Data analytics may seem like the catchphrase of the day, but that’s because there’s real value in the technology. Analytics platforms are useful outside the confines of the in-store operations as well. Techniques like machine learning and artificial intelligence (AI) can also be extremely beneficial when it comes to supply-chain operations.
LP Magazine recently asked Guy Yehiav, CEO of Profitect, a data analytics solution provider in the loss prevention industry, about the ways in which retailers might fine-tune their omni-channel processes through analytics. Yehiav, who has 25 years of experience in the industry, shared some powerful advice. From the article:
LPM: How is analytics used differently in supply chain versus store
YEHIAV: Supply chain is using analytics to maximize on‑shelf availability and optimize their
processes through just‑in‑time delivery, full truckloads, on‑time shipment complete, forecast accuracy, efficiency at the distribution centers (DCs), and other factors.
So in a nutshell, predictive analytics makes predictions, while prescriptive analytics makes
predictions and tells you what to do about them to drive optimal ROI, revenue, and margin
Store operations is using analytics to maximize customer satisfaction, optimize P&L, optimize labor, minimize shrink, execute promotions, optimize merchandise displays and promotions, and other examples.
Yehiav goes on to list the six major failure points with using predictive analytics and/or exception-based reporting rather than a prescriptive analytics solution. He also talks about the “Amazon model” and shares the details of several success stories from retailers that chose to leverage prescriptive analytics in their operations. Read the full article in the June 2018 issue of LPM Online: “Using Analytics to Maximize On-Shelf Availability and Supply-Chain Processes.”
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