Manufacturing Analytics FAQs

By connecting MES, SCADA and ERP into one model, analytics shows OEE, downtime cause, scrap and bottlenecks at the point of production rather than at month-end. Efficiency improves when a supervisor sees the loss the same shift and can act — the value is the decision moving earlier, not the dashboard itself.

Power BI serves OEE, output, downtime and quality KPIs from a governed model, refreshed every few minutes through Fabric pipelines. On a shared shop-floor screen that means the current shift's performance, not yesterday's. Real-time in manufacturing is usually a 15-minute requirement — cheaper to run and enough to act on.

OEE improves when availability, performance and quality are calculated once from machine-state and production data with the denominator visible — not gamed to hit 85%. Downtime is coded by reason and attributed to line, shift and cause, so improvement targets the real loss. Publishing OEE does not make it honest; publishing it with its denominator does.

ERP orders, MES execution and IoT machine signals land in OneLake and are conformed to a common work-order and machine grain. The hard part is mapping machine identifiers to ERP work centres — without that, OEE by product is impossible however much telemetry you collect. That mapping needs a named owner, not a project.

The core set is OEE and its three components, downtime by reason, scrap and first-pass yield, output versus plan, and schedule adherence. Each defined once with numerator, denominator and time base written down — the definition work, not the visuals, is what stops three departments quoting three different OEE numbers.

Models trained on vibration, runtime and failure history can flag a developing fault 48–72 hours ahead, turning an unplanned stop into a planned intervention. It works where the sensor and maintenance history exist and someone owns acting on the alert; predictive maintenance on a broken data foundation just produces confident false alarms.

A governed lakehouse conforms ERP, MES, quality and maintenance data into one model with agreed definitions, so production, quality and finance read the same numbers. The single source of truth is a governance achievement — one definition with an owner — not a piece of software you install.

Fabric lands ERP, MES, historian and quality data into OneLake, models work orders, batches and downtime at a common grain, and serves Power BI through Direct Lake. It is the analytical layer beneath your plant systems — it does not replace the MES or scheduler, and expecting it to is the most common source of disappointment.

We migrate one subject area at a time — the strangler pattern — keeping the legacy stack authoritative until each workload is proven on Fabric, so finance keeps closing on numbers it trusts. A big-bang rebuild usually stalls in month four; a slice-by-slice migration earns permission to continue every few weeks.

Cost tracks estate complexity, not company size — source count, whether sources are accessible or gated, plant and entity count. A first working slice can start with a Fabric Starter Sprint from USD 1,500; a full platform is a larger, scoped engagement. We price against your actual sources rather than quote a headline figure before seeing them.

A first production slice — one production-order fact replacing a manual report — is realistic in about six weeks where ERP data is reachable and work-centre identifiers are mappable. Adding downtime and quality typically takes a further 6–8 weeks. Widening OT ingestion across a plant usually takes longer, because of network and gateway approvals.

Typically ERP (SAP, Dynamics, Epicor, NetSuite), MES, SCADA or historian, a quality system, a CMMS, and the spreadsheets between them. OPC-UA and MQTT telemetry reach Fabric through an edge gateway. The inventory is easy; the value is conforming their different grains and time bases into one model.

Data quality improves with declared checks at each layer boundary, an immutable raw layer so bad loads are replayable, and a named steward for master data like item and work-centre codes. Quality is a running process, not a one-off cleanse — a plant that codes materials differently will keep doing so until someone owns the master.

With production, machine and quality data unified, AI can surface recurring bottlenecks, anomaly patterns and quality drift that manual review misses. The models are only as good as the data foundation beneath them; start by unifying and defining the metrics, then layer AI where a decision genuinely turns on it.

MyData Insights is a practitioner-led, Microsoft-native consultancy that integrates ERP, MES, SCADA and IoT data for manufacturers across India, the GCC, Singapore, the UK and North America — on a fixed-scope or Fractional Data Consultant basis, with first value in six weeks. We start with an honest assessment before recommending what to build.