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.

DANIEL GERHARD, University of Erlangen-Nürnberg
Email: gerhard@industriebetriebslehre.de
ALEXANDER BREM, University of Erlangen-Nuremberg, VEND consulting GmbH
Email: brem@industriebetriebslehre.de
KAI-INGO VOIGT, University of Erlangen-Nuremberg
Email: voigt@industriebetriebslehre.de

Developing new innovative products in the automotive industry means investing huge sums in advance, as one does not know if the product will be successful on the market after launch. Hence, companies are interested in knowing and measuring the critical success drivers within the development steps. The paper discusses the results of a qualitative meta-analysis of 16 empirical studies on New Product Development (NPD) success, which was carried out to gain deeper insight into these success drivers. Furthermore, based on the identification of three main dimensions (development process, resources and strategy), an explorative study in the German automotive industry shows that the findings are confirmed in practice as well. However, the results also indicate that there is still a gap between knowledge about practical relevance of those dimensions and the systematic assessment of these in the process. The study shows interesting approaches of best practices, such as the assessment of product advantage in combination with scenario analysis or the identification of appropriate innovations.