Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse

• Published in: International journal of information management Vol. 57; p. 102304
• Main Authors: Zhang, Dan, Pee, L.G., Cui, Lili
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.04.2021; Elsevier Science Ltd
• Subjects: Algorithms; Artificial intelligence; Case studies; E-commerce; Electronic commerce; Error reduction; Fulfillment center; Information systems; Labor economics; Optimization; Productivity; Resource orchestration; Smart warehouse; Warehouses; E-commerce; Artificial intelligence; Smart warehouse; Fulfillment center; Resource orchestration
• ISSN: 0268-4012; 1873-4707
• DOI: 10.1016/j.ijinfomgt.2020.102304

•Successful AI applications were analyzed to identify key resources and capabilities•Key AI-related resources include data, human workers, existing facilities and IS•Resources must be coordinated, leveraged, and deployed to work with one another•Key AI-related capabilities include forecasting, planning, and learning•AI capabilities interact and coevolve with human capabilities to create value Despite heightened interest, integrating artificial intelligence (AI) into businesses remains challenging. Recent surveys show that up to 85 % of AI initiatives ultimately fail to deliver on their promises. Studies on successful AI applications that could provide invaluable lessons for organizations embarking on their AI journey are still lacking. Therefore, this study aims to understand how AI technology, people, and processes should be managed to successfully create value. Building on the resource orchestration perspective, this study analyzes the successful applications of AI at Alibaba's e-commerce fulfillment center. The findings indicate that the key AI resources include data, AI algorithms, and robots. These resources must be orchestrated (e.g., coordinated, leveraged, deployed) to work with other related resources, such as warehouse facilities and existing information systems, to generate strong AI capabilities. The key AI capabilities generated include forecasting, planning, and learning. More importantly, AI capabilities are not independent – they interact and coevolve with human capabilities to create business value in terms of efficiency (e.g., space optimization, labor productivity) and effectiveness (e.g., error reduction). The implications of understanding these social informatics of AI for research and practice are discussed.