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Fresh optimization: Catching grocery retail’s white whale

Jan 19, 2024 5 min

For many retailers, fresh management remains the final fish to catch when introducing new or upgraded technologies to improve demand forecasting and optimize replenishment. The unique characteristics of managing fresh grocery retail have made it a hidden treasure amid a sea of AI and machine learning technologies. Still, many businesses rely on base-level solutions and fear taking the next investment leap. That situation must – and now can – change. It is time to be bold and wave goodbye to imperfect inventory data in fresh grocery. 

Considering the importance of managing fresh inventory, why has it been such a challenging beast to tackle so far? As a fast-moving product assortment, fresh’s primary challenge comes from the need to juggle both short- and long-term inventory management. From a stock perspective, grocers must make real-time decisions for the next day or week. Simultaneously, they must translate evident trends into future decisions that impact business strategies. 

Further, grocery retailers are under pressure to anticipate required stock levels while reconciling the products’ various and, in many cases, rapid spoilage rates. When so many companies strive for improved carbon footprints and eco impacts, the sweet spot of avoiding empty shelves and customer frustration while ensuring availability and presentation without driving waste is a narrow plank to walk. 

On top of all the inherent challenges of managing fresh inventory, retailers must cope with labor shortages and high staff turnovers that leave them struggling to leverage long-term skills and experience. 

There has long been an acknowledgement that technology is critical to finding the availability-waste sweet spot, mitigating labor issues and addressing the sheer pace of fresh retail. However, until now, there haven’t been enough viable solutions to convert that acknowledgement into action. 

Spoilage and shrink: An imperfect combination 

RELEX has sought to address a cliché that has almost become a running joke in fresh grocery in recent years: “Inventory data is never correct”. It’s an exaggeration, perhaps, but it reflects the unique challenge of products like fruits and vegetables. 

They are more challenging than any other products to monitor across the supply chain journey, up to – and including – the point of sale. Produce is frequently sold loose, creating an immediate difficulty in terms of monitoring. Each item spoils, but at a different rate to every other item – even those in the same batch. Occasionally, items spoil during distribution, making “How many actually arrived at the store?” a common question. 

There is also the issue of shrink. Products may lose moisture, which can skew weight-based modes of monitoring or tracking inventory. Stores may label items incorrectly or accidentally mix similar products or batches. Theft, loss, and unrecorded waste are also factors that lead to shrink and compromised inventory data. 

Figure 1: Multiple variations among products in fresh categories, including spoilage rates and storage requirements, make manual planning nearly impossible.  

The result of these issues is that imperfect or missing data is a constant challenge for retailers and one that legacy systems are ill-equipped to handle when forecasting future needs. Typically, at that stage, best-guess estimates are conducted by store associates surrounded by more accurate and automated demand forecasting processes in almost every other part of the store.  

No more firefighting: It’s time to be proactive 

The good news is that the bottlenecks and fears retailers have encountered when managing fresh grocery inventory can now be overcome. 

It all starts with a simulation. By bringing an organization’s data into software designed to manage fresh grocery’s specific requirements, planners can calculate where availability levels have historically been too low and waste levels too high. From these metrics, they can analyze the financial impacts of losing sales and procuring and distributing items that have perished before consumption. 

From this data, it is also possible to dig into efficiency-based metrics such as labor hours used to reach these suboptimal outcomes and environmental performance resulting from waste figures. Planners can then aggregate these statistics to assess the impacts of such a shortfall over a year or even longer. 

Figure 2: A system designed to handle the unique complexities of fresh management can process and aggregate data from multiple sources, even imperfect data, and model the impacts for various timelines.

It is this introspection that most fresh grocery players have been actively seeking for some time now and finally have access to it. Immediately, a sense of proactivity seems possible. Planners become more confident in the accuracy of automatically triggered inventory levels for the next day and week. Meanwhile, more accurate data is built up over time to help decision-makers determine optimal strategies for the future. 

AI is a critical element in the next phase of automation, which should augment associates’ capabilities and expertise. While AI certainly isn’t a replacement for their decision-making skills, it can drive speed, efficiency, and accuracy to their daily tasks, enabling them to spend less time on manual number-crunching and more time on customer service. 

Getting as far away from imperfect as possible 

Variations in spoilage rates, in-store conditions, sizes, and even seasons are a few of the complexities that have made fresh categories historically tricky for retailers to manage. These complexities naturally result in imperfect inventory data sets, as the transition from supplier to store to consumer leads to information gaps. 

Fresh optimization from RELEX unravels this complexity. It is now possible to see how much of each product made it to the store, what consumers bought, and, therefore, how trends manifest. Even starting with poor-quality data, the solution can deduce accurate estimations of stock levels required in-store. Best of all, it doesn’t require expansive change management programs, rebuilding of the supply chain, or massive reconfigurations of store processes to turn the tide. 

Two charts showing the before and after impacts of predictive inventory on balance estimations.
Figure 3: Shrink causes a growing discrepancy between estimated and actual inventory balances. Powered by machine learning, predictive inventory generates accurate inventory estimations to drive order proposal calculations even when data is poor. 

All that is needed is one solution. A solution that runs in the cloud but that can be integrated into retailers’ other software. Configurable and flexible enough to be customized to a retailer’s unique strategies, objectives, and priorities, it is a solution that can yield unrivalled accuracy for a product group that has been historically problematic. 

Even if “perfect data” isn’t possible, recent advances in AI and machine learning have made great strides for retailers and enabled them to use data that would have previously been deemed useless. Fresh retailers finally have the chance to get as far away from ‘imperfect’ as possible—an opportunity to catch their white whale. 

Learn more about how RELEX can help you get a handle on fresh optimization. 

Written by

Ulla Huopaniemi

Lead Product Marketing Manager