Recent business trends and advances in consumer behavior modeling have clearly shown that demand for goods and services, and in turn, the profits of companies and the satisfaction of their customers, are significantly shaped by price and lead time decisions. Numerous examples from practice as well as the literature have shown the potential of effective price and lead time quotation to influence demand, manage congestion, and improve customer service.
We discuss the problem of price and lead time quotation in a make-to-order (or service) system with two customer classes: (i) contract customers whose orders are always accepted and fulfilled based on a contractual agreement, and (ii) spot purchasers who arrive over time and are quoted a price and lead time pair dynamically, depending on the current system status. Spot purchasers, in turn, have the option of not accepting the price-lead time quote provided by the company. The objective is to maximize the expected average profit (i.e., revenues minus delivery delay penalties) per unit time. Assuming that the manufacturer has perfect information on the probability that a spot purchaser will accept a given quote (i.e., price-lead time pair), we first analyze the various structural properties of an optimal dynamic quotation policy for spot purchasers, given the contract parameters (i.e., price and lead times promised to contract customers). In particular, we discuss the impact of spot purchasers’ preferences on price and lead times on the optimal policy structure, and provide a simple rule to predict the situations where dynamic price and/or lead time quotation policies are improving profits significantly. We then provide some results on designing contracts so that a desirable mix of contract customers and spot purchasers can be achieved for maximum profitability.
Esma S. Gel is an Associate Professor and Program Chair of Industrial Engineering at the School of Computing, Informatics and Decision Systems Engineering of Arizona State University. She received her M.S. and Ph.D. from Northwestern University in 1995 and 1999, respectively. Dr. Gel is the recipient of the 2008 Hamid K. Eldin Outstanding Young Industrial Engineer Award from the Institute of Industrial Engineers. Her research focuses on the use of applied probability techniques for management and design of production/service systems and supply chains. Her recent work has focused on healthcare logistics, medical decision making, product portfolio design in high-tech environments, and dynamic pricing and lead time quotation in make-to-order/service systems. Dr. Gel has presented her work in national and international conferences, and published extensively in leading archival journals of her area. Her research has been funded by the National Science Foundation (NSF), as well as several industrial companies such as Intel, IBM and Infineon. Dr. Gel is a member of the Institute for Operations Research and the Management Sciences (INFORMS), and the Institute for Industrial Engineers (IIE).