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Description: Material supply often accounts for more than 50% of the cost for industrial projects, such as those in construction, build-to-order manufacturing and R&D. Material supply also often serves as bottleneck to prevent on-time completion of the projects. While both project management and supply chain management have a vast literature, their interface -- project management with consumable resource supplies receives far less attention. In practice, material supplies and project schedules are mutual constraints and thus coordinating project decisions and material supply decisions holds out the promise of significantly improving system-wide performance. My research in this area focuses on developing models and methodology for (1) joint supply chain and project planning for recurrent projects, such as those in the construction industry, (2) supply chain management for clinical trials. This research is supported by National Science foundation Award # 0747779.

Chen, C.Y., Y. Zhao (2008). "Integrating Supply Chain Planning with Project Management in Project-Driven Supply Chains." Working Paper, Rutgers Business School. Abstract: We consider the strategic planning of recurrent projects and their material supplies over an extended period of time. We propose a mathematic modeling framework -- the project-driven supply chain (PDSC) -- to integrate project management (resource planning and scheduling within each project) with material supply management (lead-time planning based on multi-project statistics). For tree structure networks, we develop an optimization algorithm based on dynamic programming. Using examples, we demonstrate that the PDSC model can lead to significant cost savings as compared to the common practice which optimizes material supply and project decisions separately. We develop insights on how the savings are generated and when they are significant. We also discuss extensions of the model to include material customization, non-consumable resource constraints and acyclic networks.

Fleischhacker, A., Y. Zhao (2007). "The Dynamic Economic Lot-size Model for Clinical Trial Supply Chains: Planning for Demand Failure." Working Paper, Rutgers Business School. Abstract: Drug supply cost frequently accounts for a significant portion of total clinical trial spending. In this paper, we developed a model to plan for the production of investigational drugs during clinical trials.  At any point during a trial, failure may occur if the drug is deemed unsafe or ineffective. The trial is immediately halted and future demand becomes zero; all unused drug supply is wasted and must be properly destroyed.  To avoid these failure costs, manufacturers could produce small batches of the drug. However, these small batches result in inefficient production and manufacturers opt for large batches to avoid multiple incurrences of high setup costs.  To optimally balance the failure costs against the cost of production inefficiencies, we extend the Wagner-Whitin (W-W) model to include the risk of failure. We show that models with the risk of failure can be transformed into W-W models where the adjusted cost parameters account for the risk of failure and destruction costs. Using the model, we demonstrate that incorporating the risk of failure into production plans often leads to both reduced batch sizes and substantial savings.

Fleischhacker, A., Y. Zhao (2009a). "Balancing Learning and Economies of Scale: The Case of Adaptive Clinical Trials." Working Paper, Rutgers Business School. Abstract: Prior to the start of an adaptive clinical trial, demand for an investigational drug can be highly uncertain. Both recommended dosages and patient recruitment can fluctuate in response to early trial results. While initial demand forecasts can be very wrong, the factors influencing future demand can be learned during the trial. To take advantage of this learning, intra-trial batches can be produced, but at the expense of scale economies. Using various learning curves, we study this balance between learning and economies of scale in a finite horizon inventory model with fixed production costs and two production options: The pre-trial batch and the intra-trial batch. We characterize the optimal policy for both production batches in regards to optimally scheduling and sizing production. Through analytical and numerical studies, we develop insights on the impact of fixed costs, learning rates, and penalty costs on the value of the intra-trial batch, the timing of the intra-trial batch, and the size of the pre-trial batch.

Fleischhacker, A., Y. Zhao (2009b). "Inventory Positioning in Global Clinical Trial Supply Chains." Working Paper, Rutgers Business School. Abstract: As a result of slow patient recruitment and high patient costs in the United States, clinical trials are increasingly going global. While recruitment efforts benefit from a larger global footprint, the supply chain has to work harder at getting the right drug supply, usually in the form of patient kits, to the right place, at the right time. Clinical trial supply chains are unique due to the fixed patient horizon, 100% guarantee to satisfy all demand and non-transferability of supplies among parallel locations.  In this paper, we provide a new class of multi-echelon inventory models to address these unique aspects. We develop optimization algorithms to find good lower and upper bounds on the objective functions. The algorithms are leveraged to provide managerial insights into the optimal supply chain configurations.