Demand Forecast Accuracy Calculator
Calculates how closely your demand forecast matched actual sales, expressed as a percentage. Use it to evaluate forecasting models, set improvement targets, and reduce costly overstock or stockout situations.
About this calculator
Demand Forecast Accuracy (DFA) measures the percentage of actual demand that your forecast captured correctly. The formula is: DFA = 100 − (|Actual − Forecast| / Actual) × 100. The absolute difference between actual and forecasted demand is divided by actual demand to produce the Mean Absolute Percentage Error (MAPE), and DFA is simply 100% minus that error rate. A score of 100% means a perfect forecast; lower scores indicate larger average errors. This metric is widely used in supply chain planning because it penalizes both over-forecasting and under-forecasting equally. Note that DFA is undefined when actual demand is zero, and it can be misleading for very low-volume items where small unit errors produce large percentage swings. Many practitioners complement DFA with bias metrics to detect systematic over- or under-prediction.
How to use
Imagine you forecasted demand of 850 units but actual demand turned out to be 1,000 units. Step 1 — find the absolute error: |1,000 − 850| = 150 units. Step 2 — divide by actual demand: 150 / 1,000 = 0.15, or 15% error. Step 3 — subtract from 100%: DFA = 100 − 15 = 85%. Your forecast was 85% accurate. A world-class supply chain typically targets DFA above 90–95%. Enter your actual and forecasted values to see your accuracy score instantly.
Frequently asked questions
What is a good demand forecast accuracy percentage for retail or manufacturing?
World-class operations typically achieve forecast accuracy of 90–95% or higher for fast-moving, stable products. For products with high demand volatility, seasonal spikes, or short life cycles, 70–85% may be realistic. The right target depends on your industry, product mix, and the cost of forecast errors. It is more useful to track accuracy trends over time than to fixate on a single benchmark number.
How does demand forecast accuracy affect inventory levels and costs?
Poor forecast accuracy forces companies to carry extra safety stock to buffer against uncertainty, tying up working capital and increasing holding costs. Overestimates lead to excess inventory and potential markdowns, while underestimates cause stockouts and lost sales. Even a 5-percentage-point improvement in forecast accuracy can translate to meaningful reductions in inventory carrying costs and service-level improvements. This is why supply chain managers invest heavily in forecasting tools and processes.
Why should I use actual demand rather than forecasted demand in the denominator of the forecast accuracy formula?
Using actual demand in the denominator anchors the error percentage to what really happened in the market, making it an unbiased measure of forecast miss. If you used forecasted demand instead, a large over-forecast would inflate the denominator and artificially reduce the apparent error rate. Actual demand reflects true customer need and is the standard used in most supply chain and S&OP frameworks. Some organizations use average demand in the denominator (symmetric MAPE) to handle extreme values, but actual-demand-based DFA remains the most common approach.