There are few challenges in retail tougher than managing groceries; optimizing a broad inventory that includes fresh and short-shelf-life products is not easy. And there are few things that show better what a supply chain professional and a supermarket inventory management system can do and the impact they can 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 best use of products’ shelf life information and category level consumer behavior to cut retail food waste.
1. Define your goals and priorities – know what you value most
Having worked with fresh goods wholesalers and retailers from big to small, high end to price-driven, supermarkets, convenience stores and cash & carry chains it’s clear that replenishment teams walk a tightrope between spoilage costs and shelf presentation, so it’s really important to get the balance right.
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. But this information isn’t always given sufficient importance in replenishment, as sell-by dates vary from delivery to delivery. However, the variables can often be worked into forecasts.
The simplest step to take is to incorporate shelf life expectation into your supermarket inventory management system’s ordering parameters. For example, it can be used to tie your safety stock calculation to a max ‘x’% of expected shelf life forecast, or building 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 can be used in order parameter calculation, but we also use it in replenishment calculations by factoring in future spoilage. In DC environments we usually do this by introducing batch level inventory balances with ‘sell-by’ date information. This helps keep availability high by replenishing before stock spoils, and it also flags items that need to be sold quickly.
Better replenishment of perishables means your displays look better, customers get fresher goods and you sell more.
In retail environments this is of course trickier as consumers don’t always operate on ‘first-in-first-out’ principle – quite the opposite actually! – but incorporating the simulated spoilage in the calculations really helps keep availability optimal even for low-volume products – but it can also increase food waste. You have to be clear that your priority is availability over spoilage if you decide to use this approach.
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 the process of 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-haves’ could run out towards the end of the evening, but that there should always be stock in all basic categories (e.g. sliced white, wholemeal, seeded etc). The optimization had the expected impact on food waste – but we were quite surprised by how much category sales and sales margins increased (over 10 percentage point on average). Fresher products, due to better inventory turnover, 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 you have to master your low level data. One good example is from a department store that’s known for its high-end food halls. When managers followed up an exception alert they discovered unacceptable levels of spoilage on fresh meat counters. An analysis of store-level data suggested that the problem only affected smaller, out-of-town stores. Drilling down further into the data to SKU-Store-level pinpointed the culprits; a small number of more expensive products, such as Beef Wellington. Further analysis of daily sales, forecasts and delivery schedules showed that sales were primarily on a Friday and Saturday – typically these premium meat products would be the centerpiece of a weekend family meal. Yet deliveries were typically on Mondays. Sales from Monday to Thursday sales were low and given the batch size 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 given it’s such a complex environment, but it’s not enough of itself. All the data in the world is of no help if you can’t access it and make sense of it easily. To do that you need a supermarket inventory management system with the power to handle big data, to interrogate it however you choose and to deliver results in real time. After all, if you’re managing fresh goods, a two hours wait for an answer to an important question is two hours too long.
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