How AI and ML can help retailers optimise inventory management

Many retailers are turning to machine learning and AI to address changes in shoppers' behaviours and the increasing popularity of e-commerce.

2 years ago   •   2 min read

By Angelique Tzanakakis

Over the past year, the frailties in today’s complex supply chains have been exposed. Retailers selling essential goods such as toilet paper, pharmaceuticals and certain food products found themselves struggling to keep up with demand as panic-buying took hold. Others selling non-discretionary items sat with piles of unsold inventory when non-essential retail was shut down during national lockdowns.

The surge in e-commerce, meanwhile, has meant that retailers needed to be agile to keep up with new shopping habits. The pandemic isn’t yet over, which means that retailers will face continued uncertainty about how their customers will shop, where they will shop and what they will buy for a few months yet. Keeping ahead of these challenges will demand the ability to react fast to changing consumer behaviours.

For that reason, we are seeing retailers step up their investments in artificial intelligence and machine learning (AI/ML) solutions that enable them to better understand and predict consumer buying patterns, so that they can, in turn, make better inventory optimisation decisions. According to Google, inventory optimisation is the AI/ML use case that will drive the most value for speciality retailers and the second most value for mass retailers through to 2023.

In using AI/ML-enabled analytics, retailers will be able to combine data and signals from various parts of their business to uncover patterns that traditional analytics often miss. This will enable them to make granular predictions even for new/ short life cycle products, even during this time of volatile demand and disrupted consumer behaviours. Algorithms could, for instance predict which product will sell best from which location or channel, so that retailers can optimise fulfilment operations and inventory assortment.

They can also tap into sophisticated AI/ML-driven analytics to better understand the quantity and location of inventory, so that they can manage customers’ expectations in terms of fulfilment. This capability is becoming a table-stakes requirement in the era of same-day delivery and guaranteed delivery windows. Another valuable way AI/ML can support retailers is in optimising pricing and promotions according to demand and the levels of stock they have available.

At DotModus, we’ve helped leading retail brands to identify sales opportunities and exceed customer expectations.  Contact us to discuss how your retail business could use advanced AI to accurately make demand forecasts to minimise stockouts, reduce excess inventory and avoid unnecessary discounting.

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