Performance Evaluation of Stochastic Multi-Echelon Inventory Systems: A Survey

Globalization, product proliferation, and fast product innovation have significantly increased the complexities of supply chains in many industries. One of the most important advancements of supply chain management in recent years is the development of models and methodologies for controlling inventory in general supply networks under uncertainty and their widefspread applications to industry. These developments are based on three generic methods: the queueing-inventory method, the lead-time demand method and the flow-unit method. In this paper, we compare and contrast these methods by discussing their strengths and weaknesses, their differences and connections, and showing how to apply them systematically to characterize and evaluate various supply networks with different supply processes, inventory policies, and demand processes. Our objective is to forge links among research strands on different methods and various network topologies so as to develop unified methodologies.Masdar Institute of Science and TechnologyNational Science Foundation (U.S.) (NSF Contract CMMI-0758069)National Science Foundation (U.S.) (Career Award CMMI-0747779)Bayer Business ServicesSAP A

Control of Supply Chain Systems by Kanban Mechanism.

This research studies the control mechanism of a supply chain system to operate it efficiently and economically under the just-in-time (JIT) philosophy. To implement a JIT system, kanbans are employed to link different plants\u27 production processes in a supply pipeline. Supply chain models may be categorized into single-stage, multi-stage, and assembly-line types of production systems. In order to operate efficiently and economically, the number of kanbans, the manufacturing batch size, the number of batches, and the total quantity over one period are determined optimally for these types of supply chains. The kanban operation at each stage is scheduled to minimize the total cost in the synchronized logistics of the supply chain. It is difficult to develop a generalized mathematical model for a supply chain system that incorporates all its salient features. This research employs two basic models to describe the supply chain system: a mathematical programming model to minimize the supply chain inventory system cost and a queuing model to configure the kanban logistic operations in the supply pipeline. A supply chain inventory system is modeled as a mixed-integer nonlinear programming (MINLP) that is difficult to solve optimally for a large instance. A branch-and-bound (B&B) method is devised for all versions of it to solve the MINLP problems. From the solution of MINLP, the number of batches in each stage and the total quantity of products are obtained. Next, the number of kanbans that are needed to deliver the batches between two adjacent stages is determined from the results of the MINLP, and kanban operations are fixed to efficiently schedule the dispatches of work-in-process. The new solutions result in a new line configuration as to the number and size of kanbans that led to simpler dispatch schedules, better material handling, reduction in WIP and delivery time, and enhancement of the overall productivity. These models can help a manager respond quickly to consumers\u27 need, determine the right policies to order the raw material and deliver the finished goods, and manage the operations efficiently both within and between the plants

Trust and Inventory Replenishment Decision Under Continuous Review System

This thesis examines the impact of inventory manager’ trust on their replenishment decision. We conduct this study in the experimental environment and design an experiment with unknown market demand, local information, and under continuous replenishment review. We also develop a multi-round trust measurement procedure through questionnaires and administer it in the context of a laboratory experiment. To conduct the study, we take the three following steps: First we investigate inventory replenishment decision under continuous review in a decentralized supply chain. Our results show that order time intervals increase along the supply chain. Inventory managers’ replenishment decisions affect their own and the other echelons’ costs. Moreover, we find that wholesaler plays the smoothing role in the decentralized supply chain. Second, we develop a multi-round trust measurement procedure through questionnaires and conduct it in the context of a laboratory experiment. This design allows us to observe inventory managers’ trust in customer and trust in supplier over time. Our results show that trust exist in a decentralized supply chain, with local information, no communication, and no access to the market demand, and trust level varies in a continuum of intensity in a decentralized supply chain. Also, we find that trust evolves and for some echelons it grows over time. We further examine trust in customer and trust in supplier along the supply chain. Our results suggest that trust in supplier is the lowest in the middle of supply chain and that trust in customer decreases while moving upstream along a decentralized supply chain. Finally, we study the impact of trust in inventory replenishment decision and analyze data at individual and echelon level. Our results show that low trust in customer is linked to high order quantity and long order time intervals at the individual levels. Also, results on the echelon level suggest that distributor exhibits the lowest trust, highest order quantity and largest order time intervals among echelons, and retailer is the only echelon that considers trust in supplier while placing order quantities to upstream supplier. We further explore the inventory holding behavior of managers and find that inventory managers hold higher inventory level when they have lower trust in customer and trust in their upstream supplier. This research fits within the behavioral operations field

Modelling and analysis of pull production systems

Ankara : Industrial Engineering and the Institute of Engineering and Science of Bilkent Univ., 1995.Thesis (Ph.D.) -- Bilkent University, 1995.Includes bibliographical references.A variety of production systems appearing in the literature are reviewed in order to develop a classification scheme for production systems. A number of pull production systems appearing in the classification are found to be equivalent to a tandem queue so that accurate tandem queue decomposition methods can be used to find the performance of such systems. The primary concern of this dissertation is to model and analyze non-tandem queue equivalent periodic pull production systems. In this research, an exact performance evaluation model is developed for a singleitem periodic pull production system. The processing and demand interarrival times are assumed to be Markovian. For large systems, which are difficult to evaluate exactly because of large state spaces involved, an approximate decomposition method is proposed. A typical approximate decomposition procedure takes individual stages or pairs of stages in isolation to analyze the system and then it aggregates the results to obtain an approximate performance for the whole system. An experiment is designed in order to investigate the general behavior of the decomposition. The results are worth attention. A second aspect of this study is to investigate an allocation methodology to achieve the maximum throughput rate with providing two sets of allocation parameters regarding the number of kanbans and the workload at each stage of the system. Together with some structural properties, the experimental results provide some insight into the behavior of pull production systems and also provide a basis for the proposed allocation methodology. Finally, we conclude our findings together with some directions for future research.Kırkavak, NureddinPh.D

Managing Emerging Market Operations

Emerging markets have been a critical part of global business, with high share of global GDP and rapid economy growth. My dissertation research focuses on studying risks and opportunities in emerging market operations. One critical characteristic of emerging markets is that agriculture remains an essential sector. The world looks to emerging countries to meet the increasing food demand. However, the output remains significantly below the potential due to limited financial, technology and policy support. Scientific agriculture such as effective planting and mechanization could potentially help farmers achieve higher yields. In the first chapter of my dissertation, we study the optimal seeding policy under rainfall uncertainty. Utilizing field weather data from Southern Africa, we investigate the advantage of the optimal planting schedule and the impact of climate conditions on this advantage in a real-size large-scale problem. Another critical characteristic of emerging markets is the low labor cost. This makes emerging markets attractive bases for global manufacturing and service operations. However, the globalization of supply chains complicates the logistics and procurement operations. In the second chapter, we focus on the warehouse outsourcing strategy in global supply chains. We establish the optimal warehousing strategy and demonstrate that excluding the logistics dynamics from contracting and making warehousing decisions unilaterally afterwards can lead to a suboptimal warehousing strategy for the retailer. Furthermore, a variety of threats such as supplier failure and transportation disruption could delay or even disrupt the operations, offsetting the low-cost benefit of emerging economies. In the third chapter, we study the optimal sourcing strategy under disruption in global supply chains. We establish the optimal sourcing strategy and provide insights on the roles of the nearshore supplier in response to supply chain disruption. Overall, my dissertation concentrates on the application of scientific methods to planting and farm machinery procurement to improve agricultural productivity in Africa and leveraging low-cost benefits in emerging markets.Doctor of Philosoph