AI for consumer packaged goods, from signal to shelf.
For CPG operators working demand signals, supplier networks, and shelf data that rarely agree with each other — and a planning system that has to hold the answer.
- Who
- CPG operators
- Frame
- Supply chain
- Built
- Forecasting & insight
- Evidence
- Founder credential
Three sources, three answers, one plan
The beatA CPG planning team is rarely short of data. It has shipment history, point-of-sale, syndicated panel data, and a sales forecast — and on any given SKU those four disagree. The work is not collecting another signal; it is producing one number the business will act on.
That is a reconciliation problem before it is a modelling problem. Which source leads for which channel, how promotional lift is separated from baseline demand, and what happens when the panel and the POS diverge are decisions that belong to the business, made explicit in the system rather than buried in a model.
And the answer has to land in the planning system. A forecast that lives in a dashboard beside SAP is a forecast someone has to re-key, which means it is a forecast that will quietly stop being used.
What we build
The workDemand Forecasting
Predictive analytics for demand planning across SKUs, channels, and regions.SKU-level demand predictionMulti-channel demand sensingPromotional impact modelingSeasonal and trend analysisSupply Chain Optimization
AI-powered optimization for inventory, logistics, and supplier management.Inventory optimization and allocationSupplier risk monitoringLogistics route optimizationProduction planning supportConsumer Insights
Purchase behavior, brand sentiment, and market trends read from the same signals.Purchase pattern analysisBrand sentiment trackingCompetitive positioning insightsMarket trend identificationTrade Promotion Optimization
Return on trade spend, planned and measured rather than estimated after the fact.Promotion effectiveness predictionOptimal pricing recommendationsRetailer collaboration insightsPost-event analysis automationProduct Innovation
Concept-to-launch support grounded in preference data rather than instinct.Consumer preference modelingIngredient and formulation optimizationLaunch success predictionPortfolio optimization analysis
What CPG demands
Supply chainThese are the conditions the systems are built to work within — not certifications SYRV AI holds. Where a requirement is the manufacturer’s to certify, the build’s job is to make the evidence retrievable.
| Requirement | What it means in practice | How the build handles it |
|---|---|---|
| ERP integration | What it means in practiceDemand, inventory, and production planning already live in SAP or Oracle, and the plan of record has to stay there. | How the build handles itForecasts land back in the planning system rather than in a parallel dashboard. |
| Retailer data agreements | What it means in practicePoint-of-sale and syndicated data arrive under terms that limit how they may be stored and combined. | How the build handles itData lineage is tracked per source so the terms travel with the data. |
| Traceability & recall | What it means in practiceLot-level traceability has to survive an audit, and a recall is a timed exercise. | How the build handles itLot and batch relationships are retained alongside the forecast that moved the product. |
| Data security | What it means in practiceConsumer data and competitive intelligence are sensitive in different ways and to different people. | How the build handles itAccess controls follow the data classification rather than the org chart. |
Why supply chain experience matters
Our viewWhy this sector is different
The forecast is not the deliverable — the decision it drives is. A model that improves accuracy on a back-test but does not change what gets produced or allocated has not earned anything.
Trade spend distorts the demand signal it is measured against. Separating promotional lift from underlying demand is the difference between a plan and an expensive echo of last year.
What that changes about the build
The planning system stays the record of truth. Integration with SAP, Oracle, or whatever holds the plan comes first, because a number that has to be copied is a number that will be copied wrong.
Reconciliation rules are written down and owned by the business. When the sources disagree, the system says which one leads and why, rather than resolving it silently inside a model.
What we have learned here
- Your data sources disagreeand deciding which one leads is a business decision, not a modelling one
- Trade spend distorts the signalpromotional lift has to be separated from baseline demand
- The ERP is the record of trutha forecast that has to be re-keyed will stop being used
- SKU proliferation outruns the plannercoverage across the tail is where the model earns its keep
- A forecast is only as good as the decision it changesaccuracy on a back-test is not the goal
Questions we get asked
ReferenceWhat is your connection to Cincinnati's CPG ecosystem?
What does a CPG AI transformation strategy include?
How do you approach demand forecasting AI?
Can you integrate with our existing ERP systems?
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