Demand forecasting
Naive Forecast
By the Supply Chain Manager team · Updated October 16, 2026
A naive forecast assumes the next period will match the last one, or the same period last cycle, and serves as the benchmark other methods must beat.
It sounds too simple to be useful, which is the point. If a sophisticated model cannot beat 'next week equals last week', it is adding cost and complexity without benefit. A seasonal naive forecast repeats the value from one season ago, for example the same weekday last week.
A naive forecast is also a useful fallback. For new SKUs with very little history, or items whose demand has just changed, a naive or seasonal naive forecast is often as good as anything else until enough data builds up to test better methods.
How to calculate naive forecast
Naive: Forecast(t + 1) = Actual(t) Seasonal naive: Forecast(t + 1) = Actual(t + 1 − season length)
Naive forecast example
Illustrative numbers: last Monday a branch sold 40 units. A seasonal naive forecast with a weekly cycle predicts 40 for next Monday, regardless of what happened on the days in between.
Why naive forecast matters for distributors
Benchmarking against naive keeps forecasting honest. It is also the denominator of MASE.
Common naive forecast mistakes
- Skipping the benchmark and assuming a complex model must be better.
- Using a plain naive benchmark for strongly weekly or seasonal demand, where seasonal naive is the fairer test.
Naive forecast in Supply Chain Manager
Supply Chain Manager includes naive and weekly seasonal naive forecasts in every SKU's model comparison.
Open the live demoExamples on this page use made-up round numbers to show the calculation. They are not benchmarks or results from real businesses.