an intelligent benchmark-based design for environment system for derivative electronic product...
TRANSCRIPT
Computers in Industry 63 (2012) 913–929
An intelligent benchmark-based design for environment system for derivativeelectronic product development
Tzu-An Chiang a,*, Rajkumar Roy b
a Department of Business Administration, National Taipei College of Business, Taipei 100, Taiwan, ROCb Department of Manufacturing and Materials, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK
A R T I C L E I N F O
Article history:
Received 15 September 2011
Received in revised form 1 May 2012
Accepted 2 August 2012
Available online 24 August 2012
Keywords:
Design for environment
Approximate life cycle inventory
Back-propagation neural network
Data envelopment analysis
A B S T R A C T
In recent years, the destruction of the ecological environment and the exhaustion of natural resources have
become increasingly severe. The demand for environmental protection has attracted worldwide attention.
If a company cannot address changes in ecological law, its products will no longer have access to world
markets. For this reason, companies should introduce the concept of design for environment (DfE) to the
product development process to alleviate the impacts of a product on the ecological environment
throughout the product life cycle. To help companies effectively meet the challenges cited above, this study
develops an intelligent benchmark-based DfE system for derivative electronic products. The architecture of
the intelligent system includes three parts. In the first part, a back-propagation neural network (BPNN) is
used to create the analysis of approximate life cycle inventory (LCI). The first part is utilised to estimate the
amounts of hazardous chemical substances produced by the material extraction, manufacturing and usage
phases as well as the amounts of energy consumed by the production and usage phases. In the second part,
data envelopment analysis (DEA) is applied to develop the models of benchmark-based DfE evaluation for a
product and a component or part. The models are used to identify which hazardous chemical substances are
excessive and to calculate the needed reductions in the quantities of these hazardous chemical substances.
The third part of the intelligent DfE system is used to generate analysis and suggestions for improvement in
the design of parts or components. The analysis and suggestion model utilises the second-part analytical
results and a BPNN to identify concrete directions for improvement and to generate suggestions that can be
used to achieve the ultimate goal of DfE.
� 2012 Elsevier B.V. All rights reserved.
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1. Introduction
Owing to ecological destruction and drastic changes in climate,design for environment (DfE) has become one of the key trends inproduct development in recent years. DfE enables productdevelopment engineers to respect the environmental impacts ofa product during the design process. DfE aims to reduce the use ofhazardous substances and decrease the environmental impacts. Toeffectively develop environmentally conscious products, thetraditional paradigm of green product development needs to bechanged. In the past, most environmental pollution preventionmeasures focused on end-of-the-pipe solutions and were thenfurther applied to improve the manufacturing process. Subse-quently, these measures have evolved into the concept of sourcecontrol [1]. Therefore, in the product development phase, thequantities of pollution and energy consumption at each phase ofthe product life cycle should be estimated and reviewedcomprehensively to achieve the objective of reducing environ-
* Corresponding author.
E-mail address: [email protected] (T.-A. Chiang).
0166-3615/$ – see front matter � 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.compind.2012.08.014
mental impacts and minimising natural resource consumption.Furthermore, the enforcement of environmental protectionregulations, such as waste electronics and electrical equipment(WEEE), restriction of hazardous substances directive (RoHS) andenergy-using products (EuP), has made eco-design an inevitabletendency. A company that cannot promptly address the require-ments of environmental protection regulations will suffer massivelosses. The EuP directive, effective August 11, 2007, primarily setthe specifications for DfE for specific energy-using electrical andelectronic products. In the future, these specifications will beextended to include all energy-consuming products. Therefore,companies must establish their capability for DfE as early aspossible. Otherwise, they will be fined, or their products will bebanned in the European Union (EU) market because of non-compliance with the EuP directive. Undoubtedly, these develop-ments will have a significant impact on enterprises whose profitscome primarily from the sales of electronics products.
In the face of intensive competition in the markets that concerntheir business, companies must accelerate product development tointroduce new electronics products. As a result, life cycle inventory(LCI) and DfE assessment occur frequently. In addition, manu-facturers are sometimes required to change the design of a product
Fig. 1. Framework of life cycle assessment [2].
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929914
to meet the requirements for DfE. To achieve DfE promptly, productdevelopment engineers must face the crucial challenges in rapidlyand accurately evaluating the energy consumption and environ-mental impacts at the major phases of a product life cycle. Besides,based on the above analytical results, product developmentengineers must objectively and effectively evaluate the performanceof DfE and analyse the amounts of hazardous chemical substances tobe reduced, and then propose design modifications. For these criticalproblems, this study will develop an intelligent benchmark-basedDfE system for derivative electronic product development. Byapplying this intelligent system in the detailed design phase, we canobjectively evaluate hazardous chemical substances and energyconsumption generated within the product life cycle. Thesehazardous chemical substances, including CO2, SOX, NOX and dust,will cause the potential environmental impacts such as greenhouseeffect, ozone layer depletion and acidification. According to theanalytical results obtained from DfE performance assessment, wecan determine the necessary reductions in energy consumption andhazardous chemical substances for various phases of the product lifecycle. Using this information, the intelligent DfE system developedhere can be used to perform further analyses to offer concretedirections and suggestions for design modifications to achieve theultimate goal of DfE.
The organisation of this paper is as follows. Section 2 reviewsrelevant areas of research, including life cycle assessment, DfE andperformance assessment. Section 3 depicts the use of back-propagation neural networks (BPNNs) and data envelopment analysis(DEA) to develop an intelligent benchmark-based DfE system forderivative electronic products. Section 4 elaborates on and verifies thefeasibility and the significant contributions of the proposed system byexamining a development project for a new wireless router. Theconclusions of the study are presented in Section 5.
2. Literature review
Life cycle assessment (LCA) measures the negative impacts andthe influences on the environment and ecology resulting from theentire product life cycle, from material extraction, manufacture,
Purposes and
scope
Data collec tion
Model operatio n
Input of each
mate rial
the weight s of impact indicators
Aggregation of
impacts
Interp retat ion
and correction
Alterna tive s
ISO-140
Definitio
ISO - 140
Impact a
ISO-140
Compar
correctio
Pollutio
Basic m Compat
ISO 140
Manufac Use: ene
Scrappin
proceduQuantific atio n
Indicator
conversion
Weight
Fig. 2. Procedure for life c
transportation and use to final scrapping, recycling and reuse. Thisconcept of ecological impact assessment is a novel approach. Theassessment method used previously focused only on the impactson the environment occurring in the manufacturing phase and didnot consider other phases of the product life cycle. TheInternational Standardisation Organisation [2] has designatedLCA as a formal international standard and has proposed the LCAframework shown in Fig. 1. The four-part framework includes thedefinition of purposes and scope, life cycle inventory, life cycleimpact assessment (LCIA) and life cycle interpretation. Fig. 2 showsa procedure for LCA. The first part of the procedure defines thepurpose and scope of LCA for a product and includes informationabout the potential users of the results of LCA. From thisinformation, we can evaluate the data and content required forthe use of the procedure. The second part principally provides dataand calculation procedures to quantify the input and output of thesystem, including detailed information on the raw materials,energy requirements, products produced, by-products and wastes.The range of data collection depends on the predeterminedpurposes and scope of the assessment procedure. This analyticalmodel is used to assess the environmental load associated withvarious phases of the product life cycle such as the extraction ofmaterials, manufacture, assembly, transportation, use/reuse,recycling and waste treatment. The LCIA model uses the analytical
40
n of purposes and sc ope
42
ssessment/impact indi cators
43
ison in inte rpre tation s of alterna tives and
n sch eme
n load
aterial da taba se
ibility da taba se
41 Invent ory an alysis
ture: energ y, material, pro cessing, tra nsportation
rgy, transpor tation, other materials
g and recycling of materials during each
re…acti vities, servic es and products
ycle assessment [2].
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929 915
results from the life cycle inventory to quantify the degree and therange of potential impacts on the environment. This phaseincorporates the linkage between the inventory data and specificenvironmental impacts. It then characterises the impacts andfurther quantifies their environmental load or potential impacts ofpollution generation and emissions on environment. This infor-mation can be applied to evaluate the effectiveness of proposedeco-design approaches. However, LCA can employ a variety of LCIAmethods. Considering data consistency, this study thus uses thequantities of hazardous chemical substances generated as anindicator to assess the performance of a DfE.
At present, a few LCA software tools, such as Boustead, GaBi,EcoPro and SimaPro, are available to help enterprises evaluateenvironmental impacts. The main function of these applications isto help enterprises perform environmental impact assessment.However, the applications lack different local LCI data. Forexample, the quantities of hazardous chemical substancesgenerated in various local manufacturing operations are notincluded. LCI is a time-consuming and high-cost activity, and itwould be a considerable burden for small and medium-sizedenterprises (SMEs). Therefore, Chiang et al. [3] developed an ISO14048-based integrated service platform to provide the LCI data forstandard parts and assist Taiwan’s SMEs in performing non-standard LCA and applying for environmental product declarations(EPDs). However, no currently available software or methods helpenterprises understand and improve the performance of DfE bycomparing a new product with similar products. Hence, companiescan only take a subjective approach to setting goals for improvingtheir DfE performance.
Because the traditional LCA procedure is expensive, complexand requires large amounts of detailed information, some studieshave used streamlined life cycle assessment [4–6]. In streamlinedlife cycle assessment, the LCA procedure is accelerated by using themaximum impacts on the environment as the assessment criteriaor by simplifying the life cycle phases. In addition, Graedal et al. [7]proposed a streamlined LCA matrix approach in which the life cyclewas divided in the five stages and five aspects of environmentalimpact were considered. This approach also used a checklist toevaluate the score (0–4) for each matrix element. A smaller scorerepresented a greater impact on the environment. This subjectiveassessment procedure can help product development engineersroughly identify which aspects of a product should be improved.However, the approach cannot provide concrete directions andsuggestions for improvement.
In the past, LCI data were collected after the manufacture of aproduct. Data were not obtained during product development tohelp gain insight into the severity of such environmental impactsas greenhouse effects, acidification and eutrophication. To solvethe abovementioned problems and to reduce the overall cost of LCI,a number of studies have used an artificial neural network as ananalytical tool to simulate an approximate life cycle assessment.For example, Sousa et al. [8] used the high-end productcharacteristics and the impact values obtained from the approxi-mate life cycle assessment of the products as sample data to trainthe artificial neural network. Based on the analytical resultsobtained through the use of this method, we can identify suitablehigh-end product characteristics and thus estimate the level ofenvironmental impact. With this method, product developmentengineers can predict the values of environmental impacts at thedetail design phase. In addition, the analytical results can be usedto decide whether the eco-design performance of a product shouldbe improved. To improve the accuracy of the environmentalimpact estimate, Sousa and Wallace [9] used a decision treealgorithm to develop a hierarchical clustering approach tosystemically guide the identification of product clusters. Aspecialised artificial neural network model was then used to
forecast the level of environmental impact. Park and Seo [10]applied an artificial neural network to develop a knowledge-basedapproximate LCA system to evaluate the environmental impacts ofproduct design alternatives in a collaborative design environment.The studies cited above show that an artificial neural network isquite suitable for environmental impact assessment. However, theassessed level of an environmental impact derived in this way is asynthesised performance assessment value and cannot furnishdetailed information for further analysis that could be used toimprove the environmental efficiency of the design. This studytherefore uses life cycle inventory data as the output of the BPNNs,including major hazardous chemical substances and energyconsumption, so as to analyse the amounts of major hazardouschemical substances to be reduced through the performanceevaluation of DfE. At the same time, this study utilises the high-endproduct attributes as the input to the BPNNs, including the weight,the material and the volume or the area of a component, and theoperating voltage, the current and the total usage time of a product.If we wish to obtain the LCI data for any new derivative product, wesimply supply the data on the high-end product attributes as inputto the existing BPNN and do not need to create a new BPNN.
In a study of DfE, Yung et al. [11] applied LCA to consumerelectronic products and proposed directions for ecological rede-sign. For example, these authors suggested that the size of aprinted circuit board or an LCD panel could be minimised as apractical measure. Moreover, the authors suggested that anautomatic detection device should be installed so that a productcould enter the power-saving mode during the usage phase if itwas idle. Jones et al. [12] developed a tree diagram for productideas to help product development engineers examine theirecologically innovative designs and to incorporate variousopinions. Based on LCA, Nielsen and Wenzel [13] proposed ananalytical procedure for integrating environmental performanceinto the entire product development process to help productdevelopment engineers identify the key points at which environ-mental efficiency could be improved and to help them choose theoptimal alternative for DfE improvement. Sakao [14] and Trappeyet al. [15] used quality function deployment for environment(QFDE) to assist product designers to identify the linkages betweenproduct specifications and environmental impacts. They thenemployed theory of inventive problem solving (TRIZ) to providesuggestions for DfE. Kuo et al. [16] applied the fuzzy multi-attribute decision-making algorithm to evaluate the DfE perfor-mance of different design alternatives. Their evaluation criteriaincluded recycling, energy consumption, hazardous substances,costs and materials. However, conversion from the original units ofmeasurement was necessary because various assessment criteriawere expressed using different units. As a result, this assessmentapproach may produce a distortion of the original data. Moreover,the method requires opinions from experts to define weights forthe different criteria. Accordingly, different opinions may yielddifferent assessment values for the environmental performance ofthe same product design alternative.
The foregoing literature review shows that few studies haveproposed objective approaches to performance assessment for DfE.Also, few studies have used a performance assessment based onbenchmarks for DfE to determine which hazardous chemicalsubstances are present in excess and to provide productdevelopment engineers with estimates of the reductions in thequantities of these substances needed in each phase of the productlife cycle. In addition, relatively little research has addressed theadditional analysis of the reduced quantities of hazardoussubstances needed to obtain concrete suggestions for improve-ment for DfE design. Accordingly, this study applies BPNN and DEAto develop an intelligent benchmark-based DfE system for theevaluation and improvement of DfE for derivative electronic
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929916
products. This intelligent DfE system can use original life cycleinventory data directly, without unit conversion, to acquire theenvironmental performance values of different product designalternatives and the reductions in the quantities of hazardouschemical substances that are required for achieving benchmarkeco-design performance. This study creates a model for the designimprovements needed for DfE. The model gives product develop-ment engineers the concrete directions and suggestions forimprovement of the product design.
3. Intelligent benchmark-based DfE system
Fig. 3 shows an architecture of the intelligent benchmark-basedDfE system. The architecture of this intelligent DfE system has
First part: The analysis o
Material extraction
Aggregate amounts of hazardous chem
approxima
Material
type
Material
weight
BPNN
Amounts of hazardous
chemical substances due
to material extraction
Manufacturin
WeightVolume
or area
BPNN
Required
power due to
production
Ratio of power gen
typ
Consumption of each powe
generation type
BPNN
Amounts of ha zardous
chemical substances due to power consumption
Second part: performance assessment of DfE and analysis of
excessive hazardous chemical substances
Amounts of
hazardous
chemical
substances during
extraction
Amounts of
hazardous
chemical
substances during
manufacturing
Amounts of
hazardous
chemical
substances during
usage
Performance assessment of benchmark-based DfE
DfE performance value
Analysis of
excessive
hazardous
chemical
substances
Amounts of hazardous
chemical substances to be
reduced in each major phase
Fig. 3. An architecture of the intellige
three parts. The first part creates the analysis of approximate LCI.The second part includes performance assessment of DfE and theanalysis of excessive hazardous chemical substances. The thirdpart is the analysis and suggestions used for improvement in thedesign of components or parts. Fig. 4 illustrates the benchmark-based DfE analytical procedure. Initially, this study uses anapproximate LCI to estimate the quantities of hazardous chemicalsubstances. In the manufacturing phase, the sources of hazardouschemical substances include the manufacturing process andenergy consumption. A benchmark-based DfE performanceevaluation model is then applied to evaluate the relative valuesfor DfE performance for the new product and for competitors’similar products. If the DfE performance value of a new product isless than 1, then the new product does not belong to an efficient
f approximate LCI
ical substances in all phases for
te LCI
Usage
Voltage Cu rrentUsage
time
BPNN
Ratio of each
power
generation type
Power
required
for usage
BPNN
Amounts of hazardous
chemical substances during
usage
g
Mat erial
each
eration
e
r
Amounts
of
hazardous
chemical
substances
during
process
Third part: Analysis and suggestions for
improvement in part or component design
Analysis of improvement in parts or component
design
Revised
amounts of
hazardous
chemical
substances
during
extraction
Revised
amounts of
hazardous
chemical
substances
during
manufacturing
Revised
amounts of
hazardous
chemical
substances
during usage
BPNN
Analysis and suggestions for improvement in
part or component design
BPNN
nt benchmark-based DfE system.
Quantiti es of hazard ous chemical sub stances for a component
duri ng manufactu ring
Quantities of hazardous chemic al su bstances du ring
usage
Quantitie s of haza rdous chem ical sub sta nces for each raw material of a component
during extra ction
Total qu anti ties of hazardo us chemic al su bstances in the maj or phases
Achieving the goal of Df E
Perf ormance assessment of benchm ark-base d DfE
Benchma rk DfE?
No
Yes
LCI data fo rcomp etitive prod ucts
Cho osing the component wi th the maximum improvement to pe rform
design change
Change in pr oduct desi gn
The maximum reduce d quantit ies of hazardous chemic al su bstances for
eac h component
Estimation of qu antiti es of haz ardous chem ical substances in the major phases
Analys is of excessiv e hazardous chem ica l
substances in the majorphases
Using th e analys is and sugges tion model for design impr ovement of
comp onents
Reduced qu anti ties of hazardo us chem ica l su bstances re quired within
the product life cycle
Fig. 4. The benchmark-based DfE analytical procedure.
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929 917
decision-making unit (DMU). In order to improve the DfEperformance value of a new product, the intelligent DfE systemperforms an analysis of the excessive hazardous chemicalsubstances to specify the reductions in the quantities of hazardouschemical substances needed in the major phases of the product lifecycle. Next, this study employs the analysis and suggestion modelfor the improvement of component designs to obtain themaximum possible reductions of the quantities of hazardouschemical substances for each component. With the foregoinganalysis, a product development engineer can choose thecomponent that offers the maximum improvement and performthe suggested design change. The analysis and suggestion modelallows product development engineers to identify concretedirections for improvement and offers suggestions for designmodification. Based on these suggestions, the product develop-ment engineers can efficiently execute design changes and then re-evaluate the quantities of hazardous chemical substances until abenchmark DfE design is reached. Each part of the intelligent DfEsystem is described in the following subsections.
3.1. Analysis of approximate LCI
In the detailed design stage, the analysis of an approximate LCIhelps enterprises evaluate the quantities of hazardous chemicalsubstances in the major phases of the product life cycle, i.e.,material extraction, manufacture and usage, as well as energyconsumption in the manufacturing and usage phases. In this study,we apply a BPNN to develop the approximate LCI models. Formaterial extraction, this paper uses the LCI data of SimaPro.Because this paper focuses on the development of derivativeelectronic products, we can obtain the detailed design
specifications and LCI data of the manufacturing phase from theproduct data management system to train the BPNN. With regardto usage, the LCI data are obtained from Taiwan Power Company.
The BPNN consists of an input layer, the hidden layers, and anoutput layer, as shown in Fig. 5. A hidden layer comprises severalneurons. This study uses formula (1) to decide the optimum quantityof neurons. The BPNN reads the values of inputs and outputs of thetraining samples and adjusts the weights on the connections todecrease the gaps between the values of the output variable and thedesired output. The BPNN immediately terminates the trainingprocess when the gap falls below a certain threshold. Usingsupervised learning, we can establish corresponding rules betweenthe input layer and the output layer to apply to new cases. Theanalysis of the approximate LCI in this study belongs to the type ofneural networks having multiple input and output variables. If thefunctional relationship between inputs and outputs is unknown, theBPNN represents an excellent prediction method. Therefore, thisstudy uses a BPNN to estimate the quantities of hazardous chemicalsubstances and the energy consumption within the product lifecycle as well as to acquire concrete directions and suggestions for themodification of the product design.
hn ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiNinput � Noutput
q; (1)
where hn is the number of units in the hidden layer; Ninput is thenumber in the input layer; Noutput is the number in the output layer.
The analysis of the approximate LCI at the extraction phase isused to predict the quantities of hazardous chemical substancesreleased during the process of extraction of various raw materialsfor a new product. The LCI software database furnishes the typeand weight of each kind of raw material and the quantities of
Fig. 5. Artificial neural network model.
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929918
hazardous chemical substances generated during extraction. Thisinformation is used to train the BPNN to establish a model forestimating the quantities of hazardous chemical substances in theextraction phase. In the manufacturing phase, the input variables ofthe model for the approximate LCI analysis, including the weight, thematerial and the volume or the area of a part or component, are usedto estimate the electric power requirement and the quantities ofhazardous chemical substances generated during the manufactur-ing process. Differences among the methods of electric powergeneration at different manufacturing sites produce dissimilaritiesin the amount of environmental pollution. This study will thereforeconsider the percentage of each type of the power generationmethod at the manufacturing site to objectively predict the amountsof hazardous chemical substances produced by the generation of theelectric power required for the manufacture of a part or component.In the usage phase, the study focuses on the estimation of theamounts of hazardous chemical substances that result from electricpower consumption. The analysis first uses a BPNN model toestimate the total consumption of electricity in the usage phase. Theinput variables of this BPNN model include the operating voltage, thecurrent and the total amount of time that a product is in use. Next,the percentage of each method of power generation at the saleslocation is considered to calculate the power consumptionrepresented by each type of power generation. The quantities ofhazardous chemical substances associated with the usage stage arethen calculated.
To ensure that the estimation of the amounts of hazardouschemical substances in all phases is sufficiently accurate to acquirethe approximate LCI, this study uses the root mean square error(RMSE) to evaluate the learning and prediction performance of anartificial neural network and to search for the optimum settings ofsuch parameters as learning rates, momentum factors and learningcycles. This method is defined in formula (2). Because the RMSEreflects the difference between observed and predicted values,smaller RMSE values correspond to better predictions. With thepreviously defined assessment criterion and the stop mechanismfor the training of an artificial neural network, the approach used inthis research can choose the ideal settings for the trainingparameters to create the analytical models of approximate LCIfor the three main phases of the product life cycle.
RMSE ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1 ðTi � EiÞ2
n
s: (2)
3.2. Performance evaluation of DfE and analysis of excessive
hazardous chemical substances
This study uses DEA to develop a DfE performance assessmentmodel for a product, as shown in formula (3). The inputs to DfEperformance assessment are the quantities of hazardous chemical
substances generated during extraction, manufacture and usagewhile the output is a product. Because the constraint function ofthe output item must be satisfied, the constraint can be ignored.This study uses the model of an approximate LCI in the first phaseto calculate the amounts of hazardous chemical substancesgenerated in the three major phases of the life cycle of a newproduct. Then, the DfE performance of the new product, i.e., thetechnical efficiency, can be obtained by comparing the approxi-mate LCI data of the new product with the LCI data of similarcompetitive products. If the DfE value of a new product is less than1, it indicates that the new product is a non-benchmark product.Using the DfE performance assessment model, this study estab-lishes an efficiency frontier formed by benchmark DfE productsand uses the slack variable analysis to calculate the reductions inthe amounts of hazardous chemical substances required in thematerial extraction, manufacturing and use phases, as shown informulas (4)–(6).
Min hk ¼ uk � e �X4
i¼1
ðSPMki þ SPUk
i Þ
s:tXR
r¼1
Plr � PRri � uk � PRk
i þ SPRki ¼ 0
XR
r¼1
Plr � PMri � uk � PMk
i þ SPMki ¼ 0
XR
r¼1
Plr � PUri � uk � PUk
i þ SPUki ¼ 0
XR
r¼1
Plr ¼ 1
SPRki ; SPMk
i ; SPUki ; lr � 0; 1 � i � 4; 0 � u � 1
; (3)
where hk is the value of DfE performance of the kth product; PRri is
the amount of the ith hazardous chemical substance generatedduring extraction for the rth product; PMr
i is the amount of the ithhazardous chemical substance generated during manufacture forthe rth product; PUr
i is the amount of the ith hazardous chemicalsubstance generated during usage for the rth product; SPRk
i is theslack variable for the ith hazardous chemical substance duringextraction for the kth product; SPMk
i is the slack variable for the ithhazardous chemical substance during manufacture for the kthproduct; SPUk
i is the slack variable for the ith hazardous chemicalsubstance during usage for the kth product; Plr is the input weightof the rth product; uk is the rough value of DfE performance of thekth product; and e is the very small positive number.
DPRki ¼ ð1 � ukÞ � PRk
i þ SPRki ; (4)
DPMki ¼ ð1 � ukÞ � PMk
i þ SPMki ; (5)
DPUki ¼ ð1 � ukÞ � PUk
i þ SPUki ; (6)
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929 919
where DPRki is the amount of the ith hazardous chemical substance
to be reduced during extraction for the kth product; DPMki is the
amount of the ith hazardous chemical substance to be reducedduring manufacture for the kth product; DPUk
i is the amount of thekth product’s ith hazardous chemical substance to be reducedduring usage for the kth product.
3.3. Analysis and suggestions for improvement in part or component
design
To perform the analysis needed for the improvement of the newproduct design, the intelligent DfE system must obtain detailedinformation on the hazardous chemical substances associated withthe design of the part or component. This intelligent DfE systemfurther analyses the DfE performance of the part or component todetermine the maximum reduction of hazardous chemicalsubstances. Formula (7) represents the DfE performance value ofthe mth part or component. If the DfE performance of the part orcomponent is less than 1, then we can use formulas (8)–(11) tocalculate the revised quantities of hazardous chemical substancesin the extraction, manufacturing and use phases. The hazardouschemical substances in the manufacturing phase are generated dueto power requirements and manufacturing process. After obtainingthe revised quantities of hazardous chemical substances for eachpart or component, the intelligent DfE system selects the part orcomponent that offers the maximum potential for improvement.By an in-depth analysis, this intelligent DfE system generatestangible suggestions for improvements in the design of the parts orcomponents.
Min mkm ¼ Muk
m � e �X4
i¼1
ðSPRi þ SPMi þ SPUiÞ
s:tXR
r¼1
lr � MRrm;i � Muk
m � MRkm;i þ SMRk
m;i ¼ 0
XR
r¼1
lr � MErm;i � Muk
m � MEkm;i þ SMEk
m;i ¼ 0
XR
r¼1
lr � MMrm;i � Muk
m � MMkm;i þ SMMk
m;i ¼ 0
XR
r¼1
lr � MUrm;i � Muk
m � MUkm;i þ SMUk
m;i ¼ 0
XR
r¼1
lr ¼ 1
SMRki ; SMMk
i ; SMUki ; lr � 0; 1 � i � 4
; (7)
where mkm is the value of DfE performance of the mth part or
component of the kth product; MRrm;i is the amount of the ith
hazardous chemical substance generated during material extrac-tion for the mth part or component of the rth product; MEr
m;i is theamount of the ith hazardous chemical substance generated due topower required during manufacture for the mth part or componentof the rth product; MMr
m;i is the amount of the ith hazardouschemical substance emitted due to manufacture for the mth part orcomponent of the rth product; MUr
m;i is the amount of the ithhazardous chemical substance generated during use for the mthpart or component of the rth product; SMRk
m;i is the slack variablefor the amount of the ith hazardous chemical substance generatedduring material extraction for the mth part or component of the kthproduct; SMEk
m;i is the slack variable for the amount of the ithhazardous chemical substance generated due to power requiredduring manufacture for the mth part or component of the kthproduct; SMMk
m;i is the slack variable for the amount of the ithhazardous chemical substance generated during manufacture forthe mth part or component of the kth product; SMUk
m;i is the slackvariable for the amount of the ith hazardous chemical substance
generated during use for the mth part or component of the kthproduct; and Muk
m is the rough DfE performance value of the mthpart or component of the kth product.
RMRkm;i ¼ Muk
m � MRkm;i � SMRk
m;i; (8)
RMEkm;i ¼ Muk
m � MEkm;i � SMEk
m;i; (9)
RMMkm;i ¼ Muk
m � MMkm;i � SMMk
m;i; (10)
RMUkm;i ¼ Muk
m � MUkm;i � SMUk
m;i; (11)
where RMRkm;i is the revised amount of the ith hazardous chemical
substance during extraction for the mth part or component of thekth product; RMEk
m;i is the revised amount of the ith hazardouschemical substance due to power required during manufacture forthe mth part or component of the kth product; RMMk
m;i is therevised amount of the ith hazardous chemical substance generatedduring manufacture for the mth part or component of the kthproduct; and RMUk
m;i is the revised amount of the ith hazardouschemical substance generated during usage for the mth part orcomponent of the kth product.
This paper uses the data produced by the first phase to train theBPNN to establish the analysis and suggestion model of theintelligent DfE system for the improvement in the design of thepart or component in the manufacturing phase, as shown in Fig. 6.First, we input the percentage of each type of power generation andthe revised quantities of hazardous chemical substances generateddue to power required during manufacture to the intelligent DfEsystem. These selected inputs are used in an estimation model forenergy consumption to obtain the revised values for electricityrequired. We then input the revised electric power required at themanufacturing phase and the revised quantities of hazardouschemical substances during material extraction and manufactur-ing into a suggestion model of the intelligent DfE system to obtainconcrete improvement suggestion in the design of parts orcomponents for reducing environmental impacts in themanufacturing phase. This suggestion model provides the revisedmaterial, weight, volume or area for the part or component. Theseconcrete suggestions for the improvement of the components orparts can help product development engineers modify the productdesign. To decrease environmental impacts in the usage phase,possible improvements in the design of the parts or componentsare generated by a suggestion model of the intelligent DfE systemused for the usage phase. This model employs the revisedquantities of hazardous chemical substances and the percentageof each type of power generation to estimate the electricityrequired for a part or component. The model then uses theelectricity required and total usage time to estimate the revisedpower for a part or component, as shown in Fig. 7. These concretesuggestions would help product development engineers define thegoals of improving energy consumption for the parts andcomponents of a new product.
These very useful suggestions for improvements in the design ofparts or components generated by the intelligent DfE system arethen used by the product development engineers. The engineerscan modify the product design efficiently. They can then use theanalysis of approximate LCI for the re-evaluation of the aggregatequantities of hazardous chemical substances associated with eachphase of the product life cycle. The revised design of the newproduct is subsequently evaluated. The method determineswhether the DfE performance of the revised new product designmeets the benchmark-based standard. If the new design does notmeet this standard, the procedure for assessment and improve-ments needs to be repeated until the goal of DfE is achieved.
Fig. 6. Analysis and suggestion model for improvements in part or component design for the manufcaturing phase.
An estimation model for energy
consumption
The revised power required during usage
The analysis and suggestion model for improvement in part or component design for the usage phase
The revised power for
a component
or part
Total usage time
The revised quantities of hazardous chemical
substances generated due to power required during
usage
The ratio of each power generation type
Fig. 7. Suggestion model for improvement in part or component design for the usage phase.
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929920
4. Case study
This paper uses a wireless router development project as a casestudy. In this case study, we compared the new product with 29other competitive products with similar functions. The keycomponents of a wireless router include a case, an antenna anda printed circuit board (PCB). The wireless router is manufacturedand used in Taiwan. This research assumes that each router is usedfor 12,000 h. Table 1 shows the relative proportions of each type ofelectric power generation in Taiwan. To perform the LCI for theproduct, this paper considers four hazardous chemical substances:dust, CO2, SOX, and NOX. The quantities of hazardous chemicalsubstances associated with each phase of the product life cycleserved as the basis for DfE performance assessment and themodification of product design. Tables 2 and 3 list the weights of
Table 3Specification of the new wireless router.
Case Antenna PCB
Volume (cm3) Weight (g) Quantity Weight (g) Area (cm2
803.682 180.3 1 14 203.5
Table 2Materials’ weights of each major component for the new wireless router.
Material Case Antenna PCB
PS PVC Cu Copper clad la
Weight 180.3 g 8 g 6 g 2 g
Table 1Ratio of each power generation type in Taiwan.
Power generation type Coal-fired Gas-fired Thermal-oi
Ratio 43.31% 22.72% 4.97%
the component materials and the specifications of the newwireless router. The specifications for all of the productsconsidered in the analysis are given in Tables A1 and A2. Thenew wireless router is identified as Product 1 in these tables. In thefirst stage, this research uses the BPNN to create the analyticalmodels of approximate LCI of the intelligent DfE system for theextraction, manufacturing and usage phases. The number of unitsin the hidden layer is determined using formula (1). The learningrate, the momentum factor and the learning cycle are determinedby a trial-and-error method. Table 4 lists the BPNN trainingparameters for the analytical models of approximate LCI. In thiscase study, this paper uses RMSE to determine whether the resultof training an artificial neural network reaches an acceptableaccuracy. In this paper, when the value of a RMSE is less than 0.002,the BPNN stop the learning process.
Power consumption Total weight (g)
) Weight (g) Voltage (V) Current (A)
92.7 5 3 287
minate (CCL) Resin fibreglass Chip Capacitor
73 g 4.2 g 13.5 g
l Nuclear power Renewable energy Co-generation
20% 4% 5%
Table 4BPNN training parameters of the approximate LCI models.
Phase Material Number of units in the hidden layer Learning rate Momentum factor Learning cycle
Material extraction ABS 2 0.7 0.7 500
PS 2 0.7 0.7 500
PP 2 0.7 0.7 500
ABS/PC 2 0.7 0.7 500
PVC 2 0.7 0.7 500
PE 2 0.7 0.7 500
Cu 2 0.7 0.7 500
Resin fibreglass 2 0.5 0.5 700
PCB 2 0.7 0.7 500
Manufacturing Case process 4 0.5 0.5 1000
Antenna process 4 0.7 0.7 500
PCB process 5 0.1 0.1 1500
Usage Prediction of power usage 2 0.7 0.7 500
Power consumption in each phase 5 0.5 0.5 700
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929 921
The analytical models of approximate LCI for the intelligent DfEsystem are created for the case study. The models use the high-endproduct attributes of the new wireless router, including thematerials, weight and volume or area of each major part orcomponent as well as the operating voltage and current of theproduct, as inputs. The data in Tables 2 and 3 are then used toestimate the quantities of hazardous chemical substances generatedduring material extraction and manufacturing as well as the electricpower required during manufacturing and usage. Tables 5–7 showthe approximate LCI for the new wireless router. Data on othercompetitive products reported in Tables A1 and A2 can be input intothe models to achieve the analysis of the approximate LCI for eachphase of the product life cycle, as shown in Tables A3–A10.
The DfE performance assessment model of the intelligentsystem employs the approximate LCI data for extraction,manufacturing and usage to calculate the DfE performance valueof each wireless router by using formula (3). The DfE performancevalue of the new product is 0.8416442. The product failed to satisfy
Table 7Approximate LCI data of energy consumption.
Phase Manufacturing
Component Case Anten
Inventory item Energy (kWh) 0.27958 0.06
Dust (g) 0.01451 0.00
CO2 (g) 179.44032 39.76
SOX (g) 0.31263 0.06
NOX (g) 0.16259 0.03
Table 6Approximate LCI data during manufacturing.
Major component Case
Inventory item Dust (g) 0.27176
CO2 (g) 487.66586
SOX (g) 1.88535
NOX (g) 2.04678
Energy (kWh) 0.27958
Table 5Approximate LCI data during material extraction.
Material PS PVC
Inventory item Dust (g) 0.27045 0.184
CO2 (g) 485.63805 23
SOX (g) 1.87692 0.39064
NOX (g) 2.03739 0.152
the benchmark-based standard for DfE performance relative tocompetitive wireless routers. Because the performance value isless than 1, the efficiency frontier of DEA is applied to determinethe amounts of hazardous chemical substances to be reduced in allphases by using formulas (4)–(6). Table 8 shows the revisedquantities of hazardous chemical substances associated with theextraction, manufacturing and usage phases of the new wirelessrouter.
Formula (7) is then used to analyse the DfE performance valuesof the major components of the new wireless router. Thecorresponding benchmarking efficiency frontier is applied todetermine the maximum reductions in hazardous chemicalsubstances for each component. If the DfE performance of acomponent or part belongs to a non-efficient decision-making unit(DMU), formulas (8)–(11) are used to analyse the maximumreductions in hazardous chemical substances during extraction,manufacturing and usage. According to the analytical results, theDfE performance values of the three major components of the new
Usage
na PCB Total
113 1.15247 1.49318 180
321 0.05989 0.07761 9.46656
945 740.67694 959.88671 117,049.91406
927 1.29037 1.67227 203.87132
598 0.67129 0.86986 106.10102
Antenna PCB Total
0.20510 695.10687 695.58373
24.83162 66.29091 578.78839
0.44039 730.20551 732.53125
0.17109 390.19507 392.41293
0.06113 1.15247 1.49318
Cu Resin fibreglass PCB Total
0.1962 0.0052 0.56370 1.21955
26.0244 1.0434 116.47119 652.17704
2.3214 0.04912 2.10826 6.74634
0.2274 0.0074 0.7326 3.15679
Table 8Revised quantities of hazardous chemical substances for the new wireless router.
Inventory item Dust (g) CO2 (g) SOX (g) NOX (g)
Material extraction 1.045502635 557.1701427 5.783566319 2.525226558
Manufacturing 541.3664261 1319.089395 573.2609057 305.5923582
Usage 8.114642196 100,334.0575 174.7564015 90.94886354
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929922
wireless router (i.e., the case, antenna and PCB) are 0.9526605, 1and 0.8524998, respectively. These results indicated that theperformance of the case and PCB could be improved. Table 9 showsthe revised amounts of hazardous chemical substances for the caseand the PCB. Because the PCB is the only key component of theproduct that consumes energy in the usage phase, the quantities ofhazardous chemical substances that can be reduced for thewireless routers during usage are equal to the maximum quantitiesof hazardous chemical substances that could be reduced for thePCB in this phase.
Because the intelligent DfE system selects the component withthe greatest reduced quantities of hazardous chemical substances,the PCB is assigned the highest priority for design change. Inparticular, the PCB generates higher CO2 and SOX levels within theproduct life cycle. The CO2 is generated because the manufacturingand usage phases consume electricity. Moreover, the process used tomanufacture the PCB produces a high level of SOX. Table 10 gives therevised quantities of hazardous chemical substances associated withthe PCB and the case of the wireless router during manufacture. Therevised quantities of hazardous chemical substances for each phaseare used as inputs for the suggestion models of the intelligent DfEsystem that perform the analysis and generate tangible suggestionsfor improvement in the design of components.
The case study uses the first-stage data to train the BPNNs. Thewell-trained models are used for analysis and to generatesuggestions for improvement in the design of parts or componentsin the manufacturing and usage phases. The RMSE is used toestimate the accuracy of a BPNN. The BPNN training parameters forthe models are listed in Table 11. The RMSE values for the model ofthe analysis and suggestions for improvement in part or
Table 10Revised quantities of hazardous chemical substances during processes of the PCB and
Inventory item Dust (g)
PCB
Material extraction 0.42226579
Manufacturing
Process 541.2825298
Power required for manufacturing 0.049464909
Case
Material extraction 0.243041845
Manufacturing
Process 0.242218035
Power required for manufacturing 0.013991429
Table 9Revised amounts of hazardous chemical substances for the case and the PCB.
Inventory item Dust (g)
Case
Material extraction 0.243042
Manufacturing
Process 0.242218
Power required for manufacturing 0.013991
PCB
Material extraction 0.461724
Manufacturing
Process 594.2665
Power required for manufacturing 0.049188
component design for the manufacturing phase are less than0.0028. The RMSE values for the estimated model of energyconsumption and the model of the analysis and suggestions for theusage phase are 0.00105 and 0.001587.
The case study first uses the revised quantities of hazardouschemical substances during the usage of the product, as shown inTable 8. These quantities are used as the inputs of in the energyconsumption estimation model of the intelligent DfE system tocalculate the revised power, 156.24 kWh, required in the usagephase. Subsequently, the electric power required after correctionand the total usage time of the product are used to obtain therevised power consumption, 13.02 W, during the usage phase ofthe PCB. The product development engineers are responsible forthe implementation of these suggestions for design modifications.Because the voltage was unchanged, a revised value of the currentis calculated and found to be 2.6 A or below. The product design isthen corrected. Table 12, based on the corrected values, shows thequantities of hazardous chemical substances generated owing toenergy consumption in the usage phase. The DfE performancevalue of the new wireless router is recalculated. This DfEperformance assessment value is 0.8436316. Accordingly, furtherrevisions of the product design by the designer are required.
The second correction of the new product design focuses onreducing the quantities of hazardous chemical substancesassociated with the manufacturing phase of the PCB. The firststep uses the energy consumption model of the intelligent DfEsystem. The inputs to the model are the revised quantities ofhazardous chemical substances resulting from the power requiredduring manufacturing, as shown in Table 9, and the proportions ofeach type of electric power generation. The model showed that the
the case.
CO2 (g) SOX (g) NOX (g)
96.870395 1.261513452 0.37202497
61.174949 569.543885 303.6069005
611.81804 1.065876244 0.554411087
411.2753477 1.810009867 1.773801588
410.8672385 1.805795966 1.773360444
173.04347 0.3008523 0.156777802
CO2 (g) SOX (g) NOX (g)
411.27535 1.81001 1.773802
410.8672 1.805796 1.77336
173.0435 0.300852 0.156778
95.376216 1.751631 0.600611
56.74196 625.0222 331.121
608.4603 1.060008 0.551359
Table 11BPNN training parameters for the models of the analysis and suggestions for improvement in part or component design.
Parameters Number of units in the hidden layer Learning rate Momentum factor Learning cycle
Manufacturing
Case 4 0.5 0.5 1000
Antenna 4 0.5 0.5 500
PCB 5 0.1 0.1 1500
Usage
Power consumption 2 0.7 0.7 500
Estimated energy consumption in each phase 3 0.7 0.7 700
Table 12Quantities of hazardous chemical substances generated owing to energy consumption in the usage phase.
LCI item Dust (g) CO2 (g) SOX (g) NOX (g)
Quantities of hazardous chemical substances 8.10913 100,299.65946 174.73663 90.88853
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929 923
revised electric power required in the manufacturing phase, is0.946035 kWh. The next step of the analysis applies the suggestionmodel to the manufacturing phase. The inputs to the model are therevised power required during the manufacture of the PCB and thequantities of hazardous chemical substances during materialextraction and manufacture for the PCB. The suggestion model ofthe intelligent DfE system indicates that the area and the weight ofthe PCB should be 156.2 cm2 and 74.4 g, respectively. The productdevelopment engineer revises the design of the PCB. The LCI for thePCB is then repeated to determine the quantities of hazardouschemical substances during material extraction and manufactur-ing. These quantities are shown in Table 13. Based on the updateddata, the DfE performance value of the new wireless router is foundto be 0.9487014. Because the performance value is still less than 1,a third design correction is made.
The third correction involves the design of the case. The revisedquantities of hazardous chemical substances resulting from energyconsumption during the manufacture of the case, shown in Table
Table 15Revised product specification.
Case Antenna PCB
Volume (cm3) Weight (g) Quantity Weight (g) Area (cm2)
670.25 152.69 1 14 156.2
Table 14Revised LCI data of the case in the phases of material extraction and manufacturing.
Inventory item Dust (g) C
Manufacturing
Process 0.22904 4
Energy consumption 0.01407 1
Total 0.24311 5
Material extraction
PS 0.22904 4
Table 13Revised LCI for the PCB in the phases of material extraction and manufacturing.
Phase Dust (g) CO
Manufacturing
Process 541.13584 6
Energy consumption 0.04894 60
Total 541.18478 67
Material extraction
Resin fibreglass 0.43240 8
CCL 0.00442
Total 0.43682 9
10, are used as inputs to the energy consumption model of theintelligent DfE system. The model calculates that the electricpower requirement for the manufacturing phase is0.270998567 kWh. We then used the revised energy consumptionand the amounts of hazardous chemical substances duringmaterial extraction and manufacture (Table 10) in the suggestionmodel for the manufacturing phase. The model indicated that thevolume and the weight of the case should be 670.25 cm3 and152.69 g, respectively, and that no change should be made in thematerial (PS) used for the case. The suggested changes to the caseindicated that lightweight should be a key aspect of the newdesign. The product development engineer modified the design ofthe case and recalculated the LCI for the case to estimate theamounts of hazardous chemical substances produced duringmaterial extraction and manufacturing (Table 14). The threedesign corrections increased the DfE performance value of the newwireless router to 1. Table 15 gives the product specifications afterthe correction.
Power consumption Total weight (g)
Weight (g) Voltage (V) Current (A)
74.4 5 2.6 241.09
O2 (g) SOX (g) NOX (g)
11.27052 1.58950 1.72540
74.08155 0.30328 0.15775
85.35207 1.89278 1.88315
11.27052 1.58950 1.72540
2 (g) SOX (g) NOX (g)
4.96712 569.40003 306.27142
5.38618 1.05467 0.54858
0.35330 570.4547 306.82000
9.34227 1.61720 0.56196
0.88689 0.04175 0.00629
0.22916 1.65895 0.56825
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929924
5. Conclusion
In recent years, the destruction of the ecological environment andthe exhaustion of natural resources have become increasingly severeproblems. There is need to mitigate ecological environmentalimpacts and reduce the consumption of natural resources. Today,environmental protection regulations are stringent in developingand developed countries. Moreover, owing to the shorter life cycle forelectronic products and the eco-design trend, an enterprise mustfrequently address problems of life cycle inventory and DfEperformance assessment. For this reason, an enterprise needs todevelop an intelligent DfE system. The intelligent system can beapplied in the detailed design stage to allow companies in theelectronics industry to predict the amounts of environmentalpollution and energy consumption occurring in major phases ofthe product life cycle. At the same time, the intelligent system canperform the DfE performance assessment of a new product throughbenchmark analysis with similar competitive products. Enterprisescan gain insight into the reasons for poor DfE performance by usingthe intelligent system to acquire information on the amounts ofhazardous chemical substances to be reduced in all major phases ofthe product life cycle. Based on the results of this study given above,concrete and valuable directions and suggestions for improvementobtained from an in-depth analysis will be helpful in achieving theultimate goals of reducing the time to market and mitigatingenvironmental impacts significantly. Accordingly, this study devel-ops a novel intelligent benchmark-based DfE evaluation andimprovement system for derivative electronic products. Thisintelligent system applies a BPNN to develop an approximate LCImodels for a product. The models estimate the amounts of hazardouschemical substances and energy consumption during materialextraction, manufacturing and usage. The intelligent system alsouses DEA to create the models for DfE performance evaluation andthe analysis of excessive hazardous chemical substances. The model
Table A1Materials’ weights of each major component for all wireless routers.
Product no. Case Antenna
ABS PS PP ABS/PC PVC
1 0 180.3 0 0 8
2 0 0 0 155.5 0
3 135.3 0 0 0 18
4 0 209.9 0 0 0
5 0 164.8 0 0 27
6 0 0 195 0 26
7 176.5 0 0 0 29.5
8 256.8 0 0 0 17
9 0 0 0 240.2 0
10 0 0 158.1 0 0
11 0 0 204.3 0 0
12 0 0 257.1 0 20
13 0 188 0 0 0
14 0 0 0 194.5 21
15 0 237.1 0 0 0
16 0 160.8 0 0 8
17 204.5 0 0 0 0
18 0 0 0 206.5 10.2
19 0 0 166.9 0 9
20 0 0 188.6 0 0
21 172.9 0 0 0 9
22 0 0 0 165.3 0
23 0 200.9 0 0 0
24 168.3 0 0 0 0
25 154.6 0 0 0 0
26 0 0 0 163.8 0
27 0 197.6 0 0 0
28 0 0 165.5 0 17
29 0 0 0 174.7 0
30 149.9 0 0 0 19
Unit: g.
includes comparisons with other competitive products and incor-porates the amounts of hazardous chemical substances to bereduced. In addition, this research uses the BPNN to create a modelthat can suggest improvements in the design of components or parts.These suggestions involve specific directions for improvement andare concrete and potentially valuable. Through iterative DfEperformance assessment and design correction, we can ultimatelyachieve the goal of benchmark-based DfE. The study used theexample of a wireless router to explain and verify the practical valueand the significant benefits of the intelligent DfE system. Afterapplying the intelligent DfE system to the wireless router develop-ment project, product development engineers gave positivefeedback. The intelligent DfE system makes them significantlyreduce the number of engineering change and the productdevelopment time because the intelligent system can effectivelyevaluate the environmental impact and DfE performance of aproduct and a component in a detail design phase, and furtherprovide product development engineers with valuable insight intohow to modify the design parameters of a new product so as torapidly achieve the benchmark-based DfE.
However, the proposed intelligent benchmark-based DfEsystem just can be used the derivative electric products. Futureresearch can develop DfE systems used in other industries. Inaddition, this paper does not consider the scrapping, recycling andreuse phases of the product life cycle. In the future, DfE works caninclude these phases of the product life cycle.
Acknowledgments
This work was supported by Taiwan National Science Councilunder Contract No. NSC 99-2221-E-141-002-.
Appendix A
PCB
PE Cu CCL Resin fibreglass Chip Capacitor
0 6 2 73 4.2 13.5
0 0 1.7 63.3 4.6 9.9
0 13 1.9 69.8 4.8 12.2
29 17 2.1 77.6 4.1 11.3
0 17 1.3 47.6 5 12.3
0 18 1.6 59.2 4.8 10.4
0 17.5 1.6 58 4.2 12.7
0 11 1.5 56.1 4.3 13.3
0 0 1.4 52.8 4 11.6
18 13 1.1 39.6 4.5 11.7
15 11 1.9 70.1 4.2 11.5
0 12 2 72 4.3 12.6
27 16 1.9 68.7 4.9 10.5
0 11 2 74.2 4.2 11.1
28 17 2 74.5 4.6 11.8
0 6 1.4 52.4 4.9 12.5
7.5 5.5 2.2 81.5 4.4 11.4
0 5.8 1.9 68.7 4.3 12.6
0 6 1.8 67.5 4.1 9.7
10 5 1.9 68.3 4.1 12.1
0 5 1.6 58.4 4.5 10.6
8 6 1.7 62.7 4.8 11.5
15 10 1.8 65.3 4.4 12.6
0 0 2 74.1 4.1 11.5
0 0 1.7 63.3 4.6 10.8
28 19 2 71.4 4.7 11.1
19 13 1.8 66 4.8 12.8
0 13 2 71.4 4.7 11.4
10 7 1.8 66.3 4.4 10.8
0 13 1.9 69.8 4.5 11.9
Table A2Specifications for all wireless routers.
Product no. Case Antenna PCB Power consumption Total weight (g)
Volume (cm3) Weight (g) Quantity Weight (g) Area (cm2) Weight (g) Voltage (V) Current (A)
1 803.682 180.3 1 14 203.5 92.7 5 3 287
2 678.08 155.5 0 0 172.5 79.5 12 0.5 235
3 817.7 135.3 2 31 195.5 88.7 5 2 255
4 643.864 209.9 3 46 210 95.1 12 1 351
5 453.6 164.8 3 44 135 66.2 12 1 275
6 556.612 195 3 44 162 76 12 1 315
7 564.3 176.5 3 47 165 76.5 5 2 300
8 500.472 256.8 2 28 157.5 75.2 5 2.5 360
9 616.208445 240.2 0 0 141.75 69.8 5 2 310
10 425.7468 158.1 2 31 108.75 56.9 5 2.5 246
11 687.5432 204.3 2 26 189 87.7 5 2.5 318
12 700.011 257.1 2 32 198 90.9 5 2.5 380
13 677.43 188 3 43 189 86 12 2 317
14 787.347 194.5 2 32 203.5 91.5 12 1 318
15 975.8331 237.1 3 45 203.5 92.9 12 1 375
16 525.9555 160.8 1 14 147 71.2 5 1.2 246
17 1147.3 204.5 1 13 222 99.5 12 2 317
18 690 206.5 1 16 192.5 87.5 12 2 310
19 688.875 166.9 1 15 178.25 83.1 12 1 265
20 855.625 188.6 1 15 190.3 86.4 5 2.5 290
21 544.5 172.9 1 14 155 75.1 5 2 262
22 569.25 165.3 1 14 170.5 80.7 9 1 260
23 788.137 200.9 2 25 184.8 84.1 5 1.2 310
24 819 168.3 0 0 198 91.7 5 2.5 260
25 618.75 154.6 0 0 170.5 80.4 12 1 235
26 889.85 163.8 3 47 197.8 89.2 12 1.25 300
27 688.2 197.6 2 32 187 85.4 5 2 315
28 674.7 165.5 2 30 198 89.5 12 1 285
29 800.625 174.7 1 17 181.5 83.3 12 1 275
30 455 149.9 2 32 192.34 88.1 5 2 270
Table A3LCI results during material extraction.
Product no. Dust (g) CO2 (g) SOX (g) NOX (g)
1 1.21955 652.17704 6.74634 3.15679
2 1.82732 512.77904 5.159662 3.31122
3 1.777945 666.12984 10.443144 3.07442
4 1.54201 813.67012 11.460785 4.06704
5 1.80143 673.19371 11.041806 3.51035
6 1.932 625.652 12.696996 3.63732
7 2.22381 836.63752 13.172231 3.81812
8 1.935595 1032.33314 11.42831 4.21429
9 2.4740175 720.070975 6.647998 4.661332
10 1.0183975 459.583445 8.700506 2.58327
11 1.23812 581.79789 9.380898 3.21411
12 1.79171 723.97502 11.339768 4.02316
13 1.41197 734.54699 10.590934 3.68553
14 3.09402 742.38749 11.6049 4.91297
15 1.561645 881.32364 11.657891 4.34073
16 1.03223 567.00356 5.943272 2.73082
17 1.41531 838.60677 8.325677 3.40089
18 2.74865 714.3191 9.20628 4.67784
19 1.1519825 479.478565 7.0017 2.649
20 1.007471 516.794532 6.77066 2.79117
21 1.30542 705.91584 7.165846 2.87819
22 2.12595 578.39576 7.800258 3.779768
23 1.188426 718.765142 8.162855 3.46961
24 1.04173 668.04006 5.16346 2.62199
25 0.925045 607.07394 4.621852 2.36707
26 2.672476 683.323852 13.413024 4.543968
27 1.29927 731.49714 9.376994 3.59354
28 1.61801 541.52542 10.29045 3.1247
29 2.27649 617.73147 8.537422 4.039682
30 1.8345318 715.2273256 10.7249564 3.247024
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929 925
Table A4The LCI results for case process during manufacture.
Product no. Dust (g) CO2 (g) SOX (g) NOX (g) Energy (kWh)
1 0.27175501 487.665863 1.885350943 2.046780586 0.279577434
2 1.279865 412.5613 3.19287 2.66395 0.281345
3 0.391227305 443.8071594 2.455382824 1.524524689 0.28384167
4 0.314393371 564.8911133 2.182566643 2.368922472 0.291738987
5 0.247678101 444.3429871 1.71807313 1.865241528 0.272957861
6 0.291325837 377.7424927 2.734634399 1.864617705 0.267403394
7 0.514607966 583.7554932 3.229968786 2.005582571 0.276646793
8 0.737629175 836.762085 4.629461288 2.874437094 0.290133089
9 2.081801414 639.5302734 5.155597687 4.154284954 0.318801939
10 0.236768723 307.017395 2.222438812 1.515221 0.275142521
11 0.304420948 394.7355042 2.857393503 1.948172092 0.268277049
12 0.382363319 495.7853699 3.589295387 2.447382927 0.285018712
13 0.283132374 508.2495728 1.964620471 2.13272357 0.282774985
14 1.683042407 516.9518433 4.167666435 3.358266592 0.299844682
15 0.356088936 639.6313477 2.471675396 2.682791233 0.303635389
16 0.241279542 432.8898621 1.673741698 1.817088366 0.271228164
17 0.588947356 668.1057739 3.696227074 2.294916391 0.275621504
18 1.780184269 547.0912476 4.409982204 3.553207159 0.304319471
19 0.24594 323.4856 2.329765 1.59985 0.263406
20 0.282650352 366.4846191 2.653313398 1.809267998 0.267318994
21 0.504350007 572.1223755 3.16552496 1.965545893 0.277331591
22 1.434667826 440.164856 3.549454212 2.860690355 0.288775802
23 0.301566839 541.670166 2.093185663 2.272048712 0.28804028
24 0.490770787 556.7246094 3.080187082 1.912515283 0.278390199
25 0.44844234 508.724823 2.814199924 1.747243404 0.282180488
26 1.420981765 435.9685059 3.515599489 2.833401203 0.288179934
27 0.296890199 533.190979 2.06057024 2.236706018 0.286696672
28 0.248836443 322.6527405 2.335853815 1.5926826 0.272166073
29 1.518417597 465.934021 3.757187605 3.028025389 0.292449147
30 0.433961123 492.2977295 2.723291159 1.690795422 0.283305943
Table A5The LCI results for antenna process during manufacture.
Product no. Dust (g) CO2 (g) SOX (g) NOX (g) Energy (kWh)
1 0.20510 24.83162 0.44040 0.17109 0.06113
2 0 0 0 0 0
3 0.417711943 52.02639008 0.888406694 0.345381856 0.066682041
4 0.083973199 53.19825363 0.470521033 0.286655545 0.069672003
5 0.606736124 76.333992 1.28497088 0.500356793 0.071722321
6 0.591885865 74.42739868 1.253363371 0.488064706 0.071296483
7 0.637273788 80.2502594 1.350449681 0.525753379 0.072627872
8 0.391790837 48.69429779 0.834384024 0.32424435 0.066022724
9 0 0 0 0 0
10 0.052152686 33.04852676 0.292279363 0.178037167 0.061579935
11 0.043493964 27.54956055 0.243676841 0.148475274 0.061539512
12 0.468625575 58.575634 0.994504571 0.386921555 0.067986138
13 0.07884147 49.95181274 0.441783428 0.269149095 0.070507213
14 0.49284631 61.69213104 1.045054197 0.40671134 0.068614744
15 0.081534788 51.6557579 0.456865817 0.278336942 0.070100188
16 0.205097452 24.83162117 0.440397471 0.171088219 0.061131343
17 0.021649472 13.71940327 0.121329568 0.073891044 0.062767975
18 0.239171878 29.15690613 0.513421714 0.199285686 0.062068038
19 0.219594344 26.66913033 0.471556395 0.183106959 0.061534055
20 0.02905477 18.41179466 0.162840933 0.099202529 0.063853472
21 0.219594344 26.66913033 0.471556395 0.183106959 0.061534055
22 0.023139656 14.66593075 0.129700005 0.078988239 0.063228771
23 0.043493964 27.54956055 0.243676841 0.148475274 0.061539512
24 0 0 0 0 0
25 0 0 0 0 0
26 0.081534788 51.6557579 0.456865817 0.278336942 0.070100188
27 0.055001307 34.85237503 0.308239907 0.187763393 0.06362994
28 0.391790837 48.69429779 0.834384024 0.32424435 0.066022724
29 0.02905477 18.41179466 0.162840933 0.099202529 0.063853472
30 0.443459094 55.33783722 0.942047894 0.366381645 0.067339316
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929926
Table A6The LCI results for PCB process during manufacture.
Product no. Dust (g) CO2 (g) SOX (g) NOX (g) Energy (kWh)
1 695.1068726 66.29090881 730.2055054 390.1950684 1.152471423
2 605.2970788 59.055485 636.6112492 336.7908534 0.942714734
3 670.8119507 66.74108887 704.8626709 374.6207581 1.13658452
4 735.1051025 59.75521469 772.2606201 408.5772705 1.095541358
5 457.4360657 68.12473297 482.0991211 260.413208 0.950746
6 566.6425171 61.55578613 595.9394531 316.1109009 0.983109474
7 557.3117065 64.01881409 586.9257813 314.3527527 1.002167821
8 537.1790771 66.42020416 566.1221924 304.3976746 1.001667619
9 501.8912659 59.87364578 528.5234985 282.6195068 0.920330584
10 375.1152954 63.01931763 395.3816223 215.5393829 0.83553189
11 663.2235107 60.54419327 697.0549927 369.7338562 1.055027962
12 684.5886841 64.37793732 719.1278687 382.9456482 1.122667432
13 654.0842896 62.54353714 687.897583 363.4129333 1.052658081
14 707.836792 59.77337265 743.9137573 393.3351135 1.071470857
15 710.9199829 64.17863464 746.9985352 395.5592957 1.138158321
16 501.9135437 68.02967834 528.8236694 284.4677734 0.993821681
17 774.7796631 61.65605164 814.1586304 429.7217102 1.156588435
18 656.9923096 64.38623047 690.5006104 368.0779114 1.098534346
19 645.3011218 55.2801201 678.4994382 358.0368889 0.972992834
20 651.5333862 61.51216125 684.663208 364.5417786 1.062249064
21 553.3016968 60.15763855 582.1508789 309.0222168 0.958749652
22 595.4354858 64.79831696 626.3565674 332.9706726 1.045423388
23 627.026001 65.12172699 659.4428101 351.8101501 1.078771353
24 698.5587158 60.08696365 733.824585 389.0478821 1.076048613
25 601.8848877 61.21020889 633.1292725 335.3320618 1.009033442
26 684.2163086 62.82150269 719.3891602 380.2308044 1.088383555
27 637.131897 68.04920959 669.7227173 357.5041809 1.125593543
28 684.2373657 63.71944809 719.251709 380.6482239 1.103095651
29 634.0682983 60.06672668 666.8840332 352.7201538 1.017365217
30 666.7963867 63.70221329 700.8776245 371.9880371 1.097315788
Table A7The total LCI results for manufacturing processes.
Product no. Dust (g) CO2 (g) SOX (g) NOX (g) Energy (kWh)
1 695.58373 578.78839 732.53125 392.41294 1.49318
2 606.5769438 471.616785 639.8041192 339.4548034 1.224059734
3 671.6208899 562.5746384 708.2064604 376.4906646 1.487108231
4 735.5034691 677.8445816 774.9137078 411.2328485 1.456952348
5 458.2904799 588.801712 485.1021651 262.7788063 1.295426182
6 567.5257288 513.7256775 599.9274509 318.4635833 1.321809351
7 558.4635883 728.0245667 591.5061997 316.8840886 1.351442486
8 538.3084972 951.8765869 571.5860377 307.596356 1.357823431
9 503.9730673 699.4039192 533.6790962 286.7737918 1.239132524
10 375.4042168 403.0852394 397.8963405 217.2326411 1.172254346
11 663.5714257 482.829258 700.156063 371.8305036 1.384844523
12 685.439673 618.7389412 723.7116686 385.7799527 1.475672282
13 654.4462634 620.7449226 690.3039869 365.814806 1.405940279
14 710.0126807 638.417347 749.126478 397.1000915 1.439930283
15 711.3576066 755.4657402 749.9270764 398.5204238 1.511893898
16 502.3599207 525.7511616 530.9378086 286.45595 1.326181188
17 775.3902599 743.4812288 817.976187 432.0905176 1.494977914
18 659.0116657 640.6343842 695.4240143 371.8304042 1.464921854
19 645.7666561 405.4348504 681.3007596 359.8198459 1.297932888
20 651.8450914 446.4085751 687.4793623 366.4502491 1.393421531
21 554.0256411 658.9491444 585.7879603 311.1708696 1.297615297
22 596.8932933 519.6291037 630.0357216 335.9103512 1.397427961
23 627.3710618 634.3414536 661.7796726 354.2306741 1.428351145
24 699.0494866 616.811573 736.904772 390.9603974 1.354438812
25 602.33333 569.9350319 635.9434724 337.0793052 1.29121393
26 685.7188251 550.4457664 723.3616255 383.3425426 1.446663678
27 637.4837885 636.0925636 672.0915274 359.9286503 1.475920156
28 684.877993 435.0664864 722.4219468 382.5651508 1.441284448
29 635.6157707 544.4125423 670.8040617 355.8473817 1.373667836
30 667.6738069 611.33778 704.5429636 374.0452142 1.447961047
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929 927
Table A8LCI results for electric power required during manufacturing.
Product no. Dust (g) CO2 (g) SOX (g) NOX (g)
1 0.07761 959.88671 1.67227 0.86986
2 0.063502886 786.0603636 1.368496409 0.73268071
3 0.07734976 956.8765717 1.666965477 0.867040295
4 0.07579301 937.8647041 1.633772202 0.849641819
5 0.067216331 831.0965996 1.448067725 0.753238071
6 0.068658812 849.0549507 1.479332291 0.769437138
7 0.070231663 868.663456 1.513417929 0.787113383
8 0.070547369 872.6446724 1.520330399 0.79068546
9 0.064201606 793.7339325 1.383104414 0.719476163
10 0.061023102 755.0348015 1.315301105 0.684077006
11 0.072010672 890.9348068 1.552104108 0.80714887
12 0.076774967 949.8658714 1.654716372 0.860618077
13 0.073111336 904.613903 1.575896561 0.819515392
14 0.074884764 926.6660309 1.614290163 0.83946665
15 0.078632889 972.7901955 1.69472196 0.88147068
16 0.06889471 852.048378 1.484510399 0.772093657
17 0.077681105 960.7734489 1.673834987 0.8706978
18 0.0762019 942.931179 1.642637141 0.854245476
19 0.067378664 833.8109623 1.449379303 0.755341658
20 0.072465358 896.5853043 1.56193655 0.812261742
21 0.067336954 832.6317291 1.4507237 0.754592758
22 0.072649954 898.9055977 1.565961398 0.814340718
23 0.074284889 919.2054977 1.601270668 0.832708832
24 0.070417905 871.2176971 1.517703414 0.789295003
25 0.06706577 829.4730835 1.445124596 0.751604244
26 0.075255204 931.2096291 1.622177862 0.84360151
27 0.07677902 949.9024582 1.654784903 0.860659879
28 0.074991126 927.8332253 1.616336316 0.840607047
29 0.07138683 883.1372147 1.538567834 0.800133031
30 0.075330795 932.1078453 1.623749159 0.844437793
Table A9Analytical results of energy consumption during usage.
Product no. Energy (kWh) Product no. Energy (kWh) Product no. Energy (kWh)
1 180 11 150 21 120
2 72 12 150 22 108
3 120 13 288 23 72
4 144 14 144 24 150
5 144 15 144 25 144
6 144 16 72 26 180
7 120 17 288 27 120
8 150 18 288 28 144
9 120 19 144 29 144
10 150 20 150 30 120
Table A10LCI results for electric power required during usage.
Product no. Dust (g) CO2 (g) SOX (g) NOX (g)
1 9.46656 117,049.91406 203.87132 106.10102
2 3.642601252 45,099.41797 78.66654205 40.7978363
3 6.111249924 75,537.41406 131.4068604 68.58152771
4 7.49724102 92,652.14844 161.2116852 84.10034943
5 7.49724102 92,652.14844 161.2116852 84.10034943
6 7.49724102 92,652.14844 161.2116852 84.10034943
7 6.111249924 75,537.41406 131.4068604 68.58152771
8 7.840816975 96,900.92969 168.6274261 87.94121552
9 6.111249924 75,537.41406 131.4068604 68.58152771
10 7.840816975 96,900.92969 168.6274261 87.94121552
11 7.840816975 96,900.92969 168.6274261 87.94121552
12 7.840816975 96,900.92969 168.6274261 87.94121552
13 13.22321701 164,000.7656 286.190094 148.2752228
14 7.49724102 92,652.14844 161.2116852 84.10034943
15 7.49724102 92,652.14844 161.2116852 84.10034943
16 3.642601252 45,099.41797 78.66654205 40.7978363
17 13.22321701 164,000.7656 286.190094 148.2752228
18 13.22321701 164,000.7656 286.190094 148.2752228
19 7.49724102 92,652.14844 161.2116852 84.10034943
20 7.840816975 96,900.92969 168.6274261 87.94121552
21 6.111249924 75,537.41406 131.4068604 68.58152771
22 5.436086178 67,210.6875 116.9466553 61.00292206
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929928
Table A10 (Continued )
Product no. Dust (g) CO2 (g) SOX (g) NOX (g)
23 3.642601252 45,099.41797 78.66654205 40.7978363
24 7.840816975 96,900.92969 168.6274261 87.94121552
25 7.49724102 92,652.14844 161.2116852 84.10034943
26 9.466556549 117,049.9141 203.8713226 106.1010208
27 6.111249924 75,537.41406 131.4068604 68.58152771
28 7.49724102 92,652.14844 161.2116852 84.10034943
29 7.49724102 92,652.14844 161.2116852 84.10034943
30 6.111249924 75,537.41406 131.4068604 68.58152771
T.-A. Chiang, R. Roy / Computers in Industry 63 (2012) 913–929 929
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Tzu-An Chiang is an associate professor in the
Department of Business Administration at National
Taipei College of Business, Taiwan. He received his
doctoral degree in the Industrial Engineering and
Engineering Management from National Tsing Hua
University in Taiwan. Dr. Chiang’s research focuses on
new product development and management, supply
chain management, and data mining.
Professor Rajkumar Roy is leading the Manufacturing
and Materials Department at Cranfield University and
Director of the EPSRC Centre for Innovative
Manufacturing in Through-life Engineering Services.
In addition, Professor Roy has led competitive design
research at Cranfield for fourteen years. He is the
Principal Investigator of four Product-Service System
(PSS) and cost engineering (IMRC, EPSRC, Industry and
MoD funded) in the areas of concept design, whole life
cost modelling, design for service and obsolescence
management.