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Retail Insight can transform food retailers’ approach to product markdowns. See WasteInsight in action and find out how it can help you to reduce waste and increase gross profit.
Reduce retail waste in-store with dynamic markdown.
In this whitepaper, we will explore how retailers could save upwards of £400m every year by utilizing fully optimized waste prevention solutions to reduce retail waste in-store.
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When we talk about the future of retail, and in particular grocery retail, there is a tendency to focus exclusively on the radical shifts required to compete in this disrupted market.
Things are changing. Brick and mortar shops are taking their sales into the digital space, while companies develop and invest in omni-channel ways to communicate with customers. Dealing with these challenges is expensive, and requires large investments in the areas of cost, capability, and cultural change.. But there are some quicker wins to be had from focusing on the last 50 yards of retail.
For most grocery retailers the potential to extract value exists everywhere and can often be realized by taking iterative steps from basic to fully-optimized or preventative solutions. In the UK, the government-funded Waste and Resources Action Programme (WRAP) estimates that over two million tonnes of fresh produce are lost or wasted each year in the supply chain alone.1 Action to reduce or prevent this retail waste could save retailers upwards of £400m every year. Across the US, this figure will run into the billions of tonnes.
Dr David Waters, Chief Product Officer at Retail Insight, says, “Obviously, there is a lot you can do to minimize the impact of out-of-stocks and waste through a more carefully designed and joined-up plan on range, space, pricing, forecasting, distribution, and labor. But that is a big plan with the need for significant coordination and change management. It could stretch into years of effort. Meanwhile, there is value to go after right now.”
Getting the balance right across the business between product availability and waste, and between long and short-term goals, is no mean feat and that is before attempting to build the technologies required to deliver a total preventative solution. However, there is still a lot that can be done beforehand.
Data analysis can drive availability measurement, insight, and action to close gaps tactically and systemically. At the same time, it can be used to understand sales rates, stock-holding levels, and the impact of price movement to appropriately mark down product price to clear before it ends up in the bin.
As Waters explains, “When retailers mark product down, if at all, usually it is in simple increments: first by 25% then 50% and to clear at 75% off. Introducing the ability to apply an item-store specific, optimized price reduction can be done quickly, adapting the price reduction dependent on factors such as stock on hand, time of day, typical store sell-through...etc. This can happen without the need for heavy resource investment on the retailer side and the returns have proven to be huge in every market launched.
“But right now there is long-term value in prevention and short-term value in reduction, so in this time of disruption and margin pressure let us take as much benefit today so that we can fight on tomorrow.”
In the case of the continued retail challenge of balancing fresh product availability and waste, the ultimate aim would be to always have just enough availability with little-to-no retail waste. But this is a complex stakeholder challenge to consider with many competing objectives:
Paul Boyle, CEO at Retail Insight, explains, “Sometimes the desire to deliver the perfect solution to the problem acts as a paralyzing factor. Perfect is the enemy of good.”
“Start with an appropriate data-based target,” he continues, “then develop a robust measure and put a basic, simple plan in place to capture quick value. If we only ever wanted an optimized solution from day one, then I think we would be waiting a long time for that day to come.”
To conclude the point, Dr David Waters adds, “Again, this is one of those examples where you apply the right mathematics at the right level and get the desired result in very short order. Sure, we can go big, we can throw AI at the forecasting problem long-term to achieve a near-perfect balance of availability and waste, and we believe we have the foundational models to achieve that long-term.”