Operations
See demand, inventory, and constraints as one system — before they become cost.
Forecasting, replenishment, and purchasing decisions are only as good as the data connecting ERP, logistics, suppliers, and demand signals. That connection is an architecture problem before it is a planning problem.
Planning tools cannot fix data the supply chain does not share.
Demand lives in orders and forecasts, supply in ERP and supplier feeds, constraints in spreadsheets and tribal knowledge. Every planning cycle re-assembles the picture by hand — too slowly to act on it.
Demand signals arrive fragmented
Orders, POS, promotions, and seasonality are never combined on one item-location-time grain.
Inventory truth varies by system
On-hand, in-transit, and committed quantities disagree across ERP, WMS, and planning tools.
Constraints surface as surprises
Lead-time drift, supplier risk, and capacity limits are discovered at expedite time, at expedite prices.
Outcomes
One demand and supply picture
Item, location, and time-grain data products that planning, purchasing, and finance share.
Forecasting on trusted history
Statistical and ML forecasts built on cleaned, governed demand data — with error tracked honestly.
Earlier constraint detection
Lead-time, supplier, and capacity anomalies visible in the data before they hit fulfillment.
How the architecture works
01
Operational integration
ERP, WMS, logistics, supplier, and demand feeds through Lakeflow into governed bronze and silver layers.
02
Planning-grade models
Demand history, inventory positions, and constraint signals modeled on planning grains with explicit rules.
03
Decision consumption
Forecasts, exceptions, and inventory analytics delivered into planning cadences and operational workflows.
What we implement
- Demand data engineering
- Demand forecasting foundations
- Inventory optimization analytics
- Supplier and lead-time intelligence
- S&OP data products
- Operational exception detection
Where this shows up
Forecast accuracy program
One governed demand history, one forecasting baseline, and error measured by item class.
Inventory position clarity
On-hand, in-transit, and committed stock reconciled across systems for one network.
What should be measured
The business case is built on a baseline, not a promise. These are the numbers this solution is accountable to.
- Forecast error by item class
- Inventory turns and aged stock
- Expedite frequency and premium cost
- Stockout and fill-rate trends
The first sensible pilot
One category, one network, one forecast cycle
Build governed demand and inventory data for a single category, run one forecasting cycle against it, and measure error against the incumbent process.
Questions
Does this replace our planning system?
No. Planning tools keep their role. The lakehouse becomes the governed source of demand, supply, and constraint data those tools and your analysts currently lack.
Can this work with poor master data?
Master-data gaps are usually the first finding. The architecture includes quality rules and stewardship signals so the problem shrinks instead of being worked around forever.
Related
Data Engineering
Lakeflow Architecture for Modern Data Engineering
Lakeflow is the engineering surface for ingestion, transformation, streaming, and orchestration. It still needs contracts, quality, and owners.
10 min
Start with the business case
Find the first data or AI opportunity worth proving.
We evaluate the business problem, systems, data, architecture, and economics behind it—then identify the smallest production engagement capable of proving whether the opportunity is real.
Business case first · Architecture-led · Production-focused