A demand-planning AI operators actually use.
Supply Chain Assistant deployed across 3,200 SKUs and 12 markets for one of India's largest FMCG operators — with demand planners in the loop, not replaced.
Client
Hindustan Unilever
Sector
Retail & Consumer Goods
Duration
14 months
Team
11 (AI engineers, supply-chain SMEs, SAP consultants, product engineers)
Where Hindustan Unilever was when we started.
Hindustan Unilever's demand planners manage SKU proliferation, promotional intensity, and monsoon-driven demand shifts across a country where the retail estate spans hypermarkets and single-shopkeeper kirana stores in equal measure. The existing planning stack was accurate on the slow-moving core and unreliable on the long tail — which was where growth was.
The problem, unvarnished.
- Legacy demand-planning tools trained on stable SKUs missed innovation SKUs and promotional volatility
- Planners were reduced to spreadsheet-overriding the model, then losing the audit trail on why
- Leadership questions between S&OP cycles were answered by hand, in a slack thread, with numbers pulled from three different reports
- The AI programme needed to earn planner trust, not force a top-down adoption fight
How we scoped and sequenced the work.
01
Planner-in-the-loop by design
The assistant doesn't publish forecasts. It proposes them. Planners see the drivers, edit the number, and the model learns from the edit. The assistant is a colleague, not a replacement.
02
Explanations, not just forecasts
Every forecast comes with the drivers that moved it — promotions, weather, macro, factory constraints. Planners see why the number changed and can push back on the reasoning.
03
Between-review answers in Slack and Teams
When leadership asks 'why is Mumbai soft?' the assistant answers in a paragraph, with the number and the drivers, in-context — not as an ad-hoc analyst request.
04
Rollout by market, not big-bang
Started with two markets and three product families. Expanded market-by-market as planners requested onboarding — not as a top-down decree.
What we shipped.
Supply Chain Assistant plumbed into HUL's SAP IBP and Kinaxis stack, with a Slack/Teams assistant surface for leadership and a planner cockpit for daily work. Forecasts, exceptions, and ad-hoc question answering across 3,200 SKUs and 12 markets.
The numbers that matter.
38%
Reduction in forecast MAPE
Weighted average across all SKUs and markets, measured against the pre-change baseline over four quarters.
3,200
SKUs under continuous forecast
Including the innovation SKUs the legacy stack couldn't handle — with planner-in-the-loop override discipline preserved.
12
Markets covered
Rolled out market-by-market on planner request. Adoption drove the schedule; the schedule didn't drive adoption.
What we built it on.
“The bit I didn't expect was how much the between-review conversation changed. Leadership stopped asking us to pull a number and started asking us what to do about the number.”
Practices involved.
Bring us your retail & consumer goods brief.
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