What Data do Retailers Need to Overcome Supply Chain Problems?
By Brad Pope
Experts are predicting that the supply chain crisis will continue for months, potentially years, to come. Retailers are continuing to experience the “feast or famine” environment we’ve all been living in throughout the pandemic, and retailers need to identify and focus on adjustments that can be made to not only help overcome today’s supply chain challenges, but that also sets them up for future success.
It’s no surprise that when shoppers can’t find the products that they need on shelves, they leave and shop elsewhere. For retailers and brands, impactful demand planning, knowing when and where consumers will want products, begins with high-quality data. But in an era of data overload, what does that mean?
High-quality demand sensing data should be timely, consistent, and regional. While applicable data sources required to prevent out-of-stocks vary based on product segment, these three characteristics remain the same across the board. Timely data is up-to-date enough to make accurate predictions about what will occur in the near- and mid-term.
Consistent data allows brands and retailers to compare demand across time periods, helping them understand if demand is higher than similar time periods in the past, and regional data is critical to understanding geographic nuances.
To showcase the impact high-quality demand sensing data can have, consider the example below:
Retailers with illness-based demand (drug, mass, grocery, and more) typically leverage medical claims-based data. For instance, the CDC publishes aggregated claims-based data from the week prior, and on the mornings before publication, the available data can be 12 days old.
Using this lagged data can have serious consequences for retailers as it paints a less accurate picture of the current state, which is why “timely” real-time data is the gold standard for demand planning.
Further, if retailers don’t have visibility into, say, what a flu season looked like in years prior, particularly as flu activity decreased when people employed preventative behaviors in response to COVID (i.e., masking), how can they understand exceptional illness levels, and their relativity to previous years?
“Consistent” data enables retailers to do so, by putting upcoming illness levels and COVID waves in context while making informed inventory and staffing decisions.
One region could be experiencing decreasing illness levels while others could be nearing their seasonal peak. Both instances require different resource strategies. With high-definition “regional” data, retailers can pivot to execute those strategies, and best serve their communities.
Retailers should leverage solutions that are rooted in timely, consistent, and regional data, to inform supply chain, and product demand decisions. This allows brands and retailers to move swiftly and confidently to plan and manage through highly variable demand. Leveraging these data sets, retailers set themselves up for success and help themselves overcome persistent supply chain issues.
Pope has over 20 years of experience in using retail data to solve supply chain and planning problems. Prior to joining Kinsa, Pope was the VP of analytics and data science at Retail Solutions Inc., where he built and led two technical teams, guiding the delivery of solutions that customers used to make decisions about supply chain, e-commerce, shelf assortment, and on-shelf availability, sales, and category management. Pope has his MS of Data Science from Indiana University, instructed retail data analysis at Northwest Arkansas Community College for over five years, and is Scaled Agile Framework (SAFe) certified. As the VP of customer success at Kinsa, Pope ensures Kinsa’s solutions and unique data signals are used to curb illness to the fullest extent possible.