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.

In a 2016 research reportWhy Artificial Intelligence is the Future of Growth, Accenture found that adoption of artificial intelligence tech across all industries may double economic growth rates by 2035. AI investment is expected to increase labor productivity by 40 percent. In fact, 70 percent of executives say they plan to “significantly increase” AI investment.

In the realm of inventory and supply-chain management, AI adoption, specifically the use of optimization algorithms, is revolutionizing inventory agility – reducing stock depletions and maximizing stock levels.

“The use of AI in supply chains is helping businesses innovate rapidly by reducing the time to market and evolve by establishing an agile supply chain capable of foreseeing and dealing with uncertainties,” says Accenture Managing Director Manish Chandra. “AI armed with predictive analytics can analyze massive amounts of data generated by the supply chains and help organizations move to a more proactive form of supply chain management.”

Supply chain processes generate giga-tons of data, and AI can deploy predictive analytics to make sense of it all. Freshly updated and analyzed data then builds a solid foundation when it comes to real-time vision and information flow. Every key player across the supply chain is empowered with the best data and maximizes it accordingly.

AI is no longer an “ain’t-it-cool” innovation in the industry but rather a necessity. With the erosion of the brick-and-mortar model and rise of real-time consumer expectations, supply chain/inventory management practices must embrace machine learning that far outpaces the speed of human thought and action. Consider these stats from the 2017 MHI Industry report concerning the speed of supply-chain transactions from just one e-tailer on Black Friday:

“A reported 426 orders per second were generated from the website throughout the day. That equates to over 36 million order transactions, an estimated 250 million picking lines at the distribution centers (DC), 40 million DC package loading scans, 40 million inbound sortation hub scans, 40 million outbound sortation hub scans, 40 million inbound regional sortation facility scans and 40 million outbound delivery truck scans.”

How should industry leaders respond? The answer, according to the report, is clear. Supply-chain companies must embed “analysis, data, and reasoning into the decision-making process. Position analytics as a core capability across the entire organization, from strategic planners through line workers, providing insight at the point of action.”

As Accenture economic research director Mark Purdy concludes, companies that survive will fully invest in the potential power of AI going forward: “To fulfill the promise of AI, relevant stakeholders must be thoroughly prepared – intellectually, technologically, politically, ethically and socially – to address the benefits and challenges that can arise as artificial intelligence becomes more integrated in our daily lives.”