Measure forecast accuracy and see where plans miss demand
An illustrative consumer goods and distribution concept connecting statistical forecasts, sales team inputs, primary and secondary sales, and promotion calendars for demand planning and forecast accuracy.
Forecast accuracy (%)
Forecast bias (%)
Weighted absolute percentage error (%)
SKUs with repeated bias (count)
THE CHALLENGE
Forecasts are made but rarely measured against what happened
Stock not visible
Closing stock is reported irregularly or not at all.
Static norms
Stock norms do not change with seasonal sales velocity.
Fast-mover gaps
High-velocity SKUs run out while slow SKUs pile up.
Ordering by habit
Primary orders follow last month's quantity rather than need.
THE SOLUTION
Forecast Accuracy Review Framework
Version history
Collect daily or weekly closing stock from every distributor.
Velocity-based norms
Set norms from recent secondary sales velocity and lead time.
Gap identification
Compare stock with norm for every SKU and distributor.
Order suggestion
Show suggested primary quantities for sales team review.
THE APPROACH
Forecast Version Capture
Standardise closing stock submissions and track missing reports.
Sales Velocity Calculation
Calculate rolling daily secondary sales by SKU and distributor.
Norm Recalculation
Recalculate days-of-cover norms with lead time and seasonality.
Below Norm Alerts
Link schemes, festivals, and launches to affected periods.
Demand Signal Comparison
Compare forecasts with secondary sales to separate demand from stock movement.
Planning Review Cadence
Run a monthly review of the largest misses with planners.
THE IMPACT
Accountable plans
Each forecast could be compared with what happened.
Bias correction
Repeated over-forecasting could be identified and discussed.
Event awareness
Promotion and festival effects could be planned explicitly.
Truer demand
Plans could reflect outlet demand, not only billing.
Controlled review
Authorized teams could review SKUs and regions with persistent forecast bias or large errors, with source evidence retained beside each review case.
Measurable evaluation
A pilot could establish baseline values for forecast accuracy (%) and assess changes after approved actions. Descriptive results alone would not establish causality.
Explore Distributor Inventory And Stock Norms
Work with RUSA Analytics to connect distributor closing stock, stock norms, secondary sales velocity, and pending primary orders and create a governed workflow to review distributors below stock norm on fast movers or holding excess slow stock. Start with a scoped concept or pilot and agreed success measures.
MORE WORK
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