Framework Model of Sustainable Supply Chain Risk for Dairy Agroindustry Based on Knolvledge Base

ICACSIS

2014

2014 International Conference on

Advanced Computer Science and

Information Systems

October 18th and 19th 2014

Ambhara Hotel, Blok M

Jakarta, Indonesia

+.IEEE

ICACSIS

INDONESIA SECTION

2014 International Conference on

Advanced Computer Science and Information Systems

(Proceedings)

ISBN : 978-979-1421-225

ICACSIS 201-1

plenary and invited speakers.

Welcome Message·from

General Chairs

On behalf of the Organizing Committee of this

International Conference on Advanced Computer

Science and Information Systems 2014 (ICACSIS

2014), we would like to extend our warm \velcome to

all of the presenter and participants, and in particular,

we would like to express our sincere gratitude to our

This international conference is organized by the Faculty of Computer Science, Universitas

Indonesia, and is intended to be the first step towards a top class conference on Computer

Science and Information Systems. We believe that this international conference will give

opportunities for sharing and exchanging original research ideas and opinions, gaining

inspiration for future research, and broadening knowledge about various fields in advanced

computer science and information systems, amongst members of Indonesian research

communities, together with researchers from Germany, Singapore, Thailand, France, Algeria,

Japan, Malaysia, Philippines, United Kingdom, Sweden, United States and other countries.

This conference focuses on the development of computer science and information systems.

Along with 4 plenary and 2 invited speeches, the proceedings of this conference contains 71

papers which have been selected from a total of 132 papers from twelve different countries.

These selected papers will be presented during the conference.

We also want to express our sincere appreciation to the members of the Program Committee for

their critical review of the submitted papers, as well as the Organizing Committee for the time

and energy they have devoted to editing the proceedings and arranging the logistics of holding

this conference. We would also like to give appreciation to the authors who have submitted their

excellent works to this conference. Last but not least, we would like to extend our gratitude to

the Ministry of Education of the Republic of Indonesia, the Rector of Universitas Indonesia,

Universitas Tarumanagara, Bogor Agricultural Institute, and the Dean of the Faculty of

Computer Science for their continued support towards the

ICACSIS 2014

conference.

Sincerely yours,

General Chairs

ICACSIS 201-1

Welcome Message from

The Dean of Faculty of Computer Science, Universitas

Indonesia

On behalf of all the academic staff and students of the Faculty of Computer

Science, Universitas Indonesia, I would like to extend our warmest welcome to

all the participants to the Ambhara Hotel, Jakarta on the occasion of the 2014

International Conference on Advanced Computer Science and Information

Systems (ICACSIS).

Just like the previous five events in this series (ICACSIS 2009, 2010, 2011, 2012, and 2013), I

am confident that !CASIS 2014 will play an important role in encouraging activities in research

and development of computer science and information technology in Indonesia, and give an

excellent opportunity to forge collaborations between research institutions both within the

country and with international partners. The broad scope of this event, which includes both

theoretical aspects of computer science and practical, applied experience of developing

information systems, provides a unique meeting ground for researchers spanning the whole

spectrum of our discipline. I hope that over the next two days, some fruitful

ｾｯｬｬ｡｢ｯｲ｡ｴｩｯｮｳ＠

can be

established.

I also hope that the special attention devoted this year to the field of pervasive computing,

including the very exciting area of wireless sensor networks, will ignite the development of

applications in this area to address the various needs of Indonesia's development.

I would like to express my sincere gratitude to the distinguished invited speakers for their

presence and contributions to the conference. I also thank all the program committee members

for their efforts in ensuring a rigorous review process to select high quality papers.

Finally, I sincerely hope that all the participants will benefit from the technical contents of this

conference. and wish you a very successful conference and an enjoyable stay in Jakarta.

Sincerely,

Mirna Adriani, Ora, Ph.D.

Dean of the Faculty of Computer Science

Uninrsitas Indonesia

ICACSIS 201-!

ICACSIS 2014

COMMITTEES

Honorary Chairs:

• A. Jain, IEEE Fellow, Michigan State University, US • T. Fukuda, IEEE Fellow, Nagoya University, JP • M. Adriani, Universitas Indonesia, ID

General Chairs:

• E. K. Budiarjo, Universitas Indonesia, ID • 0 . I. Sensuse, Universitas Indonesia, ID • Z. A. Hasibuan, Universitas Indonesia, ID

Program Chairs:

• H. B. Santoso, Universitas Indonesia, ID • W. Jatmiko, Universitas Indonesia, ID • A. Buono, Institut Pertanian Bogor, ID

• O. E. Herwindiati, Universitas Tarumanegara, ID

Section Chairs:

• K. Wastuwibowo, IEEE Indonesia Section, ID

Publication Chairs:

• A. Wibisono, Universitas Indonesia, ID

Program Committees :

• A. Azurat, Universitas Indonesia, ID

• A. Fanar, Lembaga llmu Pengetahuan Indonesia, ID • A. Kistijantoro, Institut Teknologi Bandung, ID • A. Purwarianti, Institut Teknologi Bandung, ID • A. Nugroho, PTIK BPPT, ID

• A. Srivihok, Kasetsart University, TH

• A. Arifin lnstitut Teknologi Sepuluh Nopember, ID • A. M. Arymurthy, Universitas Indonesia, ID • A. N. Hidayanto, Universitas Indonesia, ID • B. Wijaya, Universitas Indonesia, ID • B. Yuwono, Universitas Indonesia, ID • B. Hardian, Universitas Indonesia, ID • B. Purwandari, Universitas Indonesia, ID • B. A. Nazief, Universitas Indonesia, ID • B. H. Widjaja, Universitas Indonesia, ID • Denny, Universitas Indonesia, ID • 0. Jana, Computer Society of India, IN • E. Gaura, Coventry University, UK • E. Seo, Sungkyunkwan University, KR • F. Gaol, IEEE Indonesia Section, ID

ICACSIS 2014

ICACSIS

2014

• H. Manurung, Universitas Indonesia, ID • H. Suhartanto, Universitas Indonesia, ID • H. Sukoco, Institut Pertanian Bogor, ID • H. Tolle, Universitas Brawijaya, ID • I. Budi, Universitas Indonesia, ID

• I. Sitanggang, Institut Pertanian Bogor, ID • I. Wasito, Universitas Indonesia, ID • K. Sekiyama, Nagoya University, JP

• L. Stefanus, Universitas Indonesia, ID • Marimin, Institut Pertanian Bogor, ID • M. T. Suarez, De la Salle University, PH • M. Fanany, Universitas Indonesia, ID • M. Kyas, Freie Universitat Berlin, DE • M. Nakajima, Nagoya University, JP • M. Widyanto, Universitas Indonesia, ID • M. Widjaja, PTIK BPPT, ID

• N. Maulidevi, Institut Teknologi Bandung, ID • 0. Sidek, Universiti Sains Malaysia, MY • 0 . lawanto, Utah State University, US • P. Hitzler, Wright State University, US • P. Mursanto, Universitas Indonesia, ID

• S. Bressan, National University of Singapore, SG • S. Kuswadi, Institut Teknologi Sepuluh Nopember, ID • S. Nomura, Nagaoka University of Technology, JP • S. Yazid, Universitas Indonesia, ID

• T. Basaruddin, Universitas indonesia, ID

• T. Hardjono, Massachusetts Institute of Technology, US • T. Gunawan, International Islamic University Malaysia, MY • T. A. Masoem, Universitas Indonesia, ID

• V ｾ＠ Allan, Utah State University, US

• W. Chutimaskul, King Mokut's University of Technology, TH • W. Molnar, Public Research Center Henri Tudor, LU

• W. Nugroho, Universitas Indonesia, ID • W. Prasetya, Universiteit Utrecht, NL

• W. Sediono, International Islamic University Malaysia, MY • W. Susilo, University of Wollongong, AU

• W. Wibowo, Universitas Indonesia, ID

• X . Li, The University of Queensland, AU

• Y. Isal, Universitas Indonesia, ID • Y. Sucahyo, Universitas Indonesia, ID

Local Organizing Committee:

• A. Y. Utomo, Universitas Indonesia, ID • Aprinaldi, Universitas Indonesia, ID • D. Eka, Universitas Indonesia, ID • E. S. Utama, Universitas Indonesia, ID • E. Cahyaningsih, Universitas Indonesia, ID • H. A. Wisesa, Universitas Indonesia, ID • H. R. Sanabila, Universitas Indonesia, ID • K. M. Kurniawan, Universitas Indonesia, ID • M. Mega, Universitas Indonesia, ID

• M. Ni'ma, Universitas Indonesia, ID

ICACSIS 201-!

ICACSIS

2014

• M. I. Suryani, Universitas Indonesia, ID

• M. Soleh, Universitas Indonesia, ID

• M. Pravitasari, Universitas Indonesia, ID

• N. Rahmah, Universitas Indonesia, ID

• Putut, Universitas Indonesia, ID

• R. Musfikar, Universitas Indonesia, ID

• R. Latifah, Universitas Indonesia, ID

• R. P. Rangkuti, Universitas Indonesia, ID

• V. Ayumi, Universitas Indonesia, ID

ICACSIS 201-t

/CACSIS 2014

CONFERENCE INFORMATION

Dates

Organizer

Venue

Official language

Secretariat

October 181

h (Saturday) - October 19th (Sunday) 2014

Faculty of Computer Science, Universitas Indonesia

Department of Computer Science, lnstitut Pertanian Bogor

Faculty of Information Technology, Universitas Tarumanegara

Ambhara Hotel

Jalan lskandarsyah Raya No. 1, Jakarta Selatan, OKI Jakarta, 12160, Indonesia

Phone : +62-21-2700 888

Fax : +62-21-2700 215

Website: http://www.ambharahotel.com/

English

Faculty of Computer Science, Universitas Indonesia

Kampus UI Depok

Depok,16424

Indonesia

T: +62 21786 3419 ext. 3225

F: +62 217863415

E: icacsis@cs.ui.ac.id

W: http://www.cs.ui.ac.id

Conference Website http://icacsis.cs.ui.ac.id

ICACSIS 2014

ICACSIS 2014

PROGRAM SCHEDULE

Saturday, October 1stti, 2014-CONFERENCE

Time Event Event Details Rooms

08.00-09.00

Registration

Opening from the Dean of Faculty of

09.00-09.30

Opening Computer Science Universitas Indonesia/General Chair of ICACSIS

2014

Or. Ir. Basuki Yusuf lskandar, MA Dirgantara

09.30-10.15

Plenary Speech I from Ministry of Communication and

Room,

2nd

Floor

Information

10.15-10.30

Coffee Break

Prof. Dame Wendy Hall

10.30-11.15

Plenary Speech II _{from Southampton University, UK}

11.15.12.30

Lunch

12.30-14.00

Parallel Session I : See Technical (Parallel Session I

Elang, Kasuari,

Four Parallel Sessions Schedule) Merak, Cendrawasih

Room, Lobby Level

14.00-15.30

Parallel Session II : See Technical (Parallel Session II

Elang, Kasuari,

Four Parallel Sessions Schedule) Merak, Cendrawasih

Room, Lobby Level

15.30-16.00

Coffee Break

16.00-17.30

Parallel Session Ill : See Technical (Parallel Session Ill

Elang, Kasuari,

Four Parallel Sessions Schedule) Merak, Cendrawasih

Room, Lobby Level

17.30-19.00

Break

19.00-22.00

Gala Dinner Dinner, accompanied by music Dirgantara Room,

performance and traditional dances

2nd

Floor

ICACSIS 201-l

ICACSIS 2014

Sunday, October 19th, 2014-CONFERENCE

Time Event Event Details Rooms

08.00-09.00

Registration

Ors. Harry Waluyo, M .Hum

from Directorate General of Media,

09.00-10.00

Plenary Speech Ill Design, Science

&

Technology Based

Creative Economy Dirgantara Room,

2nd

Floor

10.00-10.15

Coffee Break

Prof. Masatoshi Ishikawa

10.15-11.30

Plenary Speech IV from University of Tokyo, JP

11.30-12.30

Lunch

Parallel Session IV : See Technical (Parallel Session IV Elang, Kasuari,

12.30-14.00

Four Parallel Sessions Schedule) Merak, Cendrawasih

Room, Lobby Level

Parallel Session V: See Technical (Parallel Session V Elang, Kasuari,

14.00-15.30

Four Parallel Sessions Schedule) Merak, Cendrawasih

Room, Lobby Level

15.30-16.00

Coffee Break

16.00-16.30

Closing Ceremony Awards Announcement and Photo Dirgantara Room,

Session

2nd

Floor

ICACSIS 201-1

Table of Contents

Welcome Message from General Chairs

Welcome Message from Dean of Faculty of Computer Science University of Indonesia

111

Committees

v

Conference Information

1x

Program Schedule

x

Table of Contents

x111

Computer Networks, Architecture

&

High Performance Computing

Multicore Computation of Tactical Integration System in the Maritime Patrol Aircraft

using Intel Threading Building Block

Muhammad Faris Fathoni, Bambang Sridadi

lmmersive Virtual 3D Environment based on 499 fps Hand Gesture Interface

Muhammad Sakti Alvissalim

Improve fault tolerance in cell-based evolve hardware architecture

Chanin Wongyai

A New Patients' Rights Oriented Model of EMR Access Security

YB Dwi Setianto. Yustina Reino W. Utami

Element Extraction and Evaluation of Packaging Design using Computational Kansei

Engineering Approach

ｔ｡ｺｾｦｩｫ＠

Djatna. Fajar Munichputranto. Nina Hairiyah. Elfira Febriani

Integrated Information System Specification to Support SSTC

Ahmad Tamimi Fadhilah. Yudho Giri Sucahyo. Denny

A Real Time Simulation Model Of Production System Of Glycerol Ester With Self

7

13

19

25

33

Optimization

39

bran Aang Soenandi.

ｔ｡Ｑｾｦｩｫ＠

Djatna

ICACSIS 2014

Development of University of Indonesia Next Generation Firewall Prototype and

Access Control With Deep Packet Inspection

Harish Muhammad Nazief. Tonny Adhi Sabastian. A/fan Presekal. Gladhi Guarddin

Reliable Data Delivery Mechanism on Irrigation Monitoring System

Junaidy Budi Sanger. Heru Sukoco. Satyanto Saptomo

E-Government

Evaluation on People Aspect in Knowledge Management System Implementation: A

Case Study of Bank Indonesia

Handre Duriana. Ida Ayu Trisnanty. and Putu Wuri Handayani

Government Knowledge Management System Analysis: Case Study Badan

Kepegawaian Negara

Elin Cahyaningsih, Sofiyanti lndriasari, Pinkie Anggia, Dana Indra Sensuse,

Wahyu Catur Wibowo

Shared Service in E-Govemment Sector: Case Study of Implementation in Developed

Countries

Ravi/ca Hafizi, Suraya Miskon. Azizah Abdul Rahman

GIS-based DSS in e-Livestock Indonesia

Arie/ Ramadhan, Dana Indra Sensuse. Muhammad Octaviano Pratama. Vina

Ayumi. Aniati Murni Arymurthy

Influence Of Presidential Candidates E-Campaign Towards Voters In 2014 Presidential

45

51

55

65

73

81

V'

Election In Republic Of Indonesia

87

Yani Nurhadryani, Anang Kurnia, lrsyad Satria

Information Security Risk Management Planning: A Case Study at Application Module

of State Asset Directorate General of State Asset Ministry of Finance

93

Sigit Prasetyo. Yudho Giri Sucahyo

Campaign 2.0: Analysis of Social Media Utilization in 2014 Jakarta Legislative Election

99

v

Dean Apriana Ramadhan

ICACSIS 201-l

Towards Maturity Model for E-Govemment Implementation Based on Success Factors

105

Darmall'an Baginda Napitupulu

The Critical Success Factors to Develop an Integrated

Application of Tuna Fishing Data Management in

Indonesia

Devi Fitrianah. Nursidik Heru Praptono. Achmad Ni=ar Hidayanto, Aniati Murni

Arymurthy

A Conceptual Paper on ICT as National Strategic Resources toward National

Competitiveness}{Basuki Yusuf lskandar and Fadhilah Mathar

Basuki Yusuf Jskandar and Fadhilah Mathar

Enterprise Computing

Quality Evaluation of Airline's E-Commerce Website, A Case Study of AirAsia and

Lion Air Websites

Farah Shajira Effendi, lka Aljina

Hotspot Clustering Using DBSCAN Algorithm and Shiny Web Framework

Karlina Khiyarin Nisa

Analysis of Trust Presence Within Commerce Websites: A Study of Indonesian

E-Commerce Websites

Muhammad Rifki Shihab. Sri Wahyuni. Ahmad Ni=ar Hidayanto

The Impact of Customer Knowledge Acquisition to Knowledge Management Benefits:

111

117

123

127

131

A Case Study in Indonesian Banking and Insurance Industries

137

Muhammad Rlfki Shihab, Ajeng Anugrah lestari

A System Analysis and Design for Sorghum Based Nano-Composite Film Production

143

Belladini LO\·e(v.

ｔ｡ｺｾｦｩｫ＠

Djatna

Moving Towards PCI DSS 3.0 Compliance: A Case Study of Credit Card Data Security

Audit in an Online Payment Company

149

Muhammad Rifki Shihab. Febriana Misdianti

ICACSIS 2014

An Analysis and Design of Traceability In FrozenVanname Shrimp Product based on _

Digital Business Ecosystem

155

Taufik Djatna and Aditia Ginantaka

Bayesian Rough Set Model in Hybrid Kansei Engineering for Beverage Packaging

Design

163

Azrifirn

1

an and Taufik Djatna

Predicting Smart Home Lighting Behaviour from Sensors and User Input using Very

Fast Decision Tree with Kernel Density Estimation and Improved Laplace Correction

169

Ida Bagus Putu Peradnya Dinata, and Bob Hardian

Visual Usability Design for Mobile Application Based On User Personality

175

J

Riva Aktivia, Taufik Djatna. and Yani Nurhadryani

Formal Method Software Engineering

Interaction between users and buildings: results of a multicreteria analysis

181

Audrey Bona and Jean-Marc Salotti

Digital watermarking in audio for copyright protection

187

Hemis Mustapha and Boudraa Bachir

An Extension of Petri Network for Multi-Agent System Representation

193

Pierre Sauvage

Enhancing Reliability of Feature Modeling with Transforming Representation into

Abstract Behavioral Specification (ABS)

199

Muhammad lrfan Fadhillah

Making "Energy-saving Strategies": Using a Cue Offering Interface

205

Yasutaka Kishi. Kyoko Ito. and Shogo Nishida

Extending V-model practices to support SRE to build Secure Web Application

21 I

Ala Ali Abdulrazeg

ICACSIS 201-l

Social Network Analysis for People with Systemic Lupus Erythematosus using R4

Framework

Arin Karlina and Firman Ardiansyah

Experiences Using Z2SAL

Maria Ulfah Siregar. John Derrick, Siobhan North. and Anthony

J. H.

Simons

Information Retrieval

Relative Density Estimation using Self-Organizing Maps

Denny

217

223

231

Creating Bahasa Indonesian - Javanese Parallel Corpora Using Wikipedia Articles

237

Bayu Distiawan Trisedya

Classification of Campus E-Complaint Documents using Directed Acyclic Graph

Multi-. . .

Class SVM Based on Analytic Hierarchy Process

245

Imam Cholissodin. Maya Kurniawati, lndriati, and Issa Arn•ani

Framework Model of Sustainable Supply Chain Risk for Dairy Agroindustry Based on

Knowledge Base

253

Winnie Septiani. Marimin. Yeni Herdiyeni. and Liesbetini Haditjaroko

Physicians· Involvement in Social Media on Dissemination of Health Information

259

Pauzi Ibrahim Nainggolan

A Spatial Decision Tree based on Topological Relationships for Classifying Hotspot

Occurences in Bengkalis Riau Indonesia

265

Yaumil Miss Khoiriyah. and /mas Sukaesih Sitanggang

Shallo\v Parsing Natural Language Processing Implementation for Intelligent Automatic

Customer Service System

271

Ahmad Eries Antares. Adhi Ku.madi. and Ni Made San·ika lnrari

SMOTE-Out. SMOTE-Cosine. and Selected-SMOTE: An Enhancement Strategy to

Handle Imbalance in Data Level

277

ICACSIS 2014

Fajri Koto

Adaptive lnfonnation Extraction of Disaster Information from Twitter

Ralph Vincent

J.

Regalado, Jenina

L.

Chua, Justin

L.

Co, Herman C. Cheng Jr ..

Angelo Bruce

L.

Magpantay. and Kristine Ma. Dominique

F.

Ka/aw

283

Citation Sentence Identification and Classification for Related Work Summarization

287

Dwi Hendratmo Widyantoro. and /maduddin Amin

Experiments on Keyword List Generation By Term Distribution Clustering For Social

Media Data Classification

293

Wilson Fonda and Ayu Punt•arianti

Tourism Recommendation Based on Vector Space Model and Social Recommender

Using Composite Social Media Extraction

299

/

Husnul Khotimah. Taujik Djatna. and Yani Nurhadryani

Leaming to Rank for Determining Relevant Document in Indonesian-English Cross

Language lnfonnation Retrieval using BM25

305

Syandra Sari and Mirna Adriani

Pattern

Recognition &

Image Processing

Forecasting the Length of the Rainy Season Using Time Delay Neural Network

Agus Buono. Muhammad Asyhar Agmalaro. and Amalia Fitranty Almira

Hybrid Sampling for Multiclass Imbalanced Problem: Case Study of Students'

Perfonnance Prediction

Wanthanee Prachuabsupakij and Nuanwan Soonthornphisaj

Multi-Grid Transfonnation for Medical Image Registration

Porawat Visutsak

Model Prediction for Accreditation of Public Junior High School in Bogor Using Spatial

Decision Tree

£11da11g Purnama Giri and Aniati Murni AIJ'l11Urthy

311

317

323

329

ICACSIS 201-1

Application of Decision Tree Classifier for Single Nucleotide Polymorphism Discovery

from Next-Generation Sequencing Data

335

Muhammad Abrar lstiadi, Wisnu Anania Kusuma, and I Made Tasma

\I

Alternative Feature Extraction from Digitized Images of Dye Solutions as a Model for

Algal Bloom Remote Sensing

341

Roger Luis Uy, Joel llao, Eric Pzmzalan. and Prane Mariel Ong

Iris Localization using Gradient Magnitude and Fourier Descriptor

Stewart Sentanoe, Anto

S

Nugroho, Reggio N Hartono, Mohammad Uliniansyah,

and Meidy Layooari

Multiscale Fractal Dimension Modelling on Leaf Venation Topology Panern of

Indonesian Medicinal Plants

Aziz Rahmad, Yeni Herdiyeni, Agus Buono, and Stephane Douady

347

353

Fuzzy C-Means for Deforestration Identification Based on Remote Sensing Image

359

Selia Darmawan Afandi, Yeni Herdiyeni, and lilik B Prasetyo

Quantitative Evaluation for Simple Segmentation SVM in Landscape Image

365

Endang Purnama Giri and Aniati Murni Arymurthy

Identification of Single Nucleotide Polymorphism using Support Vector Machine on

Imbalanced Data

371

Lai/an Sahrina Hasibuan

Development of Interaction and Orientation Method in Welding Simulator for Welding

Training Using Augmeneted Reality

377

Ario Sunar Baskoro. Mohammad A=11'ar Amat. and Randy Pangestu Kuswana

Tracking Efficiency Measurement of Dynamic Models on Geometric Particle Filter

using KLD-Resampling

381

Alexander A S Gunawan. Wisnu .latmiko. Vektor Delranto.

F.

Rachmadi. and

F.

Jm·an

Nonlinearly Weighted Multiple Kernel Learning for Time Series Forecasting

Agus Widodo. Indra Budi. and Belall'ati Widjaja

385

ICACSIS 201.J

Distortion Analysis of Hierarchical Mixing Technique on MPEG Surround Standard

391

lkhll'ana E/jitri. Mumuh Muharam. and Muhammad Shobirin

A Comparison of Backpropagation and L VQ : a case study of lung sound recognition

397

Fadhilah Syafria. Agus Buono, and Bib Pan1hum Silalahi

ArcPSO: Ellipse Detection Method using Particle Swarm Optimization and Arc

Combination

Aprinaldi. Jkhsanul Habibie, Robeth Rahmatullah, and A. Kurniawan

3D Virtual Pet Game "Moar" With Augmented Reality to Simulate Pet Raising

Scenario on Mobile Device

C/iffen Allen, Jeanny Pragantha, and Darius Andana Haris

Automatic Fetal Organs Segmentation Using Multilayer Super Pixel and Image Moment

Feature

R. Rahmatullah, M Anwar Masum , Aprinaldi.P. Mursanto, B. Wiweko. and Herry

Integration of Smoke Dispersion Modeling with Earth's Surface Images

A. Sulaiman. M Sadly

403

409

415

423

Performance of Robust Two-dimensional Principal Component for Classification

429

Diah

£.

Hern

1

indiati, Sani M Isa. and Janson Hendryli

Pareto Frontier Optimization in Soccer Simulation Using Normalized Normal

Constraint

Darius Andana Haris

Fully Unsupervised Clustering in Nonlinearly Separable Data Using Intelligent Kernel

K-Means

Teny Handayani and Ito Wasito

Robust Discriminant Analysis for Classification of Remote Sensing Data

Wina. Dyah

£.

Henrindiati. and Soni

Al

Isa

Automatic Fetal Organs Detection and Approximation In Ultrasound Image Using

437

445

449

Boosting Classifier and Hough Transform

455

ICACSIS 201-1

M.

Anll'ar Ma'sum. Wisnu Jatmiko.

M.

Iqbal Tawakal. and Faris Al Aft/

Particle Swarm Optimation based 2-Dimensional Randomized Hough Transform for

Fetal Head Biometry Detection and Approximation in Ultrasound Imaging

463

Putu Satwika. Jkhsanul Habibie.

M.

Anll'ar Ma'sum. Andreas Febrian. Enrico

Budianto

Digital Library

&

Distance Learning

Gamified E-Leaming Model Based on Community of Inquiry

Andika Yudha Utomo, Aftfa Amriani, A/ham Fikri Aji, Fat in Rohmah Nur

Wahidah. and Kasiyah

M.

Junus

Knowledge Management System Development with Evaluation Method in Lesson Study

Activity

Murein Miksa Mardhia. Annein Z.R. Langi, and Yoanes Bandung

Designing Minahasa Toulour 3D Animation Movie as Part of Indonesian e-Cultural

469

477

Heritage and Natural History

483

Stanley Karouw

Leaming Content Personalization Based on Triple-Factor Learning Type Approach in

E-leaming

Mira Suryani. Harry Budi Santoso. and Zainal A. Hasibuan

Adaptation of Composite E-Leaming Contents for Reusable in Smartphone Based

489

Leaming System

497

Herman Tolle. Kohei Arai. and A1yo Pinandito

The Case Study of Using GIS as Instrument for Preserving Javanese Culture in a

Traditional Coastal Batik, Indonesia

503

Tji heng Jap and Sri Tiatri

..

ICACSIS 201 4

Framework Model of Sustainable Supply Chain Risk

for Dairy Agroindustry Based on Knolvledge Base

Winnie Septiani

1

•

Marimin' . Yeni

ｈ･ｲ､ｩｾ･ｮｩ Ｒ＠

Liesbetini Haditjaroko

1

1

Department <f.--1.groindustrial Tt!ch110/ogy·. Fucult_r vf.--l.grirn/111ra/ Tt!ch110/vgy, Bogor

.--1.gricul!ural C'11irersi1y

: Departme/1/ of Comp II/er Sciem:e. Faculty of Alathematics and .\"atural Sciences. Bog<n·

.--l.grirn/111ral Unirersity

Camous /PB Darmaea. PO Box

l20.

Bmwr

16002.

!11do11esia

Email:

ｷｩｮｮｩ･｟ｳ･ｰｴｩ｡ｮｩ Ｇ ｾｹ ｡ｨｯｯ Ｎ ｣ｯｭ ［＠

marimin@ipb.ac.id:

ｹ･ｮｩＮｨ･ｲ､ｩｹ･ｮｩｾｩｰ｢Ｎ｡｣ Ｎ ｩ､Ｚ＠

I iesbetirfrµ' ipb.ac. id

.·lhs1rnc1-The objective of this paper was to develop a framework model for sustainable suppl)· chain risk for dairy ｩｮ､ｵｳｴｾ ﾷ＠ based on knowledge

base. It presented a conceptual framework with

integrated risk supply chain and knowledge base systems. The critical point of dairy located on the product which has the characteristic easy damage. Risk-damaged dairy contaminated with bacteria due to improper handling of dairy. Risk occurred in each activity in the supply chain network ranging from farmer. cooperative and dairy processing _industry. The structured ap1>roarh of supply chain risk divided into the phases of risk

identification, risk measurement and risk

assessment, risk evaluation and risk mitigation and contingency plans; ;ind risk control and monitoring s.\·stem based on kno'' ledge base system. Adding h'.nowledge base component to risk supply chain will produce the following process: knowledge base risk rapture, knol\ ledge base risk discoYer)', knowledge base risk examination, knowledge base risk sharing. knowledge base risk evaluation and knowledge base risk repositor)·. The relationship between risk factor, risks and their consequences are represented on Failure l\lode and Effect

Anal)·sis (F:\IEA) and llierarrhical Risk

Breakdown Structure (II RBS). Likelihood of risk C\'ent occurring. the Incl of dependence bet\\ een risks and severity of risk event are quantified using linguistic 'ariables and fuzz)· logic. The proposed S,\'Stem was designed b)" Intelligent Derision Support S,\·srem (IDSS). The design of this model was able to improve lhe effectiveness of decision-making with regard to the organization of knol\ ledge. storage and sharing of knowledge in the agro-indust1)· suppl)• chain risks ､｡ｩｾ ﾷ ﾭ

h'.ep\Ords : suppl)· chain risk, dai1)· agroindust1·,\. fuzzy logic. knowledge hase

R

i'k management Jctinl·J ｡ｾ＠ an effort tn rcJucc the risk anJ minimi/c 1hc 11,sscs ari,ing frnm

ｵｮｮＺｲｬ｡ｩｮｬｾ＠ ｲｩｾｉＮＮ＠

11.lj.

ｒｩｾｉＮＮ＠ ;rnd ｵｮ｣｣ｲｬ｡ｩｬｬｬｾ＠ 11n ｳｵｰｰｬｾ＠

chain risk nf Jaii: ohwincJ fn>m the charal'lcris1ics of

p..:rishabk Llaii: pwJuclS

I

181

and

1271.

This risk aris<;"s from th..: acti\ ｩｴｾ＠ of a S<;"ries of ｡ｧｲｯＭｩｮ､ｵｳｴｲｾ＠

suppl) chain activities in dairy from farms. shipping Llaii: to coop<;"rati\ ..:s. cooperati\..: storag..: and J..:li\ ..:1: of ､｡ｩｲｾ＠ in th<;" coopaati\..: to Dair) Proc..:ssing Industry (IPS ). Th..: risk of dai1: supply chain thut has a major impact is th..: risk of <lair) that contuminat<;"LI

｢ｾ＠ bact..:rial of antibiotics.

The impkm..:ntation of ｳｵｰｰｬｾ＠ chain risk

manag<;"m<;"nt can impru' e th..: quantit) and qualit) of knowkdg..:. r..:ducing the chanc..:s or risks and risk impacts

161.

According to

1111

th..:r"' is a strong inllu..:nc..: or suppl) chain risk manag..:mcnt to continuous impnl\·..:m..:nt in th..: suppl) chain proc..:ss.

Data and information relatcJ to the achin ..:111..:nt or proJuction and qualit) of ｊ｡ｩｲｾ＠ available and som..: have hc..:n well Llocum..:ntcd in th..: coop..:rath..: anJ th..: industry. I lown-..:r. th..:sc data hm·..: not b..:..:n anal) zed further to sen..: as a useful source orkml\1 kt.lg..: for ull stak..:holJers in th..: dair) supply chain n..:t\\ ork. R..:scarch has h..:..:n don..: hy

121. I 51. 118 I

and

121q

only at th..: stage id..:nt i ｉｾ＠ and ｡ｮ｡ｬｾ＠ z..: th..: risks and problems that occur in the Jai1: supply chain. R..:scarch has not h..:..:n Linne in a compn:hcnsin: tr..:<1tm..:nt plan and its clfrct on th..: m era II suppl) chain p..:rlormanc..:. lmpro,·ing prodlll:tion anJ quality or Jairy is n<.:CCSS<Jr) to ｩｮ｣ｲＮＮＺ｡ｾｃ＠ access IO information and knowledge sharing ｳｰＮＮＺ｣ｩｴｩ｣｡ ｬｬｾ＠ in unLl..:rstanding th..: characteristics of th..: Llain i11dust1Y. JairY

production and marketing S) stems

f

ｾＸＱＮ＠

.

.

This ｳｴｵ､ｾ＠ is in line "ith the parlncrship program Llai1: larmcrs and sustainable food sccurit) faciliti..:s arc r..:alizcd through a partnership bet\\ ccn the Frisian Flac . the Dutd1 en' crnmcnt. the c1n crnm..:nt or

ｬｮｊｾｮ｣ｳｩ｡

Ｎ＠ ｬｫｩｮｊｵｮ

ｾｧ＠

Callie

ｬｬｲＮＮＺ｣ｊ｣Ｑ

ｾＧ＠

Coopcrati\ c '\orth (f.,'.PSBI 1

) I cmhanl! anJ Ba11Junc Southern

Ca11lc lln:cdcrs Cnnpcra1i, c ( J.,'.l'BSl l';ncakncan . l"his partnership program i.; ha<;Cd Oil th;CC ｬｬｾ｡ｩｮ＠

pillars of ｱｵ｡ｬｩｴｾ＠ imprmcmcnt 1hn1ugh the

nptimi1ation and impnncmcnt \1ilk Collection Point 1\1CP1. an inrn:as..: in the ｱｵ｡ｬｩｴｾ＠ ;md ｱｵ｡ｭｩｴｾ＠ or l..111>\\ ledge to fonncr> anJ Llaii: l'1h'l'..:rati\ c, anJ incrca,cd cmpln1 cc proJucti\ it\ sustainahk <lair> fam1 business.

ｔｬ［｣

｟ ｴｬｾｩｲ､＠

main piliar in the partn..:rshii>

ICACSIS 2014

program is dose!) related to the research to be condu.:ted.

This re search will de' elop the integration or kno11 ledge management 11 ith a sustainable suppl) chain ri sk in the dair) agro-industry. Aspects of the sustainable supply chain consists of em·ironmental. social and economic [7]. In this study a model or suppl) chain risk or dair) agro-industry base on kno11 ledge base 11ill be designed as a S) stematic

S) stem to regul ate the organization of kno11 ledge

which 11 ill be used to ｩ､･ｮｴｩｦｾ Ｎ＠ anal) ze and risk management plans that spe1.: it) the impat:t on suppl) diain risk rnn be minimi zed through the dair) agro-industr) intc:gration of suppl) chain risk management and kno11 ledge base. The design of this model is expected to imprO\ e the effecti' eness or decision-making with regard to the organization or kno11 ledge. storage and sharing or knowledge in dair) agro-industr) suppl) chain ri sks. His influence on the performance of supply diain risk can be measured quantitatively that will assist stakc:holders in deci sion making.

II. METHODS

. ·/. Framework

According to [211. the risk of suppl) chain

influenced by avoidable risk exposure and

unarnidable risk exposure. In this study. the ri sk 11 ill be identified based on both . There rm.: three components to be considered in the design or models i.e. perfonnance profile. risk profile and ri sk exposure. Frame11ork of the integration of suppl) chain risks with the knowledge base can be seen in Figure I.

Rosi< FaC!or • category of risk

supply chain

· dimension of sustainability supply chain environment charactenshc analysis Supplycham conrigur ation f()f

dairy industry

Supporting tool .

I kjentification risk i

of sustainab'e

supply chain

New Fuuy Multi Critena

, - - . Fuzzy Entropy

Analy51S nsk of

sustainable

supply chain

M1hgahon nsk of sustainable supply

chain

ｓｵｳｴ｡ｭ｡ｾ･＠ supply

chain nsk in dairy

1ndustn based on

knowledge base

System design by

fnfehgence Deos-on Support System 11DSSi

' -Fuzzy Multt. Attibutte

1

U/l/tty The<;Yy(MAUT)

F ramewal< know4edge

based · supply chain risk

Knowledge based · Supply Cham Risk

Capture

Knowledge based •

Supply Cham Risk

Discovery

Knowledge based •

Supply Cham R1sk Examma/IOn

Knowledge base ·

Supply Cham R1sk Shanng

Knowledge base ·

Supply Cham E valuafl(Jn

Knowledge base ·

I 1;: ＱＭｲ｡ｭｾＢＧＢｫ＠ moJd o f ＧＢｾ ｴ｡ｲｮ｡ｨｫ＠ ｾｵｰ ｰｨ＠ cha111 fl'k for Jia" ｡ｾｲｯＱＱＱ､ＱＱｾ ｴ ｲＺＭ baS<'J on kmm ｫｊｧｾ＠ "'"''

Fra1111:11nr" 11111d.:I of sust:iinahlc suppl) d1;:iin risk li1r dair) agroindustr) has.:d on "mm lcdg.: has.: ''ill he d.:,·elnp.:d h) thr.:.: main phases. that is

I. Phase I. d.:terminati<m nf ri sks factors

lkt.:rminati on or risk fat:tnrs using full) .:ntrnp) and Fun) \lulti-:\ttrihute 1 ·tilit) (F\IAL I)

171

to

.:1 ;ilu:n.: ;111d ..:1>111p;1rl' th.: ' l"t:1in:1hk 'u ppl) diain ri sk 1;t<.:l<>rs.

;1. I k1em1 i11;11i,111 ,,f ri'" 1;1ct1>rs 1h;t1

,lbtain-ibk , uppl) ch.iin fl>r indu,1r:.

h. C11llccting lhL· data.

inlluenee the dair:

agro-.: . lkterminati1•n ,,f ri'" faL·tpr-; ''eight using fut I) entr111': .

d. I:"duatilln 11f ri'" 1;1cll>rs that dc·tnmin.:J by

I t1 11: \lulti-.\ttr ibu te l tili1: 11· \l.\l I l.

2. l'ha>c' 2. nwJ el de1 el<l!'111elll

'1odel I. llll•,kl risl.. idL'lllili..::11i1•n 11f 'ustainabk ,u ppl) dwin in d;1ir: agn•industr: .

Ris" idc111iliL«l1i11n <111 a1.:ti1 itie, 111 farms. c·11operati1 e and dair: pn11.:L',.;i11g industril', using l·ailurl' \h>,k an d LlkL't .\nal)'i' ti \IL :\l Jan I lil'rard1i1.:al Ris" llrl'a"d1 111 n StructurL· (I IRBS). I· \IL ,.\ i, U, L'd to dc·t.:r111in.: the , 1agl'' prot.:e,ses. p11tL'lllial foilurl' flllld<: S. f;li lur<: efl°.:L't'. plltt:lltial. p11telllial 1.:ausl', .. \ l IRllS has hec·n de\ c·l11pc·d and the strut:turc ,,r thi s pnn idc·s the hasi, for a 'tratili.:d 1.:lassilit.:ali1111 ofri,b and dl'1L'iopmcnt of a 1111111.:nclattirc for de"-rihing tr;111spor1a1iun risk. '1odel 2. \h1dcl ri s" anal) sis or ,ustai nahle suppl: t.:hain in dair: agroindustr: .

l.ik.:liho1>d or risk e\ L'tll on:urring. th e· k\ L'I nf dep.::ndL·n1:.: bct\1 ecn risb and ' L'\ nit: of risk c1 cnt art.: quamif"ied u,ing lingui, tit.: 'ariahks and fuu.: logic.

\loclel 3. :\h•dt.:I ri,k 111itigatiun of ,ustainahlc ,uppl: chain in d:1ir: agroi11d1is1r:.

Ri s" 111anagc·111cnl ,1ratc·gic·, It> he tksigncd r.:krrillg . to titL• gn>uping or ri,I., fll<lfl'1gCl11enl , 1ralL'git.:s

14 I ''

hi..:h 1.:onsi,h of lin1r group': risk "' oidant:L'. ri '" rt.:duc tion. ri,I., transli:r ( tran ,Ji.-rring ri'" land ri'" rctcnt i1111 (11\\ n ri.;k ).

:\II three modcb 11 ill ht.: the· ha'i' "n'"' kdgc liir de.;igning the· prolot: J'L' ') ,(L'l11 or 'l"tainahlc "1 ppl: d1ain ris" pf dair: ag1"<> -i11d11, tr).

3. l'ha >c· 3. I hl' dc,ign ') 'lL'lll of 'usuin:thk "1ppl: ch ;tin ri,k 1;ir dair: agro -indthlr: based <•11 k n"11 kdgc h;hL ..

I he· ': ' lL'l11 i, dL·, ignL·d lo u>c· lnl L' lli gc·111 I kci,i1111 St11'1'"rl

S:

,1L·111 111

>S"

l.

Ii I Jut.1 < ·,,11,, ri .. 11 .111.! /null 'i'

S"11r..:c·, p( d;it;t thc·d i11 tlti , ' l11d: \\c·rc Pbtai11L·1.I

frp111 ＬＬﾷｵｮ､Ｎｴｲｾ＠ d;tl;i '<'t ll"L'c·, .111,l ＱＧｲｩＱＱＱＮＱｲｾ＠ d.1t;i '"111·c1.>. S<•11rt.:L·,

"r

'cn111d:1r' d.1 t. t 11h1 :1 i11c·d fm111 !Ill· Ｇｴｵ､ｾ＠ ,,f

lilL' raturL·. thL· rL·,1111, ,,,- /'I L"I i1•t1' ,111dic'. ,.;iL·nti lic journah ;ind d<'ClllllL'lll.1ti1111 11( ｣｜ｩＬＱｩＱＱｾ＠ ,l;it;i i11 lhL' rck ' .int i11,1i1111i111h. " ••llrl'c·, 11f !'rim.tr' d.11.1 11h1.1i11c,I fr<•lll di rc·L't ""'LT\ .tli1•11. i111L'1' ic·\\ '· 'fl!L''li<•llll.lilc' afhl ,li,c11"i1•11, "ith 1.'\J'L'rt- .11hl ,l:tkc·h,•l,kr, in thL· d:t in ＮｴｾｲＱ＾ Ｍ ｩＱＱＬｨｨｬｲＧ＠ 't'i'l'I: L·lt.tin l1L'l\111rk. I l'L°li' (ir<'llj' l)i , ct1"1"11 1l(ill1 "ilh ,.\f'l°rl' .111<! st .1k ,·h11l ,kr' 111 .1''"" .t11,! n .t! 11.1!L' thL· ri,k 1.'\l''"mc 1h .1l ＱＱＱｩｾｨｬ＠ 11c-:11r ind.tin ＮＱｾＱＱＱＭ ｩＱＱ､ＱＱＬＱｲＺ＠ '"l'l'I: ch.tin.

E \f'LTI <:.11111.· lr11111 .1c.hk111 i'' an,1 1'1.ll i..: i. •111.·r,

( f;irlllLT'. 111.!ll.1gn ,., thL· (•'•'l'L'l".ili\ l. ＱＱＱＮＱＱＱＮｬｾｌＧｉ＠ ,,,

d:tin l''"''°c" i11.:.: i11d11,ll\ >. 111 .i-ldi1i1>11 I (ii> rc,1!11, .11,·

JCACSIS 2014

also uscd to build a kno" kdgc: base: associated in dctc:rmining the t) pcs and soun:cs of risk. measuring the k\ d of ri sk and C\ aluatc: its impact and cost of handling. risk.

Sccondar: data useJ include: data ddi1er) to the dair) cooperati' es. dair) qualit) data ( specilic g.rm it) . alcohol). CO\\ mortal it) dat a (number and causes ). concc:ntratc: frc:d price: data. de1 ta on the: number of Ii' c.:stlick. dat a of amount dair) rc.:cci1 c.:d from thc farmers. data o f amount dair) delh c.:r) to IPS. data of daif) accc:ptance of cooperati1e. data of milk p<)lldc:r production quant1t1c.:s. and process failure: de1ta. Primar) data c.g. em ironmc.:nt diaractcri stic of dair) agro- industr) suppl) chain e1nd dair) agro-industr) suppl) chain configurntion.

c s:\'S/(!/11

.\/vdcli11g

The: S) stc.:m 11i11 be: designc.:d to intcgratc: thc suppl) chain risks with th..: kno\\ ledge base: ＱＲｾ＠

1-

as sho11 n in Figure 2.

Datalwe f.atalogue :

• Supply win risk

In Dalry Agro·

Industry

Knowledge Utllitation [ Knowledge Consolidation J

111. RJ:Sl ' I. IS AND DISl° lJSS IONS

..I. f)i.'/er111i11arin11 o( Risk Fae/or

Risk factor hc important think to dc.:1 c.:lop a model of sustainable suppl) chain risk in <fair) agroindustr) hasc.:d llll k1w" kdgc hasc. Risk factor 11 ill dct<:rminc hascd {Ill risk suppl) chain catcgorics

1251.

risk transpn11atinn catcgorics ｉｾ＠

I

and dimc11si1111 nt

sustainable ｳｵｰｰｬｾ＠ chain consisting or sosial.

cct11111mic and cm irnnmL·nt dimcnsion .

Risk suppl) diain catcgorics

1251

consisting or dcmanJ ri sk. dela) risk. disruptiPn risk. i111 ｣ｮｴｯｲｾ＠

risk. manufacturing fpniccsq hrcakd1n111 ri-;k .

ph) sical plant fl:apacit) I ri sks. suppl) Cprocurcmcnl) risk. S) slcm risk.;. soi crcign ri sks and transp11rtatio11 risks. Risk trans1111rtation catcgcirics ｉｾ＠

I

consisting c1f pn,duct Joss ( prndul·t pi J fi:ra gc. shipllll'llt .ictti S!lll. pir;ic) and hijacking!. pn1Juct Jamagc (cquij'mcnt accidcnts. fi•or frcight handlini:=. impn,pcr L'qu ipmL'llt L

prPduct cc,11taminati11n Cclimatc c1>11tr11J 1;1ilurc. prndu<.:1 t;imJ'L'ring ). dcli1 Cf) ddil) 1 su ppl) chain i111crrup tiP11 . ,ccmil) hrcach). L.\amrlc of dctinini:= ri,k catcgoric, al .;uppl) chain ri'k 111 Jaif) ;1gn•i11duslr) can l'c Sl'Cll in I ahlc I.

TABLE I

DEFINl:\G RISKS CATEGORIES AT SUPPLY CHA I\ RISKS IN DAIRY AGROINDL'STRY

Risk C3t ories

Di:nunJ n:tk

D1:trupl1l•n Ri;k,

Pr ... ｾ･Ｚｴ Ｚｴ＠

h1c:aJ.. .. k'\'!1

!bk::.

Defining ｲｩｾＢＭ cattgor-its The nsJ.. of m.'11 fulfillment

..,,( dc:m.1nd 111 tenm. l."•f

ｱｵ｡ｬｩｴｾ＠ anJ ｱｵ｡ｮｴｮｾ＠

The nsk of ,IJ1" ｊ｡ｭｊｾ＼＠

.. iuc: lC" nJh.ir.tl ... ·au:ioc;'.!I 0r m1!>hanJl1ng

Ri sk

Tht'.' i:0ntent 1..1f fat:i t1nd pr\'lt'.'Hh ttut d\.' lh.'t meet \\ nh ｭｊｵｳｴｾ＠ 5-t:mJJrJ

• Thl" rhJ.. 0f ｊｊＱｾ＠

C-..'ntJmma1ed ｴｩｾ＠ b1h.: tenj,l

1 TPC i:\intc:nt i.;reatt"r th.m .• m1Jh ... )n ｭｬｾ＠

• The: n :tk .. ,f ｊ｡ｮｾ＠

ｾ｜ Ｑ ｮｴｊｊｬＱＱＱＱｊｬｴ＾ｊ＠ ｢ｾ＠ JOlll'h,ltH,.'

• Ri sk:)'-''" c1..1lmterf1nt J.11r:

• The: n:ik 0fh.1\\t'.'T

Ja1r:-pr .. xllh .. ·t1..:1n • The R1sJ.. of ,.fama:;e 111

Ci.'\.llmg Ullil • lht'.' R15J.. 0 f p<""IT ､ＮＱＱｾ＠

ｾｵｊｬｮｾ＠ th.u rc:1..·e1,eJ from

i.:-1.. ... ,peratn ｣Ｚｾ Ｎ＠i:,,.rerati ' e I L' fonners

ｉｮ､ｵ ｳ Ａｾ＠ • The: R1sl.. C'f 'ar:m;; ､ｊＱＱｾ＠

l uJJn,

B. .\Ive/el risk idi.'11t{lirnrio11 o.f s11srai11ahle ｳＱＱｰｰ ｾｲ＠

chai11 in dairi· agroi11d11s1rr

Risk idcntitication is thc lirst stage in ri sk managcmc:nt . Risk idcntilication is donc b) using Fl\ IL\. which includcs thc basic clcmcnts as fol1011 s: I. Stagcs or process/input. is defined as the stagc of

thc prnccss that occurs in c:ach or thc stakcholdcrs ( farmcrs. coopcrati' cs. JPS)

2. J>otcntial failurc modc is dclin..:d as thc potcntial risk that occurs in cach proccss/spccitic acti,·it).

3. J>otcntial failurc clfrcts. dclincd as thc impact of f11llcn1ial risk if thc risk occurs in a proccss. 11 hich 11 i II bc mcusurcd b) thc 1 aluc of sc' crit).

ｾ Ｎ＠ J>otcntial causcs failure:. Jctincd as the causc ofthc rotcntial risk in cach proccss/acti' ity to bc mcasurcd h) thc ,·aluc of thc likclihood of risk (valuc prohahilil) ).

Risk idcntitication is dividcd into thrcc suh-s) stcms . 11<11111:1) forms . co\1pcrati\CS and Dair) Processing lndust1: (IJ'S) . Funhn acti1itics 11ill hc Jctcrmincd thc critical point of cach sub-S) stem. ldcntili<:illi<>n risk al dair) agroinJuslr) suppl) chain 11 ill bc Ji, idcJ into 1110 l) pc:

:\cti' it) in L'ach suppl) chilin nctwork . 11 hich wnsists of ｾ｣ｴｩ｜＠ itics on farms. coopcrati\CS and dair: pn>ccssing industr).

1\1.:ti1 itics dai1: deli' c·1:. 11 hich is Ji1 idcJ intn 11111: dair) 1kli1 cr) fn1111 farmers to <:oopcrati1 cs and dair) dcli1 cr) fn,111 cm1pcrati1 c to daif) prnccS>ing industr).

\lappini:= pnic<:ss on cach nc111ork ;It dair)

agrnindustf) ,uppl) chain :

Fam1L-r: mir,l'fil''· fc,·Jing. cilgt: sanitatio11. milking. crn1 IK·alth cht:cks. dai1: pnices>ing. Jaif) saks.

C11opcra1i1c: <:nllcctilH! milk from frm11crs. dair) qualit) chccks. dair) p;icing .

Dair) prncc·s,ing indu,11:: ;11.:ccplancc ur th .. ·

C\l-OJ'lTati1 c daif). Jair) prnci:s,ing and dair) storage IJcntitication rcsults can bc ,ccn in thc tahlc I I.

[image:22.597.38.571.20.556.2]

ICACSIS 2014

TABLL II

IDENTIFIC\TION OF SUPPLY CH . .\IN RISK HlR IJ.\IRY ,\(iR0-1'\Dl ·s rnY The Central _Actfrity Potmtial Failure Potential F11i/11re

Ris/i Mode Effect

Feedings CO\\ ｄ｡ｩｾ＠ fat content do<'S Dairy ｣ｯｭｦＩＨＮＱ ｾＱｴＱｬ ｮ＠

nol comply "ith 1he ､ｾｳ＠ no1 m;nch the quali!) standards of quality SIJnJ:irJ ,

JPS

l\·lllking ｔｨｾ＠ low produc1ion L''" dair- rroJuu, of ､｡ｩｾ＠ I an a1 ｾｲ｡ｧ･＠

of -ｉｾ＠ ｬｩｴｾｲｳ＠ ｾｲ＠ da1 J

Farmer _{ｄ｡ｩｾ＠ ｣ｯｮｴ｡ｭｭ｡ｴｾ､＠ Daif)} bc-1.."l'rll

l.'::-by ba.:tena damaged and re_1ected b1 I I'S

ｄ｡ｩｾ ﾷ＠contaminated ｄ｡ｩｾ＠ be ...

·1..'nh.:':-by bacteria Jamaged and rejected b' ＱＱＧｾ＠

Acceptance of Forgery of milk b' ｄ｡ｩｾ＠ ｾＱＮＺ ｯ ｮｨＺ ＺＭＮ＠

ｊ｡Ｑｾ ﾷ＠fatmers formers damageJ anJ rejected h' 1 l''i

QualitY of milk rnnes Lo11 dam yuaht'

ｃｯｯｾｲ｡ｴｩｮ＠ _､｡ｩｾLow handling of _{ﾷ＠to ｰｲｯ｣ｾｳｳ･､＠} 51011 _rnlue-ad<kd gn"' th 111 _of

products (product fresh da1r. diversification)

ｄ｡ｩｾ＠ ｳｴｯｲ｡ｧｾ＠ in ｄ｡ｩｾ＠ ｣ｯｮｴ｡ｭｩｮ｡ｴｾ､＠ Da1f) bewmc' .:oohng unit bi ba.:tena damaged anti

rejec1ed h1 11''> Acceptance of The high ､｡ｩｾ＠ Low abs<'fpt1on of IPS ､｡ｩｾ ﾷ＠ impons for JPS ra11 dairy fa.Tllll'fS

materials

C. .lfodel risk ｡ｮ｡ｾＱ ﾷｳ ｩｳ＠ of sustainable ｳｵｰｰｾ｜Ｇ＠ chain

in dairy agroi11dus11:v u _Nol

Risk analysis starts "ith a risk assessment acti,·ities. l\h:asurements were made on three Jimensior1s. namely the probability or occurrence of the risk (probability). the impact or the risk (Se\·erity) and non-detectability. Risk measurement is done hy using a rull.) logic approach.

important

1

Pmewiul ( °i111w.1

\\ t..•ak.n"·:-:-\)f m.ma:; ... ·nh.'llt f1..·1.•d • fl..',.'d

pr IL"I..'!:- O.Ht.' high I

f ｦ｜ＡＧｬＩｬｨＮＧｉｬｬﾷｾ＠ uf ｭｩｬｾｲｮｧ＠ i.rnJ

111111..' ｊＱＡＺＭｬｾｴｮｴＮＮＧＱＮＮＧ＠ th.'1\\1..'\..'0

mdh111g

Cl1nJ1lll'll' anJ nuJl.,111:; <.:'qtupmL'nt ｾｈｴＮＺ＠ ｵｮｨｾ＠ ｾｨＮﾷＱＱ ＱＱＮＮ Ｚ＠

l.rn11teJ a1:111:!hd1t 1 of

''a11..·r

rhl.:' Ill,.'\\ ｊ｡Ｑ ｾ＠ \:(\\\ '.'

lllj\.'l,.'11.:'J \\ llh ＼Ｎｵｴｬｬ｢ｬｬｬｬｬｃｾ＠

dul.:'1 0 illnl.:'%

Cl11..·attng fm1m .. ·1s

lnl'orptlfathrn ,,f ::-1.' \ 1.•ral

ｊ｡ ｩｲｾ＠ ｦ｡ｲｭｬ ﾷｲｾ＠

l' l1or1.·r;.i11\ l.' and human

car11al l111111a11on

lhc lack uf hi ｾＱ･ｮ･＠

cool ｩｮｾ＠ unit :md

supportrn;; l' qu1pml.'nt

(jo\ 1.:rn111l.'nl ｲｾｩ［ｵｬｊｴｬｯｮ＠

thJt (:;I\ l'S frl.'l.'dom to thl.'

I I'S IO prcll 1Je l<a11 i\lakr1 ;.1h

Low Moderate

( ·urrent tlt!sig11 cuntru/

'1..'1..'k::-h .. ' 1mrro\1..' 1hi:

｜Ｌ ＺｬｬｭｰｵｳＱＱＱＱＮＮｾｮ＠ l'f ｦｬＮＮＧｾｊ＠

S\..·t 111111.; ｭｩｬｫＮＱＱＱｾ＠

ｐＱＮＮＧｮ ｴＱｊ ｨＮ Ｚ｡ｬ ｬ ｾ＠ ｲｫｾＱｮＱ ｮ ｧ＠

1..·qu1rm\.·n1

ｌｦｦｯｮ ｾ＠ rh.·i.tn ｜｜ｾｴｴ･ｲ＠

't11'pl1c'' I h1..• ｾ ｬ｜ Ｚｫ＠ (ll\\ 1;.;. Ot.ll mdkcJ

I hi.:' l.:''\;1 111111<.lllOn

or

ＱＱＱＱＱｾ＠ ｦ＼ＮＧｲＺ［ｬﾷｲｾ＠

\:o dftl fl !' maJl.'

'.'\o ｬＮＧｦｦｯｮｾ＠ maJl.'

Pl.'fl llll1c ｲｨＺｾｲｮＱＱＱＺ［＠ of

thl.:' t:llllling urnt

ｾｯ＠ l'llon:;-; ITlJlk .

Jl'l'l.' lldlllg (Ill

go\ l'fnllll'llt ｰｯｬｩｲｾ＠

important Very

The main components : _{0 100 200 300 400 500 600 700 800 900 1000}

Fuzzy Ii cat ion

Interpretation or descriptive n:presentatic111 or the membership fun ct ions.

Graphical representation of rncmhership functillns for fuzzy linguistic rnriahlc sc\ ･ｲｩｴｾＮ＠ possihility. non-Jctectahility can be seen in Figure 2.

10

I 't-'. 3 ｬ ＼ ｱＱｲ ｬＧｾ ｬＮﾷＱＱＱｾＱＱＱＱｈＱＱｦ＠ ＱＧＱＱＱＱｾ＠ 1111 .. ﾷ ｭｴＧｬｬ ﾷ ＱｾｨＱｲ＠ funt1H1n d1;1rt:--for \1:mahk·:-ｬＱＱＱ ｾｵＱ＠ .... t1r ｨＱＱＱｾ＠ H1"k ｐｔＱｮｲＱｴｾ＠ \ umhl' 11! l<l'\ 1

I ,\Ill I 111

I H l\ I \I''-.\,S I >l.I \ 1111°' It JR \· _.\Il l \ Ill I· l >I I l't I 11 //\

I l'\1;11s1 ll s

\ ·ariahk ( lt11pu1

I inL-ui,tio \ 111 /111;1urf<1/l f

I .mi If,

.c1,.,.,

11 , .

! 111;•11rf(/ilf

I ", •/T flll/ ){)rfc/lif

l)1,111ai1h .\, ><1<.:ia1i<111 1'11111

<11 .0. I / :' l ( Ｗ ＺＧＮＲＺＧＱＱＮ Ｍ ｌｾＺＧｩｬ＠

1."\2:' .:'00.h 7:' ) I (1IHI . ＷＺＧ ＱＱＮ Ｑ_{ＱＲｾ＠ l}

PC:'.lllllll.111

I 1;; 2 <iraph1cal rcprc, cntallnn of ｭ｣ｭ｢ＮＺｲｾｨＱｰ＠ funct1<111> for fut,'\

ｨｮｧＱＱ Ｑ ｾ ｴＱｃ＠ 1 ari a hie '<?H' l 111 . f'l»'1hil111. ［ＭＮ［ＬＱｮＭｊ･ｴ｣ＮＺ Ｑ｡ｨ Ｑｨｴｾ＠

\ k111hcr,hi p I°u11i.:1ion

Inference ｓｾ＠ stem <FISl mamdani. '' ith the

ｰｯｳｳｩｴ＾ｩｬｩｴｾ＠ 11r input fl111: linguistic \itriahlcs. impact anJ c\posure. and the output is a fuu: linguistic I· RI''.': ( F uu: Risk Priorit: Number).

I hi:n to assess the le\ cl of risk' ariahlcs used Fuu:

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1d

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x

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ICACSIS 2014

r

uzz: rule base dt'snibing the nitical k\ d of risk "ith an: combinatil'n of input ' ariabks. Ruk formulatt'd in the form of linguists and t'xpresst'd in the form llf IF-Tl IE'.\ . Ruic describes all possibk combinations of input factors. Propt,sitilln \\ hich folk1" s IF calkd thc antecedent. "hik the proposition that fol km s the THE:\ calkd the conscqucnt.

Rl : IF.\is:\liTHE'.\: is'.\ ,. i=l.:U .. .. .. K x input '<1riabk ( JX'Ssibilit:. ddectabilit:. SC \ crit:) \I rnnstant linguistic antecedents ( qualitati' cl:

ddined functilln)

Y output 'ariable ( FRP'.\)

'.\! constant consequent linguistic as an cxampk :

IF Possibilit: high ａｾｄ＠ dt'tcctahilit: is Moderate

A:\ D SC\ erit: is 'er: high THE:'\ the risk of damage is' er: important.

For simplif: ing the computation of the fuzz:

representation it ma: be used non numeric

representation as suggested h:

I

1-t ].

IJ. .\lucid risk 111i1iga1iu11 uf s11s1ai11able supph ·

chai11 doirv a?,roind11s11:v

The selection of appropriate risk handling proposa ls " ·ill be determined based on the '<ilue of risk exposure and <.:ost wnsid..:rations ( Fig.-t ). In this modd "ill be collected kno\\ ledge to formulate plans. strategics and m:tions to redu<.:c the chances of risks and reduce or minimize the impact of risk. The design of this model is made up of three main stages. i.e. the C\ aluation of risi... (risk ranking and risk acccptancc). risk response planning and risk monitoring.

Poh'nt1Jl

f<lllUrl·t'l fl•ft- Potl·nt1 al

_ _ t _ _ _ . f.11lurt.1

-rote ntu l fJ1!un .. ·

-(,l l/ $C'S

Catt:guric'i

of SCR

Cr nl rr of

rl'ik

ｾ＠

\°Jr1,1hd

hllj:Ul\tJ\.:

-'('\l'rl l !

_ _ .,. pn1nlof

fllliik

ｾ＠ R1<>k

lmr:111..i1k ..__... ｾ ｒｐ ｜＠ - • Ot.fu111i1L1' 1 __.

-ｾ＠ r.1nk1nc

FRP:\ Jfh' 1

lmpril\C' mrnt

\'.n1Jhk

hn:.::ui,t r\.: _prC'\·inus Compare _FRPS \\·ith +

-non

F Ilic i111egr(//in11 o( ＯＮＮＱＱｮＱＱＭｬ ･ ＱｾｩＺ･＠ hose 1111</ .\llf1ph ·

chc1i11 r is k 111c111c1:t!<' llll'1// i11 1hc 11r n11nsed /rcmu·1u1rk

h;im<.:\\ork Knn" lcdg,· Ba'..: Risk \lanag..:mcnt !KllR\I) dcsign..:d ｮＱＱＱｾｩｳｴｩＱＱｧ＠ or:

I. Kl1RC - Kr1l1\\ kdgc !lase ｒｩｾｫ＠ Caplllre

.-\t this >tagc. the risk 01· caplllrc is d1,nc based 1111 the ｲ･ ｾ ｵｬｴｳ＠ or pre\ ious rc<;c;irch. literatur<.: <;tud: . 11bscr1 ati(>ns dir..:l·tl: In the tic Id. rclc\ ;1111 arlicles as c\plicit k11P\\ ledge and Jacit knn\\ ledge fn1111 <.:\p.:ricncc or C\p..:rb and prac1i1i1111crs in th..: dair: apo-industr: ｾｵｰｰｬＺ＠ ..:hain. I he KllRC 11utp11t is a

risk catalog of the all identified risk ranging from farms. cooperati\ es until the Dair: Prncessing lndustr: (!PS).

2. KBRD - Knowkdg..: Base Risk DisnH ..:r:

The de' elopment of tacit and explicit knO\\ ledge from data and information that alread: l!.\ist and ha\ c b..:en identified pre' iousl:. At this stage "ill find regularities. patterns or relationships "ithin a data set.

3. KBREx - Kno" ledge Base Risi... bamination Testing of risk knowkdge from the le\ d of accurac: and correctness. Elimination of risk is dctcrmin..:d based on the objectiH:s tu be achi..:,ed in the design model or knowledge management suppl: chain risk for dairy agro-industr: .

-t. KBRS - Kno\\ ledge Base Risk Sharing

The dissemination kno" kdge on th.: risk of all stakeholders <lair: agro-indus11: suppl: chain net\\ork. Stages of risk management related to sharing knm\ kdgc cir.: the stages of risk anal: sis and ri sk handling.

5. KBRE - Kno\\ ledge Base Risk Evaluation This proc..:ss will serve as a sustainable process to cancel the existing risks or idcntilication of n..:\\ risks. Performanc..: C\ aluation is r..:lated to the execution of risk and risk OYcrsight.

6. KBRR - Knowledge Bas<.: Risk R..:pository To Scncs to unify information that has b..:cn stored and s..:lect..:d to he a kno\\ ledg<.: bas.: of dair:· agro-indus11: suppl: chain risks.

7. KBR Edu - Kmm ledge Bas.: Risk Education Design implementation h) educating stak..:holdcrs dair: ｡ｧｲｮＭｩｮ､ｵｳｴｲｾ＠ ·suppl) chain . Education can he

a training or group discussion .

F. ＮｾＩ＠ -.11c111 IJ<'s ig 11

The l'rotot: pc of suppl: chain risi... 1.ksign S) stem is c:-.pccted to fa<.:ilitat..: the decisi1111 mai...ing process to determining the risk management acti1,ns that must he performed based (Ill the results of the risk a'Sl'SSmenl (Fig 5). In addition. the s: stem can als11 trace" here is the risk. its causes and ctfrcts caused if the risk \\as occurcd . ·1 hi.: S) 'tern can also displa: the anwunt of risk (risk c:-.posurc) "hi ch arc us..:d as th..: has is for Jctcrmining the risl-. management strategy .

IV . Co" n t s1 o"s .·\ " l> Kl.<. O\J\11 ." DAJ 1c '"" \lode! of susJainahlc suppl: chain risk f(,r dair: agroindustr: based on kno\\ lcdgc-hase 111ilk \\as designed as a s: stc·nwtic s: s1cr11 to r..:gulalc the· nrgani1at i11n of kmm ledge "hi ch "as us..:d tn idcnt i ｉｾ Ｎ＠

anal: 1c and detcnnine the i111pact pf ri>k 111anagcmc·nt plans so that th<.: ri ' k 11f ac n1-indu>tric·s in d.iin agn1industr: suppl: d1ai11 ｴｨ ｲｾｵ ｧ ｨ＠ Jhc integratiPn 1;( suppl) chain ri'k managcn11:11t and kmm kd t: c l'a>c

s:

stem lbscd on idrnJilicatinn and anal: sis nt" ri,ks to the thrc..: agrnindusll") ｳｵｨＭ ｳ ｾ＠ stems. na111cl: agroindustr: farms. rnopcrati' cs and dai1: prPccs,ing industries shn\\ ,·d th at there "crc nian' ri ' k' fo ccd h' ;1gmindus11: stakd1 nldcr.;. ,uch a, ri>k.·dama gcd ､｡ｩｾ＠

ICACSIS 2014

contaminated \\ ith bacteria. the risk of decn:as..:d produ..:ti,·ity of dairy. milk fat content risk not in accordance '' ith industry speci lications and so on. These risks needed to be measured and anal) zed as a basis for determining appropriate risk management strategies. The design of this model was expected to impro\'e the effecti ,eness of decision-making "ith regard to the organization of knowledge. storage and sharing of kno'' ledge in the agro-industr) supply chain risks dail) .

f"Ofl"Nf ... ｦＧｾ＠ 'l:rTl:CT

fo ... ,,_tr_ ... , ... c.., •rct ＬＮＬＮＮＬＮｾＮＮＮｲｴｲ＠ ... •f"!I.

PQJ£H1W.Cill<ilnCS

ｾｨ ･Ｍ ＮＭＮＭＢＢｾ＠ ｾｯ Ｍｾ＠ • .-.1n•o:d - •tt> • n••t>oo"u a ... ' " .u""'"

ｾＮＬ＠ . . . · - .. - , . _ - - 11 .. i...,.

•1-_ ... •1-_....,..•1-_7 ｾ＠

Fig 5 Examples of risk management strategy system usage at daiJ"\ agroinduStJ"\

Future research would aim impro,·c frame,,·ork modd and implement it at ,·ariouse i:ompani..:s and report the findings In addition the futur..: research ..:ncourage to im estigate th..: oiher component. such as drin:rs. risks categories. supplier. e\'alution criteria and perfc:;rmance measurement.

111 121 131 1-11 In! I; I R EFERENCES

:\lha\\an S. Karadshch L. Talct AN and t\tansour E. .. Kno"lcdgc-hascd nsk management frame\\ork for 1nforma1um technolog) ··. Int .I of lnformat10n ｾｴ｡ｮ｡ｧ･ｭｲｮｴＮ＠

vol J. pp 50-tiS. ｾＰＱｾ＠

.-\ ssefa 11 . Eg11abher TG . Seha1 F and Tcgq; nc A. .. Agricultural knm"cdge management in ､｡ｩｲｾ＠ production 1mpro,emcn1 The Case of Bure\\'oreda. \\est Go_1pm Zone. .-\mharaRcg1C1n·· . Int J of Agricultural Fconom1cs. Hll J. no -1 . pp J0-10. 2011

Caner CR. RC1gcrs DS . .. .'\ Frame\\Ork of sustainahk suppl\ chain management mo' ing IC1 \\ard new thcof\ ·· Int J of l'h,s1cal D1s1rihu11on and l.og1st1c 1\lanagcmcnl. \OI 38. no 5.

rr 360-3R7. 2110R

Cmk J. :-;C1,ack RA . (i1h<Clll f3J . l:!ard1 FJ Transror1a11on /I Suppl\ chain rc«l'-'Cll\ e I ' S·\ South" crn Cengag,· I carmng . ｾ ｏ＠ 11

De,endra C. .. Conrna1111 anahs1s to 1mpro'c dial"\ production ">tems 111 de' ｣ｬｯｰＱＱＱｾ＠ counmc; rl1c importance Clf p.1rt1C1pator- rural appra"ar · Trop ·\n1111 llcalth l'r1•d \"ol

3q . no R. l'P 5-IQ-:':' h. )}()7

ｆｬｫｾ｡ＮＱｲ､＠ C · Suppl' 11; 1. manai;cmcnt 111 a ;mall w111pam pcr;JX'Cll\<' :\n Int J '.'>uppf\ Cham \lanagcment" ·_ ,ol 13 . ""

t>. pp ＭＱＲＵＭｾ ＮＱ Ｍｬ Ｎ＠ ｾｦｬ＼ ｊｾ＠

rr<'l I. Scnccr Sand Sari R A. ·· ;-.;c" fun' 111ult1-cr1tcria framc..•\\ork fru ｭ｣｡ｾｵｲｩｮｧ＠ ｾｵ＠ ... Ｑ｡ｩｮ［Ｑｨ､Ｑｴｾ＠ performance.: of a ; upph chain· ·. J 1111 of l:cological l'.conom1c.\ol ill. l'P I OSR-1 IOO. 2011 er JI. . l"hamp111n D. 1Ja111d;. KJ . 1l;11nl\ .-\JI> ... ,\n ln,111u11onal I hc"r- JX'r'JX"''"c '"' <11, 1a111aPlc

ｰｲＱＮｨＮＮ ﾷ ＱＱ ｌ ﾷ ｉＮＮＧｾ＠ ｮｮｴｬｾｾ＠ th .. • ､Ｎｈｲ ｾ＠ ｾｵｲｲｬｾ＠ 1.:hain·· Int J l'roJuc11on 1'.n>nomlC> I'" · rr l"c-111. ｾＱｊｬＮｪ＠

( 8] t..iu11 Y ··Rcscmd1 1111 k1w" ｬ｣ｊｾ｣＠ 1>ncn1cJ ;uppf\ cha111 nsk

ｭｾｴｮ｡ｫＧＮｬＮＮＧＱＱＱＱＮＺｮｴ＠ Ｇｾ＠ ｾｨＺｭ＠ nw\h:I · .I int ..,,( \ ｴｮｮＮＱｾｴＮＮＧｬｬｬｬＮＮＧｮｴ＠ and

ｓｴｲｮｨＮＺＧｾｾ＠ \1,,ll : . n1..l : . Pl' 7:- - :-. : 1111

141 KaraJ,hch l. . \l;111'Clur I: . .-\ lh:i"an S .\1ar (i anJ El-Bath' \:.·\ ···1 ht.'t\nt11. .. ·.1J fra1111.·'''-'rJ... for l-.n ... H\ 11 . ＮＧ､ｾｬＧ＠ ｭＧＱｮ｡ｧｾｭ･ｾｴ＠

ｰｲｵｩＮＮＮＧｬＧｾｾ＠ ｴ ｯ｜｜｡ｲｊｾ＠ tmpru' 111:; J...mn' ｫｊｾｬＧ＠ pl:'rfurmance. L"1•111111u111-:;1111>n s (•fthc llll\1.-\. H•l 7. pp 6i-7<./. 21104

( 10 ) Kern D. \l0scr R. llan111ann I: . \h>dcr \I 21112 Supph n sk

managcmt.'nt nwdd dl'' 1..'lopn11.:nt anJ 1.:mr1111..:al ｡ｮｾｨ＠ Ｇ Ｇ ｾ＠ Int J

ｐｨｾ ＺＭｩｈＮＺ ＳＱ＠ D1:-.tr1but10n imd l.\1g1:-.t1i.: ｜Ｑ ｾＱＱｨｬｾ ｬＮＡｬｬｨＮＧｮｴＮ＠ ' Cl! ·-1: . n(l I.

rr ('''· 82. ｾ ｵ＠ 12

(1 IJ ＱＮ｡ＬＮｩＭｩｲｾ＠ ll ｃｩｵｮ｡Ｇ＾ｾ｢ｲＭｴｮ＠ .-\. '.'>rlan;v.;u11 ＮＭ ｜ Ｎﾷﾷ ｾｵｲｲｬ Ｇ＠ d1a1n risk

ＱＱＱ［ＱＱＱ｡ｾ･ｭ｣ｮｴ＠ 111 I· rcnd1 CClmpan1c;;·· .I 111t of l.k<1s1on

Suppl1rt S) ｾ ｴｴＮ ﾷｲｮｾ＠ ...

't''

Ｕｾ Ｎ＠ rr s:s-S3S. 2111 ｾ＠

( 121 \fanu1 I. \1cn11,·r I. ·· lil1>bal suppl' cha111 r1'k management"·

.I Hu,1111.:_,, log1,111.. ' · 1u/. 19. no I. pp 133-1:':' . ｾＱＱＱＱＸ＠

1131 .\lar1 m111. l '111a1w \I. ll awn11 I. l ;1111ura II ·son-.\umenc

ｭｾｴｨｯ､ｦｯｲ＠ p;un\ ｉ Ｇｾ＠ fut../.) grllllJ'·\k·i.:1:--1l1n ｾ ｲｮ｡ｬＩ＠ ｾ ｉ ｓ＠..

ｬｮｴＱＮＺｬｬＱ ｧｾｮ ｴ＠ anJ ｆｵｴＮＯＮｾ＠ ｓｾＺＺｯ ｴ｜ＮＺＧｬｬｬ Ｎ＠ H115 . no 3. pp 25i -ｾｨ｜ｊ＠

( 1-11 \!1ar11111n . Scp11a111 I\". S11karJ1. lluna>nr ·1 K . .-\ ｬｮｴ｣ｬｬＱｾ｣ｮｴ＠

ｳｾ＠ >!(111 for l'a>tcumcJ \Jilk l.'ualll\ :\;;,cs111cn1 -and

Pr1..·<l1rt1on . ｾＺＭｴ＠ :\nnual nh.·1..· tin g of thi: 1ntt .. ·-rnat1llnal ｳｯｮＱＮＮﾷ ｴｾ＠ for

the ' "tcm' sn,·ncc ISS . l ok'Cl. ＺＧＮＱＱＱＬ｜ｾｵ ｳ ｵＢ＠ ｾｬｬｕＷ Ｎ＠ pp -15 ).. -165 . c(IUi

(15( '.1"rra M. !lo 11· anJ FJ\\arJ ; JS . ·· <; upph diam ｫｮ ｃｬ｜｜ｫ ｊｧｾ＠

managcm,·nt .-\ l.11cra1urc RC\ IC\\ .. \lll .N. rr r->1113-ol 10. 2Ulc

( 16( ｜ＡＱ｡ ＾ ｨＱＱＱｾｬＱ＼ＱＱＱＱ＠ I'. .. Kmrn ｫ､ｾ｣＠ r"k manai:cmc nt :\ l·ralllC\\\llk .. .I Kn <•\\kJi:c ｜Ｑ｡ｮ｡ｾＬﾷＱＱＱ｣ｮｴ＠ \OI 1-1 . 1111 3. pp -16-1--185. 2(1111

( 17( :vt1>hra l'K . Shc khar llR . ·· 1111pac1 111° risk mid unccna11111cs on supph chain ,\ Dair- lnJu>tn l'crspt.Ttl\ c·· .I 1111 n/

ｍ｡ｮ｡ｾ｣ｭ｣ｮｴ＠ Rc;;cardt. "'I J. m>I . pp 1.11 . "(111

( 181 l'cdro;c>. M L" . Na kann D. ﾷﾷ ｴ［Ｚｮｭ＼ｬ｣ｊ ｾ｣＠ and 111li1rma11on rlo\\ S 111 'upp h d1a1n > .-\ stud\ on phar111at:cu11cd compan1c,··. Int J

of Prn<lurllon ｆｲｯｮｯｭＱｲｾ Ｎ＠ \ til ＱＲｾ Ｎ＠pp 376-JX-1 . ｾＰＰ Ｙ＠

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