AI-Enabled Demand Sensing for a Beverage Company
Deployed a demand sensing engine across 500,000+ outlets for a major beverage company, improving forecast accuracy from 62% to 84% and reducing wastage from ₹80 Cr to ₹38 Cr.
Industry
CPG
Offering
Kaara Build
The Challenge
The company's demand planning relied on 30-day rolling averages with manual adjustments by regional planners. Forecast accuracy stood at 62% at the SKU-location level, leading to ₹80 Cr in annual wastage (expired inventory) and significant lost sales from stockouts during peak demand periods. Weather events, local festivals, and competitive launches created demand spikes that the existing system consistently missed. Each product line had its own planning team with no shared intelligence.
The Kaara Approach
Kaara deployed a unified demand sensing engine using Kaara.Code that connected weather data, local event calendars, POS signals, and distributor secondary sales data into a single intelligence layer. The Enterprise Memory Layer encoded the company's specific distribution network topology, outlet classification system, and product-weather-event correlations discovered over 3 years of historical analysis. Rather than building separate models per product line, Kaara created a shared intelligence architecture where demand signals from one category enriched predictions across all categories. The system enabled real-time demand adjustment at the outlet-cluster level, while providing regional sales teams with a conversational interface to query and override forecasts with local market intelligence.
Measurable Impact
- Forecast Accuracy: 62% to 84%
- Wastage Reduction: ₹80 Cr to ₹38 Cr
- Stockout Recovery: ₹28 Cr
- Planning Time: 5 days to 4 hours
The Compounding Build Advantage
- Weather-demand correlations learned from carbonated beverages automatically informed juice and water forecasting
- Outlet-level demand patterns accumulated across seasons, making each year's planning more precise than the last
- Distributor secondary sales intelligence from one region enriched demand models for similar market clusters
- Product launch demand curves from past launches informed new product forecasting with increasing accuracy