Have you ever looked at a system-generated demand forecast, scratched your head, and thought, "...what?"
When a machine spits out a number that misaligns with human intuition or historical preconceptions, it is reasonable to start asking questions. Cautious skepticism, especially when rooted in deep expertise, is what makes retail planners so irreplaceable.
But as advanced AI becomes more commonplace in the average retailer, the questions after “what?” become increasingly critical.
Too often, the follow-up question I hear from planners and allocators is “why?” When this happens, it’s often because we’re trying to apply linear, human logic to a non-linear machine. The reality is that modern demand forecasts derive from incredibly sophisticated engines that, by their very nature, will always be opaque.
The goal of a modern demand forecast is not to provide a "perfect" number that demands blind obedience. Instead of wasting time trying to solve the mystery of why the machine arrived at a specific number, here’s a proposal that I hope saves a bit of sanity for anyone reading: accept the number for what it is — a high-confidence, data-driven hypothesis — and focus your time and expertise on the exceptions, nuances, and strategic adjustments where human intelligence adds the most value.
Reframing the relationship: Your forecast as a GPS
In high-pressure retail environments, when system numbers don't align with intuition, it’s easy for the relationship to turn adversarial. This usually happens when planners feel they must either "win" against the machine or be held responsible for its mistakes. Countless hours are spent debating why the forecast was “wrong” instead of focusing on what truly matters: making the best possible decision with the information available.
When you consider that global inventory distortion, the combined cost of overstocks and out-of-stocks, costs the retail industry roughly $1.7 trillion annually, the stakes are simply too high for internal gridlock.
To build a truly agile retail enterprise, we must transition from viewing the forecast as a static report card. Think about how you use other "forecasts" in your daily life.
Ideally, you don't throw your phone out the window when your GPS app suggests an unexpected route. You ask questions that help inform next steps (“What am I missing? Can I afford to take a risk?”) before either trusting your intuition or following along. As an allocator, you're a leg up on the driver; you can do a reduced run or a test allocation — you can’t split your car in two.
Similarly, you don’t walk out in a T-shirt despite seeing gray clouds just because the weather app says there’s a low chance of rain; you ask yourself how comfortable you are with the risk of getting wet before grabbing a “safety stock” raincoat.
In both scenarios, expertise (or common sense) plus productive lines of questioning keep you feeling in control while still benefitting from what the forecast is offering: probable insight into future outcomes.
Asking the right questions of retail demand forecasts
It’s no different in retail planning. Channel skepticism into productivity by reframing each forecast as a hypothesis you can test, adjust, and refine through the right line of questioning.
There are four mental shifts I always recommend that planners make when they’re feeling frustrated with their planning system:
- From “forecast as truth” to “forecast as a hypothesis”: A forecast offers a baseline, but allocators and planners can enrich it with their knowledge of local events, regional tastes, and micro-seasonality.
- From “errors as failures” to “errors as learning signals”: A missed forecast is an opportunity. It prompts us to ask what external variables were missing and how we can incorporate them next time.
- From “adversarial relationship” to “collaborative relationship”: Instead of criticizing, the team’s role is to contribute unique insight to refine the numbers.
- From “success as accuracy” to “success as better decisions”: The ultimate measure isn’t accuracy alone; it’s a better decision that leads to higher sell-through and fewer markdowns.
And instead of looking for reasons to override the forecast entirely, productive questions include:
- "What data signal is the system picking up on that I might be missing?" (Using the machine to expand your view).
- "What real-world context do I know that the system hasn't learned yet?" (Adding your expertise to the machine).
Put another way, the solution to forecast skepticism is product engagement. You don't need to see every line of code in an ML model to work with it; you just need to be able to interact with it.
The best of both worlds
When planners and demand forecasts work in partnership — the machine handling the massive data scale, the human applying the nuanced context — you stop fighting over the baseline and start driving strategic value. Moving from "accuracy at all costs" to "collaborative decision-making" is how you win in a volatile market.
A collaborative tool is one that allows for easy editability, direct feedback loops, and learning mechanisms that capture human adjustments. When a planner can inject their context, like a local event or a sudden trend, and see how the system reacts, they aren't just "overriding" a number. They are closing the loop, training the system, and ensuring that human intelligence and machine scale are working in tandem.
We’re building this partnership approach into all of our intelligent retail applications. Anaplan’s Allocation and Replenishment Planning application empowers your teams to generate granular, store-level plans by incorporating everything from size curves and seasonality to new store profiles. With the ability to inject local context and run complex “what-if” scenarios, you can ensure the right product gets to the right place at the right time.
