Nowadays firms have to react quickly to changing markets creating a need for accurate forecasts of demand and supply. In a data-rich environment as it is within the field of supply chain management, much information needs to be stored, processed, and transformed for decision making. To deal with the increasing amounts of data, firms must be aware of chances in supply chain management such as supply chain analytic capabilities to stay agile, flexible, and make use of (complex) data. Supply chain analytics can predict patterns and trends, even in high velocity markets in real-time supporting decision making by using supply chain analytic tools based on data. The benefits of successfully implementing supply chain analytic processes are enormous and result in competitive advantages for companies such as lowering costs while increasing revenues. As many companies fail to apply supply chain analytic processes and tools, this paper examines the challenges, benefits, and factors for the introduction of supply chain analytics using the input-output model.
Engel, Tobias; Meier, Nils; and Möller, Thorsten, "Proposing A Supply Chain Analytics Reference Model As Performance Enabler" (2017). MCIS 2017 Proceedings. 38.