Decision makers in supply chains are seeking ways to effectively manage big data sources to increase agility and efficiency.
When it comes to large volumes of data accumulating at an accelerating rate in supply chain operations, complex questions arise: how much data –and from which source and in what structure and format – is needed to make accurate, timely and beneficial trade-off decisions about supply chain processes?
Making well-informed decisions in context involves a wide range of supply chain operations – from demand sensing and forecasting of inventory planning and logistics planning to execution and warehouse management, just to name a few.
Now more than ever data sources are abundant and in various forms and constructs ranging from GPS data to enable dynamic routing and scheduling of deliveries, point of sales (POS) data, operational data of warehouses, production line data, inventory data and many different forms of structured and unstructured data from numerous parties across the entire logistics network.
In this first paper of a four part series, we explore the concept of big data and how it is differentiated from small data. We then move on to identify big data sources and the applications of big data solutions in supply chain operations, and the skills required for supply chains to gain analytical competence and avoid paralysis by analysis.
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