Read the press and you’ll be forgiven for thinking artificial intelligence (AI) is all about the rise of customer-facing robots, threatening to unleash an unprecedented wave of job cuts.
But AI, especially when used in the retail supply chain, is more about performing additional functions that lie beyond the capability of conventional computing – adding new value and, dare I say it, potentially creating more jobs.
That’s because modern omnichannel retailers are swimming in data - producing terabytes of information across the sector on a daily basis. This torrent of data is so large that, until recently, retailers have had no practical way of making use of the vast majority of it – using either the human brain or conventional computing.
AI, however, promises a step change in data analysis, enabling retailers to harness big data and use it to deliver a clarity of insight never experienced before.
Here are five ways AI promises to turbocharge the retail supply chain, unleashing new retail opportunities and value – potentially creating, rather than cutting, jobs.
Dramatically cutting stock-outs
Grocery retailers, such as the UK supermarket chain Morrisons, can dramatically increase their margins by using AI to improve stock replenishment.
The retailer uses an AI solution enabling it to make 430 million calculations and 13 million automatic decisions every day. The result is that Morrisons has reduced shelf gaps by up to 30%.
The retailer’s system can automatically forecast orders per store and per SKU to meet customer demand, while ‘self-adjusting’ capabilities mean demand can be optimised down to product level – factoring in influences such as store demand patterns, seasonality, weather and promotions. McKinsey recognises the power of AI, calculating that retailers introducing AI and data analytics to their supply chains over the past five years have enjoyed a 19% uplift in their operating margins.
Retail-time supply chain management
A wealth of data can be collected the full length of the retail supply chain – inventory origins, transit routes, times a product is scanned or its location and status are reported by radio frequency (RF) tags.
Using this wealth of data it is possible to build a machine learning model capable of making a prediction about any aspect of an operation. For example, an algorithm can be created to determine if an order will be late and then another to calculate the implications and knock-on effects of this missed deadline.
Management can then be alerted and a decision made on how to rectify the problem or issue an apology to any impacted customers.
Supply chain driven by real-time customer demand
AI is already being used to provide customers with purchasing recommendations but retailers are now taking this one step further. Armed with historic data based on a customer’s purchasing behaviour it is possible to build a machine learning model capable of predicting what a customer will buy and when. It is also possible to pinpoint the most likely causes of basket abandonment and the chances a customer will return their order.
Thanks to this analysis, retailers can make critical business decisions based on science, rather than the gut instinct and intuition. They can ensure the predicted product is in stock and ready to ship and any factors that impact on that sale are mitigated.
AI enables a ‘pull’ business model in which all supply chain activities are focused on consumer demand, fulfilling the individual consumer’s expectations and delivering a truly responsive, personalised service.
Dealing with shrinkage
AI can interrogate historic ordering and sales data and factor-in expected shrinkage levels into future orders.
A wide range of technology is used the full length of the modern retail supply chain, and sooner or later some of it will break.
With the right data you can build an AI model that can predict the average length of time between malfunction and factor in the lead time for replacing broken parts so that technology is out of action for the shortest possible period.
Don’t confuse AI with automation
As these examples show, if you confuse AI with automation you miss the greatest AI benefit – AI augmentation – a combination of human and artificial intelligence, where both complement each other. Augmentation gives retailers a really compelling opportunity to re-imagine old tasks and create new opportunities.
Rather than triggering job losses, Svetlana Sicular, research vice president at Gartner, predicts AI will ‘generate millions more new positions of highly skilled, management and even entry-level and low-skilled roles’ globally across all sectors. I have little doubt, after a period of re-adjustment, retail will be among the beneficiaries of this upturn in jobs.