Dairy Cooperative — FMCG
Forecast error achieved for the top 100 SKUs, against a sub-10% target
Cross-sell revenue lift from AI-generated bundle offers
About
A national dairy cooperative asked for a two-year AI playbook to master 10,000 SKUs across India's vast cold chain, where festival demand can double within 48 hours and monsoons cut off routes overnight. We ran a maturity scan, designed a cloud data lake, deployed weather- and festival-aware forecasting models, and wrapped order-to-cash in automation under a scaled-agile rollout plan.
Industry
Dairy Cooperative — FMCG
Company size
10,000+ employees
Founded
1946
The Company
A cooperative network moving milk and dairy products at national scale
The cooperative federates dairy production and distribution across thousands of village-level collection points into a national network selling more than 10,000 SKUs through a cold chain that has to function through monsoon disruption and festival demand spikes that can double overnight.
Forecasting and planning had grown organically across the network, region by region, with no single framework connecting local demand signals to the enterprise-level view leadership needed to plan capacity and investment.
The challenge
Spreadsheet forecasts lagging reality by weeks
Festival demand could double within 48 hours in specific regions, and monsoon routes could disappear overnight — neither pattern was captured in forecasts built on spreadsheets that updated on a weekly cycle at best. By the time a spreadsheet forecast reflected a demand shift, the shift itself was already weeks old.
With no unified data architecture, leadership also lacked a clear view of which regions, SKUs, or capabilities most needed investment to close the gap between the cooperative's scale and its forecasting maturity.
The Solution
A maturity scan, a data lake, and a two-year scaled rollout
We ran a maturity scan across the network to identify where forecasting and planning capability gaps were largest, then designed an Azure Data Lake Storage architecture to unify demand signals across regions. XGBoost forecasting models were enriched with weather and festival-calendar signals so the top SKUs could anticipate, not just react to, the swings the cooperative's demand pattern is known for.
GPT-driven bundle-offer generation was introduced to lift cross-sell, and UiPath automation was wrapped around order-to-cash processing, all sequenced under a scaled-agile rollout so regions adopted the new capability in phases rather than all at once.
The Results
Forecast error under 9% on the SKUs that matter most
Forecast error for the top 100 SKUs — the highest-volume, highest-impact items — fell to 8.7%, beating the sub-10% target. AI-generated bundle offers lifted cross-sell revenue by 9.3%, directly attributable to recommendations the GPT-driven engine surfaced that a manual merchandising process hadn't been generating.
The roadmap gave the cooperative's leadership a sequenced, prioritised view of where to invest next across its 10,000-SKU portfolio, rather than treating every region's forecasting gap as an equally urgent fire to fight.
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