Data Analytics for Manufacturing: OEE, Yield, Quality, Cost
Analytics that connect Historian time-series to ERP transactions to quality records — so you can finally answer why yield dropped on Tuesday and what it cost. SPC, OEE decomposition, yield analysis, and scrap attribution built by analysts who can read a control chart.
Why Manufacturing Analytics Doesn't Answer the Right Questions
Most manufacturing analytics stops at descriptive dashboards: yesterday's OEE was 72%, scrap was 3.1%, downtime was 47 minutes. The plant manager looks at it, nods, and goes back to firefighting. The data exists to answer harder questions — what caused the yield drop on line 3 last Tuesday, which operator-shift-material combination correlates with scrap on the AB7 SKU, what the gross margin actually is per unit after rework — but the data lives in three different systems with three different timestamp conventions and nobody has joined them properly.
Useful manufacturing analytics requires three things most projects skip: time alignment between Historian (sub-second) and MES (transaction-level) and ERP (period-end), product-and-process genealogy that tracks a unit from raw material lot through every operation, and the SPC literacy to know when a control chart signal is real versus noise. With those three in place, analytics moves from "yesterday's KPIs" to "what to change tomorrow morning."
How Manufacturers Apply It
OEE Decomposition & Loss Analysis
OEE broken down into Availability × Performance × Quality, then drilled into the Six Big Losses (breakdown, setup, idling, reduced speed, defects, startup). Tied to specific assets, SKUs, shifts, and operators. Identifies the 2-3 loss buckets that account for 60% of OEE gap — the ones worth fixing.
Yield, Scrap & Rework Analysis
Yield analytics from raw material receipt through final inspection, with scrap reason coding and cost attribution. Identifies which SKU × line × shift × material lot combinations drive disproportionate scrap. Connects the financial cost of scrap (materials, labor, overhead, opportunity) back to the operational cause.
SPC & Process Capability Analysis
Statistical Process Control charts (X-bar R, individuals, p-charts) calculated from Historian data with proper subgrouping. Cp, Cpk, Pp, Ppk for capability studies. Western Electric rule violations surfaced as actionable alerts — not as 8,000 false positives that get filtered out.
What You Receive
Manufacturing analytics delivered for decisions, not dashboards: data model joining Historian / MES / ERP / QMS with proper time alignment, OEE and yield models with loss decomposition, SPC infrastructure with proper subgrouping logic, scrap cost attribution to operational drivers, and the analyst training that lets your continuous improvement team run their own DMAIC projects on it.
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Data Analytics for Manufacturing — FAQ
MES analytics typically show what happened on that MES — they don't reach into ERP for cost data, or QMS for inspection results, or Historian for the underlying tag data, or HR for shift and operator context. The interesting questions in manufacturing analytics almost always require joining at least three of those systems. That's why a dedicated analytics layer matters.
We model time at the resolution each system supports — sub-second for Historian, transaction-level for MES, shift or daily for ERP — and aggregate up rather than down. Our standard pattern uses a manufacturing event model with time buckets calibrated to the analysis. This avoids the most common pitfall: averaging Historian data to MES granularity and losing the variation that explains the yield problem.
Yes. Pre-qualified data analysts and analytics engineers with manufacturing domain experience — OEE modeling, SPC literacy, MES/Historian integration, lean and Six Sigma backgrounds. 4-stage consulting-led matching, 92% first-match acceptance.
Analytics That Answers
"Why Did Yield Drop?"
Joined data, proper time alignment, and SPC literacy — so the plant manager can act on Monday instead of asking for another report.