Manufacturing Analytics in New York
Most plants we walk into have OEE measured by a shift supervisor in Excel, reviewed Monday morning, three days after the decisions that mattered. We build the infrastructure that changes that.
Real-time production intelligence for plants that are tired of making decisions on yesterday's numbers. US-based clients tend to have higher awareness of modern data stack tooling — Snowflake, dBT, and Databricks are well-understood at the technical level. The challenge is more often the domain knowledge gap: understanding how to apply those tools to the specific operational environment of a manufacturing plant in Dubai or a supply chain network spanning India and Southeast Asia. That's the combination MDI provides.
What we hear from operators
The problems we solve
These aren't hypothetical pain points assembled from industry reports. They're observations from actual plant floors, warehouse ops, and finance desks — written down because they come up in almost every first conversation.
OEE exists on paper only
The MES captures downtime. SAP captures production orders. Nobody has connected them. So OEE is calculated at shift end by whoever remembers to fill in the form — and by the time the plant manager sees Monday's report, the week's production schedule is already locked. The data that should drive decisions is arriving three days after they were made.
Scrap is measured, not managed
Month-end scrap reports show you what you lost. They don't show you which machine, which shift, which operator, which material batch caused it. You can't act on aggregated scrap data — you can only wince at the number. The fix is granular tracking at the point of production, connected to the ERP batch records.
Quality deviations arrive too late
By the time a quality deviation surfaces in the SAP quality module, the batch has already shipped or the downstream impact has already cascaded. The SPC data exists in the quality system. The production parameters exist in the MES. Nobody has built the bridge that catches the pattern before it becomes a non-conformance.
Planned vs actual has no teeth
Every plant has a production plan. Most have planned vs actual reporting — weekly, in a spreadsheet, presented in a meeting where everyone nods and moves on. The gap between plan and actual is accepted as normal. It isn't. It's a symptom of a scheduling system that isn't connected to real capacity data.
How we work
Our approach
01
Connect the production data sources
Before anything is built, we map what exists: SAP PP/PM, the MES, SCADA if it's in scope, the quality system. We identify where the data lives, what the gaps are, and what integration method makes sense — API, database replication, or message queue. This is the foundation. Everything else depends on getting it right.
02
Build a single live production dashboard
One dashboard. OEE by line, by shift, by machine — updated every 15 minutes or faster. Downtime with reason codes. Scrap by batch, by SKU, by cause. Planned vs actual with drill-through to the order level. We start here because this is what operators and supervisors will actually use. If the first thing we build isn't used, nothing else matters.
03
Add intelligence once the foundation is trusted
Once the numbers are trusted — and it takes 4–6 weeks for a plant team to stop double-checking the dashboard against their spreadsheets — we layer in predictive capability. Maintenance alerts based on vibration or runtime patterns. Demand-driven scheduling that adjusts to real capacity. Quality deviation early warning before the batch is complete. This is where the ROI compounds.
What changes
Outcomes
These are specific, measurable shifts — not benefit statements. Every outcome listed here has been achieved with a client.
OEE visibility: end-of-shift lag → real-time or 15-minute refresh
Production teams stop waiting for reports and start managing the shift as it happens. Downtime decisions get made in minutes, not at the next morning's standup.
Scrap traceability: monthly aggregate → per-batch, per-machine, per-shift
Quality teams can isolate the root cause of a scrap event within the same production shift it occurred. Waste walk findings get backed by data, not gut feel.
Planned vs actual: weekly review meeting → live exception alerts
Plant managers get notified when production is tracking more than 5% off plan — while there's still time to respond, not after the week closes.
Month-end production close: 3-5 days → same-day or next morning
When SAP is connected to the MES, month-end production reconciliation stops being a manual exercise. Numbers close automatically against the system of record.
Technology stack
Common questions
What buyers ask us
These are questions that come up in almost every first or second conversation. If yours isn't here, it will be in the first call.
We already have SAP. Why isn't that enough?
SAP captures what happened — production orders, goods receipts, quality results. What it doesn't do well is show you what's happening right now, in a form that a shift supervisor or plant manager can act on in real time. The data is in SAP. The problem is getting it out in a usable form, fast enough to matter. That's the gap we fill.
We have a BI team. Why do we need external help?
Internal BI teams are good at maintaining what exists. They're usually under-resourced to design and build new data infrastructure — especially the integration layer between OT systems (MES, SCADA) and IT systems (SAP, ERP). That integration work requires both industrial domain knowledge and data engineering capability at the same time. That combination is rare internally.
How long before we see something working?
A first working OEE dashboard connected to live production data typically takes 6–8 weeks. That assumes we can access the MES and SAP environments and that the IT team can provide integration credentials within the first two weeks. We don't wait for a perfect data model — we build something useful, show it to the plant team, and iterate from there.
Our MES is old and doesn't have a proper API. Is that a problem?
Older MES systems are common. Most can be integrated via database-level access, ODBC, or file-based export if API access isn't available. We've worked with Wonderware, FactoryTalk, Siemens SIMATIC, and a range of custom-built systems. The integration method changes — the outcome doesn't.
Do we need to replace our ERP first?
No. We work with whatever ERP is in place — SAP B1, SAP S/4, Oracle, Dynamics 365. An ERP migration is a multi-year project. We can build meaningful analytics on top of your current ERP while that migration is planned or underway. The analytics layer we build is ERP-agnostic by design.
Ready to move
Start with a conversation, not a proposal
First call is 45 minutes. No deck. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like. No obligation.