How Research in Production and Operations Management May Evolve in the Era of Big Data

• 成果类型: Article
• 署名作者: Feng, Qi; Shanthikumar, J. George
• 署名单位: Purdue University System; Purdue University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.12836
• 发表日期: 2018
• 页码: 1670-1684
• 关键词: big data; demand learning and planning; Manufacturing; individualization; mass customization

摘要

We are living in an era in which data is generated in huge volume with high velocity and variety. Big Data and technology are reshaping our life and business. Our research inevitably needs to catch up with these changes. In this short essay, we focus on two aspects of supply chain management, namely, demand management and manufacturing. We feel that, while rapidly growing research on these two areas is contributed by scholars in computer science and engineering, the developments made by production and operations management society have been insufficient. We believe that our field has the expertise and talent to push for advancements in the theory and practice of demand management and manufacturing (of course, among many other areas) along unique dimensions. We summarize some relevant concepts emerged with Big Data and present several prototype models to demonstrate how these concepts can lead to rethinking of our research. Our intention is to generate interests and guide directions for new research in production and operations management in the era of Big Data.