Scheduling, contracting, and capacity planning in project-based supply chains [electronic resource]

Project supply chains are complex systems of organizations, information, and processes that aim at delivering specific resources required by respective project tasks at scheduled times so that the project can be carried out smoothly and delivered within the target time and budget. Lots of anecdotal evidence from a variety of industries, such as the construction industry and the aviation industry, suggests that supply chain issues have caused significant project delays and cost overruns, creating the need to carry out research on project supply chains. The body of literature on project-based supply chain management, however, is relatively new and lacks quantitative modeling and analysis. In this dissertation, we provide quantitative models and analysis to address the scheduling, coordination, and capacity planning problems in project supply chains facing uncertainties in time, demand, and technology. In Chapter 2, we study the optimal material release scheduling problem in project supply chains with processing time uncertainties. In contrast to the classical studies in product supply chains, where the focus is on the quantity of inventory to be stocked, this chapter focuses on the timing of releasing the materials required by each project task, which affects the profitability of the project, as late releases could delay the project while early releases could incur substantial financing costs. We characterize the optimal material release schedules. Additionally, we quantify the value of quick response and production flexibility and we show that quick response is more desirable when the processing times are more volatile, the project margin is less, and the correlation of the processing times are smaller. In Chapter 3, we study the incentive and coordination problem in decentralized project supply chains. Since most projects are unique and customized, project supply chains are usually configured with many specialized suppliers involved. Each supplier takes the responsibility of delivering the materials required by a respective task at a specific time. We examine some commonly used contracts and explain the reason why those contracts cannot provide proper incentives to the suppliers. We introduce an optimal contract that can coordinate the supply chain, which requires three critical components: target material delivery schedule, deposit and balance terms, and penalty/bonus terms. We show how to coordinate the supply chain by properly setting the target material delivery schedule and the timing of payments. In addition, we show that under certain conditions, the contract that we introduced can still coordinate the channel even if the manufacturer's estimate of the supply lead times is inaccurate. Last, we also show that the manufacturer's expected profit does not depend on the mean of the supply lead times and it is not necessarily a decreasing function of the variance of the supply lead times, suggesting that lead time reduction may not benefit the manufacturer as much as one would expect. In Chapter 4, we study the optimal capacity planning problem in data center capacity expansion projects. There are several ways to expand data center capacity. For example, one is through extending data center space capacity, and another way is through replacing the fleet of existing equipment by new equipment. Extending data center space capacity can be regarded as a unique project and can be analyzed using the models developed in the previous chapters. The study of this chapter, however, focuses on expanding data center capacity through new technology introduction. In particular, we consider a two-period problem. In the first period, there is only one type of equipment and one type of demand; while in the second period, new equipment is available and there is an additional type of demand. We study the optimal capacity plans in the two periods. Moreover, we compare three strategies: solo-technology rollover strategy, dual-technology rollover strategy, and a strategy that allows an upgrading option for the old equipment. We find that under the dual-technology rollover strategy, which is the most flexible strategy, the optimal capacity level of the old equipment is the lowest in the first period and highest at the end of the second period.

Keywords: Management Science and Engineering.
Advisor: Lee Hau Leung primary advisor. Thesis advisor
Hausman Warren H. advisor. Thesis advisor
Plambeck Erica L. advisor. Thesis advisor