Thesis (M.S.)--Ohio University, November, 1987. / Title from PDF t.p.

Thesis (Ph. D.)--Ohio State University, 1987. / Includes vita. Includes bibliographical references (leaves 399-417). Available online via OhioLINK's ETD Center.

Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 79-85). Also available in electronic version. Access restricted to campus users.

Thesis (M.S.)--Ohio University, August, 1993. / Title from PDF t.p.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves [55]-60).

Thesis (M.S.)--University of Louisville, 2008. / Department of Industrial Engineering. Vita. "December 2008." Includes bibliographical references (leaves 82-85).

This work considers inventory systems with two demand classes, where advance demand information is available. Three related scenarios are presented: three-stage production-inventory systems are studied in first two, whereas pure inventory systems are studied in the last scenario. In the first scenario, continuous review production-inventory systems are considered, where only one class provides advance demand information and early demand fulfillment is permitted. A new approach for production replenishment and order fulfillment in such systems is proposed, which combines the benefits of early fulfillment with Kanban-based pull systems. Simulation is used to compare the performance of the resulting policy with two other policies for a variety of scenarios (depending on the arrival rates, system utilizations, cost structures, arrival ratio, priority levels and amount of the advance demand information). A simulation-based lower bound on the optimal cost is established for some specific scenarios. The proposed policy outperforms the existing policies in every setting considered. Also, the proposed policy has added advantage of both retaining the benefit at high system utilizations and increasing the benefit up to the maximum level of advance demand information provided. A small fraction of customers providing advance demand information with early fulfillment acceptable is shown to have higher benefit than all customers providing same advance demand information with no early fulfillment. In second scenario, both classes provide advance demand information in production-inventory systems, though only one class accepts early fulfillment. Different levels of system utilization, arrival ratio and backorder cost are considered in the simulation experiments to show the superiority of early fulfillment. Also, experiments suggest that lowering the expected supply lead time may be more beneficial than increasing the demand lead time by the same amount for production-inventory systems with utilization dependent supply lead times. In third scenario, pure inventory systems are considered, where the demand classes provide different amount of advance demand information, and only one class accepts early fulfillment. The structure of an optimal policy is analytically characterized for periodic review systems under some specific conditions. / Ph. D.

Thesis (Ph.D.)--McMaster University, 2001 / Includes bibliographical references (leaves 144-156). Also available via World Wide Web.

Supply chain agility has been receiving a lot of attention in recent literature as a way for organizations to become more responsive to change and improve customer service levels. However, agility is typically dealt with qualitatively, and organizations are usually unsure of the steps to take to improve their agility and the customer service level to target. This research studies supply chain agility based on a case study of Intel Corporation, a large semiconductor manufacturer. Here, agility is defined as the ability to satisfy customer demands by reacting effectively to changes in market stimuli. Reacting effectively does not mean reacting to every change in supply or demand. Doing so means increasing supply chain variability unnecessarily, which is amplified by the bullwhip effect. The essence of supply chain agility is determining the degree to which variability should be managed through artificial means such as safety stock, and appropriate triggers for changing production levels and inventory targets. The purpose of this research is to examine factors that influence supply chain agility and identify a cost-effective plan for achieving it. The first phase addresses the problem of identifying target inventory and customer service levels based on regression analysis of historical data and financial analysis of inventory holding costs and stock-out costs. The impact of three factors (forecast error, order lead-time, and demand variability) on the relationship between inventory and customer service level is also examined. The second phase of the research evaluates strategies for production and inventory control with the goal of finding the appropriate trade-off between minimizing cost (of holding inventory and stock-outs) and minimizing variability. Control policies based on the Exponentially Weighted Moving Average (EWMA) control chart with control limits on demand forecasts are proposed to detect when tighter control of processes is necessary. A Monte Carlo supply chain simulation is used to evaluate the performance of these policies under various levels of forecast error and demand variability. Results indicate that several control chart-based policies outperform Intel's current planning policy in terms of cost without significantly increasing variability. The selection of the appropriate policy must be based on the decision-makers' desire to minimize cost compared to the desire to minimize variability, as each policy results in a trade-off between these two objectives.

Thesis (doctoral)--Universität, Magdeburg. / Includes bibliographical references.