Michael Taylor, COO of Retail Services at Advantage Solutions, oversees Daymon, a global Private Brand agency.
Perfect execution in retail means delivering the right product, on the right shelf, at the right time and at the right price. That’s a lot to get right. Converting shoppers at physical and digital shelves to make a purchase is at the mercy of a foundational function of retail: in-stock inventory. Out-of-stock inventory causes a breakdown in achieving revenue that otherwise would be captured spend, with physical empty shelves only furthering the hit to profit by holding costly retail space for air that can’t be sold.
The task of maintaining accurate planograms, which are the schematic drawings detailing product placement on shelves, is, to me, both a strategic and operational art form. The amount of strategy work that goes into it is anything but happenstance, involving contractual obligations between retailers and vendors and careful monitoring and stocking of products on the floor.
Truth be told, out-of-stocks are inevitable simply because we’re human. No matter how great procedural processes are, human error or mother nature will get in the way, with anything from missed ordering, late deliveries and low crop yields affecting availability. While most retailers strive for operational perfection, it’s been an unachievable goal. The pandemic brought about record-high out-of-stocks, so the industry has taken a hard look at solving the decades-old operational problem, and as the old saying goes, “Necessity is the mother of invention.”
Enter technology like artificial intelligence (AI), and suddenly, the retail world has the potential to achieve perfection—or at least close to it. As a veteran in the fast-moving consumer goods (FMCG) industry, just when I thought I’d seen it all, AI came along. Can AI really help solve out-of-stock issues, revenue deficiencies and on-shelf blind spots? While we haven’t yet seen all the expansive ways AI can impact retail, today, there are key opportunities in activation.
Ensure Planogram Compliance
For strategy and contractual obligation, making sure planograms are executed in-store as intended is important. AI technology can be used to identify on-shelf inconsistencies and errors, using machine learning to quickly identify discrepancies between the shelf set and the intended planogram that humans can’t always quickly catch. Some AI tools use a photograph of in-store assortments to perform what I consider no less than modern-day magic. The technology identifies variances within minutes by comparing the existing in-store shelves to the planogram, creating a succinct punch list of identified action items to be corrected to fully reach planogram compliance.
Enhance Inventory Management
A specific example of utilizing AI to reduce out-of-stock items on-shelf is solving for phantom inventory with heightened speed and agility. A retailer’s ordering system may show there is inventory in-store on a specific item, but the inventory is not actually in the back stock room—also known as phantom inventory. This can happen when products are not reconciled at delivery, accidentally thrown away, damaged or stolen. The inventory ordering system might not allow for a reorder of the item because it shows ample in-stock inventory, which means phantom inventory is an operational challenge affecting inventory systems and sometimes is not identified or solved for lengthy periods of time. Once inventory systems become flawed, they need to be corrected with speed, or the entire inventory management process begins to break down.
Improve Cashflow
Store on-shelf visibility ensures inherent contractual obligations between retailers and vendors that impact retailer rebates, promotional funding and negotiated space or slotting rates are executed as agreed upon for all parties. AI can be used to reconcile planogram compliance with vendors and brand suppliers for improved cash flow due to faster payables.
Mitigate Price Discrepancies
Another function of AI tools at retail is to find pricing discrepancies—a common pain point driving customer dissatisfaction and can also be a hefty expense for retailers in states where weights and measures laws are heavily regulated, fining organizations for price errors. This discrepancy can result from an item placed above the wrong tag, from pricing at check-out being more expensive than on-shelf, or a promotional tag being displayed that shouldn’t be. An AI-curated punch list helps to correct these errors quickly and efficiently.
Conclusion
The implementation of AI requires executive wisdom to best manage and execute. Budget, strategy, testing and iteration and resource change management are all important elements to consider for best practices. For example, budgetary planning should not be considered a short-term investment but rather a long-term windfall with continuous investment likely critical to long-term success. Investing in the right technology and software available that best meets your organization’s strategic objectives means considering the testing, learning and iteration phases of development to make the program work idyllic to plan.
Resource change management is a function that many organizations struggle to execute and talk about. Organizational change in any capacity can be hard, especially as it relates to processes and new technology, with the pushback on AI especially heightened due to fears of workforce reduction. To that point, I believe it is absolutely imperative for executives to provide a transparent explanation of what AI actually means to the organization, reassuring them that it does not mean workforce reduction. I compare AI implementation to that of when the computer started making waves, and it is important to note many had the same fears about the computer. Look where we are today—certainly more jobs, and new and different jobs than prior to the computer entering the business world. A changing world is the one constant, and encouraging everyone’s involvement to embrace change and educating that using AI can enhance workforce performance by operating in ways that allow us all to work smarter and not harder will drive impactful efficiencies.
Impressively, AI can help solve historic retailer blind spots to lift sales and increase customer satisfaction. From increasing on-shelf availability that, in turn, impacts revenue and shopper experience to validating shelf tags that reconcile planogram compliance and accurate pricing, AI technology can help solve some long-standing operational challenges. I see AI solutions only further integrating into the retail ecosystem to better the business for all.
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