跳到主要內容

臺灣博碩士論文加值系統

(34.236.36.94) 您好!臺灣時間:2021/07/24 21:19
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:王舜郁
研究生(外文):Shun-Yu Wang
論文名稱:以總體損失為基之生產系統關鍵改善決策分析機制
論文名稱(外文):Key Improvement Decision Analysis Mechanism based on Overall Loss of a Production System
指導教授:蕭堯仁蕭堯仁引用關係
指導教授(外文):Yau-Ren Shiau
口試委員:蕭堯仁王姿惠鄧志峰李旻陽洪永祥黃美玲
口試日期:2021-04-12
學位類別:博士
校院名稱:逢甲大學
系所名稱:工業工程與系統管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:英文
論文頁數:80
中文關鍵詞:時間損失性能損失材料損失總體生產線損失總體製程損失
外文關鍵詞:Time LossPerformance LossMaterial LossOverall Line LossOverall Process Loss
相關次數:
  • 被引用被引用:0
  • 點閱點閱:18
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在工業4.0的訴求下,有效應用製造執行系統(Manufacturing Execution System, MES)的即時反饋訊息,進行關鍵改善決策分析,以善用有限資源,並落實有效的品質改善,已成為企業運作之必需。
本研究以手工具為例,為提升其品質與鋼刄生產系統綜效,首先掌握原物料品質的穩定供應,訂定供應商精實評核機制,藉由製程價值流程展開,分析時間損失、性能損失、及材料損失之總體損失,以發展總體製程損失(Overall Process Loss, OPL)及總體生產線損失(Overall Line Loss, OLL),作為篩選出生產系統關鍵改善目標之雙指標,並建立生產系統關鍵改善決策分析機制。首先,本研究以Pareto分析,找出關鍵改善製程,接續展開損失肇因之改善決策,以執行損失項目改善;最後,再更新OLL分析,重新決定新的關鍵改善標的並執行改善。
本研究具體貢獻,首先是發展以總體損失為基的OPL及OLL雙指標,以快速找出總體損失源。其次是提出改善決策模型,及時運用有附加價值的改善流程進行改善。其三是建構可持續改善總體損失之機制,可持續找到新目標,並進行重點改善,達到生產系統之可持續改善之綜效。

關鍵詞:時間損失,性能損失,材料損失,總體生產線損失,總體製程損失。

Following Industry 4.0, the effective use of instant feedback from manufacturing execution systems to conduct decision-making analysis of key improvements, thereby maximizing the use of limited resources and facilitating quality enhancements, has become a must for enterprise operation.
This study used hand tools as examples. To increase their quality and the comprehensive performance of a steel blade production system, the stable supply of good-quality raw material was first controlled. Next, a mechanism for the solid assessment of suppliers was established. By expanding the production value procedure, total loss—time loss, performance loss, and material loss—was analyzed. Through this process, overall process loss (OPL) and overall line loss (OLL) were developed as dual indicators for screening key improvement targets for a production system. Furthermore, a mechanism for the decision-making analysis of key improvements in a production system was established. A Pareto analysis was first performed to identify the key improvement process. Subsequently, decisions to improve loss items contributing to loss causes were made. Finally, the OLL analysis was updated to determine the new key improvement target and to implement the improvement.
The contributions of this study are as follows. First, the dual indicators of overall process loss and OLL were identified on the basis of total loss, the source of which can be rapidly determined. Second, an enhanced decision-making model and improvement procedures that added value via timely execution were proposed. Third, a mechanism that could continue to improve total loss and find new targets, achieve key improvements, and continue to improve the production system was constructed.

Keywords: Time Loss, Performance Loss, Material Loss, Overall Line Loss, Overall Process Loss.

1 INTRODUCTION 1
1.1 GENERAL BACKGROUND AND MOTIVATION 1
1.2 RESEARCH PURPOSE 5
1.3 RESEARCH FLOW AND SCOPE 7
1.4 LIMITATIONS 8
2 LITERATURE REVIEW 9
2.1 DISCUSSION ON SUPPLIER ASSESSMENT 9
2.1.1 Supplier assessment mechanism 9
2.1.2 Decision-making model for supplier assessment 11
2.2 PRODUCTION MANAGEMENT SYSTEMS 13
2.2.1 Manufacturing Execution System, MES 13
2.2.2 Lean Production 15
2.3 PRODUCTION SYSTEM MEASUREMENT INDICES 19
2.3.1 Process capability 19
2.3.2 Overall Equipment Effectiveness 20
2.3.3 WIP quality inspection 25
2.4 SPC 27
2.4.1 Introduction 27
2.4.2 Introduction to control charts 28
2.4.3 Introduction to common control charts in precision processing 29
3 RESEARCH DESIGN AND IMPLEMENTATION 32
4 STUDY ANALYSIS AND RESULTS 36
4.1 MECHANISM FOR THE SOLID ASSESSMENT OF SUPPLIERS 36
4.1.1 Supplier assessment analysis 36
4.1.2 Assessment process standardization 40
4.2 PROCESS ANALYSIS OF THE PRODUCTION SYSTEM 41
4.2.1 Manufacturing process analysis 41
4.2.2 Overall loss analysis 44
4.3 ANALYSIS AND ESTABLISHMENT OF THE OVERALL LOSS MODEL 46
4.3.1 OPL analysis 46
4.3.2 OLL analysis 48
4.4 ESTABLISHMENT AND ANALYSIS OF THE KEY DECISION-MAKING IMPROVEMENT MECHANISM OF THE PRODUCTION SYSTEM 49
4.4.1 Key manufacturing process improvement analysis 49
4.4.2 Key improvement item analysis 50
4.4.3 Production system (OPL/OLL) updates 52
4.5 CONTROL CHART ESTABLISHMENT 53
4.5.1 Control chart selection 53
4.5.2 Control boundary establishment 53
4.6 PROCESS MONITORING 55
4.6.1 OPL51: Interpretation and analysis of overall loss in process monitoring and the execution of improvement decisions 55
4.6.2 Implementation of assessment, analysis, and the improvement decision for OPL52: process monitoring of total loss 58
4.6.3 Implementation of assessment, analysis, and the improvement decision for OPL53: process monitoring of total loss 60
4.7 RESULTS AND DISCUSSION 62
4.7.1 Loss item–based correlation analysis of each process 62
4.7.2 Production line–based correlation analysis of out-of-control processes 64
5 CONCLUSION AND RECOMMENDATIONS 66
5.1 CONCLUSION 66
5.2 RECOMMENDATIONS 68
REFERENCES 69
APPENDIX I. HYPOTHETICAL DATA FOR DMU INPUT AND OUTPUT ITEMS 77
APPENDIX II. OPL LIST 78
APPENDIX III. OLL LIST 79
APPENDIX IV. OPL51 LOSS SOURCE CHART FOR VARIOUS MANUFACTURING PROCESSES 80


English references:
Abbasi, S. A., Khaliq, Q. U. A., Omar, M. H., & Riaz, M. (2020). On designing a sequential based EWMA structure for efficient process monitoring. Journal of Taibah University for Science, 14(1), 177-191. doi:10.1080/16583655.2020.1712011
Alayón, C., Säfsten, K., & Johansson, G. (2017). “Conceptual sustainable production principles in practice: do they reflect what companies do?” Journal of Cleaner Production, 141, 693-701. doi:10.1016/j.jclepro.2016.09.079
Allwood, J. M., Ashby, M. F., Gutowski, T. G., & Worrell, E. (2011). Material Efficiency: A White Paper. Resources, Conservation and Recycling, 55(3): 362–381. doi:10.1016/j.resconrec.2010.11.002.
Azid, I. A., Ani, M. N. C., Hamid, S. A. A., & Kamaruddin, S. (2020). Solving production bottleneck through time study analysis and quality tools integration. International Journal of Industrial Engineering, 27(1), 13-27.
Besutti, R., de Campos Machado, V., & Cecconello, I. (2019). Development of an open source-based manufacturing execution system (MES): industry 4.0 enabling technology for small and medium-sized enterprises. Scientia cum Industria, 7(2), 1-11. doi:10.18226/23185279.v7iss2p1
Bork, C. A. S., de Souza, J. F., de Oliveira Gomes, J., Canhete, V. V. P., & De Barba, D. J. (2016). Methodological tools for assessing the sustainability index (SI) of industrial production processes. The International Journal of Advanced Manufacturing Technology, 87(5-8), 1313-1325. doi:10.1007/s00170-014-6684-8
Braglia, M., Castellano, D., Frosolini, M., & Gallo, M. (2018). Overall material usage effectiveness (OME): a structured indicator to measure the effective material usage within manufacturing processes. Production Planning & Control, 29(2), 143-157. doi:10.1080/09537287.2017.1395920
Carmignani, G. (2017). Scrap value stream mapping (S-VSM): a new approach to improve the supply scrap management process. International Journal of Production Research, 55(12), 3559-3576. Doi: 10.1080/00207543.2017.1308574
Chang, B., Chang, C. W., & Wu, C. H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert systems with Applications, 38(3), 1850-1858.
Chen, H. K., Lin, Y. H., & Yih, J. M. (2018). Comparisons on the Benchmarking of School Efficiency Based on SFA and DEA. International Journal of Intelligent Technologies & Applied Statistics, 10(4).
Chen, Y. J. (2011). Structured methodology for supplier selection and evaluation in a supply chain. Information Sciences, 181(9), 1651-1670.
Chien, C. F., Chu, P. C., & Zhao, L. (2015). Overall resource effectiveness (ORE) indices for total resource management and case studies. International Journal of Industrial Engineering, 22(5), 618-630.
Chien, C. F., Diaz, A. C., & Lan, Y. B. (2014). A data mining approach for analyzing semiconductor MES and FDC data to enhance overall usage effectiveness (OUE). International Journal of Computational Intelligence Systems, 7(sup2), 52-65. DOI 10.1080 / 18756891.2014.947114
da Cunha Alves, C., Konrath, A. C., Henning, E., Walter, O. M. F. C., Paladini, E. P., Oliveira, T. A., & Oliveira, A. (2019). The Mixed CUSUM-EWMA (MCE) control chart as a new alternative in the monitoring of a manufacturing process. Brazilian Journal of Operations & Production Management, 16(1), 1-13. Doi: 10.14488/BJOPM.2019.v16.n1.a1
Dobos, I., & Vörösmarty, G. (2014). Green supplier selection and evaluation using DEA-type composite indicators. International Journal of Production Economics, 157, 273-278.
Durán, O., & Durán, P. A. (2019). Prioritization of Physical Assets for Maintenance and Production Sustainability. Sustainability, 11(16), 4296. doi:10.3390/su11164296
Durán, O., Capaldo, A., & Duran Acevedo, P. A. (2018). Sustainable overall throughputability effectiveness (SOTE) as a metric for production systems. Sustainability, 10(2), 362, doi:10.3390/su10020362
Feyzioglu, O., & Büyüközkan, G. (2010). Evaluation of green suppliers considering decision criteria dependencies. In Multiple criteria decision making for sustainable energy and transportation systems, 145-154. Springer, Berlin, Heidelberg.
Garza-Reyes, J. A. (2015). From measuring overall equipment effectiveness (OEE) to overall resource effectiveness (ORE). Journal of Quality in Maintenance Engineering, 21(4), 506-527. doi:10.1108/JQME-03-2014-0014
Ghadimi, P., Dargi, A., & Heavey, C. (2017). Sustainable supplier performance scoring using audition check-list based fuzzy inference system: A case application in automotive spare part industry. Computers & Industrial Engineering, 105, 12-27.
Hafezalkotob, A., Ketabian, H., & Rahimi, H. (2014). Balancing the production line by the simulation and statistics techniques: A case study. Research Journal of Applied Sciences, Engineering and Technology, 7(4), 754-763.
Hashemia, S.H., Karimi, A., and Tavana, M., (2015). An integrated green supplier selection approach with analytic network process and improved Grey relational analysis. International Journal of Production Economics, 159, 178–191.
Huang, D., & Lv, J. (2020). Run-to-run control of batch production process in manufacturing systems based on online measurement. Computers & Industrial Engineering, 141, 106298. doi:10.1016/j.cie.2020.106298
Kumar, R., Singh, K., & Jain, S. K. (2019). Agile manufacturing: a literature review and Pareto analysis. International Journal of Quality & Reliability Management., 37(2), 207-222. doi: 10.1108/IJQRM-12-2018-0349
Lopes, Y. K., Rosso Jr, R. S., Leal, A. B., Harbs, E., & Hounsell, M. D. S. (2012). Finite Automata as an Information Model for Manufacturing Execution System and Supervisory Control Integration. IFAC Proceedings Volumes, 45(6), 212-217. 10.3182/20120523-3-RO-2023.00238
Maria Vanalle, R., & Blanco Santos, L. (2014). Green supply chain management in Brazilian automotive sector. Management of Environmental Quality: An International Journal, 25(5), 523-541.
Mirmousa, S., & Dehnavi, H. D. (2016). Development of criteria of selecting the supplier by using the fuzzy DEMATEL method. Procedia-Social and Behavioral Sciences, 230, 281-289.
Mohammed, W. M., Ramis Ferrer, B., Iarovyi, S., Negri, E., Fumagalli, L., Lobov, A., & Martinez Lastra, J. L. (2018). Generic platform for manufacturing execution system functions in knowledge-driven manufacturing systems. International Journal of Computer Integrated Manufacturing, 31(3), 262-274. doi:10.1080/0951192X.2017.1407874
Nachiappan, R. M., & Anantharaman, N. (2006). Evaluation of overall line effectiveness (OLE) in a continuous product line manufacturing system. Journal of Manufacturing Technology Management, 17(7), 987-1008. doi:10.1108/17410380610688278
Novikov, V. A., Sapun, O., & Shipulina, L. (2016). Accounting the scale and synergies in the DEA-Analysis. LogForum, 12(2), 123-128.
Pereira, A. M., Silva, M. R., Domingues, M. A., & Sá, J. C. (2019). Lean Six Sigma Approach to Improve the Production Process in the Mould Industry: a Case Study. Quality Innovation Prosperity, 23(3), 103-121. doi:10.12776/qip.v23i3.1334
Ramesh, S., & Vasu, B. (2019). Application of EWMA chart for monitoring process mean in paper industry. Management Science Letters, 9(4), 571-576. doi: 10.5267 / j.msl.2019.1.006
Rozs, R., & Ando, M. (2020). Collaborative Systems, Operation and Task of the Manufacturing Execution Systems in the 21st Century Industry. Periodica Polytechnica Mechanical Engineering, 64(1), 51-66. doi:10.3311/PPme.14413
Sanusi, R. A., Teh, S. Y., & Khoo, M. B. (2020). Simultaneous monitoring of magnitude and time-between-events data with a Max-EWMA control chart. Computers & Industrial Engineering, 142, 106378. Doi: 10.1016/j.cie.2020.106378
Sarkis, J., & Dhavale, D. G. (2015). Supplier selection for sustainable operations: A triple-bottom-line approach using a Bayesian framework. International Journal of Production Economics, 166, 177-191.
Shewhart, W. A. (1926). Quality control charts. The Bell System Technical Journal, 5(4), 593-603.
Shiau Y. R., Wang S. Y., & Chien C. K. (2020). Lean Assessment Mechanism of Supplier based on Multiple-to-multiple Benchmarking Learning. Journal of Management & Systems, 27(2), 123-144. doi:10.29416/JMS.202004_27(2).0002
Shiau Y. R., Wang S. Y. (2021). Key improvement decision analysis mechanism based on overall loss of a production system. Journal of Industrial and Production Engineering, Vol. 38, No. 1, pp. 66–73. https://doi.org/10.1080/21681015.2020.1841687
Simić, D., Kovačević, I., Svirčević, V., & Simić, S. (2017). 50 years of fuzzy set theory and models for supplier assessment and selection: A literature review. Journal of Applied Logic, 24, 85-96.
Wudhikarn, R. (2016). Implementation of the Overall Equipment Cost Loss (OECL) Methodology for Comparison with Overall Equipment Effectiveness (OEE). Journal of Quality in Maintenance Engineering, 22(1): 81–93. doi:10.1108/JQME-12-2011-0001.
Yüksel, H., & Uzunovic, Z. F. (2019). Application of Value Stream Mapping in a Manufacturing Firm in Bosnia and Herzegovina. Yonetim ve Ekonomi, 26(1), 201-219. Doi: 10.18657/yonveek.499994
Zhang, L., Chen, X., & Suo, Y. (2017). Interrelationships among critical factors of work flow reliability in lean construction. Journal of Civil Engineering and Management, 23(5), 621-632. doi:10.3846/13923730.2016.1217921
Zhang, W., Zhang, S., Guo, S., Yang, Y., & Chen, Y. (2017). Concurrent optimal allocation of distributed manufacturing resources using extended teaching-learning-based optimization. International Journal of Production Research, 55(3), 718-735.
Chinese references:
Chan, K.C., Ho, Y., & Cheng, C.S. (2019). Using Six Sigma Methodologyto Improve the Copper Plating Uniformity in Flip Chip Ball Grid Array. Journal of Quality, 26(2), 114–125. DOI: 10.6220/joq.201904_26(2).0003
Cheng, K.H., Hsia, C.C., Yeh, J.B., Chiang, Y.L., Lin, S.C., & Tsai, H.W. (2016). WIP Quality Detection and Defect Classification based on Ensemble Learning Methods. Journal of Information and Communications Technology, 80–89.
Chiang, K. W. (2018). Maximizing the production efficiency of small and medium-sized manufacturing enterprises via optimizing industrial processes: A case study of semiconductor protection device assembly and testing company. National Taiwan University. Master’s thesis, Taipei City.
Huang, Y.C., Lin Y.R., & Zhang, Y.X. (2017). A Comparative Study on Shewhart, CUSUM and EWMA Attribute Control Charts. Management Research, 17, 43–86.
Lin, W.T., Huang, C.T., & Tung, H.P. (2015). A Study of Applying Six Sigma to Improve the Process of Hex Bit Socket. Journal of Innovation and Business Management, 6(2), 50–63. doi:10.6270/JIBM.2015.6(2)50
Pan, J.N. & Lee, W.R. (2003). Quality Management, first edition. Hwa Tai Publishing, Taipei City.
Pan, J.N., Li C.I., & Yang J.R. (2019). Monitoring the Process Quality for Multistage Systems Using New Mixed EWMA-CUSUM Control Chart. Journal of the Chinese Statistical Association, 57(3), 232–262.
Tseng, W.K (2019). How to use MES in Smart Manufacturing—The Case Study of Metal Processing. Journal of Industrial Mechatronics, 440, 81–89.
Wu, C.F., Lin, Kai-Chieh, Yang, Chao-Yang, & Liao., S.F. (2016). The Influence of Relationship between Distinct Hand Tools and Universal Design Scales with Screwdriver. Journal of Design, 21(4),25–43.
Yang, C.C. (2019). The Implementation of Quality Management in Taiwan. Quality Journal, 55(3), 6–18. DOI:10.29999/QM
Yu, K. Y & Su, L.P. (2019). Integrating Six Sigma and Lean Production into ISO 9000 Quality Management System. Journal of Quality. 26(2), 92–113. DOI:10.6220/joq.201904_26(2).0002

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
無相關點閱論文