We build production, supply chain and plant-floor analytics for manufacturers — designed by an engineer who spent years inside General Motors' own data operations.
Production data lives in the MES, inventory in the ERP, quality in spreadsheets, and none of them agree. Plant managers wait until month-end to learn a line underperformed. Forecast error is absorbed as safety stock, tying up working capital nobody can quantify.
We map every production and supply chain data source, re-engineer the pipelines that move it, and build a governed reporting layer on top. The same approach used to rebuild GM's supply chain ETL — not a template adapted from another industry.
Plant, line and SKU performance visible daily rather than monthly. Forecast accuracy that shrinks safety stock. Downtime attributed to real causes. One number for production that finance and operations both trust.
What We Build
Overall equipment effectiveness broken down by line, shift and SKU, refreshed from the MES rather than rekeyed.
Inbound materials, WIP and finished goods in one view, with exception alerts when a lane slips.
Forecast-versus-actual tracking at SKU level, surfacing where error concentrates and what it costs in stock.
Rebuilt pipelines that cut processing time and make same-day reporting possible.
Unplanned stoppages attributed to cause, asset and shift, so maintenance spend follows evidence.
Turns, ageing and coverage by location, connecting operational decisions to the balance sheet.
Proven Results
Technical Stack
Context
Manufacturing analytics fails for a reason that has nothing to do with tooling. The data is generated by machines on a shop floor, recorded by an MES that was specified a decade ago, and consumed by people who need an answer within the shift. Most BI projects treat it like any other reporting problem, model it in a warehouse, and deliver a dashboard that is technically correct and operationally useless because it refreshes overnight.
The second failure is definitional. Overall equipment effectiveness sounds like a standard metric until you ask two plants to calculate it. Planned downtime, changeover and minor stoppage are treated differently almost everywhere, so the number that leadership compares across sites is frequently comparing nothing at all. Fixing that is a governance exercise before it is a technical one.
We start from the constraint rather than the tool: what decision needs making, how fast, and which source can actually support it. That is the approach used to rebuild supply chain ETL at General Motors, where the pipelines were the bottleneck and no amount of dashboard design would have moved the number.
How It Runs
FAQ
We integrate with what you have. We have connected legacy MES platforms, SAP S/4HANA, custom shop-floor databases and quality systems that only export CSV. Replacing a working MES to enable reporting is almost never the right trade.
We treat it as a definition problem first. We document how each site currently calculates it, facilitate agreement on one standard, then implement that logic centrally so every plant is measured identically. The technical work is the easy half.
Both. Reporting forecast error at SKU level usually reveals that the error concentrates in a small subset of items. That is actionable on its own, and it is also the foundation for any predictive work that follows.
It depends on the number of source systems and sites. A single-plant dashboard build is a focused three to four week engagement. A multi-site supply chain rebuild runs longer. We give you a fixed scope and price in the proposal, not an hourly estimate.