Knowledge that Transforms

We consider the problem of determining (for a short lifecycle) retail product initial and replenishment order quantities that minimize the cost of lost sales, back orders, and obsolete inventory. We model this problem as a two-stage stochastic dynamic program, propose a heuristic, establish conditions under which the heuristic finds an optimal solution, and report results of the application of our procedure at a catalog retailer. Our procedure improves on the existing method by enough to double profits. In addition, our method can be used to choose the optimal reorder time, to quantify the benefit of leadtime reduction, and to choose the best replenishment contract.

We extend previous work evaluating the quality versus time-to-market trade-off for a single product generation to the case of multiple generations. While a single generation framework is appropriate when either the technology is not extendable or when additional launch costs outweigh benefits, we find that it is important to recognize whether a technology is extendable and explicitly consider the potential for multiple generations. We evaluate the factors determining optimal development-cycle length and intensity using a forward-looking model that allows for multiple product generations. Comparisons are made with restricted versions of the model that reflect pure single generation and sequential single generation approaches. Against an active competitor, the multiple generation approach is much more profitable, with the greatest differences in fast-moving industries. More total time is spent in development when a multiple generation model is used. Further, this time is dedicated to the more frequent introduction of improved product generations—a “rapid inch-up” strategy—resulting in more, higher quality products over time. Factors affecting optimal time-to-market differ substantially for the single versus multiple generation approaches. A key difference is that faster rates of quality improvement lead to longer development cycles for the single and sequential single generation models, but shorter cycles with the forward-looking multiple generation model. With a single generation, variable costs have the biggest impact on cycle length (higher costs shorten cycles), but with multiple generations, fixed costs have the biggest impact on cycle length (higher costs lead to longer cycles).