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Predictive Analytics and Retail Inventory Management

Date posted: October 21, 2015

How a manufacturing organization handles inventory management has a direct bearing on its likelihood of success. Do it well, and it can depend on having the materials it needs on hand when it needs them; do it poorly, and significant problems ensue.

To have the right products at the right place at the right time, retailers put a lot of time, thought and effort into inventory management, allocation, and replenishment processes. of their business process, then purchase the inventory and allocate it stores from a distribution center (DC). When inventory runs low at a particular store, the DC replenishes it. That’s the idea.

An article on Predictive Analytics Times speaks to how inventory distortion can derail the idea, and how predictive analytics are working to keep them on track.

Inventory distortion is a term used by retail technologies consultant IHL Group to describe the challenges faced in the typical inventory management process. Examples of what causes this would include:

  • When products don’t sell well at specific stores, retailers are forced to markdown inventory in order to clear the inventory.
  • When there is no more inventories in the DC, or even before inventory is replenished, retailers experience lost sales at stores where these products are still in demand.
  • Customers resolve to purchase from a local competitor in order to fulfill the sale.
  • Lost sales paint an inaccurate picture of lower demand, which in turns makes a retailer order and allocate less inventory to stores in the future. (This cycle repeats itself.)

According to IHL Group, inventory distortion costs retailers nearly $800 billion globally. Increasingly, notes the article, retailers are fighting this by using predictive analytics for inter-store inventory balancing:
In order to properly forecast demand, and build the optimal inventory management strategy a retail solution must be tailored to the specific business process of the retailer, which means that it must take all factors that affect demand into account. Predictive analytics technology will significantly improve demand forecast accuracy, and suggest better allocation and replenishment strategies. Moreover, there are specific tools that enable a retailer to further decrease inventory distortion. One such tool is inter-store inventory balancing.

Inter-store inventory balancing leverages predictive analytics to proactively analyze all the influencing factors of a retail supply chain, then recommends an optimal inter-store transfer schedule to move slow-selling products to stores where there is a higher demand for them. Retailers that use predictive analytics for this technique report significant benefits, with tangible results such as:

  • A 25-40% decrease in inventory costs
  • Sales increases of up to 20%
  • Increasing turnover by a factor of up to 3.5x

This is just one way retailers are leveraging predictive analytics, but one that’s indicative of why its use in inventory management is destined to grow.





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