Enhancing buffer management in theory of constraints replenishment system

• 主要作者: Wong, Desmond Jian Wen.
• 其他作者: Lee Ka Man, Carman
• 格式: Final Year Project
• 語言: English
• 出版: 2010
• 主題: DRNTU::Engineering::Systems engineering; DRNTU::Engineering::Industrial engineering::Engineering logistics
• 在線閱讀: http://hdl.handle.net/10356/39690
• 機構: Nanyang Technological University

This globalised world has seen a growing dispersion of production functions and new emerging markets sprouting out across the globe. Outbound logistics has grown to become a complex network of distribution. Yet, the objectives of a distribution system still remain the same – to have the right inventory in the right place at the right time. A Theory of Constraints (TOC) Replenishment System is one such model to have emerged over the last decade to confront this complicated issue. It uses a central warehouse in the factory to aggregate inventory buffers and changes the mode of operation from push to pull. The solution is powerful, but the TOC methodology of finding the optimal amount of buffer remains generic at best. In a real world, a TOC Replenishment System practitioner must give consideration to the stochastic demand and replenishment lead time. The Theory of Constraints also proposes a Dynamic Buffer Management (DBM) approach to complement the Replenishment System. It offers a methodology to monitoring and adjusting the buffer stock level. DBM is feasible and effective but is insufficiently rigorous. This report elucidates on a responsive (s*, S*) methodology to enhance buffer management in a Replenishment System by proposing a responsive reorder level (s*) and order-up-to level (S*) to capture customer demand trends. By having a responsive (s*, S*), a timely adjustment can be made to the buffer stock level to react to changing customer demand trends. A timely adjustment to the buffer stock level will result in a lower average inventory level leading to inventory savings and better service level to achieve higher customer satisfaction. A case study will demonstrates the feasibility of the proposed (s*, S*) methodology