PFI (MCA): Price and Lead Time Quotation for Make-to-Order Firms with Contingent Demand and Demand Learning

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

The broader impact/commercial potential of this Partnerships for Innovation – Mid-Career Advancement (PFI(MCA)) project is to enhance the operations and customer service of make-to-order (MTO), customized service, and healthcare delivery organizations. The research will focus on developing and enabling the commercialization of decision support tools to generate real-time, operationally attainable pricing and lead-time quotes. The success of healthcare delivery organizations, many of which are small-to-medium size enterprises, is complex due to uncertainty. If a firm bids too aggressively, they often experience a demand surplus, resulting in poor service and penalties from unfulfilled proposal terms. If a firm proposes too cautiously, they become noncompetitive, resulting in business loss and under-utilization of capacity. This research will build a general decision support framework, which will be validated in the context of the industrial partner’s sales and operations environment. The project contributes directly to undergraduate and graduate recruitment, education and training in STEM, leadership, innovation, and entrepreneurship through participation and outreach activities. Underrepresented students will be recruited by leveraging the pull of working with dynamic industry partners and various supporting programs. This project seeks to develop decision support tools that integrate demand learning and revenue management techniques while explicitly considering the presence of ‘contingent demand.’ Dealing with the underlying uncertainty associated with contingent jobs, which may vary wildly in their requirements, necessitates robust approaches built around learning customer behavior and the stochastic system performance resulting from the exponential number of possible realizations of the demand backlog. The real-time quoting of arriving jobs in the presence of contingent demand has not received much attention in the literature. The development of optimization approaches that balances exploration and exploitation in operational decision making is recent and presents additional challenges in this context. The problem will be approached in a comprehensive fashion building on data science and operations research techniques to optimize system performance over a time horizon. Various customer behavior functions and special cases will be studied to gain a deeper understanding of the theoretical properties of systems subject to contingent demand and leverage them to address general cases. An online simulation platform will be built for practical evaluation and performance comparison of various approaches. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.