Analytics applications, limitations, and opportunities in restaurant supply chains

• 成果类型: Article
• 署名作者: Swink, Morgan; Hu, Kejia; Zhao, Xiande
• 署名单位: Texas Christian University; Vanderbilt University; China Europe International Business School
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13704
• 发表日期: 2022
• 页码: 3710-3726
• 关键词: Business analytics; food service supply chain; restaurant industry

摘要

Technology, market, and competitive dynamics are requiring firms in restaurant/food service supply chains to improve their analytics capabilities, which have tended to lag behind other comparable industries. The global COVID-19 pandemic has further encouraged industrial leaders to evaluate new challenges and opportunities. Our research provides insights into current applications of analytics technologies and organizationally integrates these insights for decision-makers in restaurant supply chains. The study applies decision theory as a framing perspective to this phenomenon, thereby advancing the academic literature on the interface between data management, analytical techniques, and computing. We combine data drawn from interviews of leading players in U.S. and Chinese-based restaurant chains with insights from trade publications and social media posts to identify best practices for analytics usage and supporting organizational changes. Our analysis provides examples of ways in which business leaders are applying analytics technologies to structured and unstructured data to address targeted objectives for demand/supply processes and to foster higher order organizational learning. In keeping with the stated objectives of this special issue of Production and Operations Management, this study provides an overview of both current state-of-the-art and next-generation capabilities for analytics in the restaurant industry. We further identify specific limitations of current processes, opportunities for development and theory-based research, and challenges to implementation.