Capacity Investment under Bayesian Information Updates at Reporting Periods: Model and Application

• 成果类型: Article
• 署名作者: Vedantam, Aditya; Iyer, Ananth
• 署名单位: State University of New York (SUNY) System; University at Buffalo, SUNY; Purdue University System; Purdue University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13402
• 发表日期: 2021
• 页码: 2707-2725
• 关键词: critical materials; capacity expansion; technology projects; dynamic programming; bayesian updates

摘要

We consider capacity addition decisions by a new product manufacturer faced with uncertain technology alternatives. The manufacturing capacity addition and technology development occurs in parallel, with preliminary results from a technology project's success providing valuable information to the manufacturer in adding capacity. We solve a stochastic dynamic program with Bayesian updates to obtain the manufacturer's expected profit-maximizing capacity investment decision. Our model and applications are motivated by the Critical Materials Institute (CMI) (funded by the Department of Energy), which manages research projects focused on mitigating critical material constraints, vital to renewable energy technologies such as direct-drive wind turbines, electric vehicles, and energy-efficient lighting. We capture three unique aspects of the problem: first, the learning from project progress depends on task-based stochastic outcomes and a project's percent-done. Second, the underlying technology's profitability is based on a model of competition. Third, we evaluate the impact of progress across a portfolio of projects based on a manufacturer's capacity addition. We develop a heuristic that produces results that are close to optimal and can thus be used for large problem sizes. The managerial insights from an application of our model to CMI projects include: (i) technology projects that report the percent-done of a project earlier increase expected manufacturer profit; (ii) careful choice of safe bets, that is, technologies with low profitability but a high probability of success, can increase expected manufacturer profit; (iii) a portfolio of projects can increase profits significantly over separate project evaluation; and (iv) dynamic management of project resources can increase overall profit.