selected cases in urban logistics and land transport …€¦ · penetration of online purchasing,...

SELECTED CASES IN URBAN LOGISTICS AND

LAND TRANSPORT USING MULTI-METHOD

DECISION ANALYSES

Volume 18-Jul-TF

A Collaboration Between

TLI – ASIA PACIFIC WHITE PAPERS SERIES

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Selected Cases in Urban Logistics and Land Transport

using Multi-Method Decision Analyses

Presented at:

Cascading Workshop:

Global View of Urban Transport Management for Education

17 July 2018

Jakarta

Selected Cases in Urban Logistics and Land Transport using Multi-Method Decision Analyses

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INTRODUCTION

This whitepaper serves as a record of a special series of case studies that have underpinned the learning in

the TFI - National University of Singapore – Republic of Indonesia Coordinating Ministry of Economic Affairs

Program in Urban Management & Land Transport for Leaders/Specialists delivered over the last 30 months

in Indonesia. This series of cases are expected to serve as a foundation for cascading the learning to a whole

host of other parties, albeit, in academia, industry, government and other agencies.

It was recognized through previous engagements that many participants had difficulty in understanding

certain concepts due to a gap in their foundational knowledge and visualizing example scenarios necessary

for demonstrating, practicing and applying new concepts introduced. It was also observed that participants

could better grasp concepts when local-context scenarios were illustrated. A simple game was also

necessary to help participants visualize different scenarios and to influence outcomes based on a myriad of

choices. It was, however, noted that there was a lack of relatable case scenario to facilitate learning, and a

simple game may not be entirely sufficient in presenting multiple scenarios to help participants fully grasp

these concepts.

To ensure sustained effectiveness in capability development, it was necessary to research, engage and

develop local content that participants could easily identify with, influence, understand and engage in

discussions with fellow practitioners.

Through the Coordinating Ministry for Economic Affairs of the Republic of Indonesia, two local universities

were selected to help the National University of Singapore (The Logistics Institute – Asia Pacific) build cases

for the programme. These were Andalas University (Padang – West Sumatra) and Hasanuddin University

(Makassar, South Sulawesi). NUS, itself the top ranked Asian University, through TLI-Asia Pacific, was tasked

to actively lead, engage and support the development of these cases and bring to bear its deep expertise

in innovative urban transportation solutions using emerging technologies. Besides the Universities, it was

also necessary to bring in a more commercial focus and thus, we were fortunate to have as a partner PT

Pos Indonesia.

During the scenario case development sessions, Kummara Studio, a serious-game development partner,

was also engaged to co-facilitate and support development of a dynamic scenario board game with

advanced gamification concepts for the various key logistics concepts in the cases.

The white paper documents four cases:

The first case emphasizes the importance of logistics facility location. It focuses on site selection for disaster

relief warehouses that require multiple criteria decision analysis across many parties. The problem of

choosing the warehouse location from a host of candidate locations is deemed very urgent and important

in West Sumatra because this geographical area is susceptible to disasters and the potential epicentre of

earthquakes and tsunami threats. The proposed model of site selection is expected to be of most relevance

in understanding and navigating conflicting criteria and reconciling different stakeholder interests. The

method used is analytical hierarchy process (AHP). This method is most often applied to site selection issues

TFI-NUS Urban Transportation Management Programme in Indonesia

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with many criteria. The result of the study led to the identification of eight criteria relevant for the case of

warehouse location selection in West Sumatra. Alternative locations are six districts that met the above

criteria. These criteria included coverage, risk, access to affected zone, access to infrastructure, access to

corridor, congestion, cost, and National Development Plan. There are three main criteria based on

weighting result, that are risk, access to affected zone, access to infrastructure. The best location

subsequently identified as a candidate is Kota Padang. The usefulness of this study can be assessed from

two things, the developed model and the results of the study. The model formulated from the results of

this study is applicable in other areas because the criteria used are highly relevant for the selection of

warehouse locations in disaster management. Second, the best location selection result is Padang City is in

accordance with the policy direction of BNPB in building an early warning system of disaster in West

Sumatra. This study focused on the development of site selection methods and the authors expect to apply

it in BPBD Kabupaten / Kota in West Sumatra through cascading workshops.

The second case uses analytical digital network design for optimization of rice distribution in Makassar,

South Sulawesi. The overarching goal is to try to satisfy the key objectives of maximizing profit or minimizing

cost subject to a wide range of constraints such as the limited budget, resources, capacity, and so on.

Because the two objectives are in conflict with each other, there has to be a trade-off between these key

objectives. Moreover, the current practice on decision making concerning supply and logistics chains are

made based on experience and intuition at different stages by the responsible functions individually and

separately. There might be some constraints from different functions which may not be included in the

overall consideration. The decisions such as selection and number of suppliers, delivery mode and route,

procurement, etc. are the important activities significantly affecting the overall organizational cost and

network performance. Therefore, the optimization concept is taken by the authors as the base framework

to carry out activities to achieve the best performance.

Distribution network optimization plays an important role in business survival. Companies inevitably need

to continuously re-optimize their business processes by reducing logistics operating costs and increasing

customer service level requirements. The case discusses the optimization model which includes the

objectives and related constraints and costs in the calculation that will help the company reach an effective

and efficient logistics design and secondly, a wide range of theoretical applications of distribution network

optimization model in many other logistics systems involving suppliers, warehouses, distribution,

transportation and customers are explored. The case analyses the current situation and maps productive

processes, creates a simplified logistics network model to accurately represent the current rice logistics

system, determines possible improvements before optimisation of the existing logistics process to identify

and select an optimal network distribution. This case is anticipated to increase competitiveness and

efficiency through the application of optimized distribution models, improve management of stock,

distribution, transportation and other logistics facilities, optimize warehouse capacity, reduce stock levels,

reduce transportation costs and subsequently optimize commodity / rice distribution across an entire

network of rice logistics operations in regional division of South Sulawesi and West Sulawesi of State

Logistics Agency (Bulog Divre Sulselbar) and simulate relevant aspects of rice flow from surplus areas to

deficit areas.

The third case explores an integrated approach to the redesign of hub/sub-hub networks. In a global

context characterized by competition shifting from company‐to‐company to supply chain against supply

chain, there is a burning need for logistics providers (3PLs) to streamline their processes and structures to

gain concurrent cost effectiveness and superior service level. One key initiative that 3PLs can undertake is


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to optimally allocate and utilize their logistics resources such as storage (distribution) and transportation

assets. Nowadays, in fact, the combination of factors such as increasing customers’ expectations, massive

penetration of online purchasing, and digital economy transformation & technological breakthroughs, are

forcing the reshaping of the logistics industry, putting the entire sector under an acute and growing

pressure to deliver a better service at an ever lowering cost. In the particular case of PT Pos Indonesia, after

a series of field visits, interactions with operations and leadership teams, and basic analytics of both

operational and structural data, it has been identified that the existing network structure in Greater

Surabaya led to high operational and distribution costs due to its oversize. Therefore, a seamless and

effective navigation of storage assets through an optimum design of supply network (SN) would be highly

beneficial for a company like PT Pos Indonesia that aims to gain a truly competitive edge, particularly in the

final leg of deliveries. On the one‐hand a good network design helps to increase supply chain performance

in regard to time responsiveness, operational and structural cost effectiveness, and customer satisfaction,

and on the other hand it enables firms to stay ahead of the curve.

A case study based approach leveraging on the integration of data analytics, green field analysis, and

network optimization is used to provide holistic support to decision making. Results to date show that

number of storage facilities, and their locations, affect speed and cost‐effectiveness of last mile distribution.

For the case at hand, 18% of savings in transportation and warehousing cost with no impact on service level

can be achieved by reducing the number of facilities in the network from 9 to 4. The usefulness of the study

lies on the fact that it cascades a solution method to an industry set of problem statements and provides

decision makers with a robust decision support framework able to tackle, rigorously, a diverse pool of

decisions on network design such as number and locations for the nodes of the distribution network as well

as decisions on transportation policies. The case further illustrates the methods to capture and analyse

quality data for the problem.

Finally, in the fourth section we explore serious gaming and its potential in enhanced learning of supply

chain management. Serious games, in any medium or channel, have been used to facilitate learning and

training processes with learning objectives to help the players understand specific and complex concepts.

In this case study, we introduce THINKLog, a board game for learning Supply Chain Management (SCM) and

logistics concepts. THINKLog serves as an interactive learning framework that can be extended to cover

different SCM and logistics concepts without changing the basic structure of the game. This provides

flexibility to: choose a suitable gameplay/scenario, and to expand it based on other SCM and logistics case

studies and research. THINKLog’s scenarios and gameplays were tested and evaluated and built on the

participants’ feedback to continuously refine the gameplay. As an interactive board game for game-based

learning, THINKLog is carefully designed to balance the entertainment components and the pedagogy (i.e.

learning objectives and outcomes). The authors posit clear objectives derived from a specific SCM concept

or case study, whilst maintaining the enjoyment and fun components. It can also be continuously extended

by modifying the rules of the game to include other concepts. Using this principle, THINKLog was able to

help the players to voluntarily learn and enhance their understanding on SCM concepts. In cascading to a

wider audience, the authors have been approached to incorporate the game in different supply chain

management courses in several universities. Two main challenges to develop this game were to balance

the entertainment components and the pedagogy (i.e. learning objectives and outcomes) and to evaluate

the learning objectives of the game. The authors address these challenges by maintaining a close

collaboration between the game designers and content experts and continuously testing and refining the

game, more importantly, in incorporating the scenarios in the three aforementioned cases.


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In conclusion, these cases and serious gaming described in this white paper are hoped to realise the

following objectives:

Understand the complex data involved in urban transport management

Understand competitiveness factors and trade-offs in urban transport management

Identify appropriate strategic policies to implement urban land transportation management

strategies and initiatives

Develop local-context scenario cases using case methods which analyse supply chain and logistics

competitiveness, supply chain and logistics strategies, urban transportation frameworks and logistics

infrastructure

Compare and contrast scenarios in Indonesia with supply chain and logistics management efficiency

and productivity initiatives and implementations in other sectors and geographies and,

Use serious gaming to depict different scenarios for illustration and exploration of outcomes based

on choices.

I hope you enjoy reading this whitepaper as much as the team, colleagues, partners and collaborators have

in sharing, contributing and putting this together as a final deliverable of the program and that the

knowledge gained within these pages is cascaded to the community of practice at large.

Dr. Robert de Souza

Executive Director

The Logistics Institute – Asia Pacific

Singapore

July 2018


TABLE OF CONTENTS

THE IMPORTANCE OF LOGISTICS FACILITY LOCATION

Decision Support Framework for Humanitarian Relief Stockpiles - The Case of

West Sumatra 3

REFERENCES 13

USING DIGITAL NETWORK DESIGN FOR OPTIMIZATION OF SUPPLY CHAIN

Logistics Network Design for Rice Distribution in Makassar, South Sulawesi 17

REFERENCES 28

AN INTEGRATED APPROACH TO THE RE-DESIGN OF HUB/SUB-HUB NETWORKS

Implementation of Multi-Method Decision Support Framework for Supply Network Design – The Case Study of PT Pos Indonesia

31

REFERENCES 41

SERIOUS GAMING AND ITS POTENTIAL IN ENHANCED LEARNING OF SUPPLY CHAIN MANAGEMENT

THINKLog – Interactive Framework for Learning of Supply Chain Management 45

REFERENCES 50

THE IMPORTANCE OF LOGISTICS FACILITY LOCATION THE IMPORTANCE OF LOGISTICS FACILITY LOCATION


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DECISION SUPPORT FRAMEWORK FOR

HUMANITARIAN RELIEF STOCKPILES

– THE CASE OF WEST SUMATRA

Giuseppe Timperio1, Rika Ampuh Hadiguna2, Insannul Kamil2, Linda William1, Robert de Souza1

1 The Logistics Institute – Asia Pacific, National University of Singapore, Singapore 2 Universitas Andalas, West Sumatra, Indonesia

SUMMARY

This case study provides a decision support framework to preposition logistics network to support certain

logistics activities. In this case study, the framework is applied to preposition humanitarian relief

stockpiles for the real life case of West Sumatra. The logistics facilities in humanitarian relief are used to

support humanitarian operation and distribution of relief goods. A novel methodology integrating GIS and

fuzzy AHP, with four layer hierarchical structure, is used to consider criteria, sub-criteria and alternative

locations.

The framework is part of solutions/case studies developed as part of “Temasek Foundation International

– National University of Singapore Urban Transportation Management Programme in Indonesia”

programme which aims to provide support and knowledge to center/local government for efficient and

effective urban freight movement.

The result of framework implementation shows that framework can be used for other region in Indonesia

for humanitarian relief or other logistics activities (such as rice distribution).

INTRODUCTION

In 2013, 337 natural disasters were registered (IFRC, 2015). This is less than the average number of natural

disasters registered yearly from 2004 to 2012 (390). However, the impact of natural calamities in terms

of both estimated damage, and number of people reported killed is still significant. In 2013, International

Red Cross estimated 118,639 million US$ worth in damages, with a death toll of 22,500, and nearly 100

million of people affected (IFRC, 2015). Asia has witnessed one of the most incidents of reported disasters

and the highest reported number of victims (Guha- Sapir, et al., 2015), with causalities over the period

2004-2013 being estimated at 652,754 - 66% of total worldwide figures (IFRC, 2015), and 4,625 observed

disasters since the turn of this century (UN ESCAP, 2015). Furthermore, factors such as climate change,

rapid urbanization of cities located in disaster prone areas, political and social instabilities, are expected


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to determine a further raise in number and scale of humanitarian crises (both man-made and natural) in

the coming decades. These premises have brought about a growing attention to humanitarian logistics,

and especially enhancement of the efficiency and effectiveness of relief operations, reduce duplication of

efforts, and in general to better manage resources (Balcik & Beamon, 2008).

Objective of emergency response is to provide relief goods (water, food, medical supply, shelter) to

disaster affected areas, to alleviate human suffering and to minimize the number of victims (Beamon, B.,

2004).

However, given the unpredictability of natural disaster events, for achieving an effective and efficient

response, preparedness phase is critical, especially with regards to design of logistics operations for relief

items (Duran, et al.et al., 2011). Moreover, the complexity of operational scenarios derived from the large

number of stakeholders involved in those crises, the massive deployment of goods and personnel, as well

as the massive financial flows to be managed, leads to the emergence of complex supply chain networks

(Blecken, 2010). Specifically, relief organizations, both local and international, are faced by ample

challenges in their relief chain design and management, such as unpredictability of demand and short lead

times (the golden window is typically 72 hours) for a wide range of supplies, lack of resources such as

people, technology, transportation capacity and money (Balcik & Beamon, 2008).

From the supply chain management standpoint, disasters are events with potentials of disrupting the

nodes (distribution centres) and arcs (logistics infrastructure) of a supply chain. Disruptions might

determine delays in the implementation of relief operations, hence having a direct impact on the survival

rate in affected zones (Nahleh, et al., 2013). Relief organizations and governments then need to minimize

risk of supply chains disruptions, and learn what actions to take in order to recover to recover fast, through

a greater engagement in preparatory activities that enhance their logistics resilience when responding to

emergencies (Celik & Gumus, 2016).

The lessons learnt from earlier worldwide large scale emergency responses suggest that supply chain

nodes, and especially their locations, affect directly to disaster response performances in terms of speed

and operations’ sustainability [(Balcik & Beamon, 2008) (Roh, et al., 2013)]. Particularly, pre-positioning

critical relief supplies in strategic locations can be an effective strategy to increase the robustness and

resilience of humanitarian supply chains (Rezaei-Malek, et al., 2016). When responding to sudden onset

large scale disasters events, an established prepositioned network would be highly beneficial for

enhancing the agility in the mobilization of emergency supplies. Immediate benefit of this strategy consist

in the complete elimination of procurement phase for relief goods (Duran, et al., 2011), but also

minimization of challenges and risk associated with post-disaster supply procurement (Balcik & Beamon,

2008).

In this case study, we propose the design of a supply chain framework for multi-stage network design in

the domain of disaster relief supply chains, providing a methodological approach to guide policy making

and inform field implementation in designing resilient humanitarian response, ultimately enhancing an

early response mechanism.


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THE CASE STUDY: LOGISTICS CLUSTER IN WEST SUMATRA

Sumatera Barat is a province in Indonesia located on the island of Sumatera with Padang as its capital.

The province occupies along the western coast of central Sumatera and a number of islands off the coast

such as the Mentawai Islands. The total area is about 42.297,30 km² and is bordered by four provinces,

namely Sumatera Utara, Riau, Jambi, and Bengkulu. The province has a population of 4,846,909 people,

consisting of 12 districts and 7 cities with administrative subdivisions after sub-districts in all districts

(except Mentawai Islands district) named as nagari.

Sumatera Barat is one of the province that facing the highest risk of natural disasters such as earthquakes,

tsunamis, floods, landslides, volcanic eruptions, tornadoes, flash floods, droughts and others. The

earthquake disaster on September 30, 2009 has been a turning point in the awareness of many

stakeholders, especially the central and local governments, to prepare for responsive, systematic and

organized mitigation scenario of earthquake and tsunami threats. This awareness refers to the number of

casualties when the earthquake occurred as published by the Regional Disaster Management Agency

(BPBD) of Sumatera Barat is estimated as many as 1,197 people died, 1,798 wounded and 6,554 people

displaced. Padang City and Padang Pariaman District are areas that suffered many deaths and injuries.

Seismicity map on land in West Sumatera Province can be seen in Figure 1.1. This map shows a number

of potential trigger points for the earthquake.

The disaster management system involves many government and non-governmental organizations with

different roles. Coordination becomes the key to achieving efficiency and effectiveness of logistics

operations. Indonesia's National Disaster Management Agency (BNPB) encourages the formation of

disaster response clusters in order to facilitate tasks in each field when faced with disasters. Such clusters

are aligned with the emergency response command system and can be divided into: Health Cluster,

Search and Rescue Cluster, Logistics Cluster, Displacement and Protection Cluster, Education Cluster,

Cluster of Facilities and Infrastructure, Economic Cluster, Cluster of Early Recovery. This case study focuses

on the logistics cluster responsible for logistics operations in the emergency response phase. The logistics

cluster is chosen because it has a direct role in disaster management, both for disaster victims and the aid

to be distributed.

Source: http://pusdalopspbsumbar.blogspot.co.id/2015/08/ancaman-gempabumi-di-sumatera-tidak.html

FIGURE 1.1. SEISMICITY MAP IN LAND OF WEST SUMATERA PROVINCE

http://pusdalopspbsumbar.blogspot.co.id/2015/08/ancaman-gempabumi-di-sumatera-tidak.html


6

The logistics cluster aims to develop coordination and collaboration of communities, governments and

businesses for the readiness and availability of logistics goods, improve logistical response on emergency

status, identify gaps, constraints and duplication of logistics operations. Its main task is to collect, analyze

and disseminate information on logistics, plan operations of logistics, provide advice and technical

assistance, provide necessary logistical facilities and mobilization and coordinate communities,

governments and businesses in disaster management. The effectiveness of the coordination of the cluster

of logistics is influenced by the availability of storage facilities of logistic goods or warehouses.

One of the strategic decisions in disaster logistics is the selection of warehouse locations. The warehouse

is a storage facility for the amount of goods needed for logistics operations in the emergency response

phase. Uncertain catastrophic events, the wide area affected by the widespread disaster, the large

number of victims, the refugee camps of the victims are widespread and the condition of infrastructure is

severely damaged is a condition that must be considered in the selection of warehouse location. This case

study aims to develop a warehouse location selection model involving multiple criteria for service level

maximization.

METHODOLOGY

The decision support framework proposed for this case encompasses the analysis of (potential) demand,

actual design of emergency response networks, with the inclusion of considerations on inventory

management policies as illustrated in Figure 1.2. Through this, decision-makers can then identify the

criticalities along their supply chains, and design efficient and effective strategies to enhance supply chain

robustness before the next major natural disaster occurs. Moreover, to meet the overarching

optimization criterion comprising “maximizing the speed” of relief operations in the immediate aftermath

of an emergency, a novel approach that integrates the Geographic Information System (GIS) and fuzzy

Analytical Hierarchy Process (fAHP), with four layer hierarchical structure, is used as shown in Figure 1.3

and 1.4.

FIGURE 1.2. MULTI STAGE STRATEGIC NETWORK PLANNING AND SUPPLY CHAIN FRAMEWORK


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FIGURE 1.3. FOUR LAYER HIERARCHICAL STRUCTURE FOR THE DECISION SUPPORT FRAMEWORK

FIGURE 1.4. METHODOLOGICAL APPROACH AT-A-GLANCE

Interview conducted to obtain:

1. Identifying alternative locations for logistics facilities

2. Determining qualifications of experts considered to provide a weighted assessment of criteria and

location alternatives

3. Determining the criteria to be used in site selection

4. Providing pairwise comparison to determine the weight value


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Next, the data were collected using questionnaires. There are two stages of assessment conducted by the

experts, namely the assessment of the criteria weight and weight assessment alternatives. The weighting

method uses fuzzy Analytical Hierarchy Process (fAHP).

IMPLEMENTATION RESULT

The initial stages carried out in this study were conduct interviews to BPBD (Regional Disaster

Management Agency) and survey conducted during September 2016 to obtain preliminary data needed

in conducting research. The data collected in this study are the locations considered eligible as warehouse

facilities. The focus of the study for alternatives is districts along the coast or west coast. A remote location

from the coast is also considered a variable control.

Then, several criteria to be considered in the selection of warehouse locations are formulated based on

literature review first. We then discussed these criteria with 3 experts from Regional Disaster

Management Agency (BPBD) West Sumatra, Non-Government Organization (NGO) and academician. The

purpose of the discussion of these criteria is to evaluate whether these criteria can be used to assess

warehouse location alternatives to support logistics operations of the earthquake and tsunami disaster in

West Sumatera Province. Formulation of criteria and description can be seen in Table 1.1.

The weight assessment for each criterion is done by involving several experts, namely: Indonesian Red

Cross (PMI), Non-Government Organization (NGO), Regional Disaster Management Agency (BPBD), Public

Health Service Office (DINKES), Social Service Office (DINSOS), and academicians (Sample statistics is in

Table 1.2). All of these experts stated that these criteria are highly relevant for considering alternative

locations. The result of weighting criteria is summarized in Table 1.3.

Results show that Risk, Access to Affected Zoned, and Access to Infrastructure are considered the most

important criteria to select the location for prepositioning strategic emergency stockpiles.


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TABLE 1.1. FORMULATION OF CRITERIA AND DESCRIPTION

# Criteria Description

C1 Coverage

The geographical coverage for each hub based on travel time to reach disaster affected populations. This involves combining geographic population distribution with hazard zones.

C2 Access to affected zones Lead time to reach the affected populations.

C3 Risk

The location should be outside of identified hazard zones (Exclusion criteria)

C4 Access to infrastructure

Access to suitable infrastructure for transport (air, sea, land), with suitable operational capacities including storage, transportation assets, commercial service providers and mechanical handling equipment’s.

C5 Access to Corridor

The need for the locations to be located within one of the major transportation corridors as pre-identified by the Indonesian Government

C6 Congestion

Heavily congested facilities (port, airports) and corresponding road access is a negative or exclusion criteria

C7 Costs

Transportation costs for resupplying HRFs and running operations from the respective locations (includes sending goods from the hub to affected areas).

C8 National development plan (NDP)

Proximity to Economic Centres identified in the National Master Plan for the Acceleration and Expansion of Indonesia -Economic Development 2011-2025

TABLE 1.2. SUMMARY OF EXPERTS FOR CRITERIA WEIGHT ASSESSMENT

Sample Size

Stakeholders Total

PMI NGO BPBD DINKES DINSOS Academic

# 4 3 3 3 4 3 20

% 20% 15% 15% 15% 20% 15% 100%

The next step is weight assessment for each alternative warehouse location. The location alternatives

considered are Padang, Pariaman, Padang Pariaman, Padang Panjang, Solok and Pesisir Selatan (see Figure

1.5 for map representation of these locations). The profile of each alternative location is obtained from

the official government website1 as follows:

1. Padang City is the capital of Sumatera Barat Province located on the west coast of Sumatra island

with an area of 694.96 km2 consisting of 11 districts. The population of 1,000,096 people.

1 http://www.sumbarprov.go.id


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2. Solok district has an area of about 3738 km2 and consists of 14 districts. The topography of the

region varies greatly between plains, valleys and hills. In addition, Solok District also has one volcano

that is Mount Talang. The population of 366,680 people.

3. Padang Pariaman District has an area of 1,328,79 km2 consisting of 17 districts and 103 Nagari. The

population of 458,746 people.

4. Pariaman City is geographically located on a strategic route across West Sumatra that connects

North Sumatra Province and West Sumatra Province consisting of four districts. The area is 73.36

km2 and the population is 83,151 people.

5. Southern coastal district has an area of 5,749.89 km2 with many subdistricts of 15 districts. The

population of 451,553 people.

6. Padangpanjang City has an area of 23 km2 which consists of the number of sub-districts sebanayk

two subdistricts of West Padangpanjang District and East Padangpanjang District. The population is

49,451 peoples. The city has three mountains, namely: Mount Marapi, Mount Singgalang and

Mount Tandikat.

TABLE 1.3. RESULTS OF WEIGHTED CRITERIA

Criteria PMI NGO BPBD DINKES DINSOS Academic Total

Coverage 0.132 0.162 0.143 0.039 0.134 0.191 0.125

Access to affected zones

0.119 0.198 0.104 0.169 0.128 0.105 0.139

Risk 0.201 0.207 0.113 0.109 0.125 0.083 0.141

Access to infrastructure

0.142 0.125 0.113 0.149 0.131 0.111 0.136

Access to Corridor 0.110 0.068 0.157 0.116 0.124 0.141 0.120

Congestion 0.094 0.091 0.145 0.152 0.125 0.091 0.116

Costs 0.124 0.105 0.113 0.108 0.116 0.112 0.118

National development plan (NDP)

0.078 0.044 0.113 0.159 0.116 0.165 0.105


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FIGURE 1.5. MAP REPRESENTATION OF THE ALTERNATIVE LOCATIONS

Assessing the priority weight of each location is the synthesis of the weight of the criteria and the partial

weight of each alternative location. This assessment involved 6 experts from Regional Disaster

Management Agency (BPBD) West Sumatra (2 experts), Indonesian Red Cross (PMI) (2 experts) and

academicians (2 experts). Table 1.4 is the weight values for each alternative both of the overall value and

of the partial weight value for each criterion. The results of the study show that Padang City is the most

prioritized location for the construction of warehouse facilities. This location gets the greatest weighting

value for all criteria except the risk criteria. These results can be understood and accepted rationally

because the geographical position is strategic both from coverage, access to affected zones, access to

infrastructure, access to corridor, congestion, costs and national development plan (NDP).

TABLE 1.4 RESULTS OF WEIGHTED ALTERNATIVES

Kriteria Padang Pariaman Padang

Pariaman Padang Panjang

Solok Pesisir Selatan

Coverage 0.347 0.155 0.229 0.096 0.067 0.107

Access to affected zones 0.338 0.203 0.201 0.070 0.078 0.111

Risk 0.126 0.109 0.102 0.239 0.264 0.160

Access to infrastructure 0.317 0.265 0.215 0.072 0.077 0.054

Access to Corridor 0.463 0.124 0.127 0.130 0.101 0.054

Congestion 0.364 0.231 0.226 0.064 0.065 0.050

Costs 0.373 0.181 0.179 0.089 0.097 0.081

National development plan (NDP)

0.401 0.188 0.134 0.106 0.117 0.053

Overall Weight 0.335 0.182 0.177 0.110 0.110 0.086

Priority 1 2 3 4 4 5


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CONCLUSION

This study has succeeded in building a model of warehouse location selection to support disaster logistics

operations especially in the emergency response phase. The criteria involved in site selection are

Coverage, Access to affected zones, Risk, Access to infrastructure, Access to Corridor, Congestion, Costs

and National development plan (NDP). This model is used to determine warehouse location in West

Sumatera Province considering six location alternatives. The result of framework implementation shows

that framework can be used for other region in Indonesia for humanitarian relief or other logistics

activities (such as rice distribution).


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References

Balcik, B., and Beamon, M. (2008). "Facility location in humanitarian relief", International Journal of

Logistics: Research and Applications, Vol.11 No. 2, pp. 101-121.

Beamon, B. (2004). "Humanitarian relief chains: issues and challenges", 34th International Conference on

Computers and Industrial Engineering,. San Francisco, CA, USA.

Blecken, A. (2010). "Humanitarian Logistics - Modeling Supply Chain Processes of Humanitarian

Organizations". Suttgard: Haupt Publisher

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preparedness and response ability of non-governmental humanitarian relief organizations", Computers &

Industrial Engineering, Vol. 101, pp. 21-34

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CARE International", Interfaces, Vol. 41 No. 3, pp.223 - 237.

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and trends". Brussels, Belgium: Centre for Research of the Epidemiology of Disasters Publications.

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report-2014/data (accessed 10 May 2016)

Nahleh, Y., Kumar, A., and Daver, F. (2013). "Facility Location Problem in Emergency". International

Journal of Mechanical, Aereospace, Mechatronic, and Manufacturing Engineering, Vol. 7 No. 10, pp. 2113-

2118.

Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Zahiri, B., Bozorgi-Amiri, A. (2016). "An interactive approach

for designing a robust disaster relief logistics network with perishable commodities". Computers &

Industrial Engineering, Vol. 94, pp. 201-215

Roh, S., Jang, H., and Han, C. (2013). "Warehouse Location Decision Fctors in Humanitarian Relief

Logistics". The Asian Journal of Shipping and Logistics, Vol. 29No. 1, pp. 103-120.

UN ESCAP. (2015). Asia-Pacific Disaster Report 2015. Bangkok: United Nations publications.

USING DIGITAL NETWORK DESIGN FOR OPTIMIZATION OF

SUPPLY CHAIN


17

LOGISTICS NETWORK DESIGN FOR RICE

DISTRIBUTION IN MAKASSAR, SOUTH SULAWESI

Rosmalina Hanafi1, Muhammad Rusman1, Mark Goh2, Robert de Souza2

1 Industrial Engineering Department, Hasanuddin University, Indonesia 2 The Logistics Institute – Asia Pacific, National University of Singapore, Singapore

SUMMARY

This case study investigates the logistics network design for rice distribution of a 4-supplier, 3-warehouse,

14-customer distribution point network in Makassar, province of South Sulawesi, Indonesia. A Mixed

Interger Problem (MIP) model is formulated, with the objective of minimising the procurement, inventory,

and transportation costs from supplier through to the warehouses and onto the distribution locations.

Computational experiments are conducted using ILOG CPLEX Optimization Studio. The problem is solved

to optimality and the total logistics cost is reduced significantly. The computational results suggest that

the proposed model is able to generate optimal solutions within an acceptable computational time.

INTRODUCTION

Rice is an essential agriculture commodity in Indonesia. As a staple food for the bulk of the population,

the daily demand and consumption for rice is naturally high. There is thus a need to match supply with

demand well especially because the population of Indonesia is found in the main island of Indonesia while

the rice fields are further afar within Indonesia.

Sulawesi is a major rice production point in Indonesia, ranked third after Java and Sumatra, contributing

about 11% of the national rice production volume annually. In Indonesia, rice production follows a

seasonal pattern, and depends on several factors such as the rice production areas, harvest period,

amount of rainfall, and the amount and quality of the fertilizer used. Given these uncertainties, the rice

yield levels can vary from district to district. At the same time, rice consumption follows a predictable

pattern as it is a staple product (Fisher, 1997). From a decision maker and government perspective, proper

planning and distribution is required to ensure rice availability and accessibility, and to improve food

security. To be able to undertake this task effectively, the logistics network optimization design for rice

distribution is called for.

The rice distribution network, as in all other networks, comprises a set of suppliers (farmers), plants (rice

mills), distribution centers (DC) (warehouses), and an elaborate transportation infrastructure down to the


18

last mile, through modality choices in transport. The challenge is bi-directional. First and foremost, new

locations for the consumption and hence storage of rice will surface. At the same time, the supply of rice

is never certain in terms of the volume that can be harvested each year. Given this situation, several

network decisions are needed on a yearly basis. For instance, which existing and new locations should be

used, the number of customers/ households to be served from which DCs, which suppliers should supply

to which mills, and so on.

THE CASE STUDY: LOGISTICS NETWORK DESIGN FOR RICE DISTRIBUTION IN MAKASSAR, SOUTH SULAWESI

Recognising the significant role of rice in maintaining the socio-economic stability, the Indonesian

government has been intervening by organizing the rice logistics business through BULOG (The State

Logistics Agency). BULOG is tasked to maintain the availability, stability, and affordability of basic food. In

the case of rice, BULOG maintains the national rice reserve stock, implements market operation to keep

the price stable, provides and distributes rice to alleviate any rice shortage during emergency situations.

BULOG has twenty six regional divisions (divre) located in the capitals of the provinces in Indonesia. The

sub-regional division (subdivre) serves as the sub-regional logistics agency at the city/district level. The

South and West Sulawesi of BULOG (BULOG Divre Sulselbar) located in Makassar is a regional division at

the provincial level dedicated to serve as the main logistics centre that handles rice logistics related

activities within South and West Sulawesi. BULOG applies the policy of purchasing domestic rice to keep

prices at the farm level and to ensure sufficient stock for domestic market consumption. BULOG also

serves the public as part of its social protection program by distributing subsidized rice to the poor under

the Raskin program. The Raskin program is aimed at alleviating the financial challenge faced by low

income households by providing subsidized rice to fulfil their food needs. The network for BULOG is shown

in Figure 2.1.

Source: www.BULOG.co.id

FIGURE 2.1. RICE FLOWS OF BULOG

BULOG purchases the grain/rice grown domestically directly from the farmers, farmer associations, and

indirectly through partners. The procurement of grain/rice is conducted using the system of Public Service

Obligation (PSO) and commercial means. The Grain-Rice Processing Unit (UPGB) processes the grain/rice

http://www.bulog.co.id/


19

and distributes the processed rice through the retailers, groceries, interisland, inter divre/subdivre, inter-

UPGB, and PSO. BULOG Divre Sulselbar covers 11 subregional divisions, one of which is BULOG sub-

regional division Makassar. Figure 2.2 shows the warehouse capacity of the subdivres/kansilog under

BULOG Divre Sulselbar.

Source: BULOG Divre Sulselbar FIGURE 2.2. WAREHOUSE CAPACITY OF BULOG DIVRE SULSELBAR

The current logistics facilities of the BULOG subregional division Makassar include three warehouses. At

the same time, the rice suppliers are found in the towns of Takalar, Gowa, Makassar, and Maros. The

BULOG Subdivre Makassar distributes the subsidized rice for low income households to 14 consumer

distribution points or municipalities in Makassar city itself. Figures 2.3 and 2.4 show the rice procurement

and rice distribution conducted by the BULOG Subdivre Makassar in 2015, respectively. As can be seen, a

higher level of rice procurement occurs during May, June, and July, which are the harvesting seasons. The

overall rice distribution tends to be constant over time.

Source: BULOG Divre Sulselbar

FIGURE 2.3. RICE PROCUREMENT IN 2015 - MAKASSAR SUBDIVRE

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

Ca

pa

city (

kg

)

Location

0

500000

1000000

1500000

2000000

2500000

3000000

1 2 3 4 5 6 7 8 9 10 11 12

Tota

l (k

g)

Month

W1 W2 W3


20


FIGURE 2.4. RICE DISTRIBUTION IN 2015 - MAKASSAR SUBDIVRE

We begin our study by analysing the rice distribution network starting at a supplier. One important entity

in the rice distribution network is the warehouses that act as a connector between a supplier and the

consumers and the warehouse helps to store the rice supplied by the supplier to be distributed to the

consumers. A quick scan and review of the current condition of rice distribution suggest that a good

warehouse management, transportation, and logistics system is required to ensure an efficient flow of

rice. An optimization model can be applied to reduce the overall logistics costs, to ensure an efficient

decision making and improve the current service levels in the rice logistics system. Therefore, in this study,

a mathematical model is developed for BULOG Subdivre Makassar. The objective of this study is to

develop an optimization model to determine the number, location and capacity of the rice distribution

centres and warehouses in Makassar, South Sulawesi. Based on the results obtained, we hope to provide

guidance and recommendations on how the logistics network of rice distribution in Makassar, South

Sulawesi can be better managed.

SOLUTION APPROACH

We first analyse the current logistics processes and map the distribution process for a 4-supplier, 3-

warehouse, 14-customer distribution point network. The logistics network model is then created to

accurately represent the current rice logistics system. Next, an optimal network distribution is identified

and validated. The problem is formulated as a Mixed Integer Problem (MIP) whose objective is to

minimize the total logistics cost using the following notations and assumptions.

Assumptions: The demand is known and given. The transportation distances are available and known. No

stochasticity is involved in the problem studied.

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

1 2 3 4 5 6 7 8 9 10 11 12

Tota

l (k

g)

Month

W1 W2 W3


21

Notations:

𝑃𝑐𝑖𝑡 Procurement cost at supplier i in period t

𝑄𝑖𝑡 Quantity supplied by supplier I in period t

𝑇𝑐𝑖𝑗𝑡 Transportation cost per unit (kg) from supplier i to warehouse j in

period t

𝑊𝑐𝑗𝑡 Cost of opening a warehouse j in period t

𝐼𝑓𝑐𝑗𝑡 Inventory fixed cost at warehouse j in period t

𝐼𝑣𝑐𝑗𝑡 Inventory variable cost at warehouse j in period t

𝐼𝑙𝑗𝑡 Inventory level at warehouse j in period t

𝑇𝑐𝑗𝑘𝑡 Transportation cost per unit (kg) from warehouse j to consumer k

in period t

𝑆𝑚𝑎𝑥 Maximum amount of rice that can be supplied

𝑊𝑐𝑎𝑝𝑗 Capacity of warehouse j

𝐷𝑘𝑡 Demand of rice at consumer location k in period t

𝑥𝑖𝑗𝑡 Amount of rice to be supplied from supplier i to warehouse j in

period t

𝑦𝑗𝑘𝑡 Amount of rice to be transported from warehouse j to consumer k

in period t

𝑧𝑗𝑡 Binary variable indicating whether warehouse j to be opened or not

in period t

Min

∑ 𝑃𝑐𝑖𝑡

𝑖∈𝐼

𝑄𝑖𝑡 + ∑ ∑ 𝑇𝑐𝑖𝑗

𝑡

𝑗∈𝐽𝑖∈𝐼

𝑥𝑖𝑗𝑡 + ∑ 𝑊𝑐𝑗

𝑡

𝑗∈𝐽

𝑧𝑗𝑡 + ∑ 𝐼𝑓𝑐𝑗

𝑡

𝑗∈𝐽

+ ∑ 𝐼𝑣𝑐𝑗𝑡

𝑗∈𝐽

𝐼𝑙𝑗𝑡

+ ∑ ∑ 𝑇𝑐𝑗𝑘𝑡

𝑘∈𝐾𝑗∈𝐽

𝑦𝑗𝑘𝑡 (1)

Constraints:

∑ 𝑥𝑖𝑗𝑡

𝑗∈𝐽

≤ 𝑆𝑚𝑎𝑥 ∀ 𝑖 ∈ 𝐼 (2) (2)

∑ 𝑥𝑖𝑗 𝑡

𝑗∈𝐽

≥ ∑ 𝑦𝑗𝑘𝑡

𝑗∈𝐽

∀ 𝑖 ∈ 𝐼 (3) (3)


𝑗∈𝐽

+ 𝐼𝑙𝑗𝑡−1 − ∑ 𝑦𝑗𝑘

𝑡

𝑗∈𝐽

≤ ∑ 𝑊𝑐𝑎𝑝𝑗

𝑗∈𝐽

∀ 𝑗 ∈ 𝐽 (4) (4)


22

∑ 𝑦𝑗𝑘𝑡

𝑗∈𝐽

= ∑ 𝐷𝑘𝑡

𝑘∈𝐾

∀ 𝑗 ∈ 𝐽, ∀ 𝑘

∈ 𝐾

(5) (5)


𝑗∈𝐽

+ 𝐼𝑙𝑗𝑡−1 ≥ ∑ 𝐷𝑘

𝑡

𝑘∈𝐾

+ 𝐼𝑙𝑗𝑡 ∀ 𝑗 ∈ 𝐽

(6) (6)

∑ 𝑥𝑖𝑗𝑡

𝑖∈𝐼

≤ 𝑊𝑐𝑎𝑝𝑗 ∀ 𝑗 ∈ 𝐽 (7) (7)

∑ 𝑦𝑗𝑘𝑡

𝑘∈𝐾

≤ 𝑊𝑐𝑎𝑝𝑗 ∀ 𝑗 ∈ 𝐽 (8) (8)

𝑧𝑗𝑡 = {

1, 𝑜𝑝𝑒𝑛 𝑎 𝑤𝑎𝑟𝑒ℎ𝑜𝑢𝑠𝑒0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(9)

𝑥𝑖𝑗 𝑡 ≥ 0; 𝑦𝑗𝑘

𝑡 ≥ 0; 𝐼𝑙𝑗𝑡 ≥ 0. (10)

The objective function (1) represents the costs to be minimized. These include the cost of opening a

warehouse, transportation, procurement, and inventory costs. Constraint (2) denotes that the total

amount of rice to be supplied from supplier i to warehouse j should be less than or equal to the maximum

quantity of rice that can be supplied. Constraint (3) ensures that the total amount of rice to be supplied

from supplier i to warehouse j in period t should be greater than or equal to the total amount of rice to

be distributed from warehouse j to consumer k in period t. Constraint (4) indicates the balance of

inventory level in which the total amount of rice to be supplied to warehouses plus the inventory level at

previous period should be less than or equal to the total capacity of the warehouses plus the total amount

of rice to be distributed from the warehouses to the consumers in period t. Constraint (5) enforces that

the total quantity of rice to be transported from the warehouses to the consumers should be equal to the

total demand of rice at the consumer locations in period t. Constraint (6) requires that the total amount

of rice to be supplied to the warehouses plus the inventory level at the previous period should be greater

than or equal to the consumer demand plus the inventory level in period t. Constraint (7) indicates that

the total quantity of rice to be supplied from supplier i to warehouse j in period t should be less than or

equal with the capacity of warehouse j. Constraint (8) enforces that the total quantity of rice to be

transported from warehouse j to consumer k in period t should be less than or equal to the capacity of

warehouse j. Eqn (9) represents a binary decision variable denoted by zjt which is whether to open a

warehouse or not. Inequalities (10) represent the decision variables xij t and yjk

t and the non-negativity

constraints on the corresponding decision variables. Next, we map the route distances using Google map

to determine the various transportation distances from the supplier to the warehouses and from the

respective warehouse to the consumer distribution points, as shown respectively in Figures 2.5 and 2.6.

The existing rice distribution network at sub-regional division Makassar is illustrated in Figure 2.7. At the

same time, detailed data is provided by BULOG on the amount of rice sent to Makassar and the demand

of rice at the consumer distribution points as shown in Tables 2.1 and 2.2 respectively.


23

FIGURE 2.5. DISTANCE FROM A SUPPLIER TO A WAREHOUSE

FIGURE 2.6. DISTANCE FROM WAREHOUSE TO A CONSUMER DISTRIBUTION POINT

FIGURE 2.7. THE EXISTING RICE DISTRIBUTION NETWORK AT SUB-REGIONAL DIVISION MAKASSAR


24

TABLE 2.1. QUANTITY OF RICE SUPPLIED TO MAKASSAR IN 2015


TABLE 2.2. DEMAND OF RICE AT CONSUMER DISTRIBUTION POINTS

No Consumer distribution point Demand (kg)

1 Kec. Wajo 90.930

2 Kec. Biringkanaya 884.760

3 Kec. Bontoala 230.250

4 Kec. Makassar 707.940

5 Kec. Mamajang 337.650

6 Kec. Manggala 475.690

7 Kec. Mariso 327.525

8 Kec. Panakkukang 1.044.120

9 Kec. Rappocini 745.020

10 Kec. Tallo 1.199.940

11 Kec. Tamalanrea 342.900

12 Kec. Tamalate 1.340.820

13 Kec. Ujung Pandang 87.300

14 Kec. Ujung Tanah 535.280

Total 8,350.125


RESULTS OF EXPERIMENT

Computational experiments are conducted using the ILOG CPLEX Optimization Studio ver. 12.2 to test the

model. The performance of the model is examined by applying different scenarios with various

combinations of suppliers and warehouse capacities. Initially, a test model is generated for the analysis of

the rice distribution network consisting of 4 suppliers, 3 warehouses and 14 consumer distribution points.

In this test, the following warehouse capacities were applied: W1 is 21 million kg, W2 is 21 million kg, and

W3 is 6 million kg. An optimal solution is obtained with a total logistics cost of Rp 81,545,340, with a CPU

time of 10 sec, and without having to open a new warehouse. Three optimal solutions are possible to

transport the rice from supplier to warehouse and to which warehouse to supply. The results obtained

are summarised in Table 2.3. As can be seen from Table 2.3, the first solution suggests that Makassar (S1)

should supply 720.000 kg of rice to warehouse W1 while the second and the third solutions indicate that

Supplier Amount of rice supplied to

warehouse (kg)

Distribution cost from supplier to warehouse (Rp)

Warehouse Distribution cost from warehouse to consumer distribution point (Rp)

Makassar (S1) 720.000 1.123.200 W1 9.852.014

Takalar (S2) 4.798.990 59.299.771 W2 5.944.469

Gowa (S3) 4.274.750 31.162.928 W3 1.833.322

Maros (S4) 7.021.650 68.461.088 Total 17.629.805

Total 16.995.390 160.046.987


25

Makassar (S1) should supply 720.000 kg of rice to warehouse W2. All solutions indicate that Takalar (S2)

does not supply to any warehouse. The first optimal solution indicates that Gowa supplies 620.820 kg of

rice to W1, 2.200.970 kg to W2, and 1.452.960 kg to W3. The second alternative optimal solution indicates

that Gowa supplies 4.274.750 kg of rice only to W2. The third alternative optimal solution indicates that

Gowa supplies 1.340.820 kg of rice to W1, 2.313.110 kg to W2, and 620.820 kg of rice to W3. Maros (S4)

supplies only to W2 with 3.355.375 kg of rice for all solutions. Each solution also gives a different

combination of rice to be distributed from the warehouses to the consumer distribution points. The

solutions obtained satisfy the demand at all the consumer distribution points with a total of 8.350.125 kg.

The quantity of rice to be distributed from warehouses W1, W2, and W3 to the 14 consumer demand

points as given by the three solutions are presented in Tables 2.4, 2.5 and 2.6 2. The optimized network

for rice distribution is shown in Figure 2.8.

TABLE 2.3. QUANTITY OF RICE TO BE TRANSPORTED FROM SUPPLIER TO WAREHOUSE

Supplier Warehouse

Amount of rice to be

supplied to

warehouse (kg)


supplied to

warehouse (kg)


supplied to

warehouse (kg)

I II III

Makassar (S1) W1 720.000 0 0

Makassar (S1) W2 0 0 0

Makassar (S1) W3 0 720.000 720.000

Takalar (S2) W1 0 0 0



Gowa (S3) W1 620.820 0 1.340.820

Gowa (S3) W2 2.200.970 4.274.750 2.313.110

Gowa (S3) W3 1.452.960 0 620.820

Maros (S4) W1 0 0 0

Maros (S4) W2 3.355.375 3.355.375 3.355.375

Maros (S4) W3 0 0 0

Total 8.350.125 8.350.125 8.350.125

FIGURE 2.8. OPTIMIZED RICE DISTRIBUTION NETWORK

2 Tables 4, 5, and 6 have been removed due to the page constraints. They are available on request from the authors.


26

Further, the optimal solution for the amount of rice to be delivered from the supplier to the warehouses

suggests that the total transportation cost can be reduced by 17.44%. At the same time, the optimal

amount of rice to be distributed from the warehouses to the consumer distribution points has reduced

total transportation cost by 5.68%, as shown by Figures 2.9 and 2.10 respectively.

FIGURE 2.9. OPTIMAL AMOUNT OF RICE TO BE SENT FROM SUPPLIER TO WAREHOUSE

FIGURE 2.10. OPTIMIZED AMOUNT OF RICE TO BE SENT FROM WAREHOUSES TO CONSUMER DISTRIBUTION POINTS

0

1000000

2000000

3000000

4000000

5000000

W1 W2 W3 W1 W2 W3 W1 W2 W3 W1 W2 W3

S1 S1 S1 S2 S2 S2 S3 S3 S3 S4 S4 S4

Supply quantity (kg) that should be transported to warehouse

Opt I Opt II Opt III

0

300000

600000

900000

1200000

1500000

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14

Delivery quantity (kg) to consumer locations to be distributed

W1 W2 W3

S = Suppliers

C = Customers

W = Warehouses

Opt = Optimal combination of rice quantity to be transported from supplier to warehouse


27

CONCLUSION

This study presents a mathematical model of the rice logistics network problem in Makassar. The objective

of the model is to find the minimum logistics cost of transporting rice from the suppliers to the

warehouses, and from the warehouses to the consumer distribution locations. The problem is solved to

optimality and the total logistics cost is reduced significantly. The computational results suggest that the

proposed model is able to generate optimal solutions within an acceptable computational time. Although

the model is presented in the context of a 3echelon optimization model, they are generic and can be

adapted for different logistics network problems within Indonesia. In addition, the proposed optimisation

model can be improved and extended to include other relevant elements of logistics systems and we

apply model to other logistics systems involving more upstream suppliers, warehouses, and other

downstream customers. Further study will be conducted to analyse the entire network of rice logistics

operations in South Sulawesi and West Sulawesi (Sulselbar), and simulate the effects of relevant aspects

of rice flow from the surplus areas to the deficit areas and to also take a bi-level robust optimisation

approach for the solutioning.


28

REFERENCES

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approach”, Omega, Vol. 32, pp. 9–15.

BULOG (n.d.), Personal communications and information retrieved from wwwbulog.co.id.

Fisher, M. (1997), “What is the right supply chain for your product?”, Harvard Business Review, March-

April, pp. 1-10.

Georgiadis, M.C., Tsiakis, P., Longinidis, P. and Sofioglou, M.K. (2011), “Optimal design of supply chain

networks under uncertain transient demand variations”, Omega, Vol. 39, pp. 254–272.

Goetschalckx, M. (2000), “Strategic network planning”, In Stadtler, H. and Kilger, C. (Eds), Supply Chain

Management and Advanced Planning: Concepts, Models, Software and Case Studies, Berlin, Springer, pp.

79–95.

Kauder, S. and Meyer, H. (2009), “Strategic network planning for an international automotive

manufacturer”, OR Spectrum, Vol. 31 No. 3, pp. 507-532.

Klibi, W., Martel, A. and Guitouni, A. (2010), “The design of robust value-creating supply chain networks:

a critical review”, European Journal of Operational Research, Vol. 203 No. 2, pp. 283–293.

Nickel, S. and Saldanha-da-Gama, F. (2009), “Logistics network design”, OR Spectrum, Vol. 31 No. 3, pp.

461-463. • Salema, M.I.G., Povoa, A.P.B. and Novais, A.Q. (2009), “A strategic and tactical model for

closed-loop supply chains”, OR Spectrum, Vol. 31 No. 3, pp. 573-599.

Schwarz, L.B. and Weng, Z.K. (2000), “The design of a JIT supply chain: the effect of lead time uncertainty

on safety stock”, Journal of Business Logistics, Vol. 21, pp. 231–253.

VanHoutum, G.J., Inderfurth, K. and Zijm, W.H.J. (1996), “Materials coordination in stochastic multi-

echelon systems”, European Journal of Operational Research, Vol. 95, pp. 1–23.

Yildiz, H., Yoon, J., Talluri, S. and Ho, W. (2016), “Reliable supply chain network design”, Decision Sciences,

Vol. 47 No. 4, pp. 661-698.

AN INTEGRATED APPROACH TO THE RE-DESIGN OF

HUB/SUB-HUB NETWORKS


31

IMPLEMENTATION OF MULTI-METHOD DECISION

SUPPORT FRAMEWORK FOR SUPPLY NETWORK

DESIGN – THE CASE STUDY OF PT POS INDONESIA

Giuseppe Timperio1, Bernado Boy Panjaitan2, Linda William1, Robert de Souza1,Yoseph Sunardhi2

1 The Logistics Institute – Asia Pacific, National University of Singapore 2 PT Pos Indonesia, Indonesia

SUMMARY

This case study presents a decision support framework for network design and transportation utilization,

in the context of last mile distribution. Several methods, namely: data analytics, green field analysis,

network optimization, and discrete event simulation, were integrated to provide holistic support for

decision making. This framework is implemented to evaluate PT Pos Indonesia (PI) distribution network

in Greater Surabaya. Findings of this work show that the number of distribution facilities, and their

locations, impact responsiveness and efficiency of last mile distribution. In the case at hand, a significant

savings in transportation and warehousing cost with no impact on service level can be achieved by

reducing the number of facilities in the network.

This decision support framework is part of the solutions/case studies developed under the “Temasek

Foundation International – National University of Singapore Urban Transportation Management

Programme in Indonesia” programme which aims to provide support and knowledge to center/local

government for efficient and effective urban freight movement.

The result of the framework implementation shows that the solution approach has the potential for

successful replication to other areas for the optimal allocation and utilization of logistics resources.

INTRODUCTION

Digital economy, such as Omni-channel commerce, changes the market context for every business all over

the world, including in Asia Pacific. It shifts the traditional supply chain to digital supply chain with changes

in the customers’ behavior and interaction, the product offerings and deliveries, the business operations

as well as the source of business services. This creates significant impacts and challenges to the

companies/stakeholders to fulfil these demands.


32

In the logistics industry, especially for Logistics Service Providers (LSPs), the digital economy has

tremendously spiked the demand for on-time, reliable and cost-effective deliveries. The market size for

the LSP in Asia Pacific is growing and it is becoming the largest in the world. Therefore, a seamless and

effective navigation of storage assets through an optimum design of supply network (SN) using the multi-

method decision support framework would be highly beneficial for an LSP that aims to gain a truly

competitive edge in both offline and online marketplaces.

Focusing on the last mile distributor network in urban areas, this Whitepaper aims to demonstrate how

the multi-method decision support framework is able to optimize and utilize the distribution network for

significant gains in term of costs with no impact on service level.

In the case of PT Pos Indonesia for Greater Surabaya, the implementation of the framework would address

the following research questions:

RQ1. How to determine the suitable number of intermediate distribution centers (DCs) to serve a

highly populated urban area in Indonesia, and what is the suitable number of DCs for the particular

case at hand?

RQ2. How to decide on the locations for the intermediate DCs of an optimized supply network, and

what is the optimum network configuration for the particular case at hand?

THE MULTI-METHOD DECISION SUPPORT FRAMEWORK

The multi-method decision support framework integrates data analytics, green field analysis, network

optimization and simulation to re-design distribution network and utilize the transportation assets. The

framework encompasses three phases, with one additional preliminary phase (i.e. Phase 0: Data

Gathering and Analysis) to gather relevant data, identify bottlenecks, criticalities, as well as areas for

improvements. The framework is illustrated in Figure 3.1.

Phase 1: Identification of Number of Suitable Locations for the DCs

Network design begins with the identification of number of suitable locations for the DCs. These decisions

are traditionally based on total cost (i.e. warehousing, transportation, lost sales). Two different strategies,

centralized and decentralized supply networks, can be adopted in Phase 1 based on logistics

requirements. Centralized warehousing is a system where a single (or few) DC is used to serve a particular

area, whereas a decentralized approach encompasses the use of several facilities spread out to cover a

particular area (Kokemuller, 2018).


33

FIGURE 3.1. MULTI-PHASE STRATEGIC INTEGRATED DECISION SUPPORT FRAMEWORK

We consider four costs to determine the number of DCs, namely:

1. Warehouse and Inventory Cost. Since fewer resources are needed to run one warehouse as

opposed to several, a lower number of DCs has a positive effect on costs related to warehousing

activities. In addition, variable costs of warehousing such as labour, warehouse management,

equipment, and training of personnel can also be kept to a minimum in case of the centralized

system.

2. Primary Transport Cost. The transportation cost of the first tier (e.g. from main DC to intermediate

DCs) are lower in the case of centralized supply network. Consolidation of shipments can, in fact,

be implemented with a positive impact on costing structure.

3. Local Delivery Cost. The transportation cost of the second tier (e.g. from intermediate DCs to final

consumers) are higher in the case of a highly centralized system. In decentralized network, the

distance in between intermediate DCs and customers is lower.

4. Cost of Lost Sales. A decentralized system helps in achieving shorter lead times and higher

percentages of on-time delivery, resulting in higher service level, and thus lower cost of lost sales.

In the phase, data analytics and Green Field Analysis (GFA) were used to determine the cost items. Several

techniques in data analytics such as classification, clustering and linear regression are used to identify

pattern and insights from the historical data. These insights include demand clustering, demand fulfilment

pattern and main factors contributing to logistics costs.

GFA is a Geographic Information System (GIS)/center of gravity based approach, which seeks to determine

the geographic coordinates for a potential new facility. Computations are typically based on minimum

transportation cost (calculated as “Distance” * “Product Amount”) in consideration of aggregated demand

for each customer and product, customer locations (direct distance between customers and

DCs/Warehouses), and service distance (or number of facilities to locate).

Phase 0

• Gather relevant data, identify bottlenecks, criticalities as well as areas for improvement

Phase 1

• Identify the optimum number of DCs to be included in the supply network using Data Analytics and Green Field Analysis.

Phase 2

• Structure and optimize the supply network using Network Optimization.

Phase 3

• Stress-test the supply network and measure performances based on pre-identified parameters using Simulation.


34

The combination of the above techniques would result in identifying the number of suitable locations for

a particular distribution network based on its relevant cost structure.

Phase 2: Structuring and Optimizing the Supply Network

Once Phase 1 is completed and the number of suitable locations for the network nodes is determined,

Phase 2 will be used to identify the optimum network configuration in terms of both nodes (location of DCs)

and arcs (connections in between DCs) using Network Optimization (NO). NO is an optimization technique

that seeks to find the best configuration of a supply chain network structure as well as the flows based upon

an objective function, which typically maximizes profits.

This phase would select the optimal network nodes out of the subset of suitable candidates from Phase 1.

It consists of finding the optimum number and location of the distribution centers as to effectively distribute

goods, while satisfying multiple constraints.

Phase 3: Stress-testing the supply network and measuring performances based on pre-identified parameters

After the ideal network configuration is defined (in Phase 2), Phase 3 will stress-test the supply network

based on performance measures (e.g. service level, profits). This final phase will assess network robustness

and resilience, as well as measure the operational performances.

The parameter setting (such as service time, lead time, number of transportation assets) would be adjusted

and fine-tuned. It can be undertaken by using various computer simulation techniques such as discrete-

event and agent-based simulation. Sensitivity analysis can also be conducted.

THE CASE STUDY

Indonesia Logistics Landscape

Located in the South East Asia region, Indonesia is the 14th largest nation by size in the world which covers

1,811,569 square kilometres of land and 5,800,000 square kilometres of water. The country spans three

time zones and counts over 260 million people spread over more than seventeen thousand islands.

According to the World Bank's Logistics Performance Index of 2016, Indonesia ranks at 63rd on a global

scale in regards to key logistics elements such as customs procedures, infrastructure, international

shipments, logistics competence, tracking & tracing, and timeliness. This translates in logistics costs

accounting for 26% of national GDP (US$ 861 billion), worse than its neighbouring countries like Singapore

(8%) and Malaysia (14%). Poor logistics performance affects a) Country’s economic competitiveness and

b) In-country disparities in terms of accessibility and pricing of primary commodities. In such a challenging

context, having in place an optimized supply network would be highly beneficial for increasing profits and

optimizing overstretched routes.


35

Overview of PT Pos Indonesia Operations

PT Pos Indonesia is a state-owned corporation, which was established, as per current structure, in 1995

(PT Pos Indonesia, 2016). It operates through three main revenue streams namely “mails & parcels”,

“financial services”, and “retails and networks”. In 2016, the organization counted 47,317 post agents,

4,657 post offices, 27,808 employees, 3,537 post agent couriers, 1,318 mobile services, 10,197 post-

boxes, and 64,161 mail-boxes (PT Pos Indonesia, 2016).

Due to the combination of changes in Indonesian regulatory law (no. 38 of 2009), intense digitalization of

supply chains, and rapid surge of e-commerce logistics, the leading role of PT Pos Indonesia in the national

logistics industry has been seriously threatened recently; thus, demanding for significant and progressive

investments to address key strategic issues and to strengthen the company’s positioning in both core and

non-core businesses (PT Pos Indonesia, 2016).

Situational Analysis & Scope of Work

In Greater Surabaya, PT Pos Indonesia manages a distribution network comprising of 1 supply point, 1

large Distribution Centre (Postal Processing Centre or PPC), 9 intermediate distribution centres (DCs), and

49 demand points (districts or “kecamatan”). The emphasis of this work will be on the optimum reduction

of the number of intermediate DCs (Figure 3.2). Based on field studies and preliminary data analytics, it

is revealed that this existing network structure is leading to high operational and distribution costs due to

its size. The ultimate goal is to optimize the current network to provide an efficient and responsive

network of distribution facilities to gain competitive capabilities in the last mile.

FIGURE 3.2. SCHEMATIC REPRESENTATION OF PT POS INDONESIA SUPPLY NETWORK (FOR ILLUSTRATION PURPOSES ONLY)


36

IMPLEMENTING THE FRAMEWORK

The implementation of the framework above for PT Pos Indonesia case was conducted to optimize PT Pos

Indonesia distribution network in Greater Surabaya. Due to time limitation and data availability, only

Phase 1 and Phase 2 were conducted.

In order to restructure an existing supply network, the necessary steps include the development of the

“AS-IS” Model, followed by the “TO-BE” (Ideal) model, and finally the “TO-BE” (Real) in consideration of

real-life constraints (Figure 3.3):

(1) “AS-IS” model. This model is required to understand the existing network configuration,

operational requirements, and identify bottlenecks and areas for improvement. Performances

of “AS-IS” network will be used to set the benchmark for any proposed changes.

(2) Ideal “TO-BE” model. This intermediate model is needed to identify the “unconstrained

solution” (ideal) in terms of number of DCs and their locations;

(3) Real “TO-BE” model. This final model will lead to the identification of real-life solution, which is

an adjustment of “TO-BE” ideal based on real-life constraints set by PT Pos Indonesia

FIGURE 3.3. STAGES TO SUPPLY NETWORK RESTRUCTURING

Phase 0: Identification of Bottleneck, Criticalities and Improvement Areas

Phase 0 was initiated by collecting structural (existing network structure, transportation assets, product

flows) and operational data (demand patterns, costs). An initial analysis of data led to the identification

of bottlenecks, criticalities, as well as areas for improvements. A sample of district-level demand patterns

is shown in Figure 3.4.


37

FIGURE 3.4. DISTRICT-LEVEL DEMAND PATTERNS

Phase 1: Identification of Number of Suitable Locations for the Nodes

Given the unique nature of business that PT Pos Indonesia operates in, the only cost item driving the

decision about the optimum number of facilities are transportation cost (including primary transport and

local delivery cost) for the following reasons:

1. Warehouse and inventory cost are not relevant because: 1) PT Pos Indonesia does not carry any

inventory, and 2) Manpower is fixed, regardless of the number of DCs;

2. Cost of lost sales does not apply since there is no other competitor offering the same service;

Transportation costs, which includes primary transport cost and local delivery cost, are calculated using

Green Field Analysis (GFA) (sample in Figure 3.5). Two years (year 2016 and 2017) of operational

information on historical demand (by location, amount, and time distribution), product flows, and costs

were used. Transportation cost and marginal cost reduction with the changed number of DCs is shown in

Figure 3.6. The results show that by increasing the number of intermediate DCs, the overall transportation

costs to serve final customers would be reduced.

FIGURE 3.5. ILLUSTRATION OF GREEN FIELD ANALYSIS


38

FIGURE 3.6. TRANSPORTATION COST AND MARGINAL COST REDUCTION WITH CHANGED NUMBER OF DCS

Phase 2: Structuring and Optimizing the Supply Network

Transportation Cost, Inbound Processing Cost, Outbound Processing Cost and Fixed Cost were used as

contraints in Network Optimization (NO) model. Other cost items were not considered due to a) Data

unavailability (opening and closing costs) and b) Limited relevance based on the nature of PT Pos

Indonesia business (supply, production, storage).

Results show that the ideal “TO-BE” (Ideal) network configuration should be 4 intermediate DCs (Figure

3.7 and Figure 3.8), which will minimize the overall supply chain cost, with no change to the service level

as compared to "AS-IS".

FIGURE 3.7. "TO-BE" (IDEAL) SOLUTION


39

FIGURE 3.8. "TO-BE" (IDEAL) MATERIAL FLOW

In order to define the “TO-BE” (REAL) network configuration, the constraint on maximum distance

travelled per delivery man (100 km) in a day was included. Results highlighted an overlap between "TO-

BE" Ideal and "TO-BE" Real.

TABLE 3.1. COMPARATIVE ANALYSIS OF "AS-IS" AND “TO-BE” NETWORK

Item “AS-IS” Existing

(Model) “TO-BE”

Optimized Network

Transportation Cost (Fuel) 100% -5% of existing

Facilities Expenses 100% -31% of existing

Transportation Cost (Fuel) + Facilities Expenses

100% -18% of existing

Total Cost3 100% -2% of existing

A comparative analysis of "AS-IS" and “TO-BE” network is illustrated in Table 3.1. The results show that

in terms of cost effectiveness the "TO-BE" network surpasses the "AS-IS" network by nearly 2% of the

total costs. Although it has not yet been assessed, further savings could be achieved via economies of

scale e.g. through consolidation of manpower and/or transportation assets.

3 Although manpower is not in the scope of the current research, total costs as defined in Table 4 include, besides transportation and warehousing, also labour cost for warehousing processing and delivery operations.


40

CONCLUSION

This case study presents the multi-method decision support framework for network design and

demonstrates the applicability to real life case on PT Pos Indonesia in Greater Surabaya. Starting from an

initial identification of suitable locations for the network nodes, the proposed framework depicts

procedures and tools to leverage on across the various phases of the problem's solution seeking. The full

implementation of the proposed approach would lead to the definition of the optimum network

configuration, assessment of its robustness and measurement of operational performances. However,

due to time constraint and limited data availability, we applied the framework partially which only

includes Phase 1 and 2.

The key findings from this PT Pos Indonesia case study are as follows:

RQ 1. Number of nodes. The suitable number of intermediate DCs (nodes) to serve a highly populated

urban area in one of the fastest developing economy of the Pacific region (Indonesia) can be

determined by combining data analytics with GFA, and network optimization. For the geography of

reference (greater Surabaya), and with the provided datasets, the number of suitable intermediate

DCs should be equal to 4.

RQ 2. Location of the nodes. Locations for the intermediate DCs of an optimized supply network can

be selected using a network optimization approach. The optimum network configuration for PI in

Greater Surabaya should include the four nodes as per Figure 8. The identified set of facilities

guarantees enhanced cost effectiveness (-18% of transportation and warehousing cost) at comparable

service level.

Managerial Implications. This study is able to support decision makers in a wide range decisions in the

context of network design. Green Field Analysis can help decision makers with the determination of

transportation cost at increased number of DCs, as well as identification of potentially suitable locations. The

Network Optimization can help logistics managers to make strategic decisions about the DCs’ locations.

Limitations. This work has few limitations. First, the dataset on demand is limited to the biennium 2016-

2017. An extension of the dataset with inclusion of more data points would provide a more accurate solution

to the problem at hand. Secondly, inclusion of cost items such as cost for opening or closing a DC and

manpower allocation would help to fine tune the proposed solution.

Next Steps. In order to reinforce the findings to date, a dynamic simulation model can be developed (Phase

3) to:

Determine the transportation (fleet size) and storage requirements:

Perform what-if analysis with comparison of alternative network configurations


41

REFERENCES

Kokemuller, N. (2018). The Advantages of a Centralized Warehouse. Retrieved June 6, 2018, from Chron:

http://work.chron.com/advantages-centralized-warehouse-16128.html

PT Pos Indonesia. (2016, December). Pos Indonesia Annual Report 2016. Retrieved from Pos Indonesia:

http://www.posindonesia.co.id/wp-content/uploads/2017/07/Pos-Indonesia-Annual-Report-2016.pdf

SERIOUS GAMING AND ITS POTENTIAL IN

ENHANCED LEARNING OF SUPPLY CHAIN MANAGEMENT


45

THINKLOG – INTERACTIVE FRAMEWORK FOR

LEARNING OF SUPPLY CHAIN MANAGEMENT

Linda William1, Eko Nugroho2, Rio Fredericco2, Za’Aba Bin Abdul Rahim1, Robert de Souza1

1 The Logistics Institute – Asia Pacific, National University of Singapore, Singapore 2 Kummara Studio, Indonesia

SUMMARY

This case study describes an interactive serious gaming framework, titled THINKLog, as a special board

game designed specifically to help players in learning about Supply Chain Management (SCM) concepts.

It aims to facilitate the teaching activities in classrooms and workshops through role-playing and

simulation. The game is designed as such that it can be expanded by including more SCM scenarios to

learn other specific topics in SCM without changing the basic game structure.

This gaming framework is part of solutions/case studies developed as part of “Temasek Foundation

International – National University of Singapore Urban Transportation Management Programme in

Indonesia” programme which aims to provide support and knowledge to center/local government for

efficient and effective urban freight movement.

Using interactive sessions with government officials, as part of the “Temasek Foundation International –

National University of Singapore Urban Transportation Management Programme in Indonesia”

programme workshops, we are able to validate that THINKLog was effective in helping the players’

understand SCM concepts.

INTRODUCTION

Serious games have been introduced as an educational gaming tool for teaching specific skills and

knowledge, promote physical activities and support social-emotional development (Ma, et al., 2011; de

Freitas and Liarokapis, 2011). It incorporates non-entertainment elements, such as Supply Chain

Management (SCM), into game-environment (Liu, et al., 2011) such that it serves as a pedagogical tool

with a learning purpose, moving beyond entertainment to deliver engaging interactive media to support

learning (de Freitas, 2006). Serious games which includes the use of any medium of games (e.g. board

games, card games, sports or digital games) are designed to distil a specific and complex learning concepts

while maintaining the entertainment factors. It provides learning engagement and motivations (Riedel

and Hauge, 2011) as well as giving hands-on experiences to the players.


46

In this whitepaper, we describe a serious game, named THINKLog, as an interactive learning framework

for learning SCM concepts. It is designed as an expandable face-to-face framework to learn various

concepts in SCM such as lead time, bullwhip effects, supply chain risk management, humanitarian logistics

and urban logistics. It can be extended to multiple scenarios, where each scenario would cover a specific

concept in SCM, without changing the basic structure of the game. We conducted two interactive sessions

with senior government officials as part of three-day SCM workshop, to evaluate the game. From the

conducted sessions, we gained evidence that serious games have the capabilities to deepen the players’

understanding about SCM concepts by providing an easy to play hands-on and actual experiences to apply

and experiment with the concepts.

THINKLOG OVERVIEW

As a board game, THINKLog has four main components, namely: main board, demand cards,

gameplay/rules and game master, as illustrated in Figure 4.1.

FIGURE 4.1. GAME COMPONENTS


47

1. Main Board

The main board in THINKLog is the base for all interactions between the players. To represent the supply

chain network and logistics activities, the board consists of several points or nodes in which players can

place their logistics facilities and other logistics assets. The logistics activities will occur between these

facilities or assets.

2. Demand Cards

The market demands that need to be fulfilled during the game are represented by the “Demand Cards”.

The demand values are dependent on the game scenarios played; it can be randomly generated or based

on a certain distribution based on the real demands for a particular good in the particular scenario. The

goal of the game is to fulfil these demands as displayed on the demand cards.

3. Gameplay

Each THINKLog scenario presents a different gameplay, which is designed carefully to translate a specific

learning objective. In the basic version, the gameplay would require the players to choose one out of four

available roles, namely: manufacturer, distributor, wholesaler and retailer. The gameplay in the basic

version is described in Figure 4.1.

4. Game Master

Game master facilitates a smooth flow of the game session. He/she will guide the players and ensure that

the players understand and follow the rules accordingly.

THINKLog – Game Design

Various SCM concepts can be introduced in the THINKLog by simply adjusting the gameplay without

changing the basic game structure of the game. The gamification process begins with the translation of

specific concepts into the games’ learning objectives. These concepts can be based on a specific SCM and

logistics concept, case study or our research. The gameplay is carefully designed to fit the learning

objectives and help players understand specific urban logistics concepts. We then test the gameplay with

our collaborators in interactive sessions and workshops and refine accordingly. This game design process

is illustrated in Figure 4.2.

THINKLog – Evaluation

To evaluate the effectiveness of THINKLog, we conducted two interactive sessions with senior

government officials and supply chain specialists as part of a three-day workshop focusing on SCM. The

sessions were held in August 2016 and May 2017 in Indonesia. Each session used different gameplay. The

first session used basic scenario while the second session used humanitarian scenario. The interactions

between players in these two sessions are illustrated in Figure 4.3.


48

FIGURE 4.2. THINKLOG GAME DESIGN PROCESS

FIGURE 4.3. INTERACTIONS BETWEEN THE PLAYERS

From these interactive sessions, we evaluate the absorption of the THINKLog’s learning objective by asking

the players to list down learning points from the game after they have played the game. We then match

it with the intended learning objectives of the game. From their feedback, we found out that their learning

points are aligned with the intended learning objectives regardless of the scenario used. It confirms that

the players were able to absorb the learning objectives of the game and the game helps to increase their

understanding of SCM concepts.


49

SUMMARY AND KEY TAKEAWAYS

In this section, we focus in developing serious games which serve as an interactive learning framework to

facilitate learning in SCM concepts. THINKLog serves as an interactive board game that able to have

multiple scenario based on the learning objectives without changing the basic game structure. Based on

the interactive sessions conducted to test and evaluate the game, we observe that the game is highly

accepted by the players to facilitate learning in SCM. The learning objective evaluations also shows that

the players are able to absorb the learning objectives better by playing the games.

ACKNOWLEDGEMENT

This section is a summary from Lindawati, Nugroho Eko, Frederico Rio, Za'aba Bin Abdul Rahim, Robert de

Souza, “THINKLog: Interactive Learning for Supply Chain Management”, In IEEE International Conference

on Teaching, Assessment and Learning for Engineering (IEEE TALE) 2017, pp. 44-51. IEEE, 2017.


50

REFERENCES

S. de Freitas, “ Learning in immersive worlds: A review of game-based learning,” Joint Information Systems

Committee, London, 2006.

S. de Freitas and F. Liarokapis, “Serious Games: A New Paradigm for Education?,” in Serious Games and

Edutainment Applications, London, Springer, 2011, pp. 9-21.

Lindawati, Nugroho Eko, Frederico Rio, Za'aba Bin Abdul Rahim, Robert de Souza, “THINKLog: Interactive

Learning for Supply Chain Management”, In IEEE International Conference on Teaching, Assessment and

Learning for Engineering (IEEE TALE) 2017, pp. 44-51. IEEE, 2017.

Y. Liu, T. Alexandrova and T. Nakajima, “Gamifying intelligent environments,” in Proceedings of the 2011

international ACM workshop on Ubiquitous meta user interfaces, Scottsdale, Arizona, 2011.

M. Ma, A. Oikonomou and L. Jain, “Innovations in Serious Games for Future Learning,” in Serious Games

and Edutainment Applications, London, Springer, 2011, pp. 3-7

J. Riedel and J. Hauge, “State of the art of serious games for business and industry,” in Concurrent

Enterprising (ICE), 2011 17th International Conference, 2011.

Visit www.tliap.edu.sg for to view our TLI-Asia Pacific Whitepaper Series under this programme:

Key Ideas on Urban Last Mile Logistics

(Vol 17-Sep-TF)

Driving E-Commerce Logistics Forward

(Vol 17-Mar-TF)

Case Studies in Urban Logistics and Transportation Management

in Indonesia

(Vol 17-Mar-TF)

Navigating Infrastructure and Assets in Digital Economy

– Industry Case Studies for Strategic Re-structuring

(Vol 18-Feb-TF)

Other publications published under this programme:

Conference Papers/Abstracts presented:

"THINKLog: Interactive learning for supply chain management" at the 2017 IEEE 6th International

Conference on Teaching, Assessment, and Learning for Engineering (TALE), Hong Kong, December 12-

14, 2017.

“Decision Support Framework for Humanitarian Relief Stockpiles: the Case of West Sumatra” at 28th

Annual Conference of Production and Operations Management Society (POMS), Seattle, Washington,

USA, May 5‐8, 2017.

“Logistics network design for rice distribution in Sulawesi, Indonesia”, The 12th International Congress

on Logistics and SCM Systems (ICLS2017) Beijing, China, August 20-23, 2017.

“Multi-Method Decision Support Framework for Supply Network Design” at HICL 2018: The Road to a

Digitalized Supply Chain Management (to be presented in Sept), Hamburg, Germany, September 13-

14, 2018.

Journal Papers accepted/submitted:

Linda William, Za'Aba Bin Abdul Rahim, Robert de Souza, Eko Nugroho, Rio Fredericco,“Extendable

Board Game to Facilitate Learning in Supply Chain Management”, Advances in Science, Technology and

Engineering Systems Journal, Special Issue on Multidisciplinary Sciences and Engineering (accepted for

publication)

Giuseppe Timperio, Bernado Boy Panjaitan, Linda William, Robert de Souza, Yoseph Sunardhi,

“Integrated Decision Support Framework for Distribution Network Design”, International Journal of

Production Research (IJPR) (submitted)

The Logistics Institute – Asia Pacific National University of Singapore 21 Heng Mui Keng Terrace, #04-01

Singapore 119613 Tel: (65) 6516 4842 Fax: (65) 6775 3391 E-mail: [email protected] Website: www.tliap.nus.edu.sgnus.edu.sg

The Logistics Institute – Asia Pacific

(TLI – Asia Pacific)

The Logistics Institute – Asia Pacific was

established in 1998 as a collaboration between

National University of Singapore (NUS) and

Georgia Institute of Technology (GT) for research

and educational programs in global logistics. TLI-

Asia Pacific’s vision is to be the Asia Pacific’s

premier institute nurturing logistics excellence

through research and education. Since its

formation, it has served as the training ground for

aspiring logisticians, equipping them with

analytical tools to meet supply chain challenges.

Over the years, the institute has won multiple

awards - Best Educational Course Provider at the

annual Asian Freight & Supply Chain Awards from

2003-2013; Best Educational Course Provider at

the Asian Freight, Logistics and Supply Chain

Awards in 2016 and 2018; and Best Training

Provider at the Supply Chain Awards from 2009-

2011 and 2014.

For more information, please visit

www.tliap.nus.edu.sg

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