Over the past two decades, Lean Production has begun to replace traditional manufacturing techniques around the world, mainly due to the success of the Toyota Motor Company. One key to Toyota's success that many American companies have not been able to emulate is the transformation of their suppliers to the lean philosophy. This lack of supplier transformation in America is due to a variety of reasons including differences in supplier proximity, supplier relationships, supplier performance levels, and the ordering policies used for supplied parts. The focus of this research is analyzing the impact of ordering policies for supplied parts of a manufacturing cell utilizing Lean Production techniques. This thesis presents a simulation analysis of a multi-stage, lean manufacturing cell that produces a family of products. The analysis investigates how the ordering policy for supplied parts affects the performance of the cell under conditions of demand variability and imperfect supplier performance. The ordering policies evaluated are a periodic-review inventory control policy (s, S) and two kanban policies. The performance of the cell is measured by the flowtime of the product through the cell, the on-time-delivery to their customer, the number of products shipped each week, the amount of work-in-process inventory in the cell, the approximate percentage of time the cell was stocked out, and the average supplied part inventory levels for the cell. Using this simulation model, an experimental analysis is conducted using an augmented central composite design. Then, a multivariate analysis is performed on the results of the experiments. The results obtained from this study suggest that the preferred ordering policy for supplied parts is the (s, S) inventory policy for most levels of the other three factors and most of the performance measures. This policy, however, results in increased levels of supplied part inventory, which is the primary reason for the high performance for most response variables. This increased inventory is in direct conflict with the emphasis on inventory and waste reduction, one of the key principles of Lean Production. Furthermore, the inflated kanban policy tends to perform well at high levels of supplier on-time delivery and low levels of customer demand variability. These results are consistent with the proper conditions under which to implement Lean Production: good supplier performance and level customer demand. Thus, while the (s, S) inventory policy may be advantageous as a company begins transitioning to Lean Production, the inflated kanban policy may be preferable once the company has established good supplier performance and level customer demand. / Master of Science

Supply chain management is one of the fundamental topics in the field of operations research, and a vast literature exists on the subject. Many recent developments in the field are rapidly narrowing the gap between the systems handled in the literature and the real-life problems companies need to solve on a day-to-day basis. However, there are certain features often observed in real-world systems that elude even these most recent developments. In this thesis, we consider a number of these features, and propose some new heuristics together with methodologies to evaluate their performance. In Chapter 2, we consider a general two-echelon distribution system consisting of a depot and multiple sales outlets which face random demands for a given item. The replenishment process consists of two stages: the depot procures the item from an outside supplier, while the retailers' inventories are replenished by shipments from the depot. Both of the replenishment stages are associated with a given facility-specific leadtime. The depot as well as the retailers face a limited inventory capacity. We propose a heuristic for this class of dynamic programming models to obtain an upper bound on optimal costs, together with a new approach to generate lower bounds based on Lagrangian relaxation. We report on an extensive numerical study with close to 14,000 instances which evaluates the accuracy of the lower bound and the optimality gap of the various heuristic policies. Our study reveals that our policy performs exceedingly well almost across the entire parameter spectrum. In Chapter 3, we extend the model above to deal with distribution systems involving several items. In this setting, two interdependencies can arise between items that considerably complicate the problem. First, shared storage capacity at each of the retail outlets results in a trade-off between items; ordering more of one item means less space is available for another. Second, economies of scope can occur in the order costs if several items can be ordered from a single supplier, incurring only one fixed cost. To our knowledge, our approach is the first that has been proposed to handle such complex, multi-echelon, multi-item systems. We propose a heuristic for this class of dynamic programming models, to obtain an upper bound on optimal costs, together with an approach to generate lower bounds. We report on an extensive numerical study with close to 1,200 instances that reveals our heuristic performs excellently across the entire parameter spectrum. In Chapter 4, we consider a periodic-review stochastic inventory control system consisting of a single retailer which faces random demands for a given item, and in which demand forecasts are dynamically updated (for example, new information observed in one period may affect our beliefs about demand distributions in future periods). Replenishment orders are subject to fixed and variable costs. A number of heuristics exist to deal with such systems, but to our knowledge, no general approach exists to find lower bounds on optimal costs therein. We develop a general approach for finding lower bounds on the cost of such systems using an information relaxation. We test our approach in a model with advance demand information, and obtain good lower bounds over a range of problem parameters. Finally, in Appendix A, we begin to tackle the problem of using these methods in real supply chain systems. We were able to obtain data from a luxury goods manufacturer to inspire our study. Unfortunately, the methods we developed in earlier chapters were not directly applicable to these data. Instead, we developed some alternate heuristic methods, and we considered statistical techniques that might be used to obtain the parameters required for these heuristics from the data available.

LD2668 .T4 1966 S836 / Master of Science

published_or_final_version / Industrial Engineering / Master / Master of Science in Engineering

published_or_final_version / Management Studies / Master / Master of Business Administration

Operations Managers of manufacturing systems, distribution systems, and supply chains address lot sizing and scheduling problems as part of their duties. These problems are concerned with decisions related to the size of orders and their schedule. In general, products share or compete for common resources and thus require coordination of their replenishment decisions whether replenishment involves manufacturing operations or not. This research is concerned with joint replenishment problems (JRPs) which are part of multi-item lot sizing and scheduling problems in manufacturing and distribution systems in single echelon/stage systems. The principal purpose of this research is to develop three new periodic review policies for stochastic joint replenishment problem. It also highlights the lack of research on joint replenishment problems with different demand classes (DSJRP). Therefore, periodic review policy is developed for this problem where the inventory system faces different demand classes that are deterministic demand and stochastic demand. Heuristic Algorithms have been developed to obtain (near) optimal parameters for the three policies as well as a heuristic algorithm has been developed for DSJRP. Numerical tests against literature benchmarks have been presented.

Tsan-Ming Choi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 117-118). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Literature Review --- p.3 / Chapter 1.3 --- Summary of Classic Results --- p.6 / Chapter Chapter 2 --- Two-Stage Single Ordering 一 Unknown Mean and Variance --- p.12 / Chapter 2.1 --- Mathematical Model --- p.12 / Chapter 2.2 --- "Order Quantities, Expected Profits and Expected Quantities of Goods Sold" --- p.16 / Chapter 2.3 --- Benefits from Forecast Update --- p.21 / Chapter 2.4 --- Applying the Model --- p.25 / Chapter 2.5 --- Application Example --- p.25 / Chapter Chapter 3 --- Two-Stage Single Ordering with Ordering Cost Difference --- p.28 / Chapter 3.1 --- Mathematical Model --- p.28 / Chapter 3.2 --- Order Quantity and Expected Profit --- p.31 / Chapter 3.3 --- Two-Stage Dynamic Programming Formulation --- p.33 / Chapter 3.4 --- Application Example --- p.37 / Chapter 3.5 --- Sensitivity Studies --- p.39 / Chapter Chapter 4 --- Two-Stage Two-Ordering with Ordering Cost Difference --- p.44 / Chapter 4.1 --- Mathematical Model --- p.44 / Chapter 4.2 --- Dynamic Programming Formulation --- p.47 / Chapter 4.3 --- Optimal Order Quantities --- p.51 / Chapter 4.4 --- Application Example --- p.52 / Chapter 4.5 --- Sensitivity Studies --- p.54 / Chapter Chapter 5 --- Multiple-Stage Single Ordering with Ordering Cost Difference --- p.60 / Chapter 5.1 --- Mathematical Model --- p.61 / Chapter 5.2 --- Order Quantity and Expected Profit --- p.64 / Chapter 5.3 --- Dynamic Programming Formulation --- p.64 / Chapter 5.4 --- Approximation by Taylor Series Expansion --- p.72 / Chapter 5.5 --- Approximation by Polynomial --- p.76 / Chapter 5.6 --- Comparison of Taylor Series and Polynomial Approximations --- p.84 / Chapter 5.7 --- Application Examples --- p.85 / Chapter 5.8 --- Sensitivity Studies --- p.91 / Chapter 5.9 --- Extension from Two Stages to Multiple Stages --- p.98 / Chapter 5.10 --- Non-Monotonicity of Cutting Points --- p.99 / Chapter Chapter 6 --- Real World Applications --- p.102 / Chapter 6.1 --- Background --- p.102 / Chapter 6.2 --- Two-Stage Cases --- p.105 / Chapter 6.3 --- Multiple-Stage Cases --- p.109 / Chapter Chapter 7 --- Conclusion and Further Studies --- p.115 / References --- p.117 / Appendix --- p.119

Photocopy of typescript. / Digitized by Kansas Correctional Industries

The first essay considers a dynamic non-stationary inventory problem in which replenishment is made in fixed lot sizes (e.g., in full truckloads or full containers). We consider two separate cases: one with exogenous pricing and the other with endogenous pricing. In the first case (exogenous pricing), we show that when the ordering cost contains only a variable component, the reorder-point lot-size policy or (r, Q) policy is optimal for both single-stage and multi-echelon inventory systems. In the presence of a fixed cost, we establish the optimality of batch based (s, S ) policies for the single-stage inventory system. In the second case (endogenous pricing), we show that when the demand function has the additive form and there is only a variable ordering cost, the (r,Q) list-price policy is optimal for the single-stage system, where inventory replenishment follows an (r,Q) policy and the optimal price in each period depends on the order-up-to level. / The second essay analyzes a periodic-review, stochastic, inventory-control system in which the fixed order-cost is a step function of the order size. In particular, if the order size is within a specified limit, C, then the setup cost is K1; otherwise it is K2, where K2 ≥ K1. This cost structure is motivated from some industrial applications and transportation/production contracts used in practice. Under the condition that K1 ≤ K 2 ≤ K1, we introduce a new concept called C - (K1 ≤ K 2) convexity, which enables us to partially characterize the structure of an optimal ordering policy. For the general condition K 1 ≤ K2 , the analysis is facilitated with a different notion called strong K-convexity. Based on this analysis, we provide a partial characterization of the optimal policy and construct an easy-to-implement heuristic method that has near-optimal performance in random test instances. Our study extends or redevelops (with different techniques) several existing results in the literature. / The third essay studies a firm's periodic-review production/inventory ordering decisions when the next period's setup cost depends on the quantity produced/ ordered in the current period. In particular, if the current period's production/order quantity exceeds a specified threshold value, the system starts the next period in a "warm" state and no fixed setup cost is incurred; otherwise the state is considered "cold" and a positive setup cost is required for production/ ordering. We develop a dynamic programming formulation of the problem and provide a partial characterization of the optimal policy under the assumption that the demands follow a Polya or Uniform distribution. We use the structural results to develop fairly simple heuristic policies, which perform highly effectively in our computational experiments. / With increased globalization and competition in the current market, supply chain has become longer and more complicated than ever before. An effective and efficient supply chain is crucial and essential to a successful firm. In a supply chain, inventories are a very important component as the investment in inventories is enormous. This dissertation consists of three essays related to stochastic inventory management. / Yang, Yi. / Adviser: Youhua Chen. / Source: Dissertation Abstracts International, Volume: 73-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 145-151). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Zhang Han. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 44-46). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Study --- p.4 / Chapter 3 --- "The Cost Function under the (s,c2,c1, S) Policy" --- p.8 / Chapter 4 --- "Determination of the Optimal (s,c2,c1,s) Policy" --- p.16 / Chapter 4.1 --- "The Auxiliary Function lγ(s, c2, C1, 5)" --- p.16 / Chapter 4.2 --- Optimizing Parameters of s(γ) and s(γ) --- p.18 / Chapter 4.3 --- Optimizing Parameters c2 (γ) and c1 (γ) --- p.21 / Chapter 5 --- "Algorithm for Computing the Optimal (s, c2, C1, S) Policy" --- p.27 / Chapter 5.1 --- Brief Description of the Algorithm --- p.27 / Chapter 5.2 --- The Statement of the Algorithm --- p.28 / Chapter 5.3 --- Interpretation of the Algorithm --- p.29 / Chapter 6 --- "The Optimality of the (s, c2，c1, S) Policy" --- p.31 / Chapter 6.1 --- Average Cost Criterion --- p.31 / Chapter 6.2 --- The Proof of Optimality --- p.34 / Chapter 6.3 --- "Optimality Proof for the Modified (s,c2,C1,S) Policy in the Re- laxed Model" --- p.38 / Chapter 6.3.1 --- Introduction of the Relaxed Model --- p.38 / Chapter 6.3.2 --- Average Cost Criteria for the Relaxed Model --- p.38 / Chapter 7 --- Conclusion --- p.42 / Bibliography --- p.43