Cutting Inventory Costs by 96%
A chocolate bar, five states, and a cost problem hiding in a spreadsheet.
A confectionery company's demand kept swinging wildly by season. A demand-forecasting model brought its inventory costs down by 96%.
Total inventory costs dropped from ₹31,25,060 to roughly ₹1,28,749 — a 96% reduction.
Huge seasonal demand swings, a 15-day lead time on supply, high ordering costs, and constant pressure between keeping enough stock and not overstocking a product that spoils.
Five years of the company's own sales and cost data.
One single demand forecast for the whole year doesn't work when demand genuinely swings by season — the model only got useful once peak and off-season were calculated separately.
Forecast demand by season, then calculate the ideal order size separately for each.
Not applicable to this project.
Five practical recommendations, including better vendor coordination and cold-chain logistics.
"A fairly simple model can produce a dramatic result, if you split it by the one variable that actually matters."
Read the complete report submitted for this project, including the research, analysis, strategy, and final recommendations.