BLOCKED INTENSIVE DOCTORAL SEMINAR

Transcription

1 BLOCKED INTENSIVE DOCTORAL SEMINAR Advanced Topics in Supply Chain Management Instructor: Prof. Ramesh Bollapragada Invited by Prof. KERBACHE July, 15 to 21, 2008 Location : HEC Paris Contact for registration sartiaux@hec.fr 1

2 Advanced Topics in Supply Chain Management COURSE OUTLINE Instructor: Prof. Ramesh Bollapragada Course Description and Motivation : This course focuses on several research models in supply chains with emphasis on inventory management models. It will also cover strategic supply chains, and the current trends in network design, transportation, coordination and information technology in supply chains. By the end of the course, students will learn to use quantitative methods and develop skills to conduct research in the above areas. In addition, the students will be able to present an analysis of a company s supply chain and make recommendations to potential clients. Exams and Grading: The grade is primarily based on two take-home research projects, one take-home problem solving exam, and two management games. The first two research projects will be based on topics in the areas of inventory management. The takehome exam is based on topics in the areas of strategic supply chains, network design, transportation, coordination and information technology in supply chains. Class Presentations: Each student will present one major research paper as a part of the course. The presentations are on the last day of the course (on a paper mutually selected by the instructor and the student). The total time for all presentations is 3 hours. Research Projects: The students are required to read a number of research papers covered in the course. The research projects are take-home exams, where you analyze and answer research questions related to the papers discussed in the class and summarize some of the papers discussed in the class. Management Games: Two simulation games that cover the key concepts in supply chain networks. The students will manage a network of factories and warehouses to supply different markets in one or multiple regions of a country. The supply chain management games are founded and developed by faculty members from the Graduate School of Business, Stanford University and Kellogg School of Management, Northwestern University. The below websites has details of the two games we cover in the class. ( and Details on registering for the games, establishing teams etc. will be given at the start of the course. 2

3 The course grade will be weighted as follows: Class Participation + Presentations 15% Management Games 15% Research Project-1 25% Research Project-2 25% Take-home Exam 20% Course Material: All course material (research papers) will be distributed prior to the start of the course. The textbook (Chopra & Mendl, Supply Chain Management, 3rd Edition, Prentice-Hall) should be obtained by the students and is used as a reference. Course Duration: The course will be a taught over a period of 1 week (specifically, there will be instruction for 5 days, with a 3 hour class each day). Thus the course has a total duration of 15 hours. The detailed breakup of the hours spent on each day and topic is listed below. Tentative Schedule: Day Time Topic Day 1 (July 15) 1.5 hours Course Introduction Strategic Framework to Analyze Supply Chains 1.5 hours Planning and Managing Inventories in Supply Chains Transportation and coordination in supply chains Day 2 (July 16) Stanford University Supply Chain Game-1 kicked off. (Game is an application of concepts of capacity planning, forecasting and inventory management for one factory, one warehouse and is based on real industrial data). 1.5 hours Stanford University Supply Chain Game-1 due. Discussion on the Game. Economic Lot-sizing Models Research paper 1 discussed 1.5 hours Applications of Forecasting Models Bell Labs Presentation 1 discussed Applications of Inventory and SCM (supply chain management) Models Bell Labs Presentation 2 discussed Research paper 2 discussed Research Project-1 handed out 3

4 Day 3 (July 17) Day 4 (July 18) 3 hours Inventory and SCM (supply chain management) Models Research papers 3 & 4 discussed 1.5 hours Research Project-1 due Inventory and SCM (supply chain management) Models Research paper 5 discussed Information technology in supply chains 1.5 hours Supply chain network design Network planning tool for Facility location problems Stanford University Supply Chain Game-2 kicked off. (Game is an application of concepts of inventory management, distribution and transportation for multiple factories, multiple warehouses and is based on real industrial data). July 19, 20 (Break) Day 5 (July 21) Research Project-2 handed out Work on Research Project-2, Supply Chain Game-2, Class Presentations 1.5 hours Research Project-2 due Stanford University Supply Chain Game-2 due. Discussion on the Game. Individual Class Presentations on Supply Chain topics. 1.5 hours Individual Class Presentations on Supply Chain topics. Take-Home Exam handed out (due on July 23 by ) Wrap-up of the course and Future directions. Research Papers: The following 5 research papers will be discussed in the course. Paper 1: Bollapragada, R. & Rao, U.S. (1999), Single Stage Resource Allocation and Economic Lot Scheduling on Multiple, Nonidentical Production Lines, Management Science, vol. 45, no. 6, pp Paper 2: Bangash, A., Bollapragada, R., Klein, R., Raman, N., Shulman, H.B. & Smith, D.R. (2004), Inventory Requirements Planning at Lucent Technologies, Interfaces, vol. 34, no. 5, pp Paper 3: Bollapragada, R. & Rao, U.S. (2006), "Replenishment Planning in Discretetime, Capacitated, Non-stationary, Stochastic Inventory Systems," IIE Transactions on Scheduling and Logistics, Vol. 38, No. 7, July,

5 Paper 4: Bollapragada, R., Rao, U.S & Zhang, J.(2004), Managing Inventory and Supply Performance in Assembly Systems with Random Supply Capacity and Demand, Management Science, vol. 50, no. 12, pp Paper 5: Bollapragada, R., Rao, U.S & Zhang, J.(2004), Managing Two-stage Serial Inventory Systems under Demand and Supply Uncertainty and Customer Service Level Requirements, IIE Transactions, vol. 36, pp Bell Labs Presentations Presentation 1: Integrated Demand Management at Lucent Technologies Presentation 2: Inventory Requirements Planning at Lucent Technologies Tool Demonstrations Tool 1: Network Planning tool for Facility location problems (This tool and the underlying methodology were awarded multiple U.S. Patents) 5

6 Overview of the topics covered in the course: Research Papers: Paper 1: Bollapragada, R. & Rao, U.S. (1999), Single Stage Resource Allocation and Economic Lot Scheduling on Multiple, Nonidentical Production Lines, Management Science, vol. 45, no. 6, pp In the deterministic ELSP environment, we consider the problem of apportioning item production to distinct manufacturing lines with different costs (inventory and production) and capabilities (production rates). We develop a concave minimization model, generate heuristic solutions and lower bounding methods and present computational results. We also explore a discrete time, dynamic version of the problem. Paper 2: Bangash, A., Bollapragada, R., Klein, R., Raman, N., Shulman, H.B. & Smith, D.R. (2004), Inventory Requirements Planning at Lucent Technologies, Interfaces, vol. 34, no. 5, pp The Inventory Requirements Planning (IRP) system is a decision support tool that has been used effectively to determine the buffering requirements for parts in a time-phased provisioning system. It measures the deviation of actual supply and demand from their planned values, and using the historical profile of these deviations, determines the safety stock levels required to meet the desired service level objectives. In so doing, it quantifies the relationships among the inventory drivers supply interval, demand and supply variability, desired service levels and part proliferation. These have been used to identify and drive process improvement initiatives aimed at reducing demand and supply variability in the provisioning process. IRP has been used successfully at several Lucent Technologies locations to drive inventory realignment and service performance improvement in a variety of manufacturing and distribution environments. IRP s contribution has been recognized through several awards that it has won over the years. It has also been a significant contributor to Lucent receiving the Malcolm Baldrige Award in 1992 (as a part of AT&T Corporation at that time), and subsequently, the INFORMS prize in Paper 3: Bollapragada, R. & Rao, U.S. (2006), "Replenishment Planning in Discretetime, Capacitated, Non-stationary, Stochastic Inventory Systems," IIE Transactions on Scheduling and Logistics, Vol. 38, No. 7, July, In this paper, we examine a single product, discrete-time, non-stationary, inventory replenishment problem with both supply and demand uncertainty, capacity limits on replenishment quantities, and service level requirements. A scenario-based stochastic program for the static, finite-horizon problem is presented to determine replenishment orders over the horizon. We present a new heuristic based on the first two moments of random variables and a normal approximation, whose solution is compared with the optimal from a simulation-based optimization method. Computational experiments show that the heuristic performs very well (within 0.25% of optimal, 6

7 on average) even when uncertainty is non-normal or when there periods without any supply.we also present insights obtained from sensitivity analyses on the effects of supply parameters, shortage penalty costs, capacity limits, and demand variance. A rolling horizon implementation is illustrated. Paper 4: Bollapragada, R., Rao, U.S & Zhang, J.(2004), Managing Inventory and Supply Performance in Assembly Systems with Random Supply Capacity and Demand, Management Science, vol. 50, no. 12, pp We consider stock-positioning in a pure assembly system controlled using installation base-stock policies. When component suppliers have random capacity, we characterize the system's inventory dynamics. We show that components and the end-product play convex complementary roles in providing customer service. We propose a decomposition approach which uses an internal service level to independently determine near-optimal stock levels for each component. Across the tested instances, the average error of the decomposition approach is 0.66%. Our computational analysis on two-echelon systems shows that cost reduction from improving supply performance is high when demand variability or the number of components or target customer service is high, or when the end-product is more expensive relative to components. We show how a multi-echelon pure assembly system may be converted into an equivalent two-echelon assembly system to which our results apply. Paper 5: Bollapragada, R., Rao, U.S & Zhang, J.(2004), Managing Two-stage Serial Inventory Systems under Demand and Supply Uncertainty and Customer Service Level Requirements, IIE Transactions, vol. 36, pp We consider a two-echelon serial inventory system with demand and supply uncertainty, nonzero lead times for component procurement and end-product assembly and a minimum customer service level requirement. Assuming that installation base-stock ordering policies are followed and that the demand is quasi-concave, we show that the chance-constrained problem of determining optimal base-stock levels which minimize the total inventory investment subject to a service constraint is a convex programming problem. We characterize the relation between the optimal base-stock levels of the component and the end-product. We also illustrate how an optimal internal service level can be computed, which permits decomposition of the two-stage serial system into two coordinated single echelon systems. Computational experiments illustrate insights on the effects of supply uncertainty and other problem parameters on stock-positioning in a two-echelon serial system. 7

8 Bell Labs Presentations Presentation 1: Integrated Demand Management at Lucent Technologies The Inventory Demand Management (IDM) system is a decision support tool that has been used effectively to determine the forecasting requirements for parts. It serves as an interface between the upstream systems such as MRP systems, forecasting packages and the downstream systems such as the Inventory Planning systems and the Supply Planning systems. The IDM system helped several Lucent manufacturing plants achieve high forecast accuracy targets. The system is used to plan to forecasts at all levels in a product s hierarchy (independent and dependent levels), and for a sufficiently long time horizon. Presentation 2: Inventory Requirements Planning at Lucent Technologies The Inventory Requirements Planning (IRP) system is a decision support tool that has been used effectively to determine the buffering requirements for parts in a time-phased provisioning system. It measures the deviation of actual supply and demand from their planned values, and using the historical profile of these deviations, determines the safety stock levels required to meet the desired service level objectives. IRP has been used successfully at several Lucent Technologies locations to drive inventory realignment and service performance improvement in a variety of manufacturing and distribution environments. The underlying process improvements through the use of this decision support system are discussed in detail here. 8