In this paper, we consider the demand for multiple, successive generations of products and develop a population-growth model that allows demand transitions across multiple product generations and takes into consideration the effect of competition. We propose an iterative-descent method for obtaining the parameter estimates and the covariance matrix, and we show that the method is theoretically sound and overcomes the difficulty that the units-in-use population of each product is not observable. We test the model on both simulated sales data and Intel's high-end desktop processor sales data. We use two alternative specifications for product strength in this market: performance and performance/price ratio. The former demonstrates better fit and forecast accuracy, likely due to the low price sensitivity of this high-end market. In addition, the parameter estimate suggests that, for the innovators in the diffusion of product adoption, brand switchings are more strongly influenced by product strength than within-brand product upgrades in this market. Our results indicate that compared with the Bass model, Norton–Bass model, and Jun–Park choice-based diffusion model, our approach is a better fit for strategic forecasting that occurs many months or years before the actual product launch.

To help a firm reduce inefficiencies associated with equipment capacity planning, we propose a dual-mode equipment procurement (DMEP) framework. DMEP combines dual-source (i.e., a less-expensive-but-slower base mode and a faster-but-more-expensive flexible mode) procurement with option contracts in three layers: a contract negotiation layer, where the firm chooses the best combination of lead time and price for each mode from the supply contract menu; a capacity reservation layer, where the firm reserves total equipment procurement quantities from the two supply modes before the planning horizon starts; and an execution layer, where the firm orders equipment from the two supply modes based on the updated demand information. We first investigate the execution layer as a dynamic dual-source capacity expansion problem with demand backlogging and demonstrate that the optimal policy lacks structure even under the simplest setting. Thus, we propose a heuristic solution for the execution-layer problem, which also serves as a building block for the other two layers. Through numerical analysis, we quantify the value of the added flexibility of DMEP for the firm. The DMEP framework has been implemented at Intel Corporation and has resulted in savings of tens of millions of dollars for one process technology.