by Angela Guess
Bernard Marr recently wrote in Forbes, “Supply chain management is a field where Big Data and analytics have obvious applications. Until recently, however, businesses have been less quick to implement big data analytics in supply chain management than in other areas of operation such as marketing or manufacturing. Of course supply chains have for a long time now been driven by statistics and quantifiable performance indicators. But the sort of analytics which are really revolutionizing industry today – real time analytics of huge, rapidly growing and very messy unstructured datasets – were largely absent. This was clearly a situation that couldn’t last. Many factors can clearly impact on supply chain management – from weather to the condition of vehicles and machinery, and so recently executives in the field have thought long and hard about how this could be harnessed to drive efficiencies.”
Marr goes on, “In 2013 the Journal of Business Logistics published a white paper calling for ‘crucial’ research into the possible applications of Big Data within supply chain management. Since then, significant steps have been taken, and it now appears many of the concepts are being embraced wholeheartedly. Applications for analysis of unstructured data has already been found in inventory management, forecasting, and transportation logistics. In warehouses, digital cameras are routinely used to monitor stock levels and the messy, unstructured data provides alerts when restocking is needed. Forecasting takes this a step further – the same camera data can be fed through machine learning algorithms to teach an intelligent stock management system to predict when a resupply will be needed. Eventually, the theory is, warehouses and distribution centers will effectively run themselves with very little need for human interaction.”
Photo credit: Flickr/ Tetra Pak