How accurate should your demand forecast really be? While many numbers are often thrown around, demand forecast accuracy has traditionally hovered between 70% and 85%. But in the era of AI-powered supply chain planning and advanced demand planning software, these numbers aren’t good enough anymore. Because what 85% accuracy doesn’t tell you: what that other 15% is costing you, and where incremental lifts can lead to big gains.
Why Isn’t 85% Forecast Accuracy Enough?
On paper, 85% forecast accuracy sounds strong. It suggests your team is getting demand mostly right, most of the time. But in distribution, “mostly right” can still be expensive. Here’s why 85% isn’t enough, and why inventory leaders should care about their accuracy misses.
- Small inaccuracies can lead to big costs: Every percentage point of error flows into purchasing decisions, safety stock requirements, service levels, warehouse capacity, supplier commitments, and working capital. Industry research shows that inventory distortion alone cost retailers $1.77 trillion annually, and that supplier missteps account for more than $300 billion of those total costs.
- Forecast accuracy is often too broad When a planning team reports 85% forecast accuracy, they’re almost always reporting an average. And averages in supply chain planning are very good at concealing problems. Consider this: that 85% company-wide figure might involve C-class SKUs running at 50% accuracy, with Z-class items that are essentially ungoverned. The headline number might look fine, but it’s often too broad, and misrepresents the underlying SKU mix.
- Forecastability is not one-size-fits-all: Not every item in a catalog should be managed the same way, and applying a single forecasting model across an entire SKU can lead to costly mistakes. But methods like XYZ classification help you drill down into each SKU. X items have low demand variation and are highly forecastable. Y items have moderate variability. Z items are erratic (seasonal spikes, sporadic orders, or products nearing end-of-life). When planners know which category each SKU falls into, the accuracy conversation becomes more specific. For some SKUs, inaccuracies might be unavoidable, while for highly forecastable SKUs, discrepancies are a red flag that demand improvement.
What Should You Do If You’re Stuck at 85%?
What should you do if demand forecast accuracy isn’t as high as your business deserves? Here’s a quick framework you can use for diagnosing what’s holding your forecast back, and steps you can take to nudge it in the right direction.
1. Break your accuracy number apart
If you’re looking at broad accuracy figures (such as company-wide, supplier-wide, or location-wide), start by breaking them down by SKU segment. Use ABC analysis to determine which items are most valuable and important to your business. Then, layer in forecastability. Which of those items have stable, predictable demand patterns, and which are erratic or sporadic?
The goal is to isolate where your accuracy is actually breaking down. If A/X SKUs (highest-value, most predictable) are running at 15% Mean Absolute Percentage Error (MAPE), you likely have a model or data problem that needs aggressive fixing. If your Z-class items are dragging the average down, that may be less about forecasting and more about stocking policy.
2. Give your model better inputs
AI demand planning tools are powerful, but they need human oversight and context to generate accurate outputs. First, strong forecast accuracy depends on clean, accurate inputs, such as consistent demand history, accuracy lead times, reliable supplier insights, and clean item/location attributes. But beyond core data, forecasting models are only as good as the signals they can see. If promotional events, customer-level demand shifts, or known market changes aren’t making it into the model, it’s going to churn out errors.
3. Improve your forecasting toolkit
Many supply chain businesses are leaning on tools that can generate demand forecasts, but are not purpose-built to do so at a sophisticated level. For example, most ERPs have forecasting capabilities, but it’s often a “mile wide and an inch deep.”
Inventory planning software such as StockIQ gives you a much more granular look at your supply and forecasts, and has a range of tools to help boost forecast accuracy. Consider the Unusual Sales feature, which flags demand spikes that you might want to omit from your baseline model. This ensures that historical data going into the forecast reflects actual underlying demand rather than noise.
How Small Accuracy Gains Translate Into Big Financial Wins
Improving supply chain forecasting accuracy can lead to dramatic gains in everything from cash flow to finances. Here are some examples of how, using StockIQ case studies:
- BuildASign is a leading web-to-print manufacturer specializing in custom signage, apparel, and home décor across three major e-commerce brands. They struggled translating unpredictable, custom consumer orders into accurate raw material requirements, leading to a bloated $4 million inventory. But StockIQ gave them custom demand logic for component-level planning, automated ABC classification, and proactive quarterly forecasting, which allowed them to reduce their inventory levels to $1.9M.
- Texas Electric Cooperatives is a member-owned utility supply organization serving 66 electric cooperatives across Texas. Operating in an industry with zero tolerance for delays, the team also needed to provide different stocking strategies for 30,000 SKUs, and manage highly variable manufacturer lead times post-COVID. With StockIQ, the team replaced generic fixed order policies with planning modules that reflect real-world variability, while sharing forecast signals with suppliers, allowing them to maintain 99.5% service levels across their warehouses.
- Buddha Brands is a North American food and beverage company specializing in plant-based snacks and beverages made with clean ingredients and low or no added sugar. They struggled with severe supply chain volatility and unpredictable demand, while relying heavily on spreadsheets and limited supply chain tools for planning. By switching to StockIQ, they gained the ability to anticipate stockouts earlier, adjust quickly to global supply disruptions, and place orders confidently without inflating inventory, leading to 99% service levels.
The Cost of “Good Enough” Is Too High
Forecast accuracy has always mattered. But with AI-powered demand forecasting tools like StockIQ, you don’t need to defend inaccuracies. Purpose-built for mid-market manufacturers and distributors, StockIQ goes well beyond what an ERP can offer. It delivers robust demand forecasting, ABC/XYZ classification, Unusual Sales detection, and zero-demand forecasting that flags end-of-life SKUs before they roll over into your forecasts.
The result? Less excess inventory, fewer stockouts, and more informed supply chain decisions.
If you’re ready to understand what your forecast error is really costing you, request a StockIQ demo today.
FAQs
1. What is a good forecast accuracy target for distributors?
A good forecast accuracy target depends on the SKU, demand pattern, and service-level goal. Predictable, high-value A-items may be able to achieve 95% accuracy, while sporadic or low-value items may be better managed through stocking policy rather than a single accuracy target.
2. Why is 85% forecast accuracy not enough anymore?
85% forecast accuracy still leaves 15% forecast error, which can translate into excess safety stock, stockouts, rush orders, and margin pressure. For distributors, the cost of that remaining error can be significant across thousands of SKU-location combinations.
3. How can distributors improve forecast accuracy?
Distributors can improve forecast accuracy by segmenting SKUs by value and forecastability, isolating unusual demand spikes, and improving their forecasting toolkit.