It’s the second week of December. Your product just landed in a high-visibility holiday promotion at a major retailer.
A customer walks in with a flyer and asks for the item. The associate checks the system: low stock. Looks at the shelf: empty. Then comes the awkward apology: “Sorry. We’re out… maybe try another location?”
To shoppers, it looks like a store problem. In reality, it’s a brand problem, and one that quickly becomes your problem with the retailer. Supply chain teams know the real story started weeks earlier: a promo calendar shifted, a pack type changed, demand moved faster than the plan, and inventory ended up in the wrong place.
The stakes are only getting higher. The National Retail Federation expects November–December 2025 holiday sales to total $1.01 to $1.02 trillion, up 3.7% to 4.2% over 2024.
It’s not a straight-line growth story. Many shoppers are leaning harder into deals and value. In a season this big and this dynamic, the advantage goes to brands that can turn day-two signals into day-two decisions, not just “update the forecast” once shelves are already empty.
A smarter forecast helps here, but the real advantage is concurrent planning (demand, supply and inventory working from the same “now”) plus orchestration that turns forecast changes into fast decisions: reallocation, replenishment shifts and tradeoffs. That’s how you avoid costly stockouts, overstocks, and markdowns.
Turn holiday volatility into a planning advantage with demand sensing
Forecasting is still about anticipating what’s next. In peak season, relying on last year’s pattern plus a few overrides won’t keep up. The world moves quickly, and your forecast has to move with it.
In consumer packaged goods (CPG), the data gets messy exactly when the stakes get high. A holiday multipack or limited-time seasonal flavor sells through early, and shoppers substitute instantly. Their baskets stay full, but your demand signal gets scrambled, right as promos, packaging, and fast-moving display SKUs are supposed to follow a plan built on last month’s assumptions.
This is where demand sensing becomes practical. Higher-frequency signals like point-of-sale (POS), e-commerce behavior, and promo performance help keep the near-term picture current while there’s still time to reallocate, protect service, or adjust inventory replenishment.
Consumer electronics has its own version of the same timing challenge: short product cycles, promotion spikes, and demand that can shift overnight.
It’s no wonder teams in either group look for a faster way to keep plans current: machine learning (ML) demand forecasting that adapts faster than manual overrides, plus demand sensing that pulls in fresher signals while the promo window is still open. In practice, “AI” here mostly means ML applied to demand forecasting: models that learn patterns and update as new signals arrive.
Make timing the goal, not “perfect accuracy”
Even with advanced demand forecasting, the hard part is what happens next: turning a forecast update into a decision fast enough to matter. When teams talk about AI in forecasting, it’s often framed as a feature: higher accuracy as the finish line. In peak season, the bigger win is timing, staying current enough to act while the window is open.
That’s exactly where machine learning demand forecasting helps. Not because it’s magic, but because it can learn from context and update continuously as conditions change: promotions, channel mix, early sell-through, e-commerce conversion, product attributes.
What machine learning demand forecasting changes (and what it doesn’t)
ML demand forecasting doesn’t throw out history. It still learns from history, but it reweights what’s happening now: promotions, channel shifts, early sell-through. This way, the baseline can move during the window that matters.
A simple holiday example: a toy promo goes live online. Within 48 hours, conversion is up and POS is accelerating in a few regions. With ML demand forecasting refreshed with near-term signals, the forecast can move during the promo window, while you still have time to reallocate inventory, adjust replenishment priorities, or protect service elsewhere.
One guardrail keeps this grounded: ML doesn’t replace planners. It augments them by automating baseline work and highlighting what’s driving change. Planners still bring judgment where the model can’t: delists, supply disruptions, one-off retailer calls, packaging changes that haven’t landed in the data yet, and shared capacity realities.
Why AI/ML demand forecasting still stalls without connected planning
Even when the forecast updates quickly, teams can struggle to act quickly. The bottleneck is often the planning process: who sees the update, who decides, and how changes ripple across demand, supply, and inventory. That’s where concurrency and orchestration make the difference.
Create the foundation that makes AI pay off: concurrency and orchestration
So, ultimately, here’s the counterintuitive part of holiday AI: AI is table stakes, while the foundation is the differentiator. Those two capabilities determine whether “the forecast updated” turns into “the business moved”:
One factor is concurrency, where demand, supply, and inventory are able to work from the same updated picture at the same time. No passing the baton. No waiting a week for the next handoff. Instead, teams get a shared version of “now.”
The other is orchestration: AI-powered demand planning that doesn’t stop at a new forecast. Instead, the new forecast drives coordinated decisions and responsive actions: inventory allocation (including reallocation), replenishment adjustments, scenario choices, and trade-offs. Teams move together instead of reacting in a sequence.
Without this foundation, even a fast-updating forecast can stall out. With it, ML demand forecasting becomes what it’s supposed to be: a way to tighten the gap between signal and action, protect service, and preserve margin, even when the season is moving at full speed.
A holiday-themed readiness check for peak season
“Ready” isn’t about buying a tool. It’s about how planning works when reality moves faster than meetings:
- Start with the data you already have and make it useful. Don’t wait for perfect. Build momentum, then layer in more signals as you mature.
- Treat demand sensing as a discipline, not a side project. The value is staying current while there’s still time to act.
- Unify signals in a connected planning process with solid data readiness. If POS, promos, and digital demand live in separate silos, the picture arrives late.
- Keep planning in sync with concurrency. Demand, supply, and inventory shouldn’t operate on different versions of “now.”
- Make orchestration real. A forecast change should trigger a coordinated decision: where inventory moves, what gets protected, what scenarios get evaluated, what trade-offs get made.
That’s the thesis, and our bottom line today, in operational terms:
AI won’t save the holiday season unless it can change decisions fast enough to capture demand on the way up, stay steady through the rush, and protect margin on the way out, so you’re not staring at leftover stock and painful markdowns come New Year’s.
