Forecasting Demand – How Machine Learning Helps Drive Retail Success

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Many retailers spend vast quantities of time perfecting their approach to planning and the tools they use to deliver it

Yet, if they are working with flawed data and inaccurate forecasting techniques, they will end up with too few or, too many staff on the shop floor and in the warehouse, and not enough stock to meet sudden surges in demand. This typically results in unfulfilled sales opportunities and increased costs and abandoned shopping baskets.

Conscious that this situation is unsustainable, retailers are increasingly focused on using artificial intelligence and machine learning to more accurately forecast demand. With computing and data processing power escalating all the time, the more-forward thinking are already assessing the benefits that more accurate forecasting could have on their sales and profitability.

Key to the success of any forecasting system is the data fed into it. Some retailers are recording footfall; categorising it against days of the week, and then using machine learning to look for patterns. Is it a weekend, a Bank Holiday Monday, or the first Saturday after pay day, for example? Time slots can also be overlaid, as can historical weather patterns while data around past and future events, such as a local festival in two weeks’ time, or an annual music festival in a particular town can also be brought into the equation.

The model, supplemented by consultancy from expert data scientists, can help retailers expect what would otherwise be the unexpected, and plan much better for likely future demand.

The benefit could be that the retailer can save money by reducing staff levels when demand is likely to be low, but still more likely capitalise on opportunities to drive sales by bringing in more staff where demand is expected to be high.

Forecasting Demand – How Machine Learning Helps Drive Retail Success TechNative
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Benefits extend beyond the shop floor into logistics and the supply chain of course. On the product side, forecasting looks at different categories, highlighting how demand for each may change, how it can be seasonal, for example, or what product lines are working well in that category. All this information should be rapidly taken up the supply chain to allow retailers to quickly engage with their suppliers and manufacturers. After all, the more awareness retailers can offer the entire supply chain as to what demand is likely to be, the more likely they are to have product ready for those retailers and remove redundant items from the supply chain, increasing profits all-round.

Labour planning in warehouses and distribution centres is also linked in this chain and is another factor that is considered. Planning and forecasting solutions, coupled with latest workforce management technology, are enabling much smarter planning of labour and stock levels in distribution centres.

Machine learning will also take warehouse management solutions to the next level. Currently, they are set up to ensure that the approach is not to pick solutions alphabetically, or by product type. However, going forwards these systems will be expected to look for more complex patterns in how items are ordered together. Instead the system will put them away depending on how often they are needed or whether they are likely to be picked together – you’re more likely to want gin and tonic rather than beer and tonic. By putting these items together within the warehouse, the end goal is to speed the process by reducing the average distance each picker needs to walk to go and pick each item.

Customising the Solution

The technology used is key here but so is consultancy. Every retailer and supply chain business is different and has specific needs and requirements. They all need expert advice about which systems are likely to work best for them and which approach is most suitable both on the shop floor, and on the warehouse and logistics side.

Times are tough in the retail market today and retailers need to look at ways they can achieve an edge over the competition. The most forward-thinking are investing in the latest in artificial intelligence and machine learning technology to drive operational efficiencies, reduce costs and maximise sales opportunities – all of which will positively impact the bottom line. Accurate demand forecasting is key to supporting the retail industry, keeping customers happy with products and stock and ensuring staff morale remains high. The retailers that capitalise on this the fastest and start implementing the technology are more likely to find themselves one step ahead of the game.


About the Author

Forecasting Demand – How Machine Learning Helps Drive Retail Success TechNativeMike Callender is Executive Chairman at REPL Group. REPL are a world-leading consultancy and technology group specialising in workforce management, supply chain, point of sale and in-store digital solutions. Working with major retailers and brands, collaboratively we overcome the challenges of the digital revolution.

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