How a leading Indian manufacturer used local weather, regional festivals, and price signals to improve forecasts while maintaining better delivery service.
Holding more inventory can help a food and beverage manufacturer manage uncertainty. But it also ties up working capital and leaves less flexibility when demand changes or products approach the end of their shelf life.
For one leading Indian food and beverage manufacturer, this balancing act had resulted in 93 days of inventory. Forecasts were missing important changes in demand, and the business relied on stock to protect service.
CBC’s case study reports how an AI demand planning approach helped reduce forecast error by 39%, bring inventory days down to 77, and improve on-time, in-full delivery by four percentage points.
At a Glance
| Planning Measure | Reported Result |
| Forecast error | Reduced from 48% to 29% |
| Overall forecasterror reduction | 39% |
| Inventory Days | Reduced from 93 to 77 |
| Inventory days reduction | 17% |
| One time, in full delivery | Improved by four percentage points |
| Plant – SKU forecasts | 89% |
The Manufacturer’s Planning Challenge
The customer is a leading Indian food and beverage manufacturer with approximately ₹2,500 crore in revenue. It serves General Trade and Modern Trade across four product categories from multiple plants.
That operating structure creates several layers of planning decisions. A product may sell differently across regions, demand may vary between channels, and each plant needs a forecast that reflects the products and markets it serves.
The company’s forecasts relied on historical sales and a national calendar. However, food and beverage demand was also responding to local temperature, regional festivals, and price changes.
Forecast errors were around 45–50%. To protect product availability, the manufacturer carried 93 days of inventory—an expensive buffer for a business handling products with limited shelf life.
Why Historical Sales Were Not Enough
Last year’s sales provide useful information, but they cannot fully explain what customers will buy next. A change in local temperature can affect beverage demand. A regional festival can influence buying patterns that a national calendar does not capture adequately. Changes in consumer prices can add another influence.
These signals do not affect every product, plant, or market in the same way.
For this manufacturer, improving demand forecasting meant reflecting those differences in the forecast itself, rather than relying on additional inventory to absorb the uncertainty.
How CBC Applied AI Demand Planning
CBC generated forecasts for each plant–SKU combination and automatically selected the best forecasting model for each one.
A SKU is an individual product variant. Forecasting at plant–SKU level allowed the approach to account for the specific product requirements associated with each manufacturing location.
The forecasts incorporated three external demand signals:
Local Temperature
Temperature data for individual plants and markets helped the forecasts reflect weather-related demand patterns. This was particularly relevant to milk beverages. The case study reports a 49% reduction in forecast error for this category from local temperature alone.
Regional Festival Calendars
Regional festival calendars helped account for demand changes that could be missed when planning relied on a national calendar. This added more locally relevant timing information to the forecasting process.
Monthly Consumer Price Index Data
Monthly Consumer Price Index, or CPI, data added a measure of consumer price changes to the forecasting inputs. Together, these signals supported a more detailed view of demand across products and locations.
The Results: Lower Forecast Error Across Products and Channels
The case study reports a 39% overall reduction in forecast error, with pooled Mean Absolute Percentage Error, or MAPE, at plant–SKU level moving from 48% to 29%.
The improvements extended across both sales channels:
- 41% lower forecast error in Modern Trade
- 36% lower forecast error in General Trade
- Improved forecasts for 89% of plant–SKU combinations
- Improvement in every category across both channels
These results show that the reported gains extended across a substantial share of the manufacturer’s planning scope.
Inventory Days Fell While Delivery Service Improved
Inventory days decreased from 93 to 77, a reported reduction of 17%. At the same time, on-time, in-full delivery—commonly called OTIF—improved by four percentage points.
That combination matters. Reducing inventory is more useful when the business can also maintain or improve its ability to fulfil customer orders.
The case study also reports an estimated ₹80 crore of working capital released, with CBC identified as a key contributor. This is an estimated working-capital benefit, rather than a claim of equivalent revenue or profit growth.
The Next Step: Connecting Forecasts with Production and Procurement
The manufacturer is extending the approach to weekly demand sensing and connecting forecasts directly to production and procurement planning.
The next step is to make changing demand information more useful in the decisions that follow: what to produce, which materials to procure, and when supply will be needed.
What Other Food & Beverage Manufacturers Can Learn
This case highlights a practical question for planning teams: does the forecast reflect the conditions that influence demand in each market?
When weather, regional events, and price changes affect buying behaviour, planning based primarily on sales history may leave important signals unaccounted for. The opportunity is to examine where those signals could improve the forecast—and then measure whether the change supports lower inventory and better service together.
Frequently Asked Questions
What is AI demand planning for food and beverage manufacturing?
AI demand planning uses forecasting models to analyze demand patterns and relevant inputs. In this CBC case study, the approach incorporated local temperature, regional festival calendars, and monthly CPI data at plant–SKU level.
What results does this CBC case study report?
The case study reports 39% lower forecast error, 17% fewer inventory days, and a four-percentage-point improvement in OTIF. Forecasts improved for 89% of plant–SKU combinations.
Can other manufacturers expect the same improvements?
The figures describe the results reported for this case. Outcomes for another manufacturer would depend on its data, product range, demand patterns, existing forecasting performance, and implementation scope.
Explore CBC Demand Planning for Your Business
If your team is balancing forecast uncertainty, excess inventory, and delivery commitments, start by examining one product category, plant, or sales channel.
CBC can demonstrate its Demand Planning approach against your planning priorities and discuss a 30-day platform evaluation.