A Simple Heuristic for Joint Inventory and Pricing Models with Lead Time and Backorders

Fernando Bernstein (), Yang Li () and Kevin Shang ()
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Fernando Bernstein: Fuqua School of Business, Duke University, Durham, North Carolina 27708
Yang Li: California State University, Sacramento, California 95819
Kevin Shang: Fuqua School of Business, Duke University, Durham, North Carolina 27708

Management Science, 2016, vol. 62, issue 8, 2358-2373

Abstract: We study a joint inventory and pricing problem in a single-stage system with a positive lead time. We consider both additive and multiplicative demand forms. This problem is, in general, intractable due to its computational complexity. We develop a simple heuristic that resolves this issue. The heuristic involves a myopic pricing policy that generates each period’s price as a function of the initial inventory level and a base-stock policy for inventory replenishment. In each period, the firm monitors its so-called price-deflated inventory position and places an order to reach a target base-stock level. The price-deflated inventory position weights the on-hand and pipeline inventory according to a factor that reflects the sensitivity of price to the net inventory level. To assess the effectiveness of our heuristic, we construct an upper bound to the exact system. The upper bound is based on an information-relaxation approach and involves a penalty function derived from the proposed heuristic. A numerical study suggests that the heuristic is near-optimal. The heuristic approach can be applied to a wide variety of inventory systems, such as systems with fixed ordering costs or fixed batch sizes. The heuristic enables us to explore the use of price as a lever to balance supply and demand. Our findings indicate that a responsive strategy (that effectively reduces the replenishment lead time) leads to a more stable pricing policy and that the value of dynamic pricing increases with lead time. This paper was accepted by Martin Lariviere, operations management .

Keywords: inventory-production; approximations-heuristics; stochastic; policies; pricing (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (12)

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