How signal processing can optimize non-seasonal product inventory

July 3, 2019

How signal processing can optimize non-seasonal product inventory

Winston owns and operates Winston’s Wristwatch Warehouse, a successful chain of stores dedicated to things that tick. They offer tens of thousands of high-end and mid-range watches from hundreds of international vendors. While Winston’s brand has been running like clockwork for years, he has noticed that several local competitors have begun to challenge his business. These rival watch merchants are now cutting into Winston’s share of the market.

How can Winston help his business prevail without sacrificing the quality and customer service that earned him his reputation?

Keeping your inventory lean

Winston’s answer lies in minimizing operating costs by optimizing his inventory. Most businesses severely limit their profitability by either spending too much on inventory or not stocking enough products to keep up with demand. The least successful among them are the ones that “follow their gut” and replenish their stock in an arbitrary and haphazard fashion. Others use previous years’ sales as a guide for reordering products. Even more statistically savvy businesses perform time-series analysis to estimate how many of each item to buy.

Forecasting inventory with machine learning
However, it’s no simple matter to accurately forecast changes in demand at each store for the thousands of products stocked by businesses like Winston’s. In this particular example, Winston’s flagship products are big-ticket items that cost thousands of dollars, have long lifespans, and lack a clear peak purchasing season. These characteristics make Winston’s business a poor fit for time-series analysis.
Worry not: digital signal processing can break this kind of complex pattern into simpler component patterns. Using signal processing for deep forecasting, data scientists can forecast demand with 60 percent less error than traditional statistical techniques.


Signal processing is a powerful technique that can be used in situations that aren’t suitable for more common time-series analysis. Combined with machine learning, this method can yield excellent accuracy for each product and store, even when sales data is incomplete.
To learn more about how Visionet’s inventory planning solution generates accurate demand forecasts using signal processing and machine learning, please download our white paper, “How to Slash Inventory Using Signal Processing & Machine Learning”.[/vc_column_text][/vc_column][/vc_row]