When computer assisted ordering systems (CAOs) first emerged, retail hopes ran high. Digital transformation! Automation! Intelligent ordering! Now, more than a decade and two global crises later, grocers are still struggling to make meaningful progress in improving fresh food ordering processes or implementing technology in a way that moves the P&L needle.
In part, CAOs have failed to deliver on their promises. To be successful they require accurate master data, including delivery times for certain products, product sizes, assortments, order minimums and supplier details. Any errors embedded in the system can result in breakdowns throughout the ordering cycle, leading to shrink, overstocking, understocking and more headaches and manual work for buyers.
CAOs also offer nothing more than basic forecasting capabilities. Teams must still spend time manually inputting data to trigger an order in the system. Orders are based on guesswork, rather than intelligent forecasting driven by inventory realities.
These flaws in CAO-type systems have stunted the potential benefits technology can deliver to grocers. As a result, decision makers at large chains wrestle with whether to keep buying new software or invest in building their own proprietary inventory management solutions. Building such a solution in house can take years and is a feat most grocers cannot undertake on their own. Given the high costs and complexity of building custom solutions, most of these initiatives fail to launch, and when they do, often do not boost margins or deliver meaningful returns.
The options for buying tech are equally inadequate. Many of today’s AI forecasting solutions offer limited feature sets and can be difficult to implement, requiring stores to upend internal operations and processes to fit the software—rather than the software adapting to the grocer’s needs.
At the individual store level, the effort needed to effectively utilize CAOs and forecasting software (and the frequency of mistakes that result from these systems) have led buyers to view technology as more work than it’s worth. Even in instances where a system is well implemented, many buyers lack the time, resources and interest to integrate tech into their day-to-day operations. They are content to fall back on their traditional processes and resist change.
To improve margins, it’s not just the ordering system or the tech that needs an overhaul. The entire business model must shift. Traditionally, grocery vendors have dismissed the viability of scan-based trade — wherein the vendor assumes responsibility for the inventory and grocers pay for only what they sell — as a solution to reduce the cost of shrink. But when a scan-based trade model is paired with intelligent forecasting and ordering, risk is significantly reduced and the technology’s benefits become tangible. The cost of shrink is eliminated, profits increase and buyers can focus on delivering a superior customer experience.
With a partner like Shelf Engine, grocers can transfer fresh and perishable SKUs and efficiently integrate all vendors to a scan-based trade model. Shelf Engine’s platform ingests inventory data daily and overlays what is actually happening in the store with machine learning and probabilistic models that generate highly accurate orders. The system submits PO’s directly to vendors and provides supply chain auditing across stores and vendors on fill rates, shelf lives and in-store operations and merchandising. No new hardware or complex system changes are required, and anything that doesn’t sell is bought back and removed from the grocer’s P&L.
Shelf Engine’s data shows that most grocers using CAOs and one-dimensional forecasting software consistently waste upwards of 20-30% of their fresh food inventory. Conversely, by combining intelligent forecasting software with scan-based trade, grocers have eliminated shrink and achieved a SKU-level gross margin improvement up to 400%, without diminishing the aesthetics of full and varied shelves.
Grocers that keep waiting for their CAO to bear fruit will find that all they have to show for their investment is a crate of bad apples. Instead of hoping for technology to solve shrink, focus instead on improving the business model. It’s only after broken systems are fixed that grocers can truly tap into the full potential AI and advanced technologies have to offer.