By James A. Cooke
Can’t put your finger on exactly what’s needed to improve your supply chain operation in your electrical distributorship? Big data analysis could provide that clue. Big data refers to the accumulation of information that’s stored on dozens of computers or on the Internet. Every time a bar code gets scanned, and email gets sent, or a software application issues an instruction, that notation gets recorded, and all that data about operational transactions keeps piling up in computer systems.
Until recently, these piles of data were untouched treasure troves of information. Advances in computer hardware and software now make it possible to pour through those heaps of data, both structured (residing in conventional databases) and unstructured (which lies in information repositories such as email accounts or social media networks). A number of software vendors, from big names like IBM to smaller upstart companies, have begun developing special applications that can sift through “big data” to spot patterns and find hidden cause-and-effect relationships.
Although big data analysis can be applied to all functions of the business from marketing to finance, it’s particularly promising for the supply chain. For starters, this type of analysis could be used to make connections between other aspects of their business that impact transportation and warehouse operations. For example, sifting through shipping records might discover a link between a particular type of packaging and product damage that occurs during shipping.
Big data analysis could be combined with real-time monitoring to improve warehouse and shipping performance. For example, cameras in a warehouse can capture pictures of bar-coded cases moving down a conveyor and those digital images analyzed to figure out ways to make adjustments to improve product flow in the warehouse.
A similar analysis could be done for delivery. If pallets or cases on shipments were marked with radio-frequency identification data (RFID) tags, then it becomes possible to precisely track movements along a route from shipping through receiving. An analysis of RFID data could pinpoint problems or issues during transit movements.
In addition, the RFID or image data could be combined with information from Enterprise Resource Planning (ERP) systems, Warehouse Management System (WMS) and Transportation Management Systems (TMS) into one common pool for analysis. That would enable a detailed “path analysis” of supply chain flows. This kind of mapping would allow distributors to visualize the supply chain process, and identify potential troublespots or bottlenecks.
But big data analysis can do more than just provide operational reports. Because it provides a granular level of data, it can serve as the basis for predictive analytics. By spotting a problems in the making, say in transportation, predictive software could suggest that shipments be rerouted to meet delivery schedules and ensure customer commitments.
In short, big data analysis is an information technology trend with major implications. It will allow distributors to dig into data to figure out what might be causing a particular supply chain problem as well as identify areas for improvement and efficiency.
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James Cooke is the editor of CSCMP’s Supply Chain Quarterly magazine, the premiere journal of global thought leadership for supply chain professionals. He has been writing and reporting on the best practices in supply chains for more than 30 years.
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