Nudging a Slow-Moving High-Margin Product in a Supply Chain with Constrained Capacity

• 成果类型: Article
• 署名作者: Zhang, Na; Kannan, Karthik; Shanthikumar, George
• 署名单位: Amazon.com; Purdue University System; Purdue University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13267
• 发表日期: 2021
• 页码: 11-27
• 关键词: recommender systems; support vector machines; field experiments; supply chain constraints; nudging customers

摘要

For a slow-moving high-margin product, we demonstrate the viability of an information-based nudging strategy. The motivation to study this problem was because a firm faced availability constraints for one of its slow-moving high-margin products, but the available quantities still exceeded the current demand. To identify customers to nudge, we develop a support vector machine (SVM) approach to rank order the customers based on their propensity to purchase the product. The underlying notion in our approach is that Type I errors, to be defined in the paper, in our classifier are not necessarily problematic but are potential nudging targets. Also, as a consequence, traditional ways of evaluating classifiers (with Type I and Type II errors) are not appropriate. Therefore, we conduct a field experiment to evaluate how well the identified customers are nudged through information and/or couponing. We find that in terms of the successful nudges, our SVM-based approach performed better than other approaches. The experiment also generated insights about when couponing as opposed to information is more effective when nudging.