Analisis dan Desain Rantai Pasok Agroindustri Responsif Multi Produk Buah Tropika

AN ANALYSIS AND DESIGN OF RESPONSIVE
AGROINDUSTRIAL SUPPLY CHAIN FOR MULTI
PRODUCTS OF TROPICAL FRUIT

ROHMAH LUTHFIYANTI

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016

DECLARATION OF ORIGINALITY
AND COPYRIGHT TRANSFER
I hereby declare that thesis entitle An Analysis and Design of Responsive
Agroindustrial Supply Chain for Multi Products of Tropical Fruit is my own work
under supervision of Dr Eng Ir Taufik Djatna, MSi and Dr Ir Akmadi Abbas,
MEngSc. It has never previously been published in any university. All of
incorporated originated references from other published as well as unpublished
papers are stated clearly in the text as well as in the references list.
Hereby, I delegate that the copyright to this paper is transferred to Bogor
Agriculture University.

Bogor, December 2016
Rohmah Luthfiyanti
Student ID F351140171

SUMMARY
ROHMAH LUTHFIYANTI. An Analysis and Design of Responsive
Agroindustrial Supply Chain for Multi Products of Tropical Fruit. Supervised by
TAUFIK DJATNA and AKMADI ABBAS.
Allocation of resources at a high complexity level, such as the allocation of
raw materials, the fulfillment of processing requirements, the rules, and
distribution control, waste management, a responsive supply chain system is
needed to multi products operation model. Moreover, competitive business
environment with short turnover period, minimum cost and lead time and risk for
decline quality are current challenges in designing and improving the responsive
agroindustrial supply chain for multi products of tropical fruit. Nowadays, Small
and Medium Enterprises (SMEs) need tight requirements to increase the diffuse
and adopt of information and communication technology (ICT), so that there is a
digital gap between large enterprises and Small and Medium Enterprises
especially at distance (geographic dimension). Digital business ecosystem (DBE)
is centralized collaboration environment of the species as stakeholder

communities within the business ecosystem.
The objectives of this research are (1) to identify the component and process
in systems analysis, (2) to develop the design responsive agroindustrial supply
chain multi products of tropical fruit for SME, (3) to verify and validate the model.
The method used to answer the first objective is decomposition the process with
business process model notation (BPMN). The method to design the supply chain
responsive for multi products of tropical fruit is a classification of tropical fruit
multi product with decision tree method, responsive supply chain model with
quantitative model to count the cost, flexibility and level of responsive
performance. The third model is to supplier selection with intuitionistic fuzzy
hedges method. Verification and validation to models are conducted through the
model’s result.
The result of identification and analysis system of supply chain responsive
for tropical fruit multi product based on DBE is the first layer in the interaction of
suppliers community (vendor procurement), SMEs community (business and
processing unit), distribution center and cross docks community (vendor inventory
finish goods);the second layer is the digital device infrastructure of the first layer.
The result from quantitative model of responsive supply chain for multi products
of tropical fruit may be found that it is in low responsiveness level. There are two
scenarios to increase the responsiveness level of responsive supply chain for

multi-product of tropical fruit to increase to a medium level and to a high level by
increasing the capacity of distribution center and cross dock. The supplier
selection based on intuitions of decision maker are considered flexible and
adaptive). There are top three alternative chosen suppliers; S12, S5 and S8. The
verification and validation models have been conducted through the supplier
selection model, which resulted in an accuracy of 80 %. It means that it is a very
good accuracy that is likely be implemented.
Keywords: analysis and design system, digital business ecosystem, intuitionistic
fuzzy hedges, responsive supply chain, multi products of tropical fruit

RINGKASAN
ROHMAH LUTHFIYANTI. Analisis dan Desain Rantai Pasok Agroindustri
Responsif Multi Produk Buah Tropika. Dibimbing oleh TAUFIK DJATNA dan
AKMADI ABBAS.
Alokasi sumberdaya pada tingkat kompleksitas yang tinggi, seperti alokasi
bahan baku, pemenuhan syarat pengolahan, aturan dan kontrol distribusi, hingga
penanganan limbah, sebuah sistem rantai pasok yang responsif diperlukan untuk
permodelan operasi multi produk. Lingkungan bisnis yang kompetitif dengan
perputaran produk yang singkat, biaya dan lead time yang minimum serta
penurunan kualitas menjadi tantangan terkini dalam mendesain serta memperbarui

pasokan yang responsif. Usaha Kecil dan Menengah (UKM) pada saat ini
membutuhkan syarat yang ketat untuk meningkatkan difusi dan adaptasi teknologi
informasi dan komunikasi sehingga terjadi kesenjangan yang besar antara usaha
skala besar dan UKM khususnya kesenjangan jarak (dimensi geografis). Digital
business ecosystem (DBE) merupakan pemusatan kolaborasi lingkungan spesies
sebagai komunitas pemangku kepentingan di dalam ekosistem bisnis. Penelitian
ini mendeskripsikan model rantai pasok responsif melalui analisis dan desain
untuk memenuhi kebutuhan pemangku kepentingan akan informasi dan keputusan,
dan juga terlibat dalam manajemen produk serta bahan baku yang mudah rusak.
Penelitian ini bertujuan untuk (1) mengidentifikasi dan menganalisis
komponen dan proses berbasis DBE, (2) mendesain rantai pasok responsif multi
produk buah tropika untuk UKM, serta (3) memverifikasi dan memvalidasi model
yang dihasilkan. Metode yang digunakan untuk menjawab tujuan pertama adalah
mendekomposisi proses dengan Business Process Model Notation (BPMN).
Metode mendesain rantai pasok multi produk buah tropika adalah klasifikasi multi
produk buah tropika dengan metode Decision Tree, model rantai pasok responsif
dengan model kuantitatif untuk menghitung biaya, fleksibilitas dan level
keresponsifan dari rantai pasok multi produk buah tropika. Model yang ketiga
adalah model pemilihan supplier dengan menggunakan metode intuitionistic fuzzy
hedges. Verifikasi dan validasi model dilakukan terhadap model yang dihasilkan.

Hasil identifikasi dan analisis sistem rantai pasok responsif multi produk
buah tropika berbasis DBE adalah lapisan pertama adalah interaksi dari proses
bisnis dari komunitas penyedia bahan baku (vendor procurement), komunitas
UKM (bussines and processing unit) dan komunitas distribution center dan cross
dock (vendor inventory finish goods); lapisan kedua adalah infrastruktur digital
device dari lapisan pertama. Model kuantitatif rantai pasok responsif multi
produk buah tropika diperoleh bahwa level keresponsifan dari rantai pasok multi
produk buah tropika berada dalam kondisi low responsive. Skenario untuk
meningkatkan keresponsifan rantai pasok multi produk buah tropika ada dua yaitu
meningkatkan ke tingkat sedang (medium responsive) dan ke tingkat tinggi (high
responsive) dengan meningkatkan kapasitas distribution center dan kapasitas
cross dock. Pemilihan pemasok bahan baku (suplier) berbasis intuisi dari
pengambilan keputusan dinilai fleksibel dan adaptif. Ada tiga alternatif teratas
supplier yang terpilih adalah S12, S5 and S8. Verifikasi dan validasi model telah
dilakukan, dan validasi terhadap model pemilihan supplier diperoleh akurasinya

sebesar 80 % yang artinya bahwa model sangat baik akurasinya dan besar
kemungkinan untuk bisa diimplementasikan.
Kata kunci: analisis dan desain sistem, digital business ecosystem, intuitionistic
fuzzy hedges, multi produk buah tropika, rantai pasok responsif

© Copyright 2016 by IPB
All Rights Reserved
No part or all of this thesis may be excerpted without or mentioning the sources.
Excerption only for research and education use, writing for scientific papers,
reporting, critical writing or reviewing of a problem. Excerption doesn’t inflict a
financial loss in the paper interest of IPB
No part or all part of this thesis may be transmitted and reproduced in any forms
without a written permission from IPB

AN ANALYSIS AND DESIGN OF RESPONSIVE
AGROINDUSTRIAL SUPPLY CHAIN FOR MULTI
PRODUCTS OF TROPICAL FRUIT

ROHMAH LUTHFIYANTI

Thesis
as partial fulfillment of the requirements
for the degree of Magister of Science
in the Agroindustrial Technology Study Program

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016

Examiner:

Dr rer nat Ditdit Nugeraha Utama

PREFACE
Praise to Allah Subhanahu Wa Ta’ala the Almighty for every blessing for
me to pursue my study and finish my thesis with a title of “An Analysis and
Design of Responsive Agroindustrial Supply Chain for Multi Products of Tropical
Fruit” in Graduate School of Bogor Agricultural University.
Firstly, I would like to express my sincere appreciation to Dr Eng Ir Taufik
Djatna, MSi and Dr Ir Akmadi Abbas, MEngSc, as my supervisors for their
supports and encouragements during my study in Bogor Agricultural University.
I am also indebted to Dr rer nat Ditdit Nugeraha Utama, as non-committee
examiner for his constructive comments on this thesis which I believe greatly

contributed to the improvement of this work.
I would like to thank Dr Ir Yoyon Ahmudiarto, MSc IPM, as the head of
Center for Appropriate Technology Development, Indonesian Institute of Sciences
Subang. I also thank all of the lecturers, colleagues, Fitri Widiyanti, Aisyah Rini,
Wenny DK, Seppa Septarianis, Dian Novitasari, Hartami Dewi, Hadi, Asrol,
Septian and especially TIP 2014 colleagues, at the Agroindustrial Technology
Study Program for their cooperation and share, their valuable ideas and insights
during my study in IPB. It has been a great pleasure to work with all of you.
Last but not the least, I want to express my deepest appreciation to my
parents Maderi bin Tjik Olah (Alm) and Eti Supiyati binti Mustofa Rohim (Alm),
my husband, brothers and sisters who have always prayed for me and give me
moral support to complete my studies.
I wish this work will be beneficial to readers and contribute to the
development of the knowledge related to the topic.

Bogor, December 2016
Rohmah Luthfiyanti

TABLE OF CONTENTS
LIST OF TABLES

LIST OF FIGURES
LIST OF APPENDICES

vi
vi
vii

1 INTRODUCTION
Background
Objectives
Benefits
Boundaries

1
2
2
2

2 LITERATURE REVIEW
System Analysis and Design

Digital Business Ecosystem
Multi Products of Tropical Fruit
Responsive Supply Chain

3
4
5
6

3 METHODOLOGY
Research Period and Location
Data and Information Acquisition
Assumption
Research Framework

8
8
9
9

4 RESULTS AND DISCUSSIONS
System Identification
Business Process Analysis
Classification of Multi Products
Modeling Responsive Supply Chain
Intuitionistic Fuzzy Hedges for Supplier Selection
Verification and Validation
Advantage and Disadvantage

23
24
25
27
32
36
37

5 CONCLUSIONS AND RECOMENDATIONS
Conclusions
Recommendations
REFERENCES
APPENDICES
GLOSSARY
BIOGRAPHY

37
37
38
41
77
78

LIST OF TABLES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Parameters list and its description for model responsive supply chain
Parameters list and its description for intuitionistic fuzzy
Patterns of multi products
The raw material purchase cost and transportation cost from supplier to
plants
Total cost and flexibility
Strategy for improving to medium responsiveness level
Strategy for improving to high responsiveness level
Comparative performance of delivery network designs
Performance of delivery networks for different product customer
characteristics
Non fuzzy data input
Rule obtained from the combination of variables and membership
function
Intuitionistic fuzzy score for supplier 2
Intuitionistic fuzzy hedges for supplier 2
E(Mi) and H(Mi) value for supplier 2
Fuzzy aggregation for supplier 2
Fuzzy aggregation output and the decision
Rule for the interestingness measures in supplier
Fuzzy entropy aggregation result

17
22
26
28
29
29
29
31
31
32
33
33
34
34
35
35
36
36

LIST OF FIGURES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Basic system entity construct
Verification and validation concept overview
Framework of responsive supply chain
The research framework
Analytical system entity construct
Use case diagram
Membership function for distance variable
Membership function for capacity variable
Membership function for transportation cost variable
Membership function for raw material price variable
Membership function for lead time variable
Membership function for demand variable
Detail of system entity construction
Fragments of business analysis diagram in responsive supply chain of
multi product
Result decision tree structure
The existing structure of mangosteen supply chain in Tasikmalaya
Design structure of responsive supply chain for multi products of
tropical fruits
Distributor storage with carrier delivery

3
4
7
10
11
12
19
19
20
20
20
20
24
25
26
27
28
32

LIST OF APPENDICES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Product diversification of mangosteen (Pohon Industri)
The research workflow
Flowchart of supplier selection steps
Comparative performance of delivery network designs
Performance of delivery networks for different product customer
characteristics
Requirement analysis
Use case
Fragment process hierarchy diagram (PHD)
Fragment business process diagram (BPD)
Fragment business process model notation (BPMN)
The fix and variable cost associated with plant operation
The variable cost of handling and inventory of products at distribution
centers (DCs) and transportation of products from plant to DCs
The transportation cost to ship multi products from DCs to cross docks
and distribution costs to ship multi product from cross-docks to
customer zone
Flexibility of the supplier’s capacity
Flexibility of the plant’s capacity

43
44
45
47
48
49
54
55
56
57
60
61
64
66
75

1 INTRODUCTION
Background
Nowadays, business competition aims for the responsiveness to information
of each stakeholder in the networking, especially in the supply chain.
Responsiveness to change holds the key to the existence of the company. In
Indonesia, small and medium enterprises (SMEs) are the frontline to overcome the
economic crisis. Meanwhile, Conchione (2009) explained that SMEs are generally
have yet to utilize the information technology in responding the information needs
of the stakeholder involved in the supply chain.
SMEs in Indonesia typically produce a wide range of product like juices,
jam, dodol, syrup and chips from tropical fruits. Multi-product is the product
diversification from the same raw material, which in this case was mangosteen as
the raw material and the product of juice, functional drink and dodol. The success
of SMEs in producing multi-product depends on the reliability and the agility in
raw material provision; latency in raw material procurement leads to
discontinuation of production and unfulfilled order. A multi-product supply chain
design for tropical fruits has been an interesting topic to answer the call for
responsive supply chain to the demand. The supply chain design initiated by the
identification of the criteria of responsiveness in supply chain as well as the
identification of the stakeholder involved in the business environment of the SME.
The design and analysis of supply chain is essential for the stakeholder to
identify the key aspect in the business environment of Indonesian tropical-fruitsbased multi-product. The system design provides the information for stakeholder
to gain information of the material flow and condition as well as to help in
decision-making. Digital Business Ecosystem (DBE) is one approach in system
design, is a digital technology consisting digital ecosystem to facilitate business
activities (Nachira et al. 2007). Digital ecosystem provides business ecosystem
activity to achieve the objectives of searching and finding information. The first
layer of DBE explains the digital business, which comprises the interaction of
involved stakeholders having the same objective. The second layer is the digital
operation of the first layer. Utilizing the digital information gives the advantage of
time efficacy and cost efficiency in responding the demand.
As identified, the issue in Indonesian tropical-fruits-based multi-product is
the information flow between the stakeholders. Furthermore, the material flow
itself comes to be crucial as the perishable property of agricultural material.
Deteriorated material leads to low-quality end-product. Thus, a responsive supply
chain management was proposed as the solution for SMEs to manage its business.
Tiwari et al. (2013) explained that a responsive supply chain performs faster
sales and more cost-efficient production. You and Grossmann (2008) discussed
that designing a responsive supply chain in the circumstance where demand
uncertainty occurs, with the responsive criteria of transportation time, scheduling
of the factory, lead time and stock management, significantly influenced the NPV
and supply chain structure. Vidyarthi et al. (2009) designed a system of MTO
(make to order) and ATO (assemble to order) to achieve a responsive supply chain
to the distribution of wide variety of product with a high demand and short

2
product lifecycle. Fresh fruit supply chain is a more complex compared to other
supply chain as it has a character of perishable, low shelf life, and fluctuating
demand and price. Fresh fruit supply is a process flow from the harvesting to
shipping/distribution, from the farmer to customer, in this case is the Small
Medium Enterprises in food industry (Shukla and Jharkharia 2013). Small
Medium Enterprises (SMEs) in their business produce multi-product from one
kind of raw material, which is a relevant key aspect in the study of responsive
supply chain.
Wasson (2016) represented a system in a form of entity consisting the
requirements that have to be analyzed before designing a responsive supply chain,
thus requirement analysis is essential to facilitate the comprehension of the supply
chain. Next step is to integrate the responsive supply chain system design to
ensure that the system is applicable in real business environment. The integration
is intended to satisfy the actual needs of the stakeholder involved in the supply
chain system.
Objectives
1.
2.
3.

As the motivation and challenges, this research aims to:
Analyze the requirement in the responsive agroindustrial supply chain system
for multi products of tropical fruit.
Design the responsive agroindustrial supply chain system for multi products
of tropical fruit in the scale of small and medium enterprises.
Verify and validate the model of responsive agroindustrial supply chain for
multi products of tropical fruit.
Benefits

1.
2.
3.

This research is expectantly contributing to the following output:
Contributing to the knowledge on the responsive supply chain analysis and
design of tropical fruits multi product.
Providing insights and recommendations on the analysis, design and
validation of the responsive supply chain system.
Providing knowledge and tools to support the decision making of the
stakeholder in the supply chain management.
Boundaries

The subject of this research was multi products of tropical fruit, particularly
mangosteen and its derivatives products such as mangosteen juice, dodol and
functional drinks, with the focus on the responsive supply chain of mangosteen
products in the scale of small medium enterprises (SMEs). Business process
analysis of the mangosteen multi product responsive supply chain used the
framework of Digital Business Ecosystem (DBE). This work was conducted to
the subject in the Regency of Tasikmalaya, West Java.

3

2 LITERATURE REVIEW
System Analysis and Design
System is an integrated set of interoperable elements, each with clearly
specified and bounded proficiency, operating synergistically to perform valueadded processing to enable a user to meet the mission-oriented operational needs
in a determined operating environment with a specified result and success
probability (Wasson 2016).
System Engineering (SE) is the multidisciplinary application of analytical,
mathematical and scientific principles to formulating, selecting, and developing a
solution that has acceptable risk, meets user operational need(s), and minimizes
development and life cycle costs while considering stakeholder interests. The
depiction of a system entity construction is illustrated in Figure 1. Basically, the
construction consists of inputs which are fed into a system that processes the
inputs and results the output. In the composition, the function of the system has to
be clearly identified and the added value it creates (Wasson 2016).

System Entity
Input(s)

 Stimuli
 Cues

Processing

Output Reponse(s)
 Products
 By-products
 Services

Figure 1 Basic system entity construct (Wasson 2016)
The systematic approach take to the analysis and design of information
systems is embodied in what is called the systems development life cycle (SDLC).
The SDLC is a phased approach to analysis and design that holds that systems are
best developed through the use of a specific cycle of analyst and user activities.
There are several opinions about the stages contained in SDLC. (Kendall and
Kendall 2011).
Requirement analysis on SDLC perform by using several tools method.
BPMN is graphical notation to depict the sequence of process in business
activities that collaborating and interacting to achieve a goal. Business process
modeling constructed to aid a communication with work colleagues inside the
organization, helping them form a shared understanding. Besides, BPMN also
used to drive the way in which work happens in the modern organization and
carry the instructions for how work should happen, who should do it, escalation
conditions if it is not systems. done in time, links to other systems etc. (Ginantaka
2015). The concepts of system validation and system verification as shown in
Figure 2 provides an illustration of system verification and system validation that
helps in better understanding these concepts (Wasson 2016).

4

Figure 2 Verification and validation concept overview (Wasson 2016)
Digital Business Ecosystem (DBE)
Digital Ecosystem (DE) is defined as a distributive, slackly coupled,
demand-driven, auto-organizing, collaborative software environment, where every
entity is quite responsive and proactive. These entities are secured in a digital
infrastructure to gain combined benefits. The concept of Digital Business
Ecosystem (DBE) is defined by many authors in different ways. DBE is one
technique to centralize the data and information from each stakeholder in the
responsive supply chain system of multi product in order to assist the decision
making process. DBE based system allows agile and cost-efficient response when
facing uncertainty. Digital business ecosystem (DBE) improves the collaborative
environments, such as centralized (client-server), distributed model (peer-to peer),
hybrid model (web services) into its own. DBE provides an interactive autoorganized software environment which is naturally distributive yet presents an
integrated view of all the business entities. The expected benefits include value
added activities and cost-effectiveness of services which are favorable to SMEs,
employees and consumers (Khalil et al. 2011).
Nachira et al. (2007) discussed that digital technology utilization in the
course of respective digital ecosystem in business activity having the similar
objective is described as digital business ecosystem (DBE). Digital ecosystem
provides the representation of business ecosystem with the objective of searching
and finding the information.
According to Conchione (2009), Small and Medium Enterprises (SMEs) has
to be in favorable conditions as to enhancing its diffusion to Information and
Communication Technology (ICT). This is to close the gap of digital era between
large enterprises and SMEs as well as among geographical area.
Hadzic et al. (2007) defines the steps in designing DBE are: (1) identify the
types of Digital Species (DS) based on its role, (2) develop the DS intelligence,

5
(3) determine cooperating system between each DS, (4) build the DS, (5) deploy
the security system to protect the digital ecosystem.
Multi Products of Tropical Fruits
The industrialization of Indonesia fruits is aimed to fulfill national demand
and export demand as to face the free trade era. There is a great potency to
improve the availability and the quality of tropical fruit is improved gradually
(LPPM IPB 2009). Balitbang Kementan in their work during 5 years from 2009
has been successful to release 58 varieties of superior tropical fruits of Indonesia
as well as the technology to cultivate the crops. Those varieties include banana (4
varieties), mangosteen (2 varieties), mango (11varieties), avocado (3 varieties),
papaya (1 variety), melon (4 varieties), salak (2 varieties) and 31 varieties of
banana, melon, salak and pineapple (Kabar bisnis 2014). The latest 5 varieties of
mangosteen, which have been launched as a result of the cooperation between
local government and Pusat Kajian Buah Tropika IPB (PKBT–IPB), are
Wanayasa, Puspahiyang, Malinau, Marel, Raya (LPM IPB 2009) and one from
Balitbang Pertanian (2015) is Ratu Kamang variety.
Mangosteen is an exotic commodity of Indonesia as a “queen of tropical
fruit” to be exported since it has a unique taste and color compared to other
commodities. Its skin can also be utilized as herbs, food ingredients, supplement,
cosmetics, preservatives and natural color, aside being a favorite fruits of both
domestic and foreign consumers.
The production of mangosteen in Indonesia in 2014 was up to 114,755 tons
from the plantation with a total area of 15,197 ha (Statistika pertanian 2015). As
much as 7,411 tons from the production was exported, meaning only about 6.57%
was qualified as exported grade, while the rest was for national consumption
(LPPM IPB 2009). One of the largest producer of mangosteen in West Java is
Tasikmalaya, with an area of 3,384 ha of mangosteen crops, producing as much as
37,523 tons of mangosteen in 2015 (Distan Kab Tasikmalaya 2016). According to
LPPM IPB (2009), the superior variety of mangosteen that had been released in
Tasikmalaya was Pupahiyang, with a registration number of Kepmentan No
301/Kpts/SR 120/5/2007.
The mangosteen in the supply chain of Tasikmalaya is marketed as raw
material for both domestic and export use. The excess products which are not
taken over the market can be utilized by the Small and Medium Enterprises
(SMEs) of food and beverages processing. Some products from these SMEs are
dodol, functional drinks, syrup, juice, puree, instant mangosteen skin powder, etc.
The detailed product diversification of mangosteen can be seen in Appendix 1.
Mangosteen juice is a healthy beverage containing vitamins, mineral and
xanthine (3.55 mg/100ml). Mangosteen juice is packed in a plastic bottle can
sustain for 3 months in the temperature around 4-8 oC, unexposed to direct
sunlight, whereas glass bottle packaging can improve the shelf life up to 8 months.
Functional drinks from mangosteen containing xanthine, a compound in the skin
that can be extracted, are beneficial as antioxidant to fight cancer cell growth. The
extract of mangosteen skin contains xanthine (123.97 mg/100 ml), vitamin B1
(20.66 mg), vitamin B2 (1.79 mg), vitamin B6 (0.948 mg) and vitamin C (17.92

6
mg). The shelf life of xanthone in a dark-glass bottle, being kept in low
temperature and unexposed to sunlight, is up to 10 months. Some SMEs produce
xanthone due to its simple processing steps. Another mangosteen-based product is
instant mangosteen skin powder, made by spray drying process. Mangosteen skin
is soluble in water, has a soft texture and contains the antioxidant of anthocyanin
(minimal of 1.13mg/g), total phenol (8.49 mg/g), antioxidant capacity of 428.72
mg/g AEAC (Ascorbic acid equivalent antioxidant capacity) and xanthone (alpha
mangosteen 0.59 mg/g) (Balitbang Pertanian 2015). This research focused on the
mangosteen multi product of juice, dodol and functional drink.
Responsive Supply Chain
A responsive supply chain is the ability to respond quickly to a multiple
demand while minimizing the lead-time, managing multi-product, creating
innovative product and serving with high quality (Chopra and Meindl 2013).
Tiwari et al. (2013) described the characteristic of responsive supply chain as (1)
high flexibility, (2) low cost, (3) responding to multiple demand, (4) managing
multi-product, (5) increasing agility or quick-responding, (6) high service level,
and (7) managing uncertainty. You and Grossmann (2008) also argued that the
criteria of being responsive are transportation time, residence time, scheduling and
stock management.
Company aims to distribute its product cost-efficiently when managing a
more responsive supply chain. Several alternatives it can endeavor are: (1)
reducing production time via slot splitting or decreasing setup time, (2) investing
in either finished products or raw materials safety stocks to buffer disruptions
coming from the downstream or upstream, respectively, (3) opening retail outlets
near the customer’s market, (4) shipping via faster transportation modes and (5)
dealing with firms resorting to highly reliable suppliers (Tiwari et al. 2013).
The challenge in today’s foodstuffs supply chain management is the high
complexity of global networking, which creates pathways from farm to consumers,
involving production, processing, and distribution, to waste management
(Nagurney et al. 2013). This calls for a more comprehensive study in the
foodstuffs supply chain modeling, analysis and solutions.
The methodological framework for perishable stuff supply chain has to be
able to manage different behavioral concept including optimization and
competitive equilibrium, as well as be sufficiently representing different supply
chain structures that are relevant to the case of perishable products. (Nagurney et
al. 2013). The methodological framework and integrated architecture can be
employed in analyzing the supply chain for perishable stuff as it facilitates the
decision making process, both for single criterion or multi-criterion. Responsive
supply chain analysis should also encompass the issue of agricultural product,
such as the quality deterioration of the product, the needs of specific treatment in
distribution and proper technology for inventory or storage. Figure 3 framework
of responsive supply chain.

7
 Demand Anticipation
 Manufacturing
Flexibility
 Inventory
 Product
 Architecture/
Postponement

Operational Factors

 Optimal Design of Supply
Chain Network
 Optimal Allocation of Materials
 Optimal Mode of
Transportation
 Optimal Inventory Allocation

 Information
Integration
 Coordinating &
Resource Sharing
 Organizational
Integration
 Spatial Integration
 Logistic Integration
Supply Chain
Integration

 Demand Uncertainty
 Demand Variability
 External Product
Variety
 Lead Time
 Control factor: Ability
to Meet Requirements
(Delivery, libilit & lit)

External Determinants

Supply Chain Responsiveness
 Higher Flexibility
 Low Cost
 Respond to a Wide Range of
Quantities Demand
 Handle a Large Variety of
Products
 Increased Speed
 Meet a High Service Level
 Handle Supply Uncertainty

Strategic Planning

Virtual Enterprises

Knowledge & Information
Technology

 Corporate & Business
Strategies
 Global Outsourcing
 Strategic Alliance
 Technology
 Continuous
Improvement

 Partnership Based on
Core Competencies
 Distributed Network
of Partners
 Integration by
Information
Technology

 Automation & IT
including E-Commerce
(B2B, B2C, and B2A)
 Tactical Management
 Training & Education
 Flexible Workforce
 Strategic Formulation

Figure 3 Framework of responsive supply chain (Tiwari et al. 2013)
You and Grossmann (2008) designed and planned an optimal responsive
supply chain for demand uncertainty under economic criterion in polystyrene
production. MINLP method was employed to achieve the multi-objectives of
maximizing NPV and minimizing the expected lead time. The model was solved
using ε-constraint method and pareto chart procedure. A hierarchical algorithm

8
was also proposed based on the decoupling of different decision-making levels in
the problem.
Vidyarthi and Jewkes (2009) developed a model design of MTO (make to
order) and ATO (assemble to order) with the consideration of customer demand
and distribution time. Cutting plane algorithm and Lagrangian heuristic algorithm
was employed in their work. The business strategy aimed to solve responsive
supply chain issue of varying products, high demand and short product lifecycle.
Leung et al. (2003) developed a responsive replenishment system with the
consideration of respond agility to the fluctuating demand and punctuality with
minimum cost. Fuzzy Logic was employed to solve the demand uncertainty and
improve the responsiveness in stock replenishment.
Wang and Ingham (2008) studied the dynamic simulation method to
improve and optimize performance of the supply chain. In their research, the
simulation was developed to examine the relationship between the stakeholders of
the supply chain so that the company is able to survive in the competitive
environment.
Chen and Song (2014) assessed and designed the ability to quickly respond
in the mechanism of supply chain coordination by using TOPSIS method and
Hesitant Fuzzy, where they revealed that it could result more efficient and
responsive system quickly to solve the problem.
Buzzone et al. (2014) developed the simulation model of supply chain for
fresh food safety and security in the distribution process using DES (Discrete
Event Simulator) technique. The model was able to prevent the contamination
while minimizing the transportation cost, inventory cost and sales loss. Seo et al.
(2012) used a mathematical model to plan the supply chain in a business
environment and integrate the planning of raw material procurement, production
and distribution. The mathematical model is capable of analyzing a high
complexity issue.

3 METHODOLOGY
Research Period and Location
This research was conducted from January 2016 to June 2016 in the
computer laboratory of the Department of Technology of Agroindustry. Primary
data was obtained by proposed judgment sampling in Tasikmalaya district region.
Data and Information Acquisition
In this research, primary data were obtained by survey with opened and
closed questionnaire. The respondents were selected by purposive sampling,
which consisted of mangosteen farmers, mangosteen collectors, SMEs and related
institution. Secondary data were obtained from historical data of the governmental
office of Agriculture in Tasikmalaya district and ventures of mangosteen
collectors.

9
Assumption
The assumption in the fuzzy intuitionistic model was that we used nonexpert respondents and their intuition as the input. In responsive supply chain, the
assumptions, which were the distance of supplier to plants (SMEs), the capacity of
supplier, SMEs (business and processing unit), distribution center and cross docks,
transportation cost, raw material price, lead time and demand/order quantity, were
based on the real-time condition on the field.
Several assumption subjected to the estimation in the modeling of fuzzy
intuitionistic and responsive supply chain were: (1) the subject commodity of
mangosteen and its diversification product of juice, functional drinks and dodol
existed, (2) data availability according to observation (raw material price per
kilogram, cost and supplier’s capacity) were unchanging during the working of
this research, (3) current distance was according to the initial observation (the
distance from supplier to the factory), (4) the raw material and the SMEs was
according to the current observation, and (5) the data used were primary data,
secondary data and simulation result data.
Research Framework
In order accomplish the research objectives, a methodology was designed
which can be seen thoroughly in Appendix 2. This work began with the
identification of system requirement of the tropical fruit responsive supply chain.
System entity analysis (Wasson 2016) was employed for to identify the
stakeholder, rule, role, mission, control, regulation, etc. Next, the digital business
ecosystem (DBE) was analyzed and the representation of the business community
interaction was illustrated by the Business Process Model Notation (BPMN) using
Sybase Power Design 16.5 (SAP 2013). The design of responsive supply chain
consisted of multi product classification model, responsive supply chain model
and supplier selection model. Multi products classification modeling was
performed using Decision Tree method and the computational process was done
using WEKA 2.6. The quantitative model of responsive supply chain referred to
Tiwari et al. (2013).
Design the model of supplier selection using intuitionistic fuzzy hedge
(Appendix 3) with the following steps: (1) determination of non-fuzzy input
variable, (2) arrangement of the membership and non-membership function limit,
(3) arrangement of rules based on the variables and membership and nonmembership function, (4) arrangement of the intuitionistic fuzzy set, (5) modeling
of supplier selection into intuitionistic fuzzy hedges, (6) aggregation of the fuzzy
hedges using intuitionistic fuzzy weight average operation, and (7) selection of the
interestingness measures in supplier selection. The third objective is to validate
the model intuitionistic used by (1) measuring input variable using entropy fuzzy
weight, (2) tabulating the result into confusion matrix, and (3) calculating the
model accuracy based on the confusion matrix. The framework of this research is
shown in Figure 4.

10
START
1st Objective

Identification of
stakeholder, roles, rules,
procedure, etc.

2nd Objective

Design of multi product
responsive supply chain

Analysis of the interaction
between each stakeholder

To map of the process and
sub process of each
stakeholder
Business process analysis

No
Is the model
suitable?

Yes

3rd Objective

Verification and validation
of system design

No
Is the system
verified and
valid?

Yes
END

Figure 4 The research framework

11
Identification of System
To achieve the first objective, we need to define the system by identifying
the stakeholder, rule, role, mission, input and output. In this work, system design
and analysis adopted the System Development Life Cycle Analysis (SDLCA)
framework, with the steps of (1) identifying problems, opportunities and objective,
(2) determining human information requirements, (3) analyzing system needs, (4)
designing the recommended system, (5) developing and documenting software,
(6) testing and maintaining the system and (7) implementing and evaluating the
system (Kendall and Kendal 2011). In designing the system, the boundary,
objective, desired and undesired input, desired and undesired output, resource,
rule, role and system disadvantages were specified. The whole step of the system
identification is illustrated in Figure 5. Nextly, we identified the needs of
stakeholder by determining the attribute and system entity which would be
detailed by using UML (Unified Model Language) (Wasson 2016). The
construction of high level design provided Use Case Diagram as shown Figure 6.

Figure 5 Analytical system entity construct (Wasson 2016)
Use case is characterized by a set of attributes that explain to the user about
the system deployment, operation, support, or disposal. The attributes, which act
as a checklist for developing use cases comprise unique identifier, objective,
outcome based result, assumptions, processing capabilities, scenarios and
consequences. Actors can be persons, places, event, and real or virtual objects.
UML represents actors as stick figures and use cases as ellipses (Wasson 2016).

12

MISSION SYSTEM
Use Case #1
User #2
(Actor)
Use Case #2
User #1
(Actor)

Use Case #3
User #3
(Actor)
Figure 6 Use case diagram (Wasson 2016)
Business Process Analysis

Process Hierarchy Diagram
In the problem analysis, firstly a process hierarchy diagram (PHD) was
constructed as the guidelines for the Work Breakdown Structure (WBS). PHD
describes the order of the activities from the highest order of a system function by
decomposing the processes into a tree of sub-process in a graphical view. At each
level of decomposition, each process is able to explain multiple function type.
Business Process Diagram
Business Process Diagram (BPD) describes a system in a high-level order. It
provides the control flow which illustrated the sequence of execution or data flow
which provided data exchange between processes at any level in a graphical view.
BPD has a more simple notation and helps the stakeholders involved in the system
building in understanding every process.
Business Process Model and Notation
Business process of the multi-product of tropical fruits supply chain was
described in BPMN 2.0. BPMN is a graphical notation for representing a business
process flow. The notation is made to provide a notation which is understandable
by all business users, for example the notation of start, event, task, intermediate
message, end event, or gateway. In BPMN, a business process necessitates an
order in business activity and supporting information. Thus, BPMN is a more
understandable workflow for analyzing and modeling a business (business
process). There are 3 levels of business modeling, those are process maps, process
descriptions and process models. BPMN requires several information, such as

13
input, process/activity, output, organization/actor, sub-process, roles, mission and
objectives (White 2008).
Classification of Multi Product
The classification of tropical fruits multi-product was done using Decision
Tree (DT) method, a technique in data mining. We used Weka 2.6 for the data
processing. Firstly, we built a dataset which represented the problem and then we
determined the data class or the concept. The algorithms starts with a training set
of tuples and their associated class labels. DT formulation should define the roots,
internal node and leaf node (Han et al. 2012). According to Bramer (2007),
formulation should follow the determination of entropy and information gain as
the following equation:
m
Info  D     pi log 2 pi
i 1

(1)

 

v Dj
Info A  D   
 Info D j
j 1 D

Gain  A  Info  D  InfoA  D

(2)

(3)

D is the entropy training set, while pi is the non zero probability that an
arbitrary tuple in D belongs to class Ci.
Modeling of Responsive Supply Chain
The quantitative model for responsive supply chain used the mathematical
model proposed by Tiwari et al. (2013). The decision variables in Tiwari’s model
are supplier, plant, distribution center, cross dock, cross dock-distribution center
and consumer zone-cross dock. The multi-products observed in this research were
juice, functional drinks and dodol.
Decision variables:
1. Supplier (s)

:

Ns 



1,
0,

if sup plier s is open
otherwise

2. Plant (p)

:

Xk 



1,
0,

if plant p is open
otherwise

3. Distribution center (k)

:

Yk 



1,
0,

if distribution center k is open
otherwise

4. Cross-Dock (j)

:

Z j 



1,
0,

if cross dock j is open
otherwise

14
5. Cross-dock –Distribution center (j,k,i):
R jki 



1, if crossdock j isassigned to distribution center k for product i
0, otherwise

6. Customer zone (m, j,i):
A mji 



1, if customer zone m isassigned to cross dock j for product i
0, otherwise

Constraints:
1. The raw material purchase cost and transportation cost from suppliers (s) to
plants (p).





P S R
   UTCR
rsp UCR rs rmp
p 1 s 1 r 1

(4)

2. The fixed and variable costs associated with plant operations.
P
I P
I P K
 FCP p  X p    UPCP ip   ip     UPCP ipk  v ipk
p 1
i 1 p 1
i 1 p 1 k 1

(5)

3. The variable cost of handling and inventory of products at distribution centers
(DCs) and transportation of products from plant to DCs.
K
I K J
M I J K
 FDC k  Y k     UTCD ikj  R jki      UCT ik  D mi R jki  A mji
i 1 k 1 j 1
m1 i 1 j 1 k 1
k 1

(6)

4. The transportation cost to ship multi products from DCs to cross docks and
distribution costs to ship multi products from cross docks to customer zone.
J
M I J
 FCD j  Z j     CSC mj  A mji
j 1
m1 i 1 j 1

a. Flexibility of the supplier’s capacity (SF)



I R S
SF     PCV rs  N s  D mi
i 1 r 1 s 1

b. Flexibility of the plant’s capacity (PF)



I P
PF    PCP p  X p  D mi
i 1 p 1





(7)

(8)

(9)

c. Flexibility of the distribution center’s capacity (DCF)



I K
DCF    CDC k  Y k  D mi
i 1 k 1



(10)

15
d. Flexibility of the cross dock’s capacity (CDF)



I J
CDF    CCD j  Z j  D mi
i 1 j 1



(11)

e. Distribution volume flexibility (DVFm)
DVF m  min  SF , PF , DCF , CDF 

(12)

f. Plant volume flexibility (PVFm)
P
I
PVF m   X p  PCP p    ip  SUP ip
p 1
i 1

(13)

The availability of required quantities of raw material with the supplier
capability can be examined by equation:
P
  rsp  PCV rs
p 1

(14)

Ensures that the raw material supplied by the supplier matches the
production requirements:
S
S
 UR ri   rsp   PCV rs
s 1
s 1

(15)

Total production quantity of products to be manufactured should not exceed
the plant capacity:
I
 SUp   ip  PCP p X p
ip
i 1

(16)

The quantity of products to be produced at a plant should be within the
minimum and maximum production capacities. This constraint is examined by
equation (17). Equation (18) administers the minimum and maximum throughput
capacities for DCs and ensures that customer zone assignments can be made only
to open DCs.
MNPV ip  X p   ip  MXPV ip  X p
I M
MNTH k  Y k    SUDC ik  D mi  R jki  A mji  MXTH k  Y k
i 1 m1

(17)
(18)

Equation (19) shows that each customer zone must be assigned to exactly
one cross-dock. Equation (20) ensures that the amount transported from plant is
equal to the quantity of products available at the plant. Equation (21) checks if all
the demand requirements are satisfied.
J
 A mji  1
j 1

(19)

16
K

 ip k

1 ipk
P K
M
   ipk   D mi
p 1 k 1
m1

(20)

(21)

The demand requirements at each distribution center are checked by
equations (22) and (23). Equation (24) limits cross-docks to be assigned to only
open distribution centers. Equation (25) ensures the distribution centers capacity
restrictions.
P
M
  ipk   D mi  R jki  A mji
p 1
m1

 ip, ipk ,  rsp  0

(22)
(23)

R jki  Z j

(24)

M l


 
m1 i 1 d mi A mji CCD j

(25)

Equation (26) represents the capacity restriction for cross-docks. Equation
(27) ensures that customer demand for products is satisfied by open cross-docks.
Constraint given (28) checks that only open distribution centers will have product
flow through its assigned cross-docks to customer zones.
M I


 
m1 i 1 R jki CCD j CDC k

(26)

A mji " Z j

(27)

R jki  Y k

(28)

Because capital availability is limited to any company, it is necessary to
check whether the cost incurred in opening DCs and CDs should not exceed the
available capital. This constraint is ensured by equations (29) and (30).
J
 Z j  MXC
j 1

(29)

K
 Y  MXD
k 1 k

(30)

As mentioned before, responsiveness is categorized in three class: high,
medium and low. Based on this classification, equation (31) ensures that the
combined responsiveness of the selected suppliers, plants, DCs and CDs should
be less than the upper bound of the high level of the performance index and
greater than its lower bound. Similarly, for medium and low level responsiveness,
the performance indexes are given in equation (32) and (33), respectively.

17
HRG l  RGS s  RGP p  RGDC k  RGCD j  HRG u

(31)

MRG l  RGS s  RGP p  RGDC k  RGCD j  MRG u

(32)

LRG l  RGS s  RGP p  RGDC k  RGCD j  LRG u

(33)

Table 1 Parameter list and its description for model responsive supply chain
(Tiwari et al. 2013)
No

Parameters

Notation

Description

1
2
3
4
5
6
7
8
9
10
11

Plant
Supplier
Distribution center
Cross dock
Raw material
Product
Decision variables for plant
Decision variables for supplier
Decision variables for distribution center
Decision variables for cross dock
Decision variables for cross dockdistribution center
Decision variables for customer zonecross dock
Unit cost of raw material (r) for supplier
(s)
Unit transportation cost from supplier (s)
to plant (p) for raw material (r)
Unit production cost for product (i) at
plant (p)
Unit production cost for product (i) at
plant (p) and distribution (k)
Unit transportation cost from distribution
center (k) to cross dock (j) for product (i)
Unit cost of throughput (handling and
inventory) for product (i) at distribution
center (k)
Cost to supply product (i) from cross
docks (j) which would be used by the
customer zone (m)
Fixed cost for plant (p)
Fixed cost for distribution center (k)
Fixed operating cost to open cross-dock
(j)
Quantity of product (i) produced at plant
(p)

p
s
k
j
r
i
Xp
Ns
Yk
Zj
Rjki

p = 1,2,3
s = 1,2,3,...15
k = 1,2
j = 1,2
r = mangosteen
i = 1,2,3
Xp = 0 or 1
Ns = 0 or 1
Yk = 0 or 1
Zj = 0 or 1
Rjki = 0 or 1

Amji

Amji = 0 or 1

UCRrs

Rp

UTCRrsp

Rp

UPCPip

Rp

UPCPipk

Rp

UTCDikj

Rp

UC