Waste Away: How New Replenishment Models Are Reducing Shrink

In grocery retail, waste is no longer the inevitable consequence of making sure customers always get what they want. Increasingly, retailers are recognising the myriad ways to mitigate waste – with many different motivations pushing them to adopt new models and techniques that reduce shrink.

waste, food waste, replenishment

For Marks & Spencer, that motivation is sustainability. The multichannel retailer recently launched ‘Plan A’, its journey to a less wasteful future. By 2025, it is committing to ensure all its factories are on a sustainability ladder, with half achieving gold standard. It is also pledging to halve net food waste relative to sales in Marks & Spencer operated and franchised locations worldwide during the same time frame.

Waste not, want not

Marks & Spencer certainly isn’t alone in its commitment to improving brand image (and profitability) through operational improvements. While there are incremental gains to be made across all sectors, grocery has the most obvious capacity to improve replenishment models, by more closely aligning stock with consumer demand.

The fresh category presents the biggest opportunity – and threat – when it comes to managing waste, and has been the focus of replenishment optimisation for many years. However, in most cases, the returns have been modest and not incremental for grocery retailers – until now. Artificial Intelligence (AI) is turning the dial on waste.

Redefining replenishment through AI

Artificial Intelligence is proving a replenishment game-changer for several reasons. First of all, AI can embrace an almost infinite number of inputs, enabling retailers to look at supply and demand holistically. This enables them to analyse the entire value chain from initial buying right through to disposal, rather than a single point.

As a result, teams can execute on a plan – rather than constantly reacting to bad decisions made earlier in the journey. The outcome is higher availability that is matched to actual demand at store level, generating a greater volume of sales.

Another benefit of an AI-driven replenishment model is that the solution learns as it goes – this is referred to as Machine Learning. Over time, Machine Learning enables even more accurate decisions, based on events and further data inputs, using both structured and unstructured data.

Reducing shrink in grocery retailing

Whether motivated by brand perception, bottom line or CSR, the goal for all grocery retailers is zero waste. While this is a journey rather than a destination for certain ultra-fresh categories, there is already evidence of waste among certain fresh items being reduced to nil.

For retailers like Marks & Spencer that commit to reducing shrink across their food business, embarking on the journey delivers a range of additional, unanticipated benefits. For instance, they have greater confidence to innovate, introducing new lines that they would once have eschewed for fear of high waste.

And by making bold changes to their replenishment model, grocery retailers can use their AI investment to stand out from their competitors when it comes to high availability and low waste; two dynamics that were once thought to be irreconcilable.

Discover how Blue Yonder’s Demand Forecast & Replenishment solution uses AI to reduce waste for leading grocery retailers.


Blue Yonder Blue Yonder

We enable retailers, consumer products and other companies to take a transformative approach to their core processes, automating complex decisions that deliver higher profits and customer value using artificial intelligence (AI).