Achieving a holistic understanding of customer demand across all categories and channels improves the entire end-to-end supply chain. The recent rise of fresh demand presents considerable challenges for retailers’ current technology to provide a complete understanding of true demand. Add to that, the events of 2020 that caused sudden and erratic shifts in shopping behavior, affecting both category and channel demand, and it’s clear that retailers have to be able to see the full picture in order to be successful.
In this second part of my series on the key questions you need to consider about your demand forecasting solution, I’ll be taking a look at the issues that arise when systems only understand certain categories and cannot factor in the entire store.
The Question: Are your forecasting methods limited to specific categories?
You may have an understanding of consumer demand across center store categories and a separate system analyzing the perimeter categories and fresh – but are you able to connect those dots to see how your customers fluctuate between categories and how all sides of the business affect each other?
What happens in one part of the store does affect the others
Research conducted by EsembleIQ shows that only 36% of retail supply chain professionals said their businesses operate on a single supply chain platform. Even where this exists, demand is not complete.
Retailers often leverage best-of-breed solutions for the center store, but then manually maintain or use home-grown solutions to manage fresh. Or, companies invest in a separate vendor provider to manage fresh, isolating it from their center-store platform.
Indeed, most fresh-specific technology is not built to understand non-food categories. And the systems in place for center-store planning don’t appreciate the nuances and intricacies of managing fresh items. Thus, organizations are often operating with disparate demand replenishment systems.
Fresh and perimeter categories (meat, dairy, produce, bakery, etc.) are particularly challenging to forecast. These products are sensitive to external events, like weather, but also have very short shelf life. Couple this with the demands of traceability and intra-daily delivery for some products and the challenge of forecasting becomes even more complex.
Using a primary system for general forecasting and a siloed method to forecast these more challenging categories leaves retailers without a holistic view of all their products and categories, further complicating things.
Your forecasting solution must be able to cope with the impact of all categories – center and perimeter – while incorporating the nuances of each.
To understand shopper demand patterns, you must understand all factors that impact demand across all your categories and, not just a portion of them. As we’ve experienced in 2020, there is much uncertainty in customer behavior and it can change rapidly. The agility to respond quickly to changes and adjust forecasts across all categories in single system is key to avoiding costly mistakes. With a unified view of customer activity, made possible with the right technology, you can gain a full understanding of customer needs and motivations.
Artificial intelligence can perceive all impacts to store-wide demand. Applied correctly, AI alleviates inconsistent inventory buys, overstocks (and the resulting markdowns), out-of-stocks, and margin erosion. In fact, without proven AI and machine learning, you cannot achieve a full understanding of consumer demand.
Seek out solution providers that have a proven track record in applied artificial intelligence.
While customers will continue to be a moving target that retailers need to strive to understand, the good news is that there is a way forward to achieve greater understanding. Acquiring artificial intelligence and machine learning capabilities, focused on a holistic understanding of demand, will bring a more complete, actionable knowledge of today’s biggest retail disruptor: the customer.
Learn more about connecting all categories to create a unified supply chain
Read part 1 of the series: Is your demand forecasting solution working for you or are you working for it?
Read part 3 of the series: Why demand forecasting must be effective with all channels to be effective with any