Why Spreadsheets Aren’t the Answer to Demand Forecasting
We've seen this story dozens of times during our conversations with prospects: a demand planner opens Excel, updates last month's numbers, tweaks a few assumptions, and calls it a forecast.
If that describes your current process, this piece will explain why even the most sophisticated spreadsheet is fundamentally inadequate for modern demand planning – and what actually works instead.
The core problem? You're using a static tool to model a dynamic system. Every spreadsheet-based forecast is built on the assumption that your business will behave predictably, when in reality, modern supply chains are anything but predictable.
The hidden cost of averaging
One of the biggest red flags we see in spreadsheet planning is the overuse of averages. It’s understandable – planners are operating with limited information, and need to make assumptions to model their business. If you need to forecast next month’s sales, averaging over last year or last quarter seems reasonable, right?
This is a safe option only for extremely simple, stable businesses. In reality, averaging masks the errors happening at a granular level, and results in a massive loss of accuracy.
Consider the following simple scenarios:
If you overforecast vanilla protein bars in California by 20% and underforecast them in New York by 20%, your total vanilla forecast looks perfect. But both operations are scrambling, because business decisions happen at the granular level.
Your historical data shows that you averaged 800 units/month last year. But during that time, your competitor had a catastrophic supply chain issue. So 800 units significantly overestimates your actual baseline demand – but your spreadsheet can't know that.
Here's the fundamental limitation: spreadsheets encourage planners to collapse the complexity of their business into averages and simple assumptions. But when you simplify reality to fit your tool, you're not modeling your actual business anymore.
The Perpetual Replanning Trap - a full-time job
This oversimplification creates a vicious cycle. Because your spreadsheet-based forecasts are inherently inaccurate, maintaining them becomes a full-time job. Your business operates in real-time, but spreadsheet assumptions are frozen at the moment you enter them.
Every market shift demands manual intervention:
An influencer's unexpected product endorsement creates overnight demand spikes
New tariffs invalidate your cost structures and lead time calculations
Your sales team discovers your largest customer is slowing down orders
Each disruption triggers a cascade of spreadsheet updates, assumption revisions, and opportunities for new errors. Planners become trapped in an endless cycle of reactive replanning rather than proactive optimization.
Ironically, the most volatile businesses are the ones that spend the most time updating spreadsheets (instead of actually planning). It's not sustainable – and in today's accelerating business environment, it's becoming impossible.
Even the best spreadsheets can’t model millions of outcomes
Even if you could somehow solve the averaging problem and automate the replanning, there's a third, insurmountable challenge: the sheer combinatorial complexity of modern supply chains.
You might be surprised to realize your supply chain has complexity that stretches into the millions or billions, but the math is straightforward:
Every SKU behaves differently across different channels and geographies
Each combination of SKU + Channel + Geography will respond differently to seasons/weather, price changes, and promotions
Unpredictable events create ripple effects: competitive dynamics, shipping disruptions, macroeconomic conditions, cannibalization from new products
Let's say you have 100 SKUs across 10 channels in 20 locations. That's already 20,000 unique demand patterns to model. Add in seasonal variations, promotional calendars, and competitive dynamics, and you're quickly dealing with millions of potential scenarios.
No spreadsheet can model and compare this many potential outcomes – for that matter, neither can the human brain.
The good news: AI is finally ready for your supply chain
The limitations we've outlined aren't failures of planning teams – they're structural limitations of spreadsheets themselves. Even the world's best Excel modelers cannot make spreadsheets that learn from everything that’s happening in your business and across the world, and adapt in real time.
Your supply chain needs a fundamentally different approach, and modern AI is up to the challenge. Over the past few years, AI has matured from headline-hype to Nobel Prize winning biology. You can feel it if you ride in a self-driving car, or have a live conversation with ChatGPT voice mode. 2025 would feel like science fiction to someone from 2022.
Applied properly, AI for supply chain can solve the spreadsheet problems we've outlined:
Instead of averaging, AI identifies patterns: Where spreadsheets force you to collapse complexity, AI can simultaneously model demand at every granular level – every SKU in every location through every channel – while still understanding the bigger picture.
Instead of static assumptions, AI adapts continuously: When market conditions change, AI-powered systems automatically adjust their forecasts based on new data. No manual replanning required.
Instead of simplification, AI embraces complexity: Those millions of potential scenarios? AI can evaluate them all, identifying optimal decisions across your entire network.
Omnifold develops purpose-built demand planning AI to deliver what spreadsheets structurally cannot:
Superior Accuracy: AI systems boost accuracy by identifying patterns invisible to traditional analysis. See our case studies for examples with leading Retail and CPG companies.
Autonomous Adaptation: Unlike static spreadsheets where assumptions can sit stale for months, Omnifold continuously learns from its own forecast performance.
Free up hundreds of hours of time: By automating the mechanical aspects of forecasting, AI frees planners to focus on strategic initiatives – new market entry, channel optimization, and competitive positioning.
With Omnifold, the shift is immediate and tangible. When you discover a new fact about your business – a competitor's promotion, a supply disruption, a trending social media post – you simply type it in and watch your forecasts update across every affected SKU, channel, and location.
Reach out to learn more, or read our Day in the Life of a Demand Planner to explore how AI will fundamentally improve how planners can succeed in their roles.