Diversification Under Supply Uncertainty

Abstract

Supply chain management is becoming an increasingly important issue, especially when in most industries the cost of materials purchased comprises 40--60% of the total sales revenue. Despite the benefits cited for single sourcing in the popular literature, there is enough evidence of industries having two/three sources for most parts. In this paper we address the operational issue of quantity allocation between two uncertain suppliers and its effects on the inventory policies of the buyer. Based on the type of delivery contract a buyer has with the suppliers, we suggest three models for the supply process. Model I is a one-delivery contract with all of the order quantity delivered either in the current period with probability \beta , or in the next period with probability 1 -- \beta . Model II is also a one-delivery contract with a random fraction of the order quantity delivered in the current period; the portion of the order quantity not delivered is cancelled. Model III is similar to Model II with the remaining quantity delivered in the next period. We derive the optimal ordering policies that minimize the total ordering, holding and penalty costs with backlogging. We show that the optimal ordering policy in period n for each of these models is as follows: for x \ge \bar{u} n, order nothing; for v\bar n \le x n , use only one supplier; and for x n , order from both suppliers. For the limiting case in the single period version of Model I, we derive conditions under which one would continue ordering from one or the other or both suppliers. For Model II, we give sufficient conditions for not using the second (more expensive) supplier when the demand and yield distributions have some special form. For the single period version of Models II and III with equal marginal ordering costs we show that the optimal order quantities follow a ratio rule when demand is exponential and yields are either normal or gamma distributed.dual sourcing, supply uncertainty, inventory

To wait or not to wait: Optimal ordering under lead time uncertainty and forecast updating

“…We refer the reader to [40] for a review of the random-yield literature. We note that the distinction between disruptions and random yield is less sharp from a manager's perspective, in that stochastically-proportional Bernoulli random-yield models are appropriate models for supply failures, e.g., [2,29,32,34,35].…”

“…In addition, we refer to the time interval between two decision epochs (i.e., the length of a period) as the decision interval. 2 The firm has a forecasting process in place: at the start of period 0 it has an initial forecast of demand, and, as time evolves, the firm updates its forecast in each period to incorporate new information. The firm's forecast becomes more accurate as the selling season approaches.…”

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

“…This literature models reliability in three different but related ways: random capacity, e.g., Ciarallo et al (1994), Erdem (1999), random yield, e.g., Gerchak and Parlar (1990), Parlar and Wang (1993), Anupindi and Akella (1993), Agrawal and Nahmias (1997), Swaminathan andShanthikumar (1999), Federgruen andYang (2007a), and random disruption, e.g., Parlar and Perry (1996), Gürler and Parlar (1997), Tomlin (2006), Babich et al (2007). Financial default is another element of supply risk and has recently been explored by Babich et al (2007) and Swinney and Netessine (2008).…”

Stochastic Comparisons in Production Yield Management

“…Typically, the buyer benefits from diversification so long as per-unit prices are commensurate with yield distributions. A number of authors have studied this problem, including Anupindi and Akella (1993), Gerchak and Parlar (1990), and Henig and Levin (1990).…”