QI FENG, University of Texas at Austin - Red McCombs School of Business
Email: Annabelle.Feng@mccombs.utexas.edu
SURESH SETHI, University of Texas at Dallas - School of Management
Email: sethi@utdallas.edu

This chapter examines the interaction between supply price uncertainty and demand uncertainty. We consider a manufacturer who sources a key component using different procurement options: a long-term order on a price-only contract, short-term orders on an adjustment contract, and short-term purchases directly from the market. At the beginning of the planning cycle, the manufacturer places a long-term order and reserves a certain amount of supply capacity for the purpose of adjusting the long-term order, if needed. Before the selling season, the manufacturer has multiple options to place supplementary orders from the reserved capacity or from the market.

We compare two types of capacity arrangements: dedicated capacity and overall capacity. Under a dedicated capacity arrangement, the manufacturer reserves capacities separately for different adjustment opportunities. On the overall capacity arrangement, she keeps the flexibility of using the reserved capacity within the given period for possibly multiple adjustments. We discuss the optimal procurement strategies and the criteria for capacity allocations, as well as the policy behavior and service performance in different situations.

MILIND DAWANDE, University of Texas at Dallas - Department of Information Systems & Operations Management
Email: milind@utdallas.edu
SRINAGESH GAVIRNENI, affiliation not provided to SSRN
Email: sg337@cornell.edu
SANJEEWA NARANPANAWE, affiliation not provided to SSRN
Email: sanjeewa.naranpanawe@sas.com
SURESH SETHI, University of Texas at Dallas - School of Management
Email: sethi@utdallas.edu

Motivated by the recent success of integer programming based procedures for computing discrete forecast horizons, we consider two-product variants of the classical dynamic lot-size model. In the first variant, we impose a warehouse capacity constraint on the total ending inventory of the two products in any period. In the second variant, the two products have both individual and joint setup costs for production. To our knowledge, there are no known procedures for computing forecast horizons for these variants. Under the assumption that future demands are discrete, we characterize forecast horizons for these two variants as feasibility/optimality questions in 0-1 mixed integer programs. A detailed computational study establishes the effectiveness of our approach and enables us to gain valuable insights into the behavior of minimal forecast horizons.

ALAIN BENSOUSSAN, University of Texas at Dallas - School of Management
Email: alain.bensoussan@utdallas.edu
METIN CAKANYILDIRIM, University of Texas at Dallas - School of Management
Email: metin@utdallas.edu
QI FENG, University of Texas at Austin - Red McCombs School of Business
Email: Annabelle.Feng@mccombs.utexas.edu
SURESH SETHI, University of Texas at Dallas - School of Management
Email: sethi@utdallas.edu

Under many circumstances, demand observations are often censored due to the lack of tracking lost sales caused by stockouts. To understand the impact of the lost sales information on the ordering decisions, a periodic-review inventory model is formulated in which only the sales information is obtained immediately upon the realization of the demand. This is equivalent to observing the demand when the sales are less than the available stock and to inferring that the demand is higher than the stock when there is a stockout. Subsequently, the lost sales information is obtained after a delay. In the resulting model, an optimal policy, if exists, reveals a very complex structure. By decomposing the derivative of the value function, we demonstrate two different roles of inventory in our model: satisfying the demand and extracting the demand information. We show that the optimal inventory levels under the delayed observation of the lost sales are always higher than those for which the demands are fully observed. Moreover, as illustrated in numerical examples, the optimal policy possesses a counterintuitive behavior with respect to the problem parameters. To understand the key drivers of the optimal decisions, we further compare the costs under different demand observations. Two important observations are made. First, a lower cost is obtained when the realized demand is observed than when the demand is only observed to be higher than the inventory level, and, furthermore, the cost difference represents the value of demand information. Second, while a higher inventory level induces a more accurate demand forecast, the value of exact demand observation is not monotone in the procurement cost. Consequently, the optimal ordering quantity is not always decreasing in the procurement cost.

HAOYA CHEN, affiliation not provided to SSRN
FRANK CHEN, Chinese University of Hong Kong - Department of Systems Engineering & Engineering Management
Email: yhchen@se.cuhk.edu.hk
TSAN-MING CHOI, affiliation not provided to SSRN
Email: tcjason@inet.polyu.edu.hk
SURESH SETHI, University of Texas at Dallas - School of Management
Email: sethi@utdallas.edu

We study a coordination contract for a supplier-retailer channel producing and selling a fashionable product exhibiting a stochastic price-dependent demand. The product's selling season is short, and the supply chain faces great demand uncertainty. We consider a scenario where the supplier reserves production capacity for the retailer in advance, and permits the retailer to place an order not exceeding the reserved capacity after a demand information update during a leadtime. We formulate a two-stage optimization problem in which the supplier decides the amount of capacity reservation in the first stage, and the retailer determines the order quantity and the retail price after observing the demand information in the second stage. We propose a three-parameter risk and profit sharing contract that coordinates the supply chain. The proposed contract is robust which permits any agreed-upon division of the supply chain profit between the channel members.