Inventory management is no longer a matter of guesswork. Thanks to machine learning and prescriptive analytics techniques, better decision-making in operations management makes it easier to predict demand for inventory based on a variety of different factors. With help from all kinds of data, inventory management decisions can be made correctly and successfully to create a competitive edge.
For a retail store, that data can be used to predict and prescribe optimal stock levels — even for products that have no prior sales history. Whether it’s a seasonal product, a new release or an item a store has never carried in the first place, relevant historical information related the product’s details can be used to predict and prescribe its sales potential.
Using big data to assist inventory management can help cut down on costs, maximize sales and ensure a store has the correct amount of stock on hand. The advent of omnichannel retailing and e-commerce offerings like “ship-to-store” options makes accurate inventory management crucial to ensure goods are where they need to be at the right time and aren’t being put to waste.
In one case study, we looked at the distribution and manufacturing arm of a global media conglomerate responsible for shipping an average of 1 billion units of CD, DVD and Blu-ray entertainment titles. Their challenge was to lower operational costs due to declining sales, diminished shelf space and the availability of digital alternatives.
For this vendor, their key goal was to maximize network-wide sell-through of their inventory. Because of the uncertainty of new releases that drive many sales as well as the lack of physical space on an endcap display, determining which titles had the best sales potential was essential.
Harvesting data from sales records, search queries from Google Trends and public sources such as Rotten Tomatoes and IMDb, we built inventory prescriptions taking into account a 150-week period. Building upon each previous week’s data, we scored our prediction performance in order to inform the next week’s prescription. With this data, we were able to make future sales predictions using both data-poor decisions and perfect-foresight decisions.
Being able to accurately manage, predict and organize retail inventory with the help of machine learning leads to retail operations that are far more streamlined and efficient. No matter the product or industry, prescriptive analytics using big data can greatly improve inventory management across the entire supply chain.
To get an in-depth understanding of the data and methods we used you can download our scientific paper Inventory Management in the Era of Big Data.