Reinventing Inventory Management in the Post-Pandemic Era

The pandemic exposed the retail industry’s reliance on long planning cycles, which hindered companies’ ability to respond quickly to supply chain challenges, then excess inventory, as consumer purchasing patterns and market conditions changed overnight. Industry leaders realized they didn’t have the fundamentals in place to react as quickly as the market demanded.

Burned out by that cycle, a growing number of companies are now looking to reinvent how they track and manage inventory. They’re looking for ways to increase the responsiveness of their inventory planning and demand sensing capabilities to be able to chase demand without overindexing. And they’re increasingly looking to artificial intelligence to make it happen.

However, while many leaders are already embracing AI to better predict demand, manage inventory, and optimize their supply chains, some common hurdles are getting in the way of successful adoption. A few key steps can help retailers maximize their investment in AI to reinvent inventory management.

Increase Your Organization’s Investment in AI

Nearly six in 10 retailers and CPG companies plan to experiment with AI, but they may not plan to invest much, according to a recent survey. Despite their reluctance to commit to AI as the solution, the survey respondents were clear about their need to improve demand forecasting accuracy, ranking the biggest threats to improving supply chain efficiency and accuracy as: rapidly shifting consumer demand volatility (55 percent), global events and disruptions (50 percent), and inaccurate sense of customer-specific demand (43 percent).

Given these challenges, it’s not enough to just dip your toe into the AI waters. You must do more than experiment with AI — you need to significantly increase your investment in these pivotal technologies to thrive. Innovators within the industry are already leveraging AI to reinvent inventory management in a few key ways, including:

• Leveraging predictive analytics to foresee which products will be in demand during a given season to improve stock planning and reduce stock-outs.
• Integrating more complex and varied data sets, including online searches, seasonal illness reports, social media trends, and weather patterns, into AI-driven forecasting tools to better anticipate and meet consumer demand.
• Harnessing AI to position inventory closer to where consumer demand is expected to be high.

Embrace a Multiyear Approach Focused on Testing, Learning, and Iterating

AI needs to learn from your data, which means that effective deployment of AI to reinvent inventory management isn’t going to happen overnight. The software relies on vast volumes of data, and it needs to both be trained on that data and learn from itself. You may discover that it needs additional data sources — e.g., store-specific weather location — in order to increase the accuracy of the models it relies on. Recognize that it will not be a one-year project; it will require a multiyear approach to collect enough data and build the algorithms AI relies on.

Lean on Leadership to Tell the AI Story and Build Trust

AI isn’t new to inventory management and the retail industry; in fact, teams have been experimenting with it in inventory management for more than a decade now. However, that experience brings with it more a sense of wariness than comfort; historically, organizations have struggled to collect the data needed to make AI effective. It wasn’t uncommon for inventory managers to implement recommendations generated by AI, only to find out that their back-of-the-envelope or spreadsheet-based calculations were far more accurate.

Executives will need to leverage storytelling to combat these preconceptions about AI. Through stories, leaders can explain how AI has advanced, using simple terms that demystify the technology. By providing clear examples of successful AI implementations and their tangible benefits, leaders can build trust and demonstrate the reliability and accuracy of AI systems while addressing common concerns such as job displacement and the potential for errors.

A compelling narrative can also paint a picture of a future where AI-driven inventory management leads to a more efficient, responsive and customer-centric retail environment. This vision can inspire stakeholders at all levels — from employees to executives — to buy into the AI strategy and support its implementation.

Looking to the Future of Inventory Management

To thrive in the post-pandemic retail landscape, executives must embrace AI-driven solutions to revolutionize inventory management. By significantly investing in AI, adopting a multiyear approach for data integration and model refinement, and leveraging storytelling to build trust, retail leaders can create a responsive, efficient and customer-centric inventory management system that meets the demands of a rapidly changing market.

As the vice president of growth at Propeller, Bryan Rogers oversees service offerings, emerging markets, go-to-market strategy, client engagement and the brand and marketing function.