Supply Chain Analytics: Overview, Emerging Issues, and Research Outlook

Supply chains (SCs) produce vast amounts of data from sourcing raw materials to manufacturing to consumption to returns. Supply chain analytics (SCA) helps organizations (profit or non-profit) to make faster, smarter, and more effective and efficient decisions. However, SCA requires advanced technology adoption, an organizational skill set, and a culture that embraces data-driven decision-making. In contemporary SC operations, a highly sought-after approach, analytics provides description, prediction, and prescription of the problems faced. Emerging intelligent technologies, such as the internet of things, blockchain, physical internet, and artificial intelligence that support SCA, can be utilized in almost every sector, including humanitarian and business logistics, procurement, marketing, pricing, and sustainable supply chain management. This chapter overviews the scaffolding concepts behind SCA. It offers a framework for bringing various stages of an SC to collaborate in data sharing, planning, and executing SC decisions at the operational, tactical, and strategic levels. It offers findings and managerial implications from the state-of-the-art literature and best industrial practices while focusing on SCA’s current concerns and research opportunities.