.
[31] I. Pires, “Moldação por Injecção - Notas,” Lisbon, 2014.
[32] D. V. Rosato, D. V. Rosato, and M. G. Rosato, Injection Moulding Handbook, 3rd ed. Norwell,
MA: Kluwer Academic Publishers, 2000.
78
[33] A. M. Brito and A. Matos, “Manual do projectista para moldes de injecção de plástico - VOLUME
2.” 2001.
[34] “Elite machinery systems - Plastic Injection Molding Machines,” 2014. [Online]. Available:
http://elitemachinerysystems.com/plastic_injection_molding.php. [Accessed: 01-Oct-2015].
[35] A. M. Brito and A. Matos, Manual do projectista para moldes de injecção de plástico - VOLUME
3. 2001.
[36] “Noble Institute Of Engineering Design Solutions.” [Online]. Available:
http://www.nieds.com/mould-design.php. [Accessed: 01-Oct-2015].
[37] “Longmold Technology Brief.” [Online]. Available:
http://www.longmold.com/mold_design_runner.php. [Accessed: 01-Oct-2015].
[38] “Liquid Silicone Injection Molding Company | SIMTEC Silicone Parts.” [Online]. Available:
http://www.simtec-silicone.com/. [Accessed: 01-Oct-2015].
[39] N. Coffee and M. Morgan, “Life Cycle Cost Analysis Handbook,” p. 30, 1999.
[40] H. S. Park and T. T. Nguyen, “Optimization of injection molding process for car fender in
consideration of energy efficiency and product quality,” J. Comput. Des. Eng., vol. 1, no. 4, pp.
256–265, 2014.
[41] J. Madan, M. Mani, J. H. Lee, and K. W. Lyons, “Energy performance evaluation and
improvement of unit-manufacturing processes: injection molding case study,” J. Clean. Prod., pp.
1–14, 2014.
[42] E. Müller, R. Schillig, T. Stock, and M. Schmeiler, “Improvement of injection moulding processes
by using dual energy signatures,” Procedia CIRP, vol. 17, no. Imm, pp. 704–709, 2014.
[43] A. Y. C. Nee, B. Song, and S. Ong, “Re-engineering Manufacturing for Sustainability,” in
Proceedings of the 20th CIRP International Conference on Life Cycle Engineering, Singapore
17-19 April, 2013, 2013.
[44] M. a. Sellés, S. Sanchez-Caballero, and E. Perez-Bernabeu, “Analysis and Review of Different
Tools to Calculate the Production Economics in Injection Molding,” 7th Int. Conf. Interdiscip. Eng.
(INTER-ENG 2013), vol. 12, pp. 439–441, 2013.
[45] G. Lucchetta and P. F. Bariani, “Sustainable design of injection moulded parts by material
intensity reduction,” CIRP Ann. - Manuf. Technol., vol. 59, no. 1, pp. 33–36, 2010.
[46] K.-F. Pun, I.-K. Hui, W. G. Lewis, and H. C. W. Lau, “A multiple-criteria environmental impact
assessment for the plastic injection molding process: a methodology,” J. Clean. Prod., vol. 11,
no. 1, pp. 41–49, 2003.
[47] K. S. Chin and T. N. Wong, “Knowledge-based evaluation for the conceptual design development
of injection molding parts,” Eng. Appl. Artif. Intell., vol. 9, no. 4, pp. 359–376, 1996.
[48] T. Spiering, S. Kohlitz, H. Sundmaeker, and C. Herrmann, “Energy efficiency benchmarking for
79
injection moulding processes,” Robot. Comput. Integr. Manuf., pp. 1–15, 2015.
[49] P. Peças, I. Ribeiro, E. Henriques, A. S. Pouzada, and A. J. Pontes, “The importance of applying
life-cycle approaches in the design decision of injection moulded plastics parts,” 3rd Int. Conf.
Polym. Mould. Innov. – PMI 2008, vol. 212, pp. 1–6, 2008.
[50] I. Ribeiro, P. Peças, and E. Henriques, “Environmental Impact of Plastic Injection Moulds,” 3rd
Int. Conf. Polym. Mould. Innov. – PMI 2008, vol. 253, pp. 246–253, 2008.
[51] I. Ribeiro, P. Peças, and E. Henriques, “Modelling the energy consumption in the injection
moulding process,” vol. X, pp. 1–21.
[52] G. Gantar, A. Glojek, M. Mori, B. Nardin, and M. Sekavčnik, “Resource efficient injection
moulding with low environmental impacts,” Stroj. Vestnik/Journal Mech. Eng., vol. 59, no. 3, pp.
193–200, 2013.
[53] P. Peças, I. Ribeiro, R. Folgado, and E. Henriques, “A Life Cycle Engineering model for
technology selection: a case study on plastic injection moulds for low production volumes,” J.
Clean. Prod., vol. 17, no. 9, pp. 846–856, 2009.
[54] I. Ribeiro, C. Pousa, R. Folgado, P. Peças, and E. Henriques, “LCC and LCA Simplified Models
to Foster the Design of Sustainable Plastic Injection Moulds,” 16th CIRP Int. Conf. Life Cycle
Eng. (LCE 2009), no. Lce, pp. 99–104, 2009.
[55] “THYROPLAST ® 2312 (Ultra Holder) Technical Data Sheet,” vol. 2312. p. 4977.
[56] “Ensiger GmbH - Polyester (PET, PBT, PC).” [Online]. Available: http://www.ensinger-
online.com/en/materials/engineering-plastics/polyester/. [Accessed: 05-Oct-2015].
[57] R. D. Duffy and W. E. Sedlacek, “Material selection guide,” J. Career Dev., 2007.
[58] I. Ribeiro, P. Peças, and E. Henriques, “A life cycle framework to support materials selection for
Ecodesign: A case study on biodegradable polymers,” Mater. Des., vol. 51, pp. 300–308, 2013.
[59] “OECD Gross value added (GVA) Definition.” [Online]. Available:
https://stats.oecd.org/glossary/detail.asp?ID=1184. [Accessed: 17-Aug-2015].
[60] “NASDAQ EBITDA Definition.” [Online]. Available:
http://www.nasdaq.com/investing/glossary/e/earnings-before-interest-taxes-depreciation-and-
amortization. [Accessed: 17-Aug-2015].
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Annexes
A1 – Exogenous Variables
Table 30 – A1, Exogenous Variables
Exogenous variables Units Working days 225 Days/Year
Wage operators 15 €/h Wage designers 18 €/h Unit energy cost 0.08 €/kW.h
Interest rate 15 Year Equipment life 10 Year
Indirect workers/direct workers 0.25 - Indirect workers/line 1 -
Building unit cost 800 €/m2 Building life 30 Year Idle space 25 %
Investment maintnance cost 2 %
A2 – Material parameters
Table 31 – A2, Material parameters
Materials Parameters Unit
Plastic (PBT)
Injection temperature (Tinj) 25 ºC Ejection temperature (Text) 120 ºC
Mould temperature (Tmould) 100 ºC Unit cost 3.3 €/kg
Density (ρ) 1300 kg/m3 Thermal condutivity (K) 0.25 W/m.K
Specific heat (c) 1000 J/kg.K Adimensional temperature (Y) 7.5 -
Steel
Market designation 2312 -
Unit cost 6.5 €/kg
Density 7.85 g/cm3
Scrap price 0.2 €/kg
The parameters for part plastic PBT are used to calculate the cooling time. This time is given by equation
70 [17].
A
(70)
Where s is the maximum part thickness (m), k is the part thickness coefficient (k=4/π if s ≤ 3 mm, K=8/π2
if s > 3 mm).
The Y parameter corresponds to the adimensional temperature. This is given by equation 71 [33].
(71)
And the average effective thermal diffusivity (αef) is given by equation 72 [33].
𝛼𝛼𝑙𝑙𝑙𝑙 �𝐸𝐸2𝑑𝑑� � =
𝐸𝐸𝜌𝜌 × 𝐾𝐾
(72)
A3 – Maintenance and Downtime data
Table 32 – A3, Maintenance data
Maintenance level (cycles/maintenance op.) Unplanned breakdowns 1000000 100 100000 35 50000 16 25000 10 2000 1
Path equation y=0,0043x^(-0,752)
Table 33 – A3, Downtime data
Downtime Type Mould production Mould use
phase
Milling EDM Wire-EDM Grinding Turning Laser Injection moulding
Line Shutdown (h/d) 14 7 7 7 7 14 0
Worker Unpaid Breaks (h/d) 1 1 1 1 1 1 1
Worker Paid Breaks (h/d) 1.2 1.2 1.2 1.2 1.2 1.2 1.2
Planned Maintenance (h/d) 0 0 0 0 0 0 0
Idle (h/d) 0 0 0 0 0 6 0 Unplanned
Downtime (h/d) 0 0 0 0 0 0 0.062176134
B
A4 – Process Variables
Table 34 – A4, Process Variables
Process Variables Unit Mould production Mould use phase
Milling EDM Wire-EDM Grinding Turning Laser Finishing Injection moulding Equipment cost + Accessories € 100,000 120,000 250,000 30,000 30,000 55,000 - 98,469
Machine Power kW 10 9 12.15 6.75 6.75 2 - 24.2 Sace Required m2 20 20 20 20 20 5 - 14.5
Production time A1 - 8SC; 4HR h 223 400 291 330 151 5 136 - Production time A2 -8MB; 4HR h 213 400 284 330 151 5 136 - Production time A3 - 8SC; CR h 203 400 291 330 151 5 136 - Production time A4 - 8SC; 2HR h 223 400 291 330 151 5 136 - Production time A5 - 8SC; 5HR h 279 500 364 413 189 6.5 136 -
Cycle time A1 - 8SC; 4HR s - - - - - - - 36.1 Cycle time A2 -8MB; 4HR s - - - - - - - 36.1 Cycle time A3 - 8SC; CR s - - - - - - - 44.0 Cycle time A4 - 8SC; 2HR s - - - - - - - 36.1 Cycle time A5 - 8SC; 5HR s - - - - - - - 36.1 Mould open and close time s - - - - - - - 4
Scrap per cycle A1 - 8SC; 4HR g/Cycle - - - - - - - 9.2 Scrap per cycle A2 -8MB; 4HR g/Cycle - - - - - - - 9.2 Scrap per cycle A3 - 8SC; CR g/Cycle - - - - - - - 68.8 Scrap per cycle A4 - 8SC; 2HR g/Cycle - - - - - - - 26.1 Scrap per cycle A5 - 8SC; 5HR g/Cycle - - - - - - - 11.5
Setup Time h 0.67 9 1 0.25 0.25 - - 0.5 Tooling cost €/h 1.6 - - 0.15 0.16 - - -
Cutting Fluid Consumption dm3/s 3.22E-07 - - 3.22E-07 3.22E-07 - - - Cutting Fluid Cost €/dm3 8.89 - - 8.89 8.89 - - -
Operators Allocated - 1 0.5 0.5 1 1 0.5 1 0.25 Tank Volume dm3 - 400 250 - - - - -
Dielectric Fluid Unit Cost €/dm3 - 2 2 - - - - - Consumables (lamp) €/h - - - - - 1.1 - -
Reject Rate % - - - - - - - 1 No. Of shifts - 1 1 2 1 1 1 2 3
Batch - - - - - - - - 100,000 Clamping force ton - - - - - - - 50
Injection pressure bar - - - - - - - 300
C
A5 – Mould reliability (Failure data)
Table 35 – A5, Weibull parameters
Weibull Parameters Fmax Fmin tmax tmin tmod t0 T b MTBF
Ejector pins 0.997669774 0.00233 500000 24000 200000 24000 246100 2.363937 220836 Nozzles 0.972440945 0.027559 1000000 200000 500000 200000 623054.2 2.00679 574899.4 Manifold 0.87037037 0.12963 2000000 1000000 1500000 1000000 1701841 2.017958 1621895
Moulding cavity/core 0.932692308 0.067308 1000000 400000 600000 400000 735352.1 1.706389 699140.3 Thin features 0.991293532 0.008706 1000000 400000 600000 400000 679010.4 2.033245 647200.5
Table 36 – A5, Cost per element
Cost per element Mould
alternative Ejector
pins Nozzles Manifold Moulding cavity/core
Thin features
A1-8SC; 4HR 100 1000 2000 4731.6 80 A2-8MB; 4HR 100 1000 2000 37296.8 80 A3-8SC; CR 100 1000 2000 4647.0 80 A4-8SC; 2HR 100 1000 2000 4731.6 80 A5-10SC; 5HR 100 1000 2000 3785.3 80
D
A6 – Mould Data
Table 37 – A6, Mould production subcontracts
Mould alternative
Subcontracts time (h) Subcontracts cost
Testing Mould design Electrodes Testing (€/h)
Mould design (€/h)
Electrodes (€/h)
Heat treatment (€)
A1-8SC; 4HR 20 181 23 30 23 20 266.12 A2-8MB; 4HR 20 181 23 30 23 20 232.52 A3-8SC; CR 20 181 23 30 23 20 232.52 A4-8SC; 2HR 20 181 23 30 23 20 266.12 A5-10SC; 5HR 20 181 23 30 23 20 299.72
Table 38 – A6, Mould production bought components
Mould alternative
Bought components cost (€)
Structure Standard parts Raw material - steel Hot
runners A1-8SC; 4HR 4500 3500 749.6 7000 A2-8MB; 4HR 4299 3500 595.3 7000 A3-8SC; CR 4236 3500 749.6 - A4-8SC; 2HR 4500 3500 749.6 4900
A5-10SC; 5HR 5625 4375 937 8750
E
Table 39 – A6, Mould dimensions and volumes
Mould alternative
Mould dimensions Structure Components Volume (m3)
Lenght (cm) Width (cm)
A1-8SC; 4HR 24.6 39.6 0.41
A2-8MB; 4HR 24.6 34.6 0.41
A3-8SC; CR 24.6 34.6 0.41
A4-8SC; 2HR 24.6 39.6 0.41
A5-10SC; 5HR 24.6 44.6 0.41
F