Every day grocery retailers throw away hundreds of thousands of pounds worth of food: items past their sell-by dates, damaged packets, or fresh produce binned to make way for a new carton. Waste is a major drain on resources but attempts to control it can be misguidedly draconian. Delisting items with a high apparent wastage rate, for example, can lead to disappointed customers and lost revenue while cutting order quantities can just increase stock-outs.
What is needed is a greater understanding of what causes waste – and that means real insights into the end-to-end replenishment process. It means matching customer demand to case size for a product or having precise control of physical distribution so that goods hit the shelves at peak selling times. Closely aligning the rate of replenishment with the rate of sale at store level really can affect profit and performance. This can require significant co-operation from suppliers to develop variable case sizes or change delivery windows.
Waste occurs in so many places that an inevitable problem is where to focus first. It is obviously important to start with the products where profit losses are greatest. That means carefully recording and monitoring waste by sku/store/day, tracking sales data to identify any downturns due to stock-outs, and keeping tabs on markdowns details which can all too frequently be lost in dump codes at the till. ‘Lost profit’ can be defined as the ‘value realised if waste is eliminated and full on-shelf availability achieved’. By assessing performance (using sku/store/day level data) any underlying issues that hinder replenishment can be identified and resolved relatively quickly.
For products with a short shelf life, having an appropriate case size is vital. Sku/store/ day level data can be used to assess just how suitable the case size is: demand needs to be matched to shelf-life and then this information used to calculate optimum case size.
It is important to focus on what happens at store level. All too often, data is aggregated to DC or region and a corporate approach to forecasting can hide a multitude of sins in the individual store. While the total volume of product distributed to stores may closely reflect the total demand, stores may have either too much or not enough stock. Correcting irregularities in historical data is also essential because future forecasts generally rely on past sales. Retailers should stop adjusting future forecasts on the fly and really tackle the errors in historic data that are causing the problems.
Choosing the right product assortment for each store is vital, as this will determine the profitability of the available space. Careful analysis of how the chosen ranges perform – at sku/store/day level – is also important. By comparing product performance in stores with similar sales profiles, for example, opportunities for improvement or excessive wastage rates can quickly be identified. Similarly, when it comes to promotions it is imperative to identify and manage the interactions between promotional and non-promotional lines proactively. Smart retailers should acknowledge and plan for the net effect of the promoted line’s sales uplift and accompanying downturn in sales of associated non-promotional lines. Blind optimism is an expensive folly.
The trick with all waste, is to identify the drivers quickly and then resolve them efficiently. Better use of data – and resource – can give every product the best opportunity to succeed as well as provide stores with a level playing field when it comes to measuring comparative performance. Any reduction of wastage, and increase in sales through improved availability, delivers immediate benefits to the bottom line. There are benefits for the wider audience too: suppliers see more stability in their forecasts/orders and experience less product churn or delisting debates while customers get the products they want, when and where they want them.
