Optimize Your Inventory Using Predictive Machine Learning
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.