Evaluating demand management effects of universal designs for product end-of-life

We examine the inventory and service level effects of a universal design for a product near the end of the product life cycle. The universal design allows the manufacturer to take advantage of the benefits of demand pooling. This document helps better to quantify some of these benefits.; Using a single-period model with normally distributed demands, the demand management benefits for N products replaced by a universal design are quantified in a closed-form solution. Profit is calculated as a function of the stocking level for the universal product. The function is shown to be concave. A close approximation to the optimal stocking level is calculated easily and provides a tight lower bound on optimal profit.; We extend the model to two periods and numerically calculate optimal profits over a range of parameters. We show that there can be significant benefit in moving from a single-period model to a two-period model. In two-period problems, the benefits of pooling are less than in single-period problems.; Our analysis suggests that firms might consider product structures to be an important inventory and service-level parameter, adjusted throughout the product's life. For some industries, such as the semiconductor industry, end-of-life effects have significant implications for the optimization of the product offering over the life of the product. In many cases it may be optimal to invest in significant redesigns as the product enters the end of life.; We also study the phenomenon of demand forecasts that do not become more accurate as they are updated. We find that forecast updates do not improve forecasts as consistently as is frequently expected for some common forecasting models. Multiple examples are presented that demonstrate the phenomenon. We provide a simple theoretical analysis supporting the phenomenon. Randomness, the use of inappropriate forecasting models, and demand information distortion are potential contributors to this counter-intuitive phenomenon. Many companies have inappropriate expectations for forecast updates and might do well not to update demand forecasts inside of three months.