Optimize Your Inventory Using Predictive Machine Learning

Optimize Your Inventory Using Predictive Machine Learning

Apr 20

Here is a common scenario for large retailers that operate dozens or hundreds of stores across large geographic regions. They pour millions of dollars into safety stock, ostensibly to prevent out-of-stocks, but at the end of the day, it looks like all this extra stock is there just for the sake of maintaining perfect fill rates. Some businesses order excess stock because their vendor minimums are too high. Other businesses accidentally treat seasonal items like year-round products, maybe because of regional differences in buying patterns.

Whatever the reason, this excess stock doesn’t move, though not necessarily for lack of demand. These products might simply be in the wrong stores and distribution centers, or are waiting for the right time of year to arrive. However, without a reliable way to generate sales estimates, most retailers are flying blind. The problem is not low demand; it is poor predictive capability.

Lost sales is just one of consequences of poor forecasting. Excess stock eventually leads to markdowns, which have a major impact on profits. Maintaining a large inventory and frequently transferring products between locations to save sales also shrink margins.

In a perfect world, retailers know exactly how many sales will occur at each location, deliver each product from the optimal source, and maintain optimal replenishment schedules with each of their vendors. Cutting-edge digital technologies have now made inventory optimization a reality, allowing retailers to cut their excess inventory in half.

Visionet Systems has helped retailers nationwide by analyzing several years of sales data, categorized by store and SKU, and using this data to train machine learning systems. After applying several regression algorithms, we were able to predict the actual sales with a high degree of accuracy e.g. sales spikes on holidays and long weekends. The optimizations suggested by our forecasting algorithms reduced our client’s inventory spend by 66%, and also reduced stock markdowns and scrapping.

Even though our algorithms were extremely good at estimating product demand, we also recognized the value of human experience. Our solution allows users to make manual adjustments to computer-generated estimates, which are then submitted to decision makers for approval. Users can make as many adjustments they deem necessary, and any approved changes are used as feedback by the forecasting system to improve future estimates. The forecasting system interfaces directly with the retailer’s ERP system, and the difference between computer estimates, manual adjustments, and actual sales are compiled and presented in detailed reports.

If you think you are spending too much on safety stock, this solution is a quick win, with most implementations taking less than a month to complete. Contact Visionet Systems for a POC that uses your own historical sales data and gives you a clear picture of your expected ROI.

The millions you save in inventory costs makes this solution extremely worthwhile.

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