How Machine Learning Is Changing the Store Paradigm
Changing demands in the retail environment have created all-new operational challenges, as retailers strive to deliver exceptional omnichannel customer experiences. With a growing need to balance inventory management with labor utilization, how can machine learning (ML) revive the physical retail store, making it a critical asset to successful omnichannel execution?
At its core, the omnichannel promise is simple: to deliver service effectively and cohesively across all digital and physical channels, while simultaneously creating a seamless, consistent and positive customer experience. Thankfully, modern advances in the application of artificial intelligence (AI) and ML are arming retailers with the real-time data and intelligence that makes this possible, from enhancing retail operations and driving inventory accuracy to even improving labor efficiencies. So, how can retailers execute this?
A retail store equipped with ML can use smart algorithms to determine the products its customers — whether in-store or online — want to purchase, facilitating accurate forecasts for planning. This data can be used to increase efficiencies and inform planning and stock management, while also enabling retailers to get a complete and accurate picture of their inventory and where it is held, whether on the shop floor or in the stockroom.
By precisely organizing and tracking store inventory, retailers can simultaneously fulfill online orders using stock stored in the backroom and ensure shelves are adequately stocked for customers visiting in person. This is helping retailers meet customer expectations, no matter what purchase platform they chose.
Les McNeill, founder and chief technology officer of Impulse Logic, based in Sam Ramon, Calif., explained: “The retail environment has shifted away from the concept of perpetual inventory, a model used by most big retailers. Here, continuous accurate counts are required, and that can only be realized with location-based inventory. This enables retailers to identify what is on the sales floor, what is in the backroom and what is committed in the supply chain, yet to be received.”
McNeill added: “Most important is the determination of the true demand for digital and in-store purchases within each product’s delivery cycle to the store. It is in these areas that Impulse Logic is applying ML to determine not just where the stock is, but where it should be, and in what quantities. Today, very few retailers can accurately expose and use such intelligence in support of omnichannel availability within the local store. Without this knowledge, it is impossible to achieve accurate supply planning in balance with demand.”
Working with the Parker Avery Group, a leading strategy and management consulting firm, Impulse Logic has outlined the five key steps retailers can take to adapt to the dynamics of the modern marketplace. In “Rethinking the Retail Store Paradigm: Five Expert Steps to Finally Solve Omnichannel Execution” they argue that accurate management of inventory is the cornerstone for omnichannel execution by understanding the need to change, fixing inventory accuracy, leveraging the backroom, intelligently aligning assortment with demand and aligning store labor to improve efficiency.”
Rob Oglesby, senior director at The Parker Avery Group, added: “Without understanding how much of a product you have and where it is stored, you will mess up your perpetual inventory. If a retailer thinks they have 100 units in store but can only find 25 in a location, they will make an adjustment to replenish, leading to an unnecessary overstock of certain products. The winners are going to be those companies that break this paradigm.”
Balancing demand and availability
By understanding the precise physical location of products, retailers can achieve accurate fulfillment — whether in the physical retail environment or via a digital platform. Here, retail technologies are combining ML to transform retail stores into micro-fulfillment centers. It means physical stores are equipped with the same capabilities that they would expect of their distribution centers, but on a local level.
Additionally, ML is not just focused on ensuring real-time inventory counts. There is a whole array of data points, pattern recognition and modeling that heightens visibility, improves replenishment and redirects labor to where it is most needed.
“By using accurate and timely data, retailers can identify what products will hit minimum count, generate a pick list and task in-store teams to action,” explained McNeill. “This removes unnecessary labor on the shop floor and ensures every store associate is focused on a task that drives efficiency and adds value to operations and the customer experience.”
So, how can retailers embrace this shift to transform their omnichannel execution? While there has been a notable trend to embrace the Internet of Things and introduce smart shelves and RFID tagging, these approaches come with a significant cap ex cost. However, the same outcome can be achieved with much smaller investments. The disruption caused by fulfilling online orders using on-shelf stock is making availability a much bigger issue affecting every consumer on every platform. By embracing ML and AI such as that offered by Impulse Logic, retailers can introduce advanced predictive analytics to create the optimal product flow through the store, optimize labor availability, ensure product availability, reduce waste and increase profits.
Discover how machine learning can transform your inventory handling by reading “Rethinking the Retail Store Paradigm: Five Expert Steps to Finally Solve Omnichannel Execution” today.