Overstocking ties up cash and godown space. Understocking means lost sales during peak demand. Indian retailers and distributors face extra complexity: festival seasons, monsoon effects, regional preferences and promotional schemes all shift demand.
What forecasting models use
- Historical sales by product, location and date
- Festival and holiday calendars
- Promotions and price changes
- Weather, where relevant
- Stock availability (to avoid treating stockouts as low demand)
Start with clean data
The biggest obstacle is usually data, not algorithms. Sales records may be split across Tally, POS systems and spreadsheets. Bringing them together and fixing gaps is the first step.
Choose the right level
Forecasting every SKU at every store may be unnecessary. Many businesses get good results forecasting at category or region level first, then refining for top-selling products.
Combine with business judgement
Models handle patterns; managers know about upcoming launches, competitor moves and local events. The best results come from forecasts that planners can review and adjust.
Measure accuracy
Compare forecasts with actual sales using a simple error measure, and track stockouts and excess stock before and after.
When ML is not the answer
If you have less than a year of reliable data, simple rules — moving averages and seasonal adjustments — may perform just as well.
FAQs
How much history do we need?
Ideally two or more years to capture seasonal patterns, though useful models can start with less.
Can forecasts feed our ERP?
Yes, as suggested reorder quantities in your ERP or inventory system.
Does it work for new products?
New products are harder; similar-product history and launch plans help.
