臺灣博碩士論文加值系統

在工業4.0中，生產系統引進預測性維修是一項重要的策略。預測性維修即為使用感測器以及數據資料進行狀態監控，以使系統能在當機前先一步進行維修。本研究考慮了一個有意願投入資源於預測性維修的製造商以及一個可承接維修計畫的外部承包商。模型的目標即在最小化成本下決定最佳預測性維修投入程度。為達成通路合作，我們提出了三種激勵性合約: (i) 維修成本補貼合約; (ii) 運轉時間指標與紅利合約; (iii) 運轉時間額外獎勵合約，吸引並提高承包商的合作意願，以降低雙方成本並創造雙贏的局面。而此三種合約都擁有激勵承包商與製造商精進各自技術的能力，並為在達成通路合作後所獲得的額外利益提供了不同的分配方式。我們也同時討論了當失效率與維修時間與成本改善時此三種合約的可行性。研究結果顯示，當失效率與維修時間或成本改善時，運轉時間指標與紅利合約以及運轉時間額外獎勵合約可在滿足特定條件下達成通路合作。

For Industry 4.0, introducing predictive maintenance into production systems is an important policy. Predictive maintenance uses sensors and data analysis to monitor production systems so that maintenance could be performed before the system shutdown. This study considers a manufacturer who willing to make the predictive maintenance effort and an external contractor who can undertake maintenance activities. The objective of this model is to determine the optimal predictive maintenance effort to minimize total costs. To achieve channel coordination, we propose three incentive contracts: (i) maintenance cost subsidization contract; (ii) the uptime target and bonus contract; (iii) the uptime reward contract, which enhances the cooperation willingness of the contractor and decreases the costs of both parties, thus leading to the win-win situation. Moreover, all contracts have the ability to motivate contractor and manufacturer to be sophisticated and provide different options for allocating the additional profit yielded from channel coordination. We also discuss the feasibility of the contracts when the failure rate and maintenance capability could be improved. The result shows that the uptime target and bonus contract and the uptime reward contract can achieve channel coordination by satisfying specific conditions when the failure rate and maintenance capability are improved.

摘要 II
ABSTRACT III
CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VII
CHAPTER 1 1
1.1 Background and Motivation 1
1.2 Research Objectives 3
1.3 Research Organization 4
CHAPTER 2 6
2.1 Channel Coordination 6
2.2 Maintenance Outsourcing 8
2.3 Predictive Maintenance 9
CHAPTER 3 11
3.1 Notations and Assumptions 11
3.2 Based Model 13
3.3 Coordination contracts 16
3.3.1 Maintenance Cost Subsidization Contract 17
3.3.2 The Uptime Target and Bonus Contract 19
3.3.3 The Uptime Reward Contract 21
CHAPTER 4 25
CHAPTER 5 28
5.1 Conclusion 28
5.2 Future Research 29
REFERENCE 30
Appendix 34

Baur, C. and Wee, D. (2015) Manufacturing’s next act. McKinsey Quarterly, Jun.Avlible:http://www.mckinsey.com/business-functions/operations/our-nsights/manufacturings-next-act.
BHARDWAJ, P. (2005) Managing Channel Profits: The Role of Managerial Incentives. Quantitative Marketing and Economics, 247-279.
Chu, W. and Desai, P.S. (1995) Channel coordination mechanisms for customer satisfaction. Marketing Science,14, 343-359.
Coresystems (2016) The Difference Between Predictive Maintenance and Preventive Maintenance. Avlible: https://www.coresystems.net/blog/the-difference-between- predictive-maintenance-and-preventive-maintenance.
Curcurù, G., Galante, G. and Lombardo, A. (2010) A predictive maintenance policy with imperfect monitoring. Reliability Engineering & System Safety,95, 989-997.
De Faria, H., Costa, J.G.S. and Olivas, J.L.M. (2015) A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis. Renewable and Sustainable Energy Reviews,46, 201-209.
Deloux, E., Castanier, B. and B erenguer, C. (2009) Predictive maintenance policy for a gradually deteriorating system subject to stress. Reliability Engineering & System Safety, 418-431.
Fiix (2017) What is predictive maintenance? Avlible: https://www.fiixsoftware.com/- maintenance-strategies/predictive- maintenance/.
Hamidi, M., Liao, H. and Szidarovszky, F. (2016) Non-cooperative and cooperative game-theoretic models for usage-based lease contracts. European Journal of Operational Research,255, 163-174.
IBM (2016) Prevent asset failure, detect quality issues and improve operational processes. Avalible: https://www-01.ibm.com/software/analytics/solutions/opera- tional-analytics/predictive-maintenance/.
Jeuland, A.P. and Shugan, S.M. (1983) Managing Channel Profits. Marketing Science,2, 239-272.
L.Lee, H. (2004) The Triple-A Supply Chain. Harvard Business Review.
Li, L., Liu, M., Shen, W. and Cheng, G. (2016) A Discrete Stress–Strength Interference Theory-Based Dynamic Supplier Selection Model for Maintenance Service Outsourcing. IEEE Transactions on Engineering Management,63, 189-200.
Li, S., Huang, H.-Z., Wang, P. and Jin, T. (2016) Design of maintenance outsourcing contracts considering fleet expansion. IEEE, pp. 1-7.
Maillart, L.M. and Pollock, S.M. (2002) Cost-optimal condition-monitoring for predictive maintenance of 2-phase systems. IEEE Transactions on Reliability,51, 322-330.
Murthy, D.N.P., Karim, M.R. and Ahmadi, A. (2015) Data management in maintenance outsourcing. Reliability Engineering & System Safety,142, 100-110.
Park, C., Moon, D., Do, N. and Bae, S.M. (2015) A predictive maintenance approach based on real-time internal parameter monitoring. The International Journal of Advanced Manufacturing Technology,85, 623-632.
Pascual, R., Santelices, G., Liao, H. and Maturana, S. (2016) Channel coordination on fixed-term maintenance outsourcing contracts. IIE Transactions,48, 651-660.
Pitt, T.J. (1997) Data requirements for the prioritization of predictive building maintenance. Facilities,15, 97-104.
Porter, M.E. and Heppelmann, J.E. (2014) How Smart, Connected Products Are Transforming Competition. Harvard Business Review.
Sarkar, B. (2016) Supply chain coordination with variable backorder, inspections, and discount policy for fixed lifetime products. Mathematical Problems in Engineering,2016.
Shin, J.-H. and Jun, H.-B. (2015) On condition based maintenance policy. Journal of Computational Design and Engineering,2, 119-127.
Smith, R. (2015) 4 Reasons Why Predictive Maintenance Does not Work. Available: https://www.linkedin.com/pulse/4-reasons-why-predictive-maintenance-does-work-ricky.
Swami, S. and Shah, J. (2012) Channel coordination in green supply chain management. Journal of the Operational Research Society,64, 336-351.
Tarakci, H., Tang, K. and Teyarachakul, S. (2009) Learning effects on maintenance outsourcing. European Journal of Operational Research,192, 138-150.
Tarakci, H., Tang, K., Moskowitz, H. and Plante, R. (2006) Incentive maintenance outsourcing contracts for channel coordination and improvement. IIE Transactions,38, 671-684.
Tarakci, H., Tang, K., Moskowitz, H. and Plante, R. (2006) Maintenance outsourcing of a multi-process manufacturing system with multiple contractors. IIE Transactions,38, 67-78.
Tseng, F.-S. and Yeh, Y. (2013) Maintenance outsourcing coordination with risk-averse contractors. Journal of the Operational Research Society,65, 1760-1769.
Wen, D., Ershun, P., Ying, W. and Wenzhu, L. (2014) An economic production quantity model for a deteriorating system integrated with predictive maintenance strategy. Journal of Intelligent Manufacturing,27, 1323-1333.
Xu, X., Cheng, X. and Sun, Y. (2015) Coordination contracts for outsourcing supply chain with financial constraint. International Journal of Production Economics,162, 134-142.
Yuanyuan, L. and Jiang, S. (2015) Research on Equipment Predictive Maintenance Strategy Based on Big Data Technology, in Intelligent Transportation, Big Data and Smart City (ICITBS), 2015 International Conference on, IEEE, pp. 641-644.
Zhang, L., Wang, J. and You, J. (2015) Consumer environmental awareness and channel coordination with two substitutable products. European Journal of Operational Research,241, 63-73.