IESS
'2015
THE 3
00INTERNATIONAL CONFERENCE
ON INDUSTRIAL ENGINEERING AND SERVICE SCIENCE
M elia Purosani Hotel - Yogyakarta, 1 - 3 September 2015
PROCEEDINGS
PROCEEDINGS
CREATING VALUE
THROUGH INNOVATION
Organized
by:
Department of Industrial Engineering
tnstilul Teknotogi Seputuh Nopember
Organized by:
Industrial Engineering Department, Institut Teknologi Sepuluh Nopember (ITS) Surabaya and Universitas Atma Jaya Yogyakarta - Indonesia
Supported by:
Rajamangala University of Technology Suvamabhumi (RMUTSB) - Thailand Universitas Islam Indonesia (UII) - Indonesia
ISSN:.2338-7939 . @20.l5 :
Edited by:
Iwan Vanany Nani Kurniati Dyah Santhi Dewi
" t .,.
Cover page
Preface
Table of Contents
• Welcome Speech from Conference Chair
• Welcome Message from Rector of ITS Surabaya
• Welcome Message from Rector of Universitas Atma Jaya Yogyakarta
• Keynote Speakers
• Industrial Engineering ITS
• Universitas Atma Jaya Yogyakarta
Committee
Conference Sponsor
List of Papers
Industrial Engineering - Decision Making
I. Annisaa Novieningtyas and Pri Hennawan. Group Model Building for Policy Making (Case Study: Beef Cattle at West Sumatra)
2. Pri' Hennawan, Yuliati Komar,and Soehartati Gondhowiardjo. A ｎ･ｴｷｯｲｫＭｾ｡ｳ･､＠ Collaborative
Decision Making Model in Reducing Cervical Cancer Spread in Indonesia. Ij
31' Erika ｆ｡ｴｾ｡＠ .. Developl!1ent ofSus.tainable Tuna ｐｲｯ｣･ｾＮｳｩｮｧ＠ Industry ｾｨｲｯｵｧｾ＠ System ｄｹｮ｡ｾｩ｣ｳ＠ .
Simulation •... ' ' . ',:' ." " . . ' '.':. " . : . '. , ..;. .' .
4. I Made Ronyastra, I Ketut Gunarta and Udisubakti Ciptomulyono. A Multi Criteria Decision Analysis for Reinvestment Action Portfolio Selection Problem in an Indonesian Real Estate
20
Company 27
S. Stefan us Eko Wiratno, Em Latimanti and Kevin Karmadi Wirawan. Selection of Business Funding Proposals Using Analytical Network Process: A Case Study at a Venture Capital
Company 36
Industrial Engineering - Logistics
I. Farlda Pulansari, Dwi Donoriyanto and Iriani. Perfonnance Assessment Mechanism for Reverse logistics Maturity Implementation toward Sustainable Manufacturing Systems: A Conceptual
Framework 42
2. Adi Budlpriyanto, Budisantoso Wirjodirdjo, Nyoman Pujawan and Saut Gurning. Berth
Allocation Problem under Uncertainty: A Conceptual Model Using Collaborative Approach S I
3. Nur Ulfa Hidayatullah and Ali Musyafa. Hazop Study on Fuel Distribution System Based on
Anfis Layer of Protection Analysis in Surabaya Installation Group PT Pertamina Tanjung Perak S9
4. Ardian Rlzaldi, Meditya Wasesa and M Noviar Rahman. Yard Cranes Coordination Schemes for
Automated Container Tenninals: An Agent-Based Approach 66
S. Meditya Wases&, M Noviar Rachman, Ardian Rlzaldi and M Mashuri. Relocating
Multiple-Tenants Logistics Center: Lesson Learned from an Air Cargo Tenninal Relocation Project 74
6. Siti Nurminarsih, Ahmad Rusdiansyah and Nurhadi Siswanto. Inventory Ship Routing Problem
(ISRP) Model Considering Port Dwelling Time Infonnation 80
7. Sonny Sanjaya and Tomy Perdana. Logistics System Model Development on Supply Chain
Industrial Engineering - Manufacturing system
1. Wlwin Widiasih, Putu Dana Kamingsih and Udisubakti Ciptomulyono. Development of Integrated Model for Managing Risk in Lean Manufacturing Implementation: A Case Study in an
Indonesian Manufacturing Company 95
2. Joko Sulistio and Tri Astuti Rini. A Structural Literature Review on Models and Methods
Analysis of Green Supply Chain Management 103
3. Sri Indrawati and Muhammad Ridwansyah. Manufacturing Continuous Improvement Using
Lean Six Sigma: An Iron Ores Industry Case Application III
4. Sri Hartini and Udisubakti Ciptomulyono. The Relathionship between Lean and Sustainable
Manufacturing on Performance: literature review 117
S. Maria Anityasari and Aulia Nadia Rachmat. Lesson Learnt from Top-Down Medium
Enterprises Selection for Green Industry Pilot Project in Surabaya 126
6 Putu Karningsih, Dewanti Anggrahini and Muhammad Syafi'i, Concurent Engineering
Implementation Assesment. Case Study in an Indonesia Manufacturing Company 133
7 Nani Kurniati, Ruey-Huei Yeh and Jong-Jang Lin. Quality Inspection and maintenance: the
framework of interaction 140
Industrial Engineering -' Operation management
t.
Suhendi Irawan. The Effect of Choosing a Transportation Vendor and the Performance ofTranspon:ation Vendor on the Performance'ofShipping Goods to Consumer: A Case Study of DB
2.
3.
4.
5.
6.
7.
8.
9.
, Schenker Freigh Forwarder
Jugkrit ,Mahoran, Sukanya Wonglakron; ｓｵｭ｡ｬ･ｾ＠ ｎ｡ｭ｡･ｨｯｴ･Ｍｬｬｮ､ﾷｎ｡ｲｵｰＧｨｰｮＮｏ｡ｮｾｭｯｮＮ＠ Risk.,
Management ofVilhige Funds hi Muang' District, Suphanburi ProvinCe.' " :. . ｾＮ＠
Susanto Sudiro and Shn'Ri Mohd Yusof. Managing WIP buffer with combination of feeding materials scenario and conventional control theory of single type of hospital bed production
Agung Sutrisno, Indra Gunawan and Stenly Tangkuman. Modified FMEA Model for Acessing the Risk of Maintenance Waste
Filemon Yoga Adhisatya, The .Jln Ai and Dah-Chuan Gong. Economic Lot Scheduling Problem
with Two Imperfect Key Modules .
Bupe Mwanza and Charles Mbohwa. An Assessment of the Effectiveness of Equipment Maintenance Practices in Public Hospitals.
Bupe Mwanza and Charles Mbohwa. Design ofa Total Productive Maintenance Model for Effective Implementation: A case study ofa Chemical Manufacturing Company
Hafid Budiman. Increasing Compressor Reliability with The Weibull Distribution Analysis
Paulus Wlsnu Anggoro and Baju Bawono. Reverse Engineering Technology in Redesign Process Ceramics: Application for CNN Plate
10. Endang Retno Wedowati, Moses Laksono Singgih and I Ketut Gunarta. Integrated Production Planning and Scheduling for Mass Customization in Food Industry: A Conceptual Framework
11. Taufik Djatna and Wenny Dwl Kurnlati. A System Analysis and Design for Packaging Design of Powder Shaped Fresheners Based on Kansei Engineering
12. Taufik Djatna and Muhammad Raja Ihsan. A Fuzzy Associative Memory Modeling for Production Equipment Status Assessment
13. Taufik Djatna and FaJar Munlchputranto. An Analysis and Design of Mobile Business
Intelligence System for Productivity Measurement and Evaluation in Tire Curing Production Line
147
152
159
167
173
179
185
194
199
205
213
220
14. Sazli Tutur Risyahadi. Scheduling Model of Harvesting Strawberry Considering Product Decay
During Storage 231
IS. Chaterine Alvina Prima Hapsari. Deny Ratna Yuniartha and Ignatius Luddy Indra
Pumama. Tour and Break Scheduling for Shift Operators in Hard Disk Drive Manufacturer 239
16. Jwannaraksu Phen. An influence of packaging design on customer purchase intention 247
17. Dewanti Anggrahini, Putu Dana Kamingsih and Martian Sulistiyono. Managing Quality Risk In
A Frozen Shrimp Supply Chain (A Case Study) 25 I
18. Anny Maryani, Sritomo Wignjosoebroto and Sri Gunani Partiwi. A System Dynamics Approach
for Modeling Construction Accidents 259
19. Naning Aranti Wessiani and Satria Oktaufanus Sarwoko. Risk Analysis of Poultry Feed
Production Using Fuzzy FMEA 265
20.
Mohamad Faisal Mohamad Sobri, Hawa Hishamuddin, Noraida Azura Md Darom' Disruption OMI
Pccovery for a Single Stage Production-Inventory System with Optimal Safety Stock
Industrial Engineering - Operation research
I. Wahyuda and Budi Santosa. Dynamic Pricing in Electricity: Research Potential in Indonesia
2. Chirag Sancheti, Aditya Balu and Amit Kumar Gupta. Simulation based optimization of productivity using Flexsim
3. Budl Santosa and 1 Gusti Ngurah Agung Kresna. Simulated Annealing Algorithm to Solve
Single S.tage Capacitated Warehouse Location Problem (Case Study: PT. Petrokimia Gresik)
4. Yuanita Handayati, Togar Simatupang and TomyPerdana. Value Co-Creation in Agri-Chains
Network: A Hard Agent Based Simulation
. 5 .. Budl Santosa·and Ade Lia Safitri. Biogeography-based Optimization. Algorithm for Single
. Machine TbtalWeighte<q'ardioess Problem" . . '. . . , . . .. ':. . . ..'
6. Gilang Almaghribi Sarkara Putra and Rendra Agus Triyono. Proposing a Neural Network
Method for Instrumentation and Control Cost Estimation of the EPC Companies Bidding
276
283
289
298
,
307
Proposal 313
7. Sattarpoom Thaiparnit, Baramee Osateerakul and Danupon Kumpanya. Algorithm
Design in Leaf Surface Separation by Degree in HS" Color ;\'Iodel and Estimation of Leaf
Area by Linear Regression 320'
8. Danupon Kumpanya and Sattarpoom Thaipamit. Parameter Identification ofBLDC Motor
Model via Metaheuristic Optimization Techniques
326
9. Sinta Dewi, Imam Baihaqi and Erwin Widodo. Modeling Strategy of Purchasing Consortium to
Optimize Total Purchasing Cost Considering the Dynamic Condition ofOrganizaion 332
10. Taufik Djatna and Imam Muharram AUtu. An Application of Association Rule Mining in Total Productive Maintenance Strategy: An Analysis and Modelling for Wooden Door Manufacturing
Industry
340
Industrial Engineering - Product Development
1. Yosephine Suharyanti, Subagyo. Nur Aini Masruroh and Indra Bastian. The Scheme of Product
Development Process as a Trigger to Product Success: A Theoretical Framework 347
2. Ishardita Pambudi Tama and Wifqi Azlia. Development of Customer Oriented Product Design
Using Kansei Engineering and Kano Model (Case study of Ceramic Souvenir)
355
3. Taufik Djatna. Lub Putu Wrasiati and Ida Bagus Dharma Yoga Santosa. Balinese
Aromatherapy Product Development Based On Kansei Engineering And Customer Personality 362
Type
4. Dyah Santhi Dewi, Bambang Syairudin and Eka Nahdliyatun Nikmah. Risk Management in
New Product Development Process for Fashion Industry (Case Study: Hijab Industry) 368
Industrial Engineering - Project Management
I. Jeyanthi Ramasamy and Sha'Ri Mohd Yusof. A Literature Review of Subsea Asset Integrity
Framework for Project Execution Phase 376
2. Baju Bawono and Paulus Wisnu Anggoro. Utilization Of Rapid Prototyping Technology to
Improve Quality Souvenir Product 384
Industrial Engineering - Supply Chain Engineering
1. lrwan Syahrir, Supamo and (wan Vanany. Healthcare and Disaster Supply Chain: Literature Review and Future Research
2. Yudi Fernando and Sofri Yahya. Challenges in Implementing Renewable Energy Supply Chain in Service Economy Era
3. Araya Uengpaiboonkit. The Marketing's Factors that Effect to Consumers Decisions of Organic Rice in Surin, Thailand
4. Layung Prasetyanti and Togar Simatupang. Proposed Framework for Service-Dominant-Logic Based Supply Chain
5. Taufik Djatna and Rohmah Luthfiyanti. An ａｮ｡ｬｹｳｩｾ＠ and Design of Responsive Supply Chain
for Pineapple Multi Products. SME Based On Digital ｂｵｳｾｮ･ｳｳ＠ Ecosystem (DBE)
6. Erwin Widodo. A Model Reflecting the Impact of Producer Substitution in Dual-Channel Supply-Chain Inventory Policy
7 .. Taufik
Ｎｄｪｾｴｾｾ＠
｡ｾ､＠ ｈ･ｾ＠
Ｚｈｾｾ､｡ｾ｡ｮｩﾷ＠
ｈｩ､｡ｹ｡ｾ＠
.. An' OptimizedｓｾｰＮｾｩｹ＠
thain· Model for . . . ..Detennination of Distribution Center and Inventory Level in A Coconut'Water Agro-Industry
8. Siamet Setio Wigati and The Jin Ai. An Integrated Production System Model for Multi Supplier Single Buyer with Non Confonning Item and Product Warranty
9. Yoshua Perwira Hartono, Ririn Diar Astanti and The Jin Ai. Enabler to Successful Implementation of Lean Supply Chain in A Book Publisher
10.
Sutrisno and Pumawan Adi W:Caksono. Optimal Strategy for Multi-product Inventory Systemwith Supplier Selection By Using Model Predictive Control
11. [wan Vanany, Anny Maryani and Bilqis Amaliah. Blood Traceability System for Indonesian Blood Supply Chain
Industrial Engineering - Safety & Ergonomic
I. Natalie Carol Skeepers and Charles Mbohwa. A Study on the leadership behaviour, safety
390
398
404
408
416
423
430
436
443
450
457
leadership and safety perfonnance in the Construction industry in South Africa
464
2. Herry Christian Palit and Debora Anne Yang Aysia. The Effect of Pop Musical Tempo during
Post Treadmill Exercise Recovery Time
470
3. Eko Nurmianto, Udisubakti Ciptomulyono, Supamo and Sudiyono Kromodihardjo. Manual
Handling Problem Identification in Mining Industry: the Ergonomic Perspective 475
4. Wiyono Sutari, Mumi Dwi Astuti, YusufNugroho Doyobekti and Yuvie Mutiarasari. Analysis of Working Posture Effect on Muscular Skeleton Disorder of Operator in Stamp Scraping in
Batik Stamp Industry
483
5. Rino Andias Anugraha. Wlyono Sutarl and lima Mufidah, The Design of Working Desk of Batik
Scraper by Using the Principles of Ergonomy 488
.
..
6. Budi Praptono, YusufNugroho Doyo Yekti, I Gede Wisuda Pura and Fransiskus Tatas Dwi Atmadji. Prevention ofMusculo Skeletal Disorders of Green Beans Farmer Through Application
of Ergonomics in Order to Developing Manual Handling Equipment 496
7. Manik Mahachandrn, Yassierli and Erdo Garnaby. The effectiveness of in-vehicle peppermint
fragrance to maintain car drivers' alertness 500
8. Yassierli, Manik Mahachandra and Iftikar Sutalaksana. Fatigue Evaluation of Fuel Truck
Drivers 506
9. Ayu Bidfawati and Eva Suryani. Improving the Work Position of Worker's Based on Quick
Exposure Check Method to Reduce the Risk of Work Related Musculoskeletal Disorders 512
10. Bernadus Kristyanto, Brillianta Budi Nugraha, Anugrah Kusumo P and Kristanto Agung
N. Head and Neck Movement: Simulation And Kinematics Analysis 518
II. Ronny Norlyati, Wisnu Rozaaq, Ali Musyafa and Adi Supriyanto. Hazard & Operability Study
And Determining Safety Integrity Level On Sulfur Furnace Unit: A Case Study In Fertilizer
Industry 525
12. Dyah Santhi Dewi and Tyasiliah Septiana. Workforce Scheduling Considering Physical And
Mental Workload: A Case Study Of Domestic Freight Forwarding 531
Industrial Engineering - Suporting topics in Industrial Egineering
I. Hatma Suryoharyo and Niken Larasati. Sustainable Livelihood Framework As An Approach To
Build Community Based Security 539
2.· Ngurah Wira, Amelia KUf!liawati and Umar Yunan. The Design of Best Practice on The Media
Transfer Activities and Preservation Based on Knowledge Conversion with SECI Method 546
, , 3,. Sri GU,na!,!1 Partiwi" EUy ａｧｾｴｩ｡ｮｩ＠ ｡ｮｾ＠ Anny Maryani. Preparation for I?esigning ｾｵｳｩｮ･ｳｳ＠
Strategy.of'Bamboo Cultivation in Bondowos,o' '. ' . ' ..
4. Yosephfne Suharyantl and Alva EdyTontowi. Market Response as a function of Design, Competition, and Socio-political Condition: An Empirical Model
Service Science - Service Business Design & Strategy
1. Tri Ramadhan, Dermawan Wibisono, Reza Ashari Nasution and Santi Novani. Design of Self
552 .
558
Service Technology on Passenger Shipping Transportation Service System in Indonesia 566
2. Ratna Hidayati and Santi Novani. A Conceptual Complaint Model for Value co-Creation
Process 574
3. Mikhael Tjlll, Jann Hidajat Tjakraatmadja and Santi Novani. Designing value co-creation
process in organic food product distribution Case srudy in Bandung 579
4. Nurtami Prihadi and Santi Novani, Value Co-Creation among Stakeholders in Solo Tourism
Development: Service System Science Perspective 591
S. Arlavianyssa Pradiva Arru and Santi Novani. Value Co-Creation in Solo Tourism by Using
Soft System Dynamics Methodology 60 I
6. Rizki S. Nurfitria and Mursyid H. Basri. Developing Clinical Pathway Model in Public Hospital
as Basic Component of Casemix System 609
7. Watcllaree Phetwong and Krisorn Sawangsire. The Development of Computer Game for
Historic Sites Learning in Suphanburi. 617
8. Lidia Mayangsari and Santi Novani. Multi-stakeholder Co-Creation Analysis in Smart City
Service Science - Service Delivery & Operations
1. Imam Djati Widodo and Hanvati Sutanto. AR MBA: Linkage Pattern of Visited Tourism
Object 628
2. Liane Okdinawati, Togar M. Simatupang and Yos Sunitiyoso. Value Co-creation Map in
Collaborative Transportation 635
3. Americo Azevedo and Maratus Sholihah. Innovative Costing System Framework in Industrial
Product-Service System Environment 642
4. Iwan Vanany, Udisubakti Ciptomulyono, Muhammad Khoiri, Dody Hartanto and Putri Nur
Imani. Willingness to Pay for Surabaya Mass Rapid Transit (SMART) Options 649
Service Science - Service Quality
1. Agus Mansur and Rizky Oestiana Hapsari. Analysis of the Public Transportation Service Quality
on Trans logja Transportation 658
2. Zya Labiba and Mulih Wijaya. Improvement Quality ofIndustrial Training Center Through
Service Quality Based on Participation Perspective 663
Service Science - Supporting topics in service science
1. Gembong Baskoro. The concept of balancing Higher Education Institution (HEI) organization
towards global and regional challenges 670
2. Samhuri Ikbal Pradana, Amelia Kurniawati and Nia Ambarsari. Knowledge Management System Implementation Readiness Measurement in ·POll LIPI Based On People and
Organizational Structure Factors . 674
•
I'roc ecflillt; IIf Im/II.Hr;1I1 ｅｬｬｧｩｬｬ･ｴＡｲｩｬｬｾ＠ flllIl SCrI'ic:c Scicllce , 2015
An Analysis
Intelligence
Measurement and
Production Line
and Design
System
Evaluation
of
for
Taufik Djfllllaa', Fajar MunichpUITOl lllOb
Mobile Business
P,"oductivity
Tire Curing
•
In
Ｂ ｬＧ ｯｳ ｬ ｧｲｾ､ｵｾｬｃ＠ I'rogr:Ull, i\gro-industrial Technology Departm ent. Ilogu r Agriculture ｕ ｮｩＢ｣ＡＧ ｾｩ ｬ ｹＮ＠ Bogor. Indoncsi3, t:IU
-filalj:lln:l'.<!. lp b.:Il' .Id. OR 1 J 19726:135
bGradlw tc l'rogram, Agro-induslria J Tcd1nology Department. Bogor Agriculture Un iversity, Bogor, Ind onesia.
fa-.hl!:!110991(t1 ,jpi!0P'b.>lc.id. OS 1 J 9697687
ABSTRACT
/Jlls/ness illlelligcllcc (lJ/) system as an architecrure of competellcies. processes, leellll%gies. applications lind
ｰｬＧｬｬ ｣ ｬｩ｣ ･Ｎｾ＠ to SIIPpO!'1 prodllctidry m easurem ent. If ｯ｢ｬＧｩｯｬｬｳｾ｜Ｂ＠ needs (/ IJI [hat .wppon organizatiol/ to dec/are lIll)'
prot/llc/ioll COlls/raillt.l· tlltll cllrrelll!y or a!I'em!y oC/,;UI'rr.w ill .weh tiS (ire ClIl'illg ｩｬＯ､ｬｬｾ Ｇ ＧＧ ＩＧＮ＠ Overall Eq uipmellt
ＱＺＮｦＯ ･｣ ｲｩｬＧｬＡ Ｎ ＯｉＨ ＡｾﾷＮｾ＠ (OEE) is used (IS (I I/II(/lititatil'e prOdllCfil'il), mca.wn!lII ellT (llId becollle Ih e beHe of compally
COli-fill/lOIiS imprOI'cmf'1II alld cralll(lliv/!. The objecfires of fhis ,wu/)' (lrc lO idell/ify crilical paramcfers of produc-tioll lille ill ｣ｊｊ･｣ ｦｩｬﾷ･ｬｬ ･ｳＮｾ＠ measuremenT and machine lIIili;:(l/ioll. (II/(I'y::e flu: reqlliremem of illfol'llwtioll ｳｹＮｾｴ｣ｭ＠ liS fill Alldroid hased !I1obile BI System (llId fo illlcgr(lle III(.' ､･ｾＧｩｧｬｬ＠ illlo a mobile system. !:,)'slem requirement
(11/(/-Iy::ed each illterdcpelldcmll1em'll/'c ill lit e rcal \l'orld of CO lllplexil), by IIsillg BPMN 2.0. ｔｨ ･Ｌ ｾ･＠ mClI.wrcs (Ire part of c/ashbo(ll'd CQmpOllelll.f ill tlte proposed Ill. The acqllired d(l{(I fro m II Na tiOllal forefrv lIf lire illdusfI)' shows Ihe OE£ ill three ralio /IIcasurcmellf of amilabililY. peltOl'mallCe, alld quality with 78%. 82.5 % and 99.8% of st;orecard respcclively. III order /() dctermille tftc statllS
0/
pmdllctioll. the dcploy mclI/0/
k-lI care.1"l neig hbor (k. NN) ｧｨＧ･ｾ Ｇ＠ 67.5 % ojaccllmcy rille, Th c aifical pm'olllclel's idemijicmioll re.w/fS to 8 (eight) ｾＧ ｩｧｬｬｩｦｩ ｣ ｷｬｉ＠ co//-,Slraim,l', which ca lculated /lsillg di,I'/(lIIce-/)met/ RELIEF mfribllte selectioll. E"enlllally litis approach ｲ ･ｳ ｬｬｬｴ ｾﾷ＠ big fOllr cOllS/milll.I' W be /loted: (a) /IIold repair, (b) mold .I·clling, (1..) grecn lire shortage alld (d) defect cwe,Ki')'ll'ords: bllSilless illfelligcllce, tire cllring, prodllctil'it)'
I.
Introdu ction
Productivity is one of the company success indicators in prod ucing goods and impro ving service quality to consumers or related pan ics. Productivity is a meas urement level to obtain the results with a variety of
aV,1 i lable resources
l
I , . To regain the ir com petit iveness wit h in th e market, com pan ies s hould apply th e Total Production Managemen t (TPM) fo r cost efficiency and higher profit margi n . Most of multinational tire man-ufacturers arc Original Equ ipment Manufacturer (OEr,'I) for automotive industry: hence the critical parame-ters arc pointed on how they produce goods wilh right quantity at the right place and at the right time. ortermed as just·i n-tim e
[21.
Just-in-t ime (J IT) is llOW ofte n termed as lea n manufacturing that wou ld ha vebecome iss ues for product ion s trategy. Recen tl y. business need ,I method 10 repair and reveals the problem as
so lu tion due 10 dy na m ic eve nts and inc ident s in production. Core problems co uld be revenled llsing business
intelligence (8 1) by applying dashboard to measure and monitor performance of production. Productivity
meas urement w ithin 131 system s is able to comb ine data ga thering. data s torage, and know ledge management
with analytical too ls 10 present complex internal and co mpetiti ve information to planne rs and decision
mak-ers. Some organizatiolls use BI to gain data-driven insights on anything rdated to business performance,
Enterprise can build busi ness intelli ge nt (I3I) system usi ng analytics illsight to trigger business even I [3]. Any measurement from analytics insig ht is s hown at specific parameters on das hboard that reflect the definition o fproducli vit y scorecard to su pport dec is ion making,
III thi s papcr, we focu s on productivi ty data min ing usi ng Overall E<l uipm cllt Effec ti ve ness (DEE) as indica
-tors. DEE is an appropriate method to measure tire curing as products arc discrete and able to revea ls the six
big losses in productivity. According to Maran el.al (2012) six constraint s arc divided into three categories
I\n An:llysis and Design of ｾ Ｇｉ ｮ｢ｩｬ｣＠ Business Intell igcnce Systcm for Productivity MC:I$UrCmcnt and Evaluation ill Tire
Curing I'roduction Line
which provide major influences on productivity [41. Constraints could be minimized by predicting and
de-term ining the priority constraints to be solved from a variety o f constraints that ma y occur during thc pro-ducti on process. Predictin g ami sequencing th e cons tra ints nre likely to support management to deci de
pre-vcn tive ac tion for coming period - especially with a mobile and fr iend ly-p ractical dashboa rd to monitor the
reports. This paper is merging the powerful busi ness analytics and fundamental OEE measu remen t (using
data mining) to revea l Ihe sil ve r linin g of produ ction and reduce business friction with mobile 131 dashboard to triggcr bu siness acti on in real time.
l3ased on the motivation above. the objecti ves of thi s paper are to identify critical parameters o f production line in effecti ve ncss measurement and machine util ization, anal yze the requirement of informati on system as a mobile BI system and to integrate the design into a mobile sys tem. Critical parameters obtai ned by produc-tion constraints OEE valucs of so mc producproduc-tion lines . OEE prod m;tio n lines with the lowest value will be anal yzed furthcr in the form o f prediction s for the nex t period o f production status using algorithm k-nearest-nei ghbor [5 }. Priority is de termined using RELIEF a lgori thm [6J. Both algorithms are used as a loss o f transparency in the productiou process so as to facilitate efforts to reduce such losses.
2. Research Method
This pape r is wrillcn in on-fie ld experi ence, which all the data was acquired from one of the biggest tire multinationa l company in Indonesia. The facts of production '1c tivity lead to build the systemat ic methodol-ogy to so lve the prOd ucti on constr<li nt s. Frequency of constra ints cou ld be minimi zed with an ticipat ion and preve nti ve action . Categori za tion of constrai nts wil l case the tlser to determinc right treatment for the con-strai nt s and computational calculation. Co ncon-straints should be prioritized and first priority or main concon-straints
should be placed 0 11 top of all production constraint s which effec tively reduce total both major and minor
constraints and brie n y in crease the producti on q uali ty. All classifica ti on and sequencing rcsult should be
monitored (dashboard) to trigger management dec ision in o rde r to main tain production l[uality. ['Ience, the
sequences abovc arc elaborated in the methodology cxplanation below.
2.1 C onstraint Analysis
:I . Ovc rall Eq uipm en t E ffecti ve ness (DEE) i\ h 'asllr"cm ent
OEE is a combined measurement of the effectiveness of the time (availability), machine perfOmlallCe
(performance) and product quality (quality) [7]. The genera l formula used to calculate the OEE va lue is written
in equati on.
OEE • tIl"CIifubili(I' )( IIcrfiJl"ll!flllCC )( IJI/fllil)' ( I )
The effectiveness of the time (availability) shows the ability o f a machine to produce product produc tion.
As sccn in equation ( 1.2). this paramctcr is the result of a comparison between the times spent during the
pro-duction takes place with a tota l time avai lable for such ac ti vities.
Avai!:tbility=RuntimclLoading Time (2)
Engine perfonnance shows the ability of a mach ine to produce product product ion (pcrformanee). This
parameter is the result of a comparison betwecn the achievements of production Ihat should result in a net time
used fo r production activi ties ta ke place. Calculation engine perfor1l1!1nce le vels seen in e(jllation.
I'erfonllance"- lotal prodllctionilarget production (3)
While the level of product quali ty (quality) on OE E is the ra tio between the total products is acceptable
(good) wi th the tota l product produced. Thi s measurement is close ly rel ated to the number o f defective products
(defects I scrap) .
Qua1ity=good product/total production (4)
b. Pr"cdi etion of Prudu ct io n St:llU S
Prediction product ion status wi th k-nearest neighbor algorithm aims to prcdict the classi lication statuS of production in th e next period. Principle of this algorithm is to find a pattem of a dat'l se t (tmining tuples) and the
class ifi cation of data o f unknown e lass iticrtlion (u nknow n tupl e)
15J.
Classilication is done by measuring theAn Analys is lind Des ign of 1t,'lobi lc Business Intelligenec System for Produc tivity f','!casurcmc nt and [\,;Il u:uion in Tire
Curing Production Li uc
distance to the k nearest training tuples where itcm data as rderence and lix data in the problem domain and un -kn own tuples which indicate data to be class ified with in thc proposed solution, In this s tud y. the closest distance is calculated by Euclidian dis tance (1.5).
ｉｾ＠ . .
2
2.
(X II-.QI ) ,XIi=!
II':l ining tuple; x
2 "" unknown wpl es (5 )
Company's hi storical yie lds data indi cate the mnount of production losses caused by any co ns traint s ha ve di ffercnt ran ges. No rmali za tion of data needs to be do ne so that any outcome class i fication has a valid va lue. The itcm of nonnalized data represented as \.' where nonll:l liza tio n proccss of data on cach :llIribute constr:lints made poss ible using the following equa tion .
v",
unnonnali zcd claw,;/ = value on dataset
(6)
c, Produ ct ion Co ns t r:l ints sequ en ce
Recursive Elimination of Features or RELI EF technique in att ri bute sekction algorithm is used to give weight to each constraint to a production status. Weight values will be sorte(1 as priorit y constraints most
exhib-itcd siglli fieantl y caus ing losses in producti on. RELI EF is the princip le o r tile technique using the Eucl idea n
dis-tance to the dat a (i nstancc) of the othcr data in the same attributc, Insdis-tance classi fied into two, namely Ncar-hi t and Nca r-mi ss. Ncar-hit arc thc instances thai arc in one classification while Ncar-miss has a difTcrcllI
classifica-tion [6J . In this s tudy, the classificat ion is dClefmincd by k-nearest nei ghbor algori thm ,
3. Res ult a nd
Discu ss ion
3, 1 Prod uction Co nst ra int
Analys is
Study case was conducted using multinational tire compan y produ ction data which has been coll ec ted in sevcral wec ks. The OEE was calculated using thc three ratios include Ihe level of effectiveness o f thc time (availabi[ity), eng ine performance (pc rfoTlI1:m cc) and product qU:llity (qua li! y). l3ased on the data availablc, the
results arc shown in Figure I as follow s. Data was compared with world standard ofO EE in manufacturing.
100
80
60
40
20
o
Availability
IiiI PT.X
IiiiI Worid Class
Performance Quality
[n relation to thc six big losses components, the production constra ints in tire manufacturcr we re directly ob-served on ficld. The resu lt of 8 (eight) main constraints of tire manufacturing (p lease 110te that constrain ts were
only focused on mach inery issues) and c:o;planation will be spelled out in Tab le L MIG Trollb le is olle of the
biggest breakdowlls of a machine bCCH use its failure in tire cllfing. Th e Backward I" Cllre and Green Tire
Shortage arc constraints eauscd by m iSmatch in scheduling or undeliverable prod uction targe t which will impact
the next production activiti es. Mold Selling and MoM Repair arc some constraints that impacted the performance
of production which causcd by adjusullents of tire size that 10 be c urcd. Bladder Changes is act ivity to change
the bladder due its durability to maintain quality of tire curin g which cou ld easil y down grade quality tire or so
callcd defect.
An An:al ysis and I) esign o f Mobile Business Intell ige nce System for Producth'ity Me:1surcmen t an d Eva luation in Tire Curing Prod uction Li ne
Tabll' I. Six ｈｩ ｾ＠ l.oJses Idcntincnlion
... 0. ｃ ｯｮ ｾ ｬｲｾ ｩｮｉ ｓ＠ Sj:r llig I.ossrs Ca lfj!ory
I. /l.I/C ｔｲ ｯ ｬｬ ｢ ｬ ｾ＠ IlrCJkd owli S
,.
Back ward I " CureJ . Gree n ｔ ［ ｲ ｾ＠ Shon asc (GTS)
,.
Mold Sellmg,.
Mold Rq •• lIr Selup and AdJuslmfnlS,.
Cleaning Tools 7. Rladdcr Changes,.
Ikfe" IkjeciAs me ntioned above. the constrain ts should be cl ilssilicd and sequenced in order to conclude the production and trigger manage ment action. The classification process is using k· nearest neighbor (K NN) as one of da ta mi ning tec hnique. T he princip le o f K· Neilrest Nei ghbor (KNN) is look ing fo r the shonest distancc between Ihe data to be eval ualed wit h K neighbor (neighbor) in the closest training data. K value can be determined experi. menta lly and we used k- 2 wi th 6 7.5% accuracy rate. Then do the classifier data test b:1sed learning data to obtain production status as call be sccn in the T able 2.
This class ification helped user to de ten11i ne the dala classificmi on o f unknown tuples. Each tuple represents
dai ly production. Thc class ifi cat ion will be interp retcd in dashboard to measure the quality of production as key
performance index (Kil l) i.e. 85% is classifi ed as G or good, 75%·85% will be classifi ed as A or acceptable and
the rest wil l be classifi ed as NO or /lot good. T his method is the lIIost :1ppro priatc compared to other techn ique
whie:1 has 67. 5% accuracy ra te. higher tho II other method i.e. N:1i"ve Bayes cl assifi er or sup pon vecto r machi ne
(SVM).
While there arc ma llY NG clussifications, Ihere shoul d be an in-depth ｡ｮ｡ｬ ｹ ｳ ｩＮｾ＠ to identify the cri tic,1)
paT<1m-eters of lire curing. Rcfer 10 fra meworks Iha t have mentioned above, the conslra ints sho uld be sequenccd or weighted to ident ify the most illlp:1cting constraint in tire curing. Constr:1i nls on the production of the weight
calcu lation to de tennine the conslraims that ha ve the most impact on production fa ilures. [n Ihis study the melh·
od used 10 calculale the RELI EF weight of each consimili!. T he pri nci ple of the ealcu lalion is 10 calculate the miss and hil each att ribute 10 each classifi cat ion
[S J.
This lechni<lue rcsu[ ted weight and ra nked to kllOw the most afTccting attribute (constmi nts) on the fie ld. G r!lph o f ranked weight is depicted in Figure 3.Tahle 2, ｃ ｬ ｡ ｾ ｳ ｩｮ ｣Ｚ Ｂｩ ｯ ｮ＠ Slllllili c of ｬ Ｇ ｲｉｬｬｊ ｜ｉ ｾｬｩｯ ｬｬ＠ ｓ ｬＺｩｬｬｉ Ｎｾ＠
i\ IIC T rou. III:ultin
GTS 1" C urr i\t(l ld i\t otd ｃ ｴ ｣ ｾｮ ｬ ｮ ＱＺ＠ ｜Ｉ ･ ｲ ｾｃ ｉ＠ Ot hers Cb u ifi ca li on 01. C h:ln er Ib el.wanl Sm lne ｬｴ ･ ｾｄｬｲ＠ TO(lts C ur t
0.735 0.552 1.000 0.000 0.000 0,769 0.000 0,245 0. 746 A
1.000 0.966 0.1135 0.000 0.000 0.000 0.800 0.51 1 0 .302 A
0.000 0,793 0.6211 0.000 0.000 0.000 0.000 0,367 0.127 NG 0,295 1.000 0,728 0.000 0.000 0,}08 0.000 0,327 0.4 .14 NG 0.344 1.000 0.50-1 0. 000 0.500 0. 000 0,000 0,0112 0.667 A
0.0-19 1.000 0.4.\.\ 0.000 0.000 0.000 0.000 0.000 0.508 NG 0.328 0. t 88 0.8 13 0.000 0.000 0.000 '.000 0.000 0.41 3 NG 0.590 0.906 0,,10 4 0.000 0.000 0,000 0.000 0. 14) 0. S7t NG
0.000 0,20-1 0.733 0. 179 O.IJ(J{) 0. 179 0.000 t,OOO 0.000 A
0.098 0.463 0.182 0.000 0.000 0.000 1.000 0.347 0.000 A
0.000 0.778 0.42 1 0.000 0.000 0.000 0.000 0. t63 0.000 NG
Nute '
A ..,. An:l.'plllbll'
,vG .. NOI G(J(J(I
Those calculations above wi ll become the core computationa l ca[culat ion ror the monitori ng dashboard of business int ellige nce ap pli cation. T he model and sys tem arch itecture will exp lained be low.
An Ana lysis aud Design of Mobi le Uusiucss Intelligence System for Producti vi ty i'>·lcasuremcnt and Evaluat ion iu Ti re Curing Production Line
"
2.'.,
,
2"
0L'
0u I.
"0
-
0.'<
Ｎｾ＠
3 0 MIC
T.o uble
GTS lst Cure Mold
M undur Settl nl:
Mold Cle3ni ng aladde r Defect
Repair Tools ｃｨｾｮｧ･＠ Cu.c
Fi ): u re l. Crilic:1I Conslr:tin ls on Tire C ur in g
Othe.s
3. 2 Business Intelligence S)'Sle lll i\ lod el and i\ lobil c-Bascd So ft wa re fo r Prod ucti vi ty Eva lua tio n
The relation of productivity mcasurcmel1l and business intellige nce arc interpreted in KP[ and dashboard.
The numbers Ihat ha ve been calcu lated using lllethods above become the parameters to de termine the quality of
production. The dashboard itse! f helps management to decide dynami c-based eventS that usuall y happe ns during
production. All the d[lIa were collected in data warehouse from an y stakeholde rs. Data were processed in
com-putational exerc ises that consist of data mining technique to extr.tct infonnation and conclusion over the
scal-lered data. The il lustrati on of rea l lime 13 1 concept is Fig urc 4 below.
-
_--::-..,.
-
H
-
ｾ＠Ａｾ＠
J
f
-Figure 2. Business Intelligcllu' Com:ell '
The requirement will be elaborated using business process mode l. Business process model is :1 model to
help identifying, illustrating and elaborating a problem in a business. Business process is one or more procedurcs
to revea l ｢ｵ ｳ ｩ ｮ ･ｾｾ＠ objectives. usuall y in thc contcxt of organizational Slructurc thai define the fun ction.!1 ro le and
its re lat ion [9J. The mode l is usi ng Business Process Model No tation 2.0 thnt de fin es the process of information
and become the basis o f business intellige nce system arc hitecture. Business process model is illustrated in F ig. ure 5 below. The highlighted process is the critical busincss intell ige nce components thai will tran slated into dashboard.
Figun' J. Process Bl.sis ｆｲ｡ｾｲｮ･Ｂ ｴ＠ of Business Intelligence
[image:13.595.133.461.331.492.2]An Anal ysis and Design of Mobi le Business IllIclligcncc System for J' roduc ti vi ty /l.lca5urCnlCnt and [v:ll ua ti on in Tire Curing Product ion Line
Design an info nnation system into mobile-based package werc then compiled wilh object-oriented programming using Unifi ed Modeling Lang uage (UML)_ UM L is used 10 meet lhe visual modeling needs in s pec ifying, illus_
trating. build ing a]](1 doc ume ntatio n of software system. UML is also termed as vis u:11 langua ge for mOdeling
and communication in relation to a system !Ising supporting diagram [IOj. In th is paper, the so ft wlIre was built
using Andro id platfonn, which could be lIpplicd in an y recent devices. The software is real -t ime connected to the
serve r and othe r
Pc.
AVAtLAO IUTY P( RfORMANC[ QUAlfTY
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l
-I _
I
-I..
' 0E
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• •
!-OVERAll EQU IP M EtlT EHECTIVEt l£SS
,
...
J ..
1-PRODUCTION STATUS,
M AI N CON STRAINTS
Mold ｓ｣Ｑｴｬｮｾ＠
M ejd Repai. De'.cl Cure G"
..
,
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I
PROOUC110N OE SCRIPTION Da, • . 1 -01 · l01S
ｓｾ ｩ ｦｬ＠ : 2 PK : Mr.X
ａｃＱｲｯｲ ｾ＠ PLAN
ｆｉ ｉＡ ｕｈ Ｇ ｾＮ＠ Bl D:.shho;. nh for Prod ucli vit y ,\ l e:,slIremt' nl
4. Conclu sion
This pape r pro posed a co nstructio n 10 build Android based mobile Business Intell ige nce a pplication by fi rst identify ing c ri tica l parameters o f producti on line in e ffectiv c ness meas ureme nt a nd machine utilizatio n, and then by a na lyz ing the requiremcnt of info rmation system as a mobil e 01 system and cventua ll y by im cgra ting
the des ign in to a mobile sys te m. These computatio nal exercises we re elaboratcd as Key Pcrfoml;lr1ce Index
(KPI ) to be monilored o n das hboard and like ly to tri gge r manage ment decision which naturall y to cover the
dy-namic-based prod uc ti o n evcnts prod uction. By compiling end to end crit ica l fac to rs in tire cur ing production, the ;-das hboard wo ul d inform ope rato rs and manage ment the rea l time s itlill ti on in produc tion !inc as th e core of bus iness int elligence (01) concept. This mob ile BI-system of produ ction evaluation is the inili,,1 s teps and ex-pected to deve lop whole tire productio n process or other manufacturing compan)'. Fu rth ennort:. in o rd er to en-hance the foca l point of business inte lligence and OE E as an integrated system of en tcrprise resource planning
(E RP ) wi th Overall Financial Effectiveness (O FE) to measure the cost redu ction and asset dep rcc iation of
ma-c hinma-cry and equi pment.
5. Referenc es
11
J Herjanto, E., '\/(lIIagemelll O,Jeraliom', ｇｲ｡ ｾｩ ｮ ､ｯ Ｎ＠Jakarta. 2007.121
[JI
Reid, Dan R. and Nada R. S .. Opcl"lI/iol1$ A-/wwgelllcl1/ 11ll11l/('gI"IIICl/ Appl"()ac!l. John Wiley & Sons. Inc .. 2011, p.233.
/l.lineUi /1.1., Chnmbers /1.'1 .. ,md Dhir:lj t\ .. Em crging Busincss !lIId ligencc (1m! Allillytic T"clldJ jo,. Today'J IJtls;mw,
Jo hn Wiley & Sons, [nc., 20 U.
141
Maran. M. , G. M:lnikandan nnd K. Thiagarajan .. FII::Y E.t pert S)'s/ell/ jo /' P/alll Ol";/rall EquiplllclIll;,jJectil"i'II t'SS .. ｾ ｵﾭropcan Jou nml o f Sc ientific Research ISSN 1 ｾ Ｕ ＰＭＲ Ｑ Ｖｘ＠ Vol. 83 No.3 . EuroJoumals Publishing, Inc .. 20 12. pp-l30-4>S.
[51 llan. J., K. Micheline nnd PeL lian .. D(I/(I ,\Iilling COIICCPU tlwl TeclmiqllC:S Third £dili(III, Elscvier Inc.,
Massachu-seilS. 20 12.
[6] Kira, K. dnn Larry /\. R., A PraclicllllQ Feall/re Se/ec/iOI,. 1992.
[7J Suk rol11o, W., Tllrning l.oss il/lo Pmj il .. PT GWl11cdia l'uslaka Utama. Jabrta, 2010.
[8 1 Iannone. R., and Nenni M . E .. Managillg OE[ IQ Ol'limi:c Fac/or)" Pelfo rll/ lIIla'. Creative Commons Allribulioll li-cense. 2013.
[9J Draheim, D., Business Process Technology: A Vilified View Oil BllSill css f'rm.·dl·. Nell' York: Springer Heidelbcrg.
20 10.
[IOJ Rosa, A. :Illd M. SlwlahmlJin. 1 ... lml ul Pem bcl:.jaran Rcknyasa J'cmngkat Lunak (Tcrs rmktllr dan Beroricnt;lsi Objckj,
I .... [odulil. n andlln g, 20 12.
