Pricing, Production, and Inventory Policies for Manufacturing with Stochastic Demand and Discretionary Sales

Abstract

We study determining prices and production jointly in a multiple period horizon under a general, nonstationary stochastic demand function with a discrete menu of prices. We assume that the available production capacity is limited and that unmet demand is lost. We incorporate discretionary sales, when inventory may be set aside to satisfy future demand even if some present demand is lost. We analyze and compare partial planning or delayed strategies. In delayed strategies, one decision may be planned in advance, whereas a second decision is delayed until the beginning of each time period, after observing the results of previous decisions. For example, in delayed production (delayed pricing), pricing (production) is determined at the beginning of the horizon, and the production (pricing) decision is made at the beginning of each period before new customer orders are received. A special case is where a single price is chosen over the horizon. We describe policies and heuristics for the strategies based on deterministic approximations and analyze their performances. Computational analysis yields additional insights about the strategies, such as that delayed production is usually better than delayed pricing except sometimes when capacity is tight. On average, the delayed production (pricing) heuristic achieved 99.3% (99.8%) of the corresponding optimal strategy.pricing, production, inventory control, discretionary sales, worst-case analysis

Revenue management for manufacturing companies

“…Elimam and Dodin (2001) use nonlinear programming in order to determine optimal price discount levels during off-peak periods at a manufacturing company. Chan, Simchi-Levi, and Swann (2003) consider dynamic pricing strategies in a finite horizon problem under non-stationary, stochastic demand. Ziya, Hayriye, and Foley (2006) investigate optimal prices for M/M/1/m and M/GI/s/s blocking systems.…”

Learning dynamic prices in MultiSeller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

“…There are many other papers that discuss issues of dynamic pricing with inventory considerations. For example, see [5], [6], [16], [19], [27], [29], [30], [34]- [36], [40], [42].…”

Managing inventory and production capacity in start-up firms

“…Their model differs significantly from ours because it does not allow the firm any control over subsequent changes in production capacity and assumes future demand is known. Chan et al (2006) use a Markov decision process model to investigate pricing, production and inventory decisions in a manufacturing context. Demand is uncertain, but unlike our model the changes to the production level incur no cost.…”