Methodologies for performance enhancement in decentralized supply chains

METHODOLOGIES FOR PERFORMANCE ENHANCEMENT IN DECENTRALIZED SUPPLY CHAINS SUNDAR RAJ THANGAVELU NATIONAL UNIVERSITY OF SINGAPORE 2009 METHODOLOGIES FOR PERFORMANCE ENHANCEMENT IN DECENTRALIZED SUPPLY CHAINS SUNDAR RAJ THANGAVELU (M.Tech., I.I.T. Kharagpur, India) (B.Tech., University of Madras, India) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF CHEMICAL AND BIOMOLECULAR ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2009 Dedicated to My Dear Parents Acknowledgement My deepest thanks to my parents and family members for their continuous support, interest and encouragement. They would be my all time assets to pursue my wishes. Next to family, I would like to thank my supervisor Dr Lakshminarayanan Samavedham (Laksh) for his kind guidance, support and timely feedback. His suggestions helped me a lot to advance further in my research. I admire his enthusiasm in motivating others to excel in diverse research areas. I personally benefited by having Dr Laksh as my supervisor in aspects such as of improving communication skills, coordinating team work with my colleagues and interactions with visitors and final year students. His encouragement allowed me to spare time for departmental activities (teaching activities and tutoring) and ChBE Graduate Student Association. Thanks Sir for all the advice and help. I was very fortunate to have Prof Karimi and Prof Karthik Natarajan as the panel members for examining my research proposal and providing their valuable suggestions to improve my research focus. I am grateful to Prof Fraser for facilitating and funding my visit to Department of Chemical and Materials Engineering, University of Alberta, Canada during September 2008 to November 2008. In spite of his busy schedule, the way he managed his time and organized himself for discussions with me is tremendous. During my tenure as a PhD student, I got a chance to assist Prof Rangaiah, Prof M.P Srinivasan, Prof Raj Srinivasan and Prof Krishnaswamy in running courses. They shared their views on different topics in a friendly manner; my sincere thanks to all of them. Nothing comes for free; especially computational facility which is very much significant requirement challenge for my research. Mr Mao Ning and Mr Boey (ChBE, NUS) and Bob Barton (UofA) ensured the required computational resources throughout my candidature. Their timely contribution and the help from the Chemical and Biomolecular Department are much appreciated. My stay at National University of Singapore has been a memorable one because of my department friends, labmates and roommates. They made my stay lively and enjoyable. I am thankful to all of them and to my present and past IPCU colleagues for their excellent assistance and discussions. Finally, a big thanks to National University of Singapore and University of Alberta for giving me the opportunity to pursue my research with advanced research facilities and financial support. Table of Contents List of Figures v List of Tables vii Abbreviations viii Symbols xi Introduction 1.1 Supply Chain System - An Overview . . . . . . . . 1.2 Supply Chain Decision Levels . . . . . . . . . . . . 1.3 Supply Chain Structure . . . . . . . . . . . . . . . 1.4 Supply Chain Cost . . . . . . . . . . . . . . . . . . 1.5 Centralized and Decentralized Supply Chains . . . 1.6 Importance of Decentralized Distribution Networks 1.7 Research Scope . . . . . . . . . . . . . . . . . . . . 1.8 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution Network and its Management 2.1 Inventory Management Methods . . . . . . . . . . . . 2.1.1 Push and Pull Inventory Systems . . . . . . . 2.1.2 Just-in-Time and Vendor Managed Inventory 2.2 Bullwhip Effect . . . . . . . . . . . . . . . . . . . . . 2.2.1 Sources of Bullwhip Effect . . . . . . . . . . . 2.2.2 Consequence of Bullwhip . . . . . . . . . . . . 2.2.3 Bullwhip Quantification and Impact . . . . . 2.3 Distribution System . . . . . . . . . . . . . . . . . . . 2.3.1 Modeling & Control of the Distribution Node 2.3.2 Distribution Network . . . . . . . . . . . . . . 2.4 Performance Metrics and their Quantification . . . . 2.4.1 Performance Metrics . . . . . . . . . . . . . . 2.4.2 Performance Benchmarking . . . . . . . . . . 2.5 Product nature and supply chain type . . . . . . . . i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 . 11 . . . . . . . . . . . . . . 14 15 15 16 18 19 21 21 22 24 25 28 28 31 32 . . . . . . . . . . . . . . Table of Contents 2.6 2.7 2.8 Demand and Responsiveness . . . . . . . . . . . . . . . . . . . . . 2.6.1 Market Demand . . . . . . . . . . . . . . . . . . . . . . . . 2.6.2 Relation between demand type and inventory requirement 2.6.3 Influence of shift in demand patterns . . . . . . . . . . . . 2.6.4 Demand Forecasting . . . . . . . . . . . . . . . . . . . . . 2.6.5 Responsiveness . . . . . . . . . . . . . . . . . . . . . . . . Supply Chain Diagnosis . . . . . . . . . . . . . . . . . . . . . . . Optimization Methodology . . . . . . . . . . . . . . . . . . . . . . Decentralized Distribution Systems 3.1 Material and Information Balances 3.2 Market Demand . . . . . . . . . . . 3.3 Replenishment Strategies . . . . . . 3.4 Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 34 34 35 36 38 38 40 . . . . 45 46 51 51 54 Performance Assessment Framework for Decentralized Distribution Network Systems 4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Motivation of this study . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Market Demand . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Performance Indicators . . . . . . . . . . . . . . . . . . . . . 4.5.3 Performance index of the Distribution node and network . . 4.6 Solution Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 Identification of inefficient distribution nodes . . . . . . . . . 4.6.2 Identification of the potential opportunities for performance improvements . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.3 Lead time information of all nodes in the network . . . . . . 4.6.4 Responsiveness . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.1 Case Study (1): Stationary Demand . . . . . . . . . . . . . 4.7.2 Case Study (2): Non-Stationary Demand . . . . . . . . . . . 4.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 59 60 61 61 65 68 68 69 70 71 73 73 75 77 77 83 89 Multi-Objective Optimization in Multi-Echelon Decentralized Distribution Networks 90 5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.2 Motivation of this study . . . . . . . . . . . . . . . . . . . . . . . . 93 5.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.4 Multi-objective Optimization and Pareto Analysis . . . . . . . . . . 94 5.5 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.6 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.7 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 104 ii Table of Contents 5.7.1 5.7.2 5.7.3 5.7.4 5.8 Customer Focused Approach . . . . . . . . . . . . . . . . . Cost Effective Approach . . . . . . . . . . . . . . . . . . . Optimal Cost Tradeoff Approach . . . . . . . . . . . . . . Performance tradeoff Strategy (with reference to Utopia Performance) . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 . 108 . 110 . 112 . 116 Entropy Based Optimization of Decentralized Supply Chain Networks 118 6.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6.1.1 Complexity and Consequences - Overview . . . . . . . . . . 121 6.1.2 Uncertainty Sources and Quantification . . . . . . . . . . . . 124 6.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6.4 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6.5 Complexity Modelling . . . . . . . . . . . . . . . . . . . . . . . . . 130 6.6 Proposed Complexity Management Methodologies . . . . . . . . . . 136 6.6.1 Strategy I (S-I) . . . . . . . . . . . . . . . . . . . . . . . . . 138 6.6.2 Strategy II (S-II) . . . . . . . . . . . . . . . . . . . . . . . . 139 6.6.3 Strategy III (S-III) . . . . . . . . . . . . . . . . . . . . . . . 140 6.6.4 Strategy IV (S-IV) . . . . . . . . . . . . . . . . . . . . . . . 140 6.7 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . 142 6.7.1 Scenario 1: Complexity Reduction . . . . . . . . . . . . . . . 143 6.7.2 Scenario 2: Complexity Optimization with desired CS . . . . 145 6.7.3 Scenario 3: Complexity Reduction (using similar replenishment rules) . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 6.7.4 Scenario 4: Complexity Minimization with desired CS (using similar replenishment rules) . . . . . . . . . . . . . . . . . . 148 6.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Divide and Conquer Optimization for Closed Loop Supply Chains152 7.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 7.1.1 Closed Loop Supply Chains . . . . . . . . . . . . . . . . . . 156 7.1.2 Decomposition based Optimization . . . . . . . . . . . . . . 159 7.2 Motivation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 7.3 Model Assumptions & Supply Chain Description . . . . . . . . . . . 162 7.3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 7.3.2 Problem Description . . . . . . . . . . . . . . . . . . . . . . 163 7.4 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 167 7.4.1 Forward Channel . . . . . . . . . . . . . . . . . . . . . . . . 167 7.4.2 Reverse Channel . . . . . . . . . . . . . . . . . . . . . . . . 169 7.4.3 Production Facility . . . . . . . . . . . . . . . . . . . . . . . 170 7.4.4 Optimization Agent . . . . . . . . . . . . . . . . . . . . . . . 171 7.5 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . 172 7.5.1 Comparison with Single/Multi Objective Optimization . . . 173 iii Table of Contents 7.5.2 7.6 Robustness of the derived solution (obtained by three optimization methods) with respect to uncertain demand and used product returns . . . . . . . . . . . . . . . . . . . . . . 176 7.5.3 Desirability of the solution with respect to the customer satisfaction constraint . . . . . . . . . . . . . . . . . . . . . . . 178 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Conclusions and Recommendations 8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Recommendations for further work . . . . . . . . . . . . 8.2.1 Diagnosis of Oscillations in Supply Chains . . . . 8.2.2 Demand (opportunity) Forecast . . . . . . . . . . 8.2.3 Sensitivity and Robust of Supply Chain Decisions 8.2.4 Optimization Efficiency . . . . . . . . . . . . . . . 8.2.5 Advanced Control Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 . 181 . 186 . 186 . 187 . 187 . 187 . 188 A Reverse and Production system Model 200 A.1 Reverse Channel Formulation . . . . . . . . . . . . . . . . . . . . . 200 A.2 Multi-purpose Production facility Formulation . . . . . . . . . . . . 202 B Publications and Presentations 206 iv List of Figures 1.1 1.2 1.3 Schematic representation of Supply Chain Architecture . . . . . . . Schematic representation of Supply Chain Decisions . . . . . . . . . Supply Chain Cost Structure . . . . . . . . . . . . . . . . . . . . . . 2.1 2.2 The Bullwhip Effect . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.1 3.2 Schematic representation of the decentralized distribution system . 46 Schematic representation of the internal strategy of a distribution node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.1 4.2 4.3 . 63 . 66 Performance Assessment Framework . . . . . . . . . . . . . . . . Schematic representation of the Decentralized Distribution System Performance of the Existing Distribution Strategy and after Dampening Aggressive Nodes . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Performance Enrichment after Improving Weak Nodes and after Restructuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Performance of similar replenishment policy at all nodes in the network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Total Cost of Decentralized Network facing Stationary Stochastic Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Excess Inventory and Backorder of heuristics rules (PI and SOP2 ) 4.8 Performance of the Existing Distribution Strategy and after Dampening Aggressive Nodes . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Performance Enrichment after Improving Weak Nodes and after Restructuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10 Performance of similar replenishment policy at all nodes in the network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.11 Total Cost of Decentralized Network facing Non-Stationary Stochastic Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.12 Excess Inventory and Backorder of Heuristic rules (PI and SOP2 ) 5.1 5.2 5.3 5.4 . 79 . 80 . 81 . 82 . 83 . 85 . 86 . 87 . 88 . 88 Schematic representation of the decentralized distribution system . Multi-echelon Decentralized distribution system . . . . . . . . . . . Multi-objective Performance Enhancement Framework . . . . . . . A Pareto analysis representation for various business strategies (symbol Θ - utopia point) . . . . . . . . . . . . . . . . . . . . . . . . . . v 92 98 100 106 Bibliography [39] J.S. Yang and J.C.H. Pan. Just-in-time purchasing: an integrated inventory model involving deterministic variable lead time and quality improvement investment. International Journal of Production Research, 42(5):853 – 863, 2004. [40] E. D’Ouville, T.H. Willis, and C.R. Huston. Traditional versus JIT production systems: A cost comparison model. Production Planning and Control, 9(5):457–464, 1998. [41] T.T. Burton. JIT/repetitive sourcing strategies: ’tying the knot’ with your supplies. Production and Inventory Management Journal, 29(4):38–42, 1988. [42] C.C. Lee and W.H.J. Chu. Who should control inventory in a supply chain? European Journal of Operational Research, 164(1):158–172, 2005. [43] M.C. Wilson. The impact of transportation disruptions on supply chain performance. Transportation Research Part E: Logistics and Transportation Review, 43(4):295–320, 2007. [44] B. John. Supply chain collaboration: Will determine future success. Semiconductor International, 24:81–86, 2001. [45] P. Kouvelis, C. Chambers, and H. Wang. Supply chain management research and production and operations management: Review, trends, and opportunities. Production and Operations Management, 15(3):449–469, 2006. [46] J.W. Forrester. Industrial dynamics: A major breakthrough for decision makers. Harvard Business Review, 36(4):37–66, 1958. [47] J. Wikner, D. R. Towill, and M. Naim. Smoothing supply chain dynamics. International Journal of Production Economics, 22(3):231–248, 1991. [48] H.L. Lee, V. Padmanabhan, and S. Whang. Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4):546–558, 1997. [49] H.L. Lee, V. Padmanabhan, and S. Whang. The bullwhip effect in supply chains. Sloan Management Review, 38(3):93–102, 1997. [50] E.A. Silver, D.F. Pyke, and R. Peterson. Inventory Management and Production Planning and Scheduling. John Wiley & Sons, New York, 1998. [51] R.D.H. Warburton. An analytical investigation of the bullwhip effect. Production and Operations Management, 13(2):150–160, 2004. [52] D.C. Chatfield, J.G. Kim, T.P. Harrison, and J.C. Hayya. The bullwhip effect - impact of stochastic lead time, information quality, and information sharing: A simulation study. Production and Operations Management, 13 (4):340–353, 2004. [53] S.M. Disney and D.R. Towill. On the bullwhip and inventory variance produced by an ordering policy. Omega, 31(3):157–167, 2003. 192 Bibliography [54] J. Nienhaus, A. Ziegenbein, and P. Schoensleben. How human behaviour amplifies the bullwhip effect. a study based on the beer distribution game online. Production Planning & Control, 17(6):547 – 557, 2006. [55] G.P. Cachon. Managing supply chain demand variability with scheduled ordering policies. Management Science, 45(6):843–856, 1999. [56] J. Dejonckheere, S.M. Disney, M.R. Lambrecht, and D.R. Towill. Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research, 147(3):567–590, 2003. [57] K. Hoberg, J.R. Bradley, and U.W. Thonemann. Analyzing the effect of the inventory policy on order and inventory variability with linear control theory. European Journal of Operational Research, 176(3):1620–1642, 2007. [58] J. Dejonckheere, S.M. Disney, M.R. Lambrecht, and D.R. Towill. Transfer function analysis of forecasting induced bullwhip in supply chains. International Journal of Production Economics, 78(2):133–44, 2002. [59] L. Zhou and S. M. Disney. Bullwhip and inventory variance in a closed loop supply chain. OR Spectrum, 28(1):127–149, 2006. [60] B.M. Beamon. Supply chain design and analysis: Models and methods. International Journal of Production Economics, 55(3):281–294, 1998. [61] S.M. Disney, M.M. Naim, and D.R. Towill. Dynamic simulation modelling for lean logistics. International Journal of Physical Distribution & Logistics Management, 27:174–196, 1997. [62] E. Perea, I. Grossmann, E. Ydstie, and T. Tahmassebi. Dynamic modeling and classical control theory for supply chain management. Computers & Chemical Engineering, 24(2-7):1143–1149, 2000. [63] E. Perea-Lopez, I.E. Grossmann, B.E. Ydstie, and T. Tahmassebi. Dynamic modeling and decentralized control of supply chains. Ind. Eng. Chem. Res., 40(15):3369–3383, 2001. [64] X. Wan, J.F. Pekny, and G.V. Reklaitis. Simulation-based optimization with surrogate models - application to supply chain management. Computers and Chemical Engineering, 29(6):1317–1328, 2005. [65] P.H. Lin, S.S. Jang, and D.S.H. Wong. Design and analyze the batch ordering supply chain system. Journal of the Chinese Institute of Chemical Engineers, 35(3):371–379, 2004. [66] H.J. Ko, C.S. Ko, and T. Kim. A hybrid optimization/simulation approach for a distribution network design of 3pls. Computers & Industrial Engineering, 50(4):440–449, 2006. 193 Bibliography [67] M. W. Braun, D. E. Rivera, M. E. Flores, W. M. Carlyle, and K. G. Kempf. A model predictive control framework for robust management of multi-product, multi-echelon demand networks. Annual Reviews in Control, 27(2):229–245, 2003. [68] P. Seferlis and N.F. Giannelos. A two-layered optimisation-based control strategy for multi-echelon supply chain networks. Computers & Chemical Engineering, 28(5):799–809, 2004. [69] S. Piramuthu. Knowledge-based framework for automated dynamic supply chain configuration. European Journal of Operational Research, 165(1):219– 30, 2005. [70] B.M. Beamon. Measuring supply chain performance. International Journal of Operations and Production Management, 19(3-4):275–292, 1999. [71] M. Stevenson and M. Spring. Flexibility from a supply chain perspective: Definition and review. International Journal of Operations and Production Management, 27(7):685–713, 2007. [72] A. Gunasekaran and B. Kobu. Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications. International Journal of Production Research, 45 (12):2819–2840, 2007. [73] F.T.S. Chan. Performance measurement in a supply chain. The International Journal of Advanced Manufacturing Technology, 21(7):534–548, 2003. [74] A. Gunasekaran, C. Patel, and Ronald E. McGaughey. A framework for supply chain performance measurement. International Journal of Production Economics, 87(3):333–347, 2004. [75] David C. Juran and Harvey Dershin. Real world supply chain assessment and improvement. In J.C. Wood and M.C. Wood, editors, Critical Evaluations in Business and Management, pages 446–468. Routledge, London, 2005. [76] W.P. Wong and K.Y. Wong. A review on benchmarking of supply chain performance measures. Benchmarking, 15(1):25–51, 2008. [77] H.L. Lee and C. Billington. Managing supply chain inventory: Pitfalls and opportunities. Sloan Management Review, 33(3):65–73, 1992. [78] A. Ross, M.A. Venkataramanan, and K.W. Ernstberger. Reconfiguring the supply network using current performance data. Decision Sciences, 29(3): 707–724, 1998. [79] M.L Fisher. What is the right supply chain for your product? Business Review, 75(2):105–116, 1997. Harvard [80] L. Yan and T.G. Woo. Information sharing in a supply chain with dynamic consumer demand pattern. In Proceedings of the Hawaii International Conference on System Sciences, volume 37, pages 1143–1152, 2004. 194 Bibliography [81] D. Caruso. Managing demand: Is this the secret to success? MSI, 22(3):39, 2004. [82] R. Fildes, K. Nikolopoulos, S.F. Crone, and A.A. Syntetos. Forecasting and operational research: A review. Journal of the Operational Research Society, 59(9):1150–1172, 2008. [83] M. Kamlesh and S. Daniel. Management Science: The Art of Decision Making. Prentice-Hall, 1994. [84] M.M. Naim and D.R. Towill. Establishing a framework for effective materials logistics management. The international Journal of Logistics Management, 5(1):81–88, 1994. [85] M.M. Naim, P. Childerhouse, S.M. Disney, and D.R. Towill. A supply chain diagnostic methodology: determining the vector of change. Computers & Industrial Engineering, 43:135–157, 2002. [86] N.F. Thornhill, S.L. Shah, B. Huang, and A. Vishnubhotla. Spectral principal component analysis of dynamic process data. Control Engineering Practice, 10(8):833–846, 2002. [87] M.M. Naim and N.F Thornhill. Detecting disturbances in a supply chain echelon via spectral pca. In 18th International Conference on Production Research, Salerno, Italy., 2006. [88] N.F. Thornhill and M.M. Naim. An exploratory study to identify rogue seasonality in a steel company’s supply network using spectral principal component analysis. European Journal of Operational Research, 172(1):146–162, 2006. [89] M.M. Naim and N.F Thornhill. Detecting disturbances in a supply chain echelon via spectral pca. In 18th International Conference on Production Research, Salerno, Italy., 2005. [90] E. Mestan, M. Turkay, and Y. Arkun. Optimization of operations in supply chain systems using hybrid systems approach and model predictive control. Ind. Eng. Chem. Res., 45(19):6493–6503, 2006. [91] C.L. Chen, B.W. Wang, and W.C. Lee. Multiobjective optimization for a multienterprise supply chain network. Ind. Eng. Chem. Res., 42(9):1879– 1889, 2003. [92] F.D. Mele, G. Guillen, A. Espuna, and L. Puigjaner. An agent-based approach for supply chain retrofitting under uncertainty. Computers and Chemical Engineering, 31(5-6):722–735, 2007. [93] K. Deb, A. Anand, and D. Joshi. A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation, 10 (4):371–395, 2002. 195 Bibliography [94] F. Cannavo and V. Nunnari. Evolutionary algorithm for inventory levels selection in a distribution supply chain. In Emerging Technologies and Factory Automation, volume 2, 2005. [95] B.J. Angerhofer and M.C. Angelides. System dynamics modelling in supply chain management: research review. In Simulation Conference Proceedings, volume 1, pages 342–351, 2000. [96] R. Bhagwat and M.K. Sharma. Performance measurement of supply chain management using the analytical hierarchy process. Production Planning and Control, 18(8):666–680, 2007. [97] H. Eschenauer, J. Koski, and A. Osyczka. Multicriteria Design Optimization Procedures and Applications. Springer-Verlag, 1990. [98] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Proceedings of 6th International Conference on Parallel Problem Solving from Nature, (Lecture Notes in Computer Science Vol.1917), pages 849–858, Paris, France, 2000. Springer-Verlag. [99] E.M. Kasprzak and K.E. Lewis. Pareto analysis in multiobjective optimization using the collinearity theorem and scaling method. Structural and Multidisciplinary Optimization, 22(3):208–18, 2001. [100] H.A. Taboada, F. Baheranwala, D.W. Coit, and N. Wattanapongsakorn. Practical solutions for multi-objective optimization: An application to system reliability design problems. Reliability Engineering and System Safety, 92(3):314–322, 2007. [101] J.M. Swaminathan, S.F. Smith, and N.M. Sadeh. Modeling supply chain dynamics: A multiagent approach. Decision Sciences, 29(3):607–631, 1998. [102] H.B. Hwarng and N. Xie. Understanding supply chain dynamics: A chaos perspective. European Journal of Operational Research, 184(3):1163–1178, 2008. [103] P. Fiala. Information sharing in supply chains. Omega, 33(5):419–423, 2005. [104] NSGA-II: A multi-objective optimization algorithm, www.mathworks.com, 2006. [105] K. Deb, Pratap. A, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):181–197, 2002. [106] O. Yeniay. Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Applications, 10(1):45– 56, 2005. 196 Bibliography [107] G. Agrawal, K. Lewis, K. Chugh, C.H. Huang, S. Parashar, and C.L. Bloebaum. Intuitive visualization of pareto frontier for multi-objective optimization in n-dimensional performance space. In Multidisciplinary Analysis and Optimization, volume of Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary, pages 1523–1533, Albany, NY, United States, 2004. American Institute of Aeronautics and Astronautics Inc., Reston, VA 20191, United States. [108] G. Agrawal, C.L. Bloebaum, and K. Lewis. Intuitive design selection using visualized n-dimensional pareto frontier. In Structures, Structural Dynamics and Materials, volume of Collection of Technical Papers AIAA/ASME/ASCE/AHS/ASC Structures, pages 145–158, Austin, TX, United States, 2005. American Inst. Aeronautics and Astronautics Inc., Reston, United States. [109] G. Agrawal, S. Parashar, and C.L. Bloebaum. Estimation of multi-objective pareto frontier using hyperspace diagonal counting. In Multidisciplinary Analysis and Optimization, volume of Collection of Technical Papers - 11th AIAA/ISSMO, pages 689–702, Portsmouth, VA, 2006. American Institute of Aeronautics and Astronautics Inc., Reston, VA 20191, United States. [110] G. Agrawal, S. Parashar, and C.L. Bloebaum. Intuitive visualization of hyperspace pareto frontier for robustness in multi-attribute decision-making. In Multidisciplinary Analysis and Optimization, volume of Collection of Technical Papers - 11th AIAA/ISSMO, pages 729–742, Portsmouth, VA, United States, 2006. American Institute of Aeronautics and Astronautics Inc., Reston, VA 20191, United States. [111] Simulink 6.6, Release 2007a, The Mathworks Inc., Natick, MA, 2007. [112] C.F. Ho, Y.M. Tai, and Y.P. Chi. A structural approach to measuring uncertainty in supply chains. Int. J. Electron. Commerce, 9(3):91–114, 2005. [113] S. Sivadasan, J. Efstathiou, A. Calinescu, and L.H. Huatuco. Advances on measuring the operational complexity of supplier-customer systems. European Journal of Operational Research, 171(1):208–226, 2006. [114] R. Hoole. Five ways to simplify your supply chain. Supply Chain Management, 10(1):3–6, 2005. [115] F.D. Mele, A. Espuna, and L. Puigjaner. Supply chain management through dynamic model parameters optimization. Ind. Eng. Chem. Res., 45(5):1708– 1721, 2006. [116] T. Sundar Raj and S. Lakshminarayanan. Performance assessment/enhancement methodology for supply chains. Ind. Eng. Chem. Res., 47(3):748–759, 2008. [117] M. Christopher and H. Lee. Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution & Logistics Management, 34(5):388–396, 2004. 197 Bibliography [118] R.D Wilding. The supply chain complexity triangle: Uncertainty generation in the supply chain. International Journal of Physical Distribution & Logistics Management, 28:599–616, 1998. [119] E. Prater. A framework for understanding the interaction of uncertainty and information systems on supply chains. International Journal of Physical Distribution & Logistics Management, 35:524–539, 2005. [120] M.G. Mann and S. Lloyd. Information measures, effective complexity, and total information. Complexity, 2(1):44–52, 1996. [121] S Sivadasan, J Efstathiou, G Frizelle, R Shirazi, and A Calinescu. An information-theoretic methodology for measuring the operational complexity of supplier-customer systems. International Journal of Operations & Production Management, 22:80–102, 2002. [122] Genetic Algorithm and Direct Search Toolbox 2.1, The Mathworks Inc., Natick, MA, 2007. [123] S. Mitra and S. Webster. Competition in remanufacturing and the effects of government subsidies. International Journal of Production Economics, 111 (2):287–298, 2008. [124] Y. Ogushi, M. Kandlikar, and H. Dowlatabadi. What determines end-of-life outcomes for consumer products? insights from the japanese experience. Progress in Industrial Ecology, 5(4):361–387, 2008. [125] N. Roche. The truth about free inkjet cartridge recycling. Bay Currents Highlighting the best neighborhood in Brooklyn, 1(6):5, 2004. [126] J. Jusko. Putting waste to work. Industry Week, 257(10):41–44, 2008. [127] D. Shah and S. Gasaway. Converting post consumer recycle PET waste into PBT based engineering thermoplastics for automotive and electrical applications. In Society of Plastics Engineers - Global Plastics Environmental Conference, GPEC 2007 - Environmental Innovation: Plastics Recycling and Sustainability, volume 3, pages 1051–1058, 2007. [128] Url. http://www.solidwastedistrict.com/information/stats.htm. accessed on 7th, January 2009. [129] S. Kumar and V. Putnam. Cradle to cradle: Reverse logistics strategies and opportunities across three industry sectors. International Journal of Production Economics, 115(2):305–315, 2008. [130] D. Vlachos, P. Georgiadis, and E. Iakovou. A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains. Computers and Operations Research, 34(2):367–394, 2007. [131] V.D.R. Guide Jr. and L.N. Van Wassenhove. Closed-loop supply chains: An introduction to the feature issue (part 1). Production and Operations Management, 15(3):345–350, 2006. 198 Bibliography [132] V.D.R. Guide Jr., V. Jayaraman, R. Srivastava, and W.C. Benton. Supplychain management for recoverable manufacturing systems. Interfaces, 30(3): 125–142, 2000. [133] V.D.R. Guide and L.N. Van Wassenhove. Closed-loop supply chains: Practice and potential. Interfaces, 33(6):1–2, 2003. [134] C. Kocabasoglu, C. Prahinski, and R.D. Klassen. Linking forward and reverse supply chain investments: The role of business uncertainty. Journal of Operations Management, 25(6):1141–1160, 2007. [135] F. Du and G.W. Evans. A bi-objective reverse logistics network analysis for post-sale service. Computers and Operations Research, 35(8):2617–2634, 2008. [136] C.L. Chen and W.C. Lee. Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers and Chemical Engineering, 28(6-7):1131–1144, 2004. [137] C. Dimopoulos. A review of evolutionary multiobjective optimization applications in the area of production research. In Congress on Evolutionary Computation, volume 2, pages 1487–1494 Vol.2, 2004. [138] B.K. Lee, K.H. Kang, and Y.H. Lee. Decomposition heuristic to minimize total cost in a multi-level supply chain network. Computers and Industrial Engineering, 54(4):945–959, 2008. [139] A.S. Tasan. A two step approach for the integrated production and distribution planning of a supply chain. Lecture Notes in Computer Science (International Conference on Intelligent Computing). Springer, Berlin, 4113 lncs - i edition, 2006. [140] H. Uster, G. Easwaran, E. Akcali, and S. Cetinkaya. Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model. Naval Research Logistics, 54(8):890–907, 2007. [141] R. Kleber. The integral decision on production/remanufacturing technology and investment time in product recovery. OR Spectrum, 28(1):21–51, 2006. [142] C.A. Floudas and P.M. Pardalos. Encyclopedia of Optimization. SpringerVerlag New York, Inc., 2006. [143] P.E. Gill, W. Murray, and M.A. Saunders. SNOPT: An SQP algorithm for large-scale constrained optimization. SIAM Journal on Optimization, 12(4): 979–1006, 2002. [144] D.R. Jones, C.D. Perttunen, and B.E. Stuckman. Lipschitzian optimization without the lipschitz constant. Journal of Optimization Theory and Applications, 79(1):157–181, 1993. [145] G.C. Derringer. A balancing act: Optimizing a product’s properties. Quality Progress, 27(6):51–58, 1994. 199 Appendix A Reverse and Production system Model Highly integrated manufacturing, product distribution and recycle systems have resulted in more and more large-scale operations. Large-scale engineering has garnered a great amount of interest from academic scholars and industrial practitioners to ensure high-level operations. This section will elaborate the modeling aspects and the dynamic behavior of collection centers, manufacturing facilities and their internal (control) strategies. A.1 Reverse Channel Formulation The main idea of a distributor node acting as a collection center ‘j’ is to recover maximum amount of value by integrating the used product returns into total supply chain picture. The common process involved in the reverse channel is used product acquisition, inspection, testing and disposition, reverse logistics, remanufacturing, and distribution. Although the common activities are well established, 200 A.2 Multi-purpose Production facility Formulation the managerial importance and understanding of these activities is different in different scenarios [133]. Usually, at all collection centers, the used products (YUkj,p ) are collected, stockpiled and pushed to the upstream nodes. They are used in the remanufacturing facilities for reproduction and also reduce the unnecessary retention of used products returns (IHUj,p ). Equations A.1 and A.2 indicate that used product inventory is the balance of used product influx from the downstream customers and used product moved to the upstream nodes. The primary concern in reverse channel is the manner in which used product inventory is reviewed and transferred to the upstream nodes. According to equation A.3, the used product inventory is reviewed at each time period and the material is pushed to the upstream nodes only when the availability of used product is more than the truck capacity (Cj,p ). IHUj,p (t) = IHUj,p (t − 1) + Y Ukj,p (t) − Y Uji,p (t) (A.1) k IHUj,p (z −1 ) = 1 − z −1 Yji,p (t) = Y Ukj,p (z −1 ) − k       Y Uji,p (z −1 ) −1 1−z (A.2) , IHUj,p (t) < Cj,p (A.3)      Cj,p , IHUj,p (t) ≥ Cj,p 201 A.2 Multi-purpose Production facility Formulation A.2 Multi-purpose Production facility Formulation The generic closed loop supply chain practices three production facilities, namely manufacturing ‘mn’, remanufacturing ‘rm’ and refurbishment facility ‘rfm’. Each production facility has own inventory target (SPpf,p , pf ∈ {mn, rm, rfm}) for each product ‘p’. The discrepancy in the desired inventory target (epf,p ) drives the production machinery at all production facilities (equation A.4-A.9). epf,p (t) = SPpf,p − IHpf,p (t) ∀ pf ∈ {mn, rm, rf m}, p (A.4) IHpf,p is the inventory of new product available at all production facilities. It is the balance of sum of products produced (Pm,p ) in the machineries ‘m’ and the product transferred to the distribution center to satisfy the distributor orders (equation A.5). Ppf,p,m − Ypf,p (t) IHpf,p (t) = ∀ pf ∈ {mn, rm, rf m}, p (A.5) m All products produced from the manufacturing, remanufacturing and refurbishment units are considered identical in quality [141]. With respect to production cost and time, the products produced from refurbishment facility have advantage over remanufacturing and manufacturing facilities. Therefore, refurbished product is preferred ahead of product from other facilities to satisfy the distributor order (equation A.6). Next to refurbished products, remanufactured products have cost 202 A.2 Multi-purpose Production facility Formulation advantage over products manufactured using fresh raw material (equation A.7). Finally, product produced from virgin raw material is used to cover the backlog caused because of insufficient product from remanufacturing facilities (equation A.8). Yrf b,p (t) =       Udw,p (t) , IHrf b,p (t) ≥ Udw,p (t) (A.6)      IHrf b,p (t) , IHrf b,p (t) ≤ Udw,p (t) Yrm,p (t) =       Udw,p (t) − Yrf b,p (t) , IHrm,p (t) ≥ Udw,p (t) − Yrf b,p (t)      IHrm,p (t) Ymn,p (t) = (A.7) , IHrm,p (t) ≤ Udw,p (t) − Yrf b,p (t)       Udw,p (t) − Yrf b,p (t) − Yrm,p (t) , IHrm,p (t) ≥ Udw,p (t) − Yrf b,p (t) − Yrm,p (t)      IHmn,p (t) , IHrm,p (t) ≤ Udw,p (t) − Yrf b,p (t) − Yrm,p (t) (A.8) Ypf,p (t) = Yrf b,p (t) + Yrm,p (t) + Ymn,p (t) (A.9) Each production facility has multiple production resources (machineries) ‘m’. All machineries have identical production capacity (200 units). All machineries have the capability to produce any product A, B, .I. The production rules followed at all production facilities for each review period (daily basis) are described in Figure 203 A.2 Multi-purpose Production facility Formulation Figure A.1: Scheduling sequence of Multi-purpose production facility A.1 and equations (a) Spot the unemployed (idle) machineries from the set m1 to mn (one by one) (b) Identify the products ‘p’ having high inventory discrepancies (c) Check whether the discrepancy is greater than the production capacity (d) Make sure the raw material/used product is available to produce the product identified in the step (b) (e) Start production and update the status of machinery, raw materials (f ) Repeat step (a) to (e) until all machineries are assigned for production or until all discrepancies in product inventory are eliminated via production 204 A.2 Multi-purpose Production facility Formulation Table A.1: Associated cost in Closed Loop Supply Chains Variables Details Refurbishment Rate 200 units/3 days/machine Remanufacturing Rate 200 units/5 days/machine Manufacturing Rate 200 units/5 days/machine Lead time (WH to DC) 12 days Lead time (DC to R) 12 days IH (at r,dc,m,rm,rfm) 1/unit/day IHU (at r,dc,rm,rfm) 1/unit/day TC (at r and dc) 1*no of units /truck Manufacturing Unit: Pmn,p∗ ,m (t + Lmn ) =       pc(t) , emn,p∗ ≥ pcm , statusmn,m = idle      (A.10) , otherwise Remanufacturing Unit: Prm,p∗ ,m (t + Lrm ) =       pc(t) , erm,p∗ ≥ pcm , rwrm,p∗ ≥ pcm , statusrm,m = idle      , otherwise (A.11) Refurbishment Unit: Prf m,p∗ ,m (t + Lrf ) =       pc(t) , erf m,p∗ ≥ pcm , rwrf m,p∗ ≥ pcm , statusrf m,m = idle      , otherwise (A.12) p∗ = p corresponds to max(ew,p ) 205 Appendix B Publications and Presentations Sundar Raj, T. and Lakshminarayanan, S. Multi-objective Optimization for Decentralized Supply Chains, Ind. Eng. Chem. Res. 2008, 47, 6661-6671. Sundar Raj, T. and Lakshminarayanan, S. Entropy Based Complexity Management in Decentralized Supply Chains, Proceedings of the 17th World Congress -The International Federation of Automatic Control, 2008, 10588 - 10593. Sundar Raj, T. and Lakshminarayanan, S. Performance Assessment/Enhancement Methodology for Supply Chains, Ind. Eng. Chem. Res. 2008, 47, 748-759. Sundar Raj, T. and Lakshminarayanan, S. A Performance Assessment Framework for Supply Chain Networks, Computer Aided Chemical Engineering, 2007, 24, 709-714. Sundar Raj, T. and Lakshminarayanan, S. Entropy based Optimization of Decentralized Supply Chain Networks submitted for possible publication to Ind. Eng. Chem. Res. 2009. Sundar Raj, T. and Lakshminarayanan, S. Divide and Conquer Optimization for Closed Loop Supply Chains, Manuscript in preparation 2009. 206 Publications and Presentations Sundar Raj, T. and Lakshminarayanan, S. Entropy Based Complexity Management in Decentralized Supply Chains, Proceedings of 17th International Federation of Automatic Control (IFAC), July 2008, Seoul, Korea. Sundar Raj, T. and Lakshminarayanan, S. A Cost Effective and Customer Focused Performance Improvement Methodology for Supply Chain Network, Proceedings of 4th International Symposium on Design, Operation and Control of Chemical Processes (PSE ASIA), August 2007, Xian, China. Sundar Raj, T. and Lakshminarayanan, S. Multi-objective Performance Assessment Framework for Decentralized Supply Chains, Chemical Supply Chain - Industry Forum, August 2007, Biopolis, Singapore. Sundar Raj, T. and Lakshminarayanan, S. A Performance Assessment Framework for Supply Chain Networks, Proceedings of 17th European Symposium on Computer Aided Process Engineering (ESCAPE17), May 2007, Bucharest, Romania. Sundar Raj, T. and Lakshminarayanan, S. Troubleshooting a Supply Chain System with Procurement Rule Fortification/Restructuring, American Institute of Chemical Engineers Annual Meeting (AICHE), November 2006, San Francisco, USA. 207 [...]... exogenous, whereas uncertainties arising due to inefficient supply chain entities practicing inappropriate internal strategies are endogenous type An uncertainty prone supply chain requires more investment than a similar but uncertainty free supply chain to satisfy similar market customers In other words, the return on investment is less in uncertainty prone supply chains Exogenous uncertainty such as customer... problems involving stationary and non-stationary demand patterns Real world supply chains aim for multiple performance criteria’s such as minimal supply chain cost and maximum customer satisfaction Chapter 5 aims to support the supply chains interested in multi-objective performance metrics A multi-objective performance optimization methodology is developed to identify the right supply chain decisions... advantage of supply chain model, forecasted uncertain inputs and optimization algorithms to synchronize the internal entities of large scale supply chains Computational studies were carried out to investigate the feasibility of the proposed performance enhancement strategies on realistically sized supply chains Based on a thorough understanding of the supply chain characteristics and business goal(s),... scale decentralized supply chain is considered to investigate the applicability of the proposed performance enhancement methodologies and support the real world supply chains with right decisions Several scenarios are used to illustrate the proposed performance enhancement methodologies xv Chapter 1 Introduction Tough competition and globalization are the two drivers for supply chain management Supply. .. handle sudden fluctuations in the demand from uncertain customers 1.7 Research Scope As discussed in the previous sections, this thesis concentrates on improving the performance of decentralized supply chains with reference to their diverse business goals In particular, this work emphasizes on the methodologies to identify right supply chain decisions for performance enhancement The methodologies developed... the supply chain entities We have taken the role of a third party supply chain consultant and exercised the following options: (a) enhance the overall performance with minimal modifications in the supply chain decisions (b) achieve superior performance at all internal entities of the network in multi-objective fashion (c) improve the predictability of the supply chain nodes by reducing the uncertainty... affecting supply chain performance Therefore, a well-oiled supply chain network is a prerequisite to compete successfully in today’s market place This research work is focused on developing efficient performance assessment and enhancement (i.e supply chain decision revision algorithm) methodologies that lead to efficient supply chain decisions or strategies with reference to the relevant yet diverse business... develop decentralized supply chain network models to study and capture the complexity of the interactions among various decision-makers in uencing the overall performance of the supply chain [27] 1.6 Importance of Decentralized Distribution Networks The prevailing challenges in the supply chain arise from the large number of the inbound and outbound material and information flows that converge in and... highly uncertain demand Inventory management is the crucial decision necessary for this kind of supply chain where (a) insufficient inventory creates unsatisfied customer and ruin the business or (b) managing excess inventory than the required level diminishes the profit margin 2.1.1 Push and Pull Inventory Systems In push-inventory system, products are produced based on long term demand forecasts using historical... against fire, flood and theft, inventory shrinkage and obsolescence The profit gained is described as the difference between the revenues and the supply 6 1.5 Centralized and Decentralized Supply Chains chain cost [23] The revenue is the monetary value of product sales at the market Figure 1.3: Supply Chain Cost Structure 1.5 Centralized and Decentralized Supply Chains In a supply chain, entities such as suppliers, . METHODOLOGIES FOR PERFORMANCE ENHANCEMENT IN DECENTRALIZED SUPPLY CHAINS SUNDAR RAJ THANGAVELU NATIONAL UNIVERSITY OF SINGAPORE 2009 METHODOLOGIES FOR PERFORMANCE ENHANCEMENT IN DECENTRALIZED. Counting IE Information Entropy JIT Just -in- Time LP Linear Programming LSSVM Least Square Support Vector Machine MILP Mixed Integer Linear Programming MINLP Mixed Integer Nonlinear Programming MIP. coordination between the supply chain entities. In this thesis, we play the role of a third-party supply chain consultant to develop methodologies to measure and enhance the performance of supply