There are few challenges in retail tougher than managing grocery retail supply chains. Optimizing a broad inventory that includes fresh and short shelf-life products is not easy, and nothing more powerfully demonstrates what a supply chain professional and a supermarket inventory management system can do together quite like the impact they have on profitability.
Better replenishment of perishables means your displays look better, customers get fresher goods, and you sell more. So, let’s look at how effective grocery store inventory management makes the best use of product shelf-life information and category-level consumer behavior to cut retail food waste.
1. Ensure a Dynamic Approach for Both Fresh and Ambient Products
From fresh goods wholesalers to grocery retailers, from high-end to price-driven supermarkets, convenience stores to cash-and-carry chains, it is clear that replenishment teams walk a thin line between spoilage costs and shelf presentation. This relationship makes it important to get the balance right.
Grocers navigate a supply chain that serves both the fresh and ambient products with the same capacity constraints and resourcing costs while handling their different characteristics like code life and shelf space. For example, fresh products need a more just-in-time replenishment model, whereas ambient items can be more cost-driven and space aware. However, both should remain agile otherwise changes in demand will lead to lost sales.
2. Never Overlook Product-Level Shelf Life When Ordering
All major retailers have contracts with suppliers that specify that items have an agreed minimum shelf life when delivered. However, this information isn’t always given sufficient importance in replenishment, as sell-by dates vary from delivery to delivery. These variables, however, can often be worked into forecasts.
Retailers need a model that can estimate the two main cost components in grocery retail business—spoilage and lost sales—in all possible real-life situations. Based on these two estimates, a cost function is formulated to lay out the total cost of ordering. By understanding the trade-offs, an intelligent system can balance the costs between spoilage and lost sales in a variety of situations. This process minimizes costs by varying the safety stock level and choosing the value resulting in minimal total costs.
Even though the majority of orders can be done with optimization alone, the final tuning often benefits from smart heuristics. The simplest step to take is to incorporate shelf-life expectations into your supermarket inventory management system’s ordering parameters.
For example, the set parameters can tie your safety stock calculation to a max ‘x’% of the expected shelf life forecast or be used to build exception reports when safety stocks are likely to creep over a set threshold. Meanwhile, setting exceptions for when a case pack’s days of supply exceed ‘y’% of shelf life can help highlight products needing close supervision.
3. Incorporate Forecasted Spoilage – Simulations Can Help
Spoilage forecasts are useful in order parameter calculation, but good systems can also use them in replenishment calculations by factoring in future spoilage. In DC environments, we usually introduce batch level inventory balances with sell-by-date information. This data helps keep availability high by replenishing before stock spoils and flagging items that need to be sold quickly.
Better replenishment of perishables means your displays look better, customers get fresher goods, and you sell more.
This is tricker in retail environments, as consumers don’t always operate on the “first-in-first-out” principle – quite the opposite, actually! Luckily, modern replenishment tools can estimate this consumer shopping behavior and generate estimates for batch balances. Thus, systems can utilize accurate simulated spoilage figures in replenishment calculations to keep availability optimal even for low-volume products.
4. Manage Each Product Individually – But Understand How Products Behave in Groups
In many perishable categories, products often substitute so readily for one another that the consumer can switch without a second thought. Fresh bread is a good example. With one particular client, we began optimizing bread replenishment by identifying “must-haves” in each sub-category via store-level ABC classification.
We ran replenishment on the basis that “nice-to-have” products could run out towards the end of the evening, but there should always be stock in all basic categories (e.g., sliced white, whole grain, seeded). The optimization had the expected impact on food waste, but we were quite surprised by how much category sales and sales margins also increased (over ten percentage points on average). Because of better inventory turnover, fresher products simply appealed more to consumers.
5. Dive into Your Day-level Data
In retail, big gains come from small improvements across countless SKU-store combinations. To get the big figures right, however, you have to master your low-level data. One good example is from a department store known for its high-end food halls. When managers followed up on an exception alert, they discovered unacceptable levels of spoilage from their fresh meat counters.
An analysis of store-level data suggested that the problem only affected smaller, out-of-town stores. Drilling further into the data to the SKU-store level pinpointed the culprits — a small number of more expensive products like Beef Wellington. Further analysis of daily sales forecasts and delivery schedules showed that sales were primarily on Friday and Saturday — as these premium meat products would generally be the centerpiece of a weekend family meal.
Since deliveries were typically on Mondays and sales from Monday to Thursday were low, most of the delivery would end up being thrown away. The store chain simply reduced the selection of expensive products available Monday to Thursday and got on top of the problem. Of course, it helped that they had a solution in place that gave them instant results and thus complete transparency.
In retail, big gains come from small improvements across countless SKU-Store combinations. To get the big figures right you have to master your low-level data.
Good data is essential for good grocery store inventory management, especially since it is such a complex environment, but the data alone is not enough. All the data in the world is of no help if you cannot access it and make sense of it easily. To achieve that, you need a supermarket inventory management system with the power to handle big data, granularize it however you choose, and deliver results in real time. After all, if you’re managing fresh goods, a two-hour wait for an answer to an important question is two hours too long.
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For in-depth best practices for managing grocery supply chains, read our guide, Winning the Food Fight: Best Practices for Managing Grocery Retail Supply Chains; for insights into how today’s leading grocers are tackling their most pressing supply chain challenges, read our report, Planning for Every Future in Grocery Retail.
