臺灣博碩士論文加值系統

供應鏈之風險是無法避免的，由於目前全球災難事件接二連三的出現，例如最近於日本發生之海嘯、龍捲風襲擊美國、洪水及地震的發生以及恐怖主義及政治的動盪，因此供應鏈將會發生中斷，而所謂的中斷意旨公司停止運作以至於產生無法使用之產品；而當供應商面臨到供應中斷時，供應商必須要有能力恢復並使其達到正常之運作狀態；而面對供應之風險，製造商必須要能做出迅速之反應並更換其供應商當原有之供應商無法履行訂單任務時，而雖然主要之供應商通常都能夠履行任務，但製造商仍然需要監督其備用之供應商。本研究由於考慮以上供應鏈之狀況，因此透過馬可夫鏈模型及可靠度工程之理論進行分析並探討供應中斷對於供應鏈所造成之影響；本研究將考慮以下兩種模式：兩個供應商（由主要之供應商及備用之供應商所組成）、三個供應商（由一個主要供應商及兩個備用之供應商組成）；而每種模式將透過兩種情況進行討論之。第一種情況假設其供應商之故障率及修復率相同，而第二種情況則是假設其故障率及修復率之參數為不相同。本研究同時也將由於供應中斷造成製造商之財務影響因素納入考慮。本研究將可以提供給予藉由使用馬可夫鏈模型及可靠度工程概念並探討供應鏈中斷時之供應商及製造商之績效的初步研究。

Supply chain risks are unavoidable due to the common occurrence of tragic events such as the recent tsunami in Japan, tornado attack in different cities of US, floods, earthquake, terrorism, and political instability. As a result, disruption will occur. Disruptions refer to the stoppage of a company’s operation resulting to the unavailability of products. When the supplier faces disruptions, it must be able to restore to its normal operation. In response to supply risk, the manufacturer must act quickly whenever its suppliers cannot fulfill its order by switching to another supplier. Though the active supplier delivers for the manufacturer, the manufacturer needs to detect its standby supplier’s failure. Considering this supply chain scenario, this study proposes a Markov chain model to analyze the effect of disruption on supply chain availability using Markov chain and employing reliability engineering concepts. The study considers two models: two supplier (one active-one standby supplier) and three-supplier (one-active-two-standby supplier). Each model is discussed in two cases. The first case assumes that the failure rate and repair rate of the suppliers are the same. While in the second case, these parameters are different. The financial impacts of disruption on the manufacturer’s profit are included in the study. This paper could serve as a preliminary study in supply chain disruption that considers the performance of suppliers and manufacturer in a supply chain by employing reliability engineering concepts and Markov chain model.

TABLE OF CONTENTS

SUMMARY
摘要 i
ABSTRACT ii
ACKNOWLEDGMENTS iii
LIST OF TABLES vii
LIST OF FIGURES viii
LIST OF NOTATIONS ix

Chapter 1 . INTRODUCTION 10
1.1 Background 10
1.2 Supply chain disruptions 12
1.3 The application of reliability engineering theories on SCM 15
1.3.1 Reliability engineering theories 15
1.3.2 Supply chain 17
1.4 Markov model 22
1.5 Research questions 24
1.6 Research objectives 24
1.7 Overview 25

Chapter 2 . LITERATURE REVIEW 26
2.1 Supply chain disruption 27
2.2 Application of reliability engineering theories on SCM 29

Chapter 3 . METHODOLOGY 30

Chapter 4 . ONE ACTIVE-ONE STANDBY SUPPLY CHAIN SYSTEM 33
4.1 CASE 1: IDENTICAL SUPPLIERS 34
4.1.1 Assumptions 34
4.1.2 Model development 34
4.1.3 Numerical example 41
4.2 CASE 2: DISTINCT SUPPLIERS 44
4.2.1 Model development 44
4.2.3 Numerical example 46
4.3 SENSITIVITY ANALYSES 48
4.4 INSIGHTS 52

Chapter 5 . ONE ACTIVE-TWO STANDBY SUPPLY CHAIN SYSTEM 53
5.1 CASE 1: IDENTICAL SUPPLIERS 54
5.1.1 Assumptions 54
5.1.2 Model development 54
5.2 CASE 2: DISTINCT SUPPLIERS 65
5.2.1 Model development 65
5.2.2 Numerical example 70
5.3 SENSITIVITY ANALYSES 71
5.4 INSIGHTS 78

Chapter 6 . FINANCIAL IMPACT 79

Chapter 7 . RESULTS, CONCLUSIONS, INSIGHTS AND RECOMMENDATIONS 91
7.1 Summary of results 91
7.2 Conclusions 92
7.3 Management insights 92
7.4 Recommendations 93

REFERENCES 95
APPENDIXES 101





LIST OF TABLES

Table 2.1 Related literature on supply chain disruption 27
Table 2.2 Related literature on supply chain reliability 29
Table 4.1 The state description of the Markov model for Model 1: Case 1 35
Table 4.2 The transition rate for Model 1: Case 1 38
Table 4.3 The state probability equation for Model 1: Case 1 40
Table 4.4 The steady-state probabilities and transition rate for Model 1: Case 1 42
Table 4.5 The transition rate for Model 1: Case 2 45
Table 4.6 The steady-state probabilities and transitions rate value for Model 1: Case 2 48
Table 4.7 The effect of the parameter on the supply chain availability for Model 1: Case 1 49
Table 4.8 The effect of the failure rate on the supply chain availability for Model 1: Case 2 50
Table 4.9 The effect of the failure rate on the supply chain availability for Model 1: Case 2 51
Table 5.1 The state description in the Markov model for Model 2: Case 1 56
Table 5.2 The transition rate in the Markov model for Model 2: Case 1 58
Table 5.3 The steady-state probabilities and transition rate value for Model 2: Case 1 64
Table 5.4 The state description in the Markov model for Model 2: Case 2 68
Table 5.5 The effect of the parameter on the supply chain availability for Model 2: Case 1 71
Table 5.6 A comparison of the effect of failure rate on the availability for Model 1 and 2: Case 1 73
Table 5.7 A comparison of the effect of repair rate on the availability for Model 1 and 2: Case 1 74
Table 5.8 A comparison of the effect of (c) on the availability for Model 1 and 2: Case 1 75
Table 5.9 A comparison of the effect of the (d) on the availability for Model 1 and 2: Case 1 76
Table 5.10 A comparison of the effect of (1/k) on the availability for Model 1 and 2: Case 1 77
Table 6.1 The state cost condition of each player for Model 1: Case 1 80
Table 6.2 The transition rate of the manufacturer for Model 1: Case 1 80
Table 6.3 The associated cost with the transition rate of the manufacturer for Model 1: Case 1 80
Table 6.4 The steady-state probabilities and transition rate for Model 1: Case 1 81
Table 6.5 The state cost condition of each player for Model 1: Case 2 82
Table 6.6 The transition rate for Model 1: Case 2 82
Table 6.7 The associated cost with the transition rate of the manufacturer for Model 1: Case 2 83
Table 6.8 The state cost condition of each player for Model 2: Case 1 84
Table 6.9 The transition rate of the manufacturer for Model 2: Case 1 84
Table 6.10 The associated cost with the transition rate of the manufacturer for Model 2: Case 1 85
Table 6.11 The steady-state probabilities and transition rate for Model 2: Case 1 85
Table 6.12 The effect of the parameters on the manufacturer's profit 89
Table 7.1 The effect of the parameters on supply chain availability 91
Table 7.2 The effect of the parameters on the supply chain availability 91
Table 7.3 The manufacturer’s profit for the two-supplier and three-supplier 92



LIST OF FIGURES

Figure 1.1 The types of supply chain risk 13
Figure 1.2 The three elements of SC disruption management 14
Figure 1.3 The application of reliability concepts on SCM 15
Figure 1.4 The association of reliability concepts and SCM in the Markov model 19
Figure 1.5 The steady-state availability 23
Figure 1.6 The framework of the study 25
Figure 3.1 Methodology of the study 30
Figure 4.1 RBD for one-active-one-standby supply chain system 33
Figure 4.2 The state transition diagram for two-supplier with the same parameter value 37
Figure 4.3 The Markov model for Model 1: Case 1 39
Figure 4.4 Model 1: Case 1 illustrated using Relex 11 42
Figure 4.5 The Markov model for Model 1: Case 2 46
Figure 4.6 Model 1: Case 2 illustrated using Relex 11 47
Figure 4.7 The effect of the parameters on the supply chain availability for Case 1: Model 1 50
Figure 4.8 The effect of the active and standby supplier's failure rate on the supply chain availability for Model 1: Case 2 51
Figure 4.9 The effect of the active and standby supplier's repair rate on the supply chain availability for Model 1: Case 2 52
Figure 5.1 RBD with three parallel components 53
Figure 5.2 The Markov model for Model 2: Case 1 61
Figure 5.3 Model 2: Case 1 illustrated using Relex 11 63
Figure 5.4 The Markov model for Model 2: Case 2 69
Figure 5.5 Model 2: Case 2 illustrated using Relex 11 70
Figure 5.6 The effect of the parameters on the supply chain availability for Model 2: Case 1 72
Figure 5.7 A comparison of Model 1 and 2 for the effect of failure rate on the supply chain availability 73
Figure 5.8 A comparison of Model 1 and 2 for the effect of repair rate on the supply chain availability 74
Figure 5.9 A comparison of Model 1 and 2 for the effect of (c) on the supply chain availability 75
Figure 5.10 A comparison of Model 1 and 2 for the effect of (d) on the supply chain availability 76
Figure 5.11 A comparison of Model 1 and 2 of the effect of the (1/k) on the supply chain availability 77
Figure 6.1 The effect of the parameters on the manufacturer's expected profit for two-supplier system 87
Figure 6.2 The effect of the parameters on the manufacturer's expected profit for three-supplier system 88

REFERENCES

1.Banisalam, S., 2008. A risk management tool for the reverse supply chain network. Thesis, The Faculty of California Polytechnic State University.
2.Better purchasing guide: Managing and monitoring supplier’s performance. Queensland Government, Department of Public Works, September 2000 edition. (Source: http://www.qgm.qld.gov.au/00_downloads/bpg_monitoring.pdf).
3.Chongchao, H., Gang, Y., Song, W., Xzanjza, W., 2006. Disruption management for supply chain coordination with exponential demand function. Acta Mathematica Scientia, 26 (4), 655-669.
4.Chopra, S., Meindl, P., 2002. Supply Chain Management: Strategy, Planning, and Operation.IIE Transactions, 34, 221-222.
5.Chopra, S., Sodhi, M.S., 2004. Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46 (1), 53-61.
6.Clausen J., Hansen, J., Larson, J., Larson, A., 2001. Disruption management. OR/MS Today, 40-43.
7.Cooper, M.C., Lambert, D.M., Pagh, J.D., 1997.Supply chain management: More than a new name for logistics. International Journal of Logistics Management, 8 (1), 1-13.
8.Duarte, S., Carvalho, H., Machado, V., 2010. Exploring relationships between supply chain performance measures. Proceedings of the Fourth International Conference on Management Science and Engineering Management, 91-95.
9.Giunipero, L. C., Eltantawy, R. A., 2004. Securing the upstream supply chain: a risk management approach. International Journal of Physical Distribution and Logistics Management, 34, 698-713.
10.Harland, C., Brenchley, R., Walker, H., 2003. Risk in supply networks. Journal of Purchasing and Supply Management, 9, 51–62.
11.Hendricks, K., Singhal, V., 2005. An empirical analysis of the effect of supply chain disruptions on long-run stock price performance and equity risk of the firm. Production and Operations Management, 14 (1), 35-52.
12.Hoyland, A., Rausand, M., 1994. System reliability theory: Models and statistical methods. John Wiley & Sons, Inc., 93.
13.Goh, M., Lim, J.Y.S., Meng, F., 2007. A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 182, 164–173.
14.Jia, X., Cui, L., 2008. A study on reliability of supply chain based on higher order Markov chain. IEEE International Conference on Service Operations and Logistics, and Informatics, 2, 2014-2017.
15.Kersten, W., Hohrath, P., Boger, M., 2007. An empirical approach to supply chain risk management: Development of a strategic framework. 18th Annual POMS Conference, Dallas.
16.Kleindorfer, P.R., Saad, G.H., 2005. Managing disruption risks in supply chains. Production and Operations Management, 14 (1), 53-68.
17.Kuo, W., Zuo, M.J., 2003. Optimal reliability modeling: Principles and Applications. John Wiley & Sons, Inc., 1.
18.Lewis, E.E., 1987. Introduction to reliability engineering. John Wiley & Sons, Inc.
19.Liang, X.L., Liu, M., Tan, L., 2009. Supply chain repairable model with the multi-suppliers and single demander. The Eighth International Symposium on Operations Research and Its Applications, 315–322.
20.Li, J., Wang, S., Cheng, T., 2010. Competition and cooperation in a single-retailer two-supplier supply chain with supply disruption. International Journal of Production Economics, 124, 137–150.
21.Lin, Y.K., 2009. System reliability evaluation for a multistate supply chain network with failure nodes using minimal paths. IEEE Transactions on Reliability, 58 (1), 34-40.
22.Liu, Y., Li, J., Jia, X., 2008. Reliability of k-out-of-n:G system in supply chain based on Markov chain. IEEE International Conference on Service Operations and Logistics, and Informatics, 1, 1390-1393.
23.Liu, Y., Peng, B., 2009. Achieving robustness objectives within a supply chain by means of reliability allocation. Proceedings of the 2009 International Symposium on Web Information Systems and Applications, 210-213.
24.Longo, F., Oren, T., 2008. Supply chain vulnerability and resilience: A state of the art overview. Proceedings of European Modeling and Simulation Symposium, 17-19.
25.Meena, P. L., Sarmah, S. P., 2011. Supplier selection under the risk of supply disruptions. Proceedings of the International Conference on Industrial Engineering and Operations Management, 711-716.
26.Nowakowski, T., 2008. Problems of supply process reliability assessment at small and medium-sized enterprises. Total Logistic Management, 125-136.
27.Pfohl, H.C., Kohler, H., Thomas, D., 2010. State of the art in supply chain risk management research: empirical and conceptual findings and a roadmap for the implementation in practice. Logistics Research, 2 (1), 33-44.
28.Pricewater house Coopers. Internal audit perspectives: Increased level of supply chain risk joins growing chain of challenges, 2009 edition. (Source: www.pwc.com/.../risk/internal-audit/.../perspectives-supply-chain-risk-2009- 07-en.pdf).
29.Pujawan, N., Geraldin, L., 2009. House of risk: a model for proactive supply chain risk management. Business Process Management Journal, 15 (6), 953-967.
30.Pukite, J., Pukite, P., 1998. Modeling for reliability analysis. IEEE Press Series on Engineering of Complex Computer Systems.
31.Qi, L., Shen, Z.J., Snyder, L., 2009. A continuous-review inventory model with disruptions at both Supplier and retailer. Production and Operations Management, 1-17.
32.Ran, L., Jia, X., Tian, R., 2009. Modeling and analyzing supply chain reliability by different effects of failure nodes. International Conference on Information Management, Innovation Management and Industrial Engineering, 4, 396-400.
33.Rice, J., 2003. Supply Chain Response to Terrorism: Creating Resilient and Secure Supply Chains. Supply Chain Response to Terrorism Project, MIT Center for Transportation and Logistics.
34.Ritchie, B., Brindley, C., 2007. Supply chain risk management and performance: A guiding framework for future development. International Journal of Operations and Production Management, 27 (3), 303-322.
35.Ross, A., Rong, Y., Snyder, L., 2008. Supply disruptions with time-dependent parameters. Computers and Operations Research, 35, 3504 – 3529.
36.Ross, S., 1972. Introduction to probability models. Academic press, Ninth edition.
37.Schmitt, A., 2011. Strategies for customer service level protection under multi-echelon supply chain disruption risk. Transportation Research Part, Article in press.
38.Schmitt, A., Singh, M., 2009. Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation. Proceedings of the 2009 Winter Simulation Conference, 1237-1248.
39.Shen, X., Fu, L., Gao, Y., 2008. Reliability analysis of supply chain based on typical irreparable system and Markov reparable system. IEEE International Conference on Service Operations and Logistics, and Informatics, 2, 2062-2065.
40.Snyder, L.V., 2003. Supply chain robustness and reliability: Models and algorithms. Illinois State: Northwestern University.
41.Snyder, L., Scaparra, P., Daskin, M., Church, R., 2005. Planning for disruptions in supply chain networks. INFORMS: Tutorials in Operations Research, 1-22.
42.Stanshine, J., Bellcore, Bank, R., 1995. Modeling silent failures in telecommunications systems. Proceedings in Reliability and Maintainability Symposium, 261-264.
43.Tang. C., 2006. Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, 9 (1), 33–45.
44.Telcordia SR-TSY_001171, 2007. Methods and procedures for system reliability analysis. Issue 2.
45.Thomas, M.U., 2002. Supply chain reliability for contingency operations. Proceedings Annual, Reliability and Maintainability Symposium, 61-67.
46.Wang, H., Liu, H., Yang, J., 2009. Dynamic analysis of a two-stage supply chain-a switched system theory approach. International Journal of Advanced Manufacturing Technology, 43, 200–210.
47.Wilson, M., 2007. The impact of transportation disruptions on supply chain performance. Transportation Research, Part E, 43, 295–320.
48.Wu, J., Zhang, X., 2005.Evaluation of reliability of distribution service. International Conference on Services Systems and Services Management, 1, 332- 334.
49.Wu, T., Blackhurst, J., 2005. A modeling methodology for supply chain synthesis and disruption analysis. International Journal of Knowledge-based and Intelligent Engineering Systems, 9, 93–105.
50.Xu, J., 2008. Managing the risk of supply chain disruption: Towards a resilient approach of supply chain management. ISECS International Colloquium on Computing, Communication, Control, and Management, 3-7.
51.Yu, H., Zeng, A., Zhao, L., 2009. Single or dual sourcing: Decision making in the presence of supply chain disruption risks. Omega, 37, 788–800.
52.Zsidisin, G., 2001. Measuring supply risk: An example from Europe. PRACTIX: Best Practices in Purchasing and Supply Chain Management, 4 (3), 1-6.