46% of grocery retailers are still basing replenishment decisions on ‘gut feeling’

A global study of 750 grocery decision-makers reveals key challenges in meeting customer expectations.

In the second blog in our customer experience research series, we reveal grocery manager and directors experience and implementation of replenishment and its impact on the delivery of their customer experience.

Customer Experience, Machine Learning, Grocery, Replenishment

Grocery retailers are struggling to optimize their replenishment processes

The global research found that grocery retailers are struggling to optimize their replenishment processes, with many of these decisions still based entirely on ‘gut feeling’ in spite of increased investment in accurate machine learning algorithms for automated replenishment and demand planning.

This suggests retailers are still not embracing the value of technologies that can accurately predict and manage their replenishment needs.

Considering that grocery retailers are caught between the need to reduce food waste and the need to ensure the consumer can get what they want, when they want and through whichever channel they want, relying on the ‘gut feeling’ of store managers to find this balance is a startlingly inefficient method.

When overstocking leads inevitably to food waste, and under-stocking leads to consumers searching elsewhere for their product, grocery retailers sorely need a way of finding the balance between the two.

Key replenishment findings:

    • 62% of directors say they have invested in replenishment optimization in the last two years.
    • 31% of directors say they will be investing in further replenishment optimization in the next two years.
    • 46% of directors said that replenishment is a manual process and a further 46% say that although the process is automated it can be overridden by managers, suggesting a reluctance to rely on automation.

Customer Experience, retail, grocery, replenishment, machine learning

From these statistics we can see that although grocery retailers recognise the need to invest in their replenishment optimization, the process is still mostly a manual one, or subject to manual override, in spite of significant investment.

Machine learning for successful replenishment strategies

Replenishment – especially for fresh and produced goods - is incredibly difficult to get right. Only by using advanced machine learning algorithms to optimize and automate decisions can grocery ensure they deliver a first-class customer experience with product availability, without risking profitability with waste.

With increasing customer demand for immediate availability on all products, grocery retailers need the marginal gains that machine learning algorithms and automation can offer in delivering the best decisions on a daily basis to their replenishment strategies.

https://www.youtube.com/watch?v=LJRiEbTujcs

Download the full findings and whitepaper here.

Learn more about Replenishment Optimiziation in our brochure!

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World-class retailers can reduce their out-of-stock rates by as much as 80% and see their revenue and profits improve by more than 5%.