Data and AI for consumer goods
Consumer goods businesses are data-rich and signal-poor: shipment, retailer, promotional and consumption data all describe the same demand through different lenses, at different grains, on different calendars.
Clients in this sector: AB InBev
What usually gets in the way
- Shipment data and actual consumption diverging, with inventory in between
- Retailer and distributor feeds arriving in inconsistent formats and cadences
- Promotional effects that distort baselines and confound forecasting
- Hierarchies — product, customer, geography — that change under you
- Forecasts produced monthly for decisions taken weekly
What we build
- Demand forecasting at the grain the planning process actually uses
- Harmonising retailer, distributor and internal shipment data onto a common hierarchy
- Promotional lift and baseline decomposition
- Inventory and service-level analytics that account for the shipment-to-consumption lag
- Automated data quality checks on inbound partner feeds
How we handle the constraints
Forecasting is only useful if it lands inside the planning cycle. We design to the decision cadence rather than the modelling ideal — a good forecast on Friday beats a better one the following Wednesday.
Ways to work with us
Fixed-scope build
Defined deliverable · weeks to months
The problem is well understood and you want it solved without opening a hiring req.
Embedded senior team
Ongoing · alongside your people
You have a capable team that is short on senior capacity, not on direction.
Advisory and roadmap
Assessment · sequenced plan
There are several plausible directions and the cost of picking the wrong one is high.
Build and run
Delivery · then ongoing operation
The system matters but does not justify hiring a permanent team to maintain it.
Common questions
Our data is messy. Do we need to fix it before starting?
No. Messy partner feeds are the normal starting condition, and cleaning them is usually part of the engagement rather than a prerequisite for it.
Can you improve forecasting without replacing our planning system?
Yes. In most cases the forecast is produced upstream and fed into the existing planning process, which avoids a system migration nobody asked for.
Working on something in consumer goods?
Describe the problem in a couple of sentences. If we're not the right people for it, we'll tell you who is.