Inventory Disruption Requires a Nimble Response from Retailers — and Machine Learning Could Help

The disruption to the back-to-school shopping season, combined with the looming holiday period, has created a number of inventory challenges for retailers. A new inventory optimization solution from Oracle Retail is aiming to help those retailers who find themselves unsure of where to direct their merchandise in the coming months.

Inventory planning usually incorporates learnings from previous years, but the unique characteristics of 2020 has left many businesses in the lurch. Without a clear roadmap to adhere to, retailers are tasked with anticipating radically shifting consumer behavior, as well as developments in the retail landscape, which has proven near impossible during this pandemic.

“Retailers are struggling to adjust decades of well-defined inventory and traditional supply chain management processes that have been thrown a curveball by COVID-19,” said Jeff Warren, VP of strategy and solution management at Oracle Retail.

While small or new businesses may be particularly struggling to adapt, even companies with a previously robust merchandising plan may find themselves challenged by the new circumstances. Production is likely to be continually put under pressure, as regions both domestically and internationally experience first and second waves of the pandemic. This may make it difficult to obtain sufficient quantity, especially across a range of sizes.

“In booms, it’s easy for a retailer to carry fringe sizes to service those customers who need, for example, a ladies 4.5 or a 12,” said Greg Flinn, solutions manager for planning and optimization at Oracle Retail. “From a supply chain POV, it will be more critical than ever to not send full size runs to every location, like may have been done in the past. Sending every other size, for example, will help keep a critical mass in warehouses to then fulfill orders from there.”

Demand is also likely to differ from the norm, as consumer activity adjusts in response to pandemic-induced restrictions. For instance, while some footwear categories, such as athletic and outdoor, have experienced spikes in popularity, Flinn warns retailers to be mindful of long-term declines in dress shoes for both men and women.

In response to this difficult environment, Oracle Retail has launched a new Inventory Optimization Cloud Service. The solution uses machine learning to take existing retail forecasting and supply chain information and turn that into date-driven recommendations.

The machine learning works by simulating and forecasting possible inventory strategies, using these results to identify optimal inventory positions and parameters — without the need for manual trial and error. The solution functions as part of the company’s existing cloud technology and is now readily available for implementation.

“Machine learning can be used to better identify where to put the available inventory by size to maximize the supply chain efficiency,” said Flinn. “The number of sizes in shoes versus ready-to-wear makes it much more challenging to understand where to put the available sizes. Machine learning can help solve that problem, especially in the absence of consistent data by category.”

Successful inventory management has several benefits, beyond simplifying logistics and the shuttling of excess product between brand locations. By identifying the appropriate amount of merchandise to promote at a given store, retailers can reduce the need for markdowns and therefore protect revenue. A planned-out inventory strategy can also ensure that product distribution and employee productivity is optimized, saving both time and money.

The use of artificial intelligence and machine learning is becoming more widespread across multiple areas of the footwear industry. This is in part due to the benefits of a system that is able to refine and improve its performance based on real-time information. The rapidly changing nature of retail during the pandemic has required fast, nimble responses from businesses in order for them to stay competitive.

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