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Praval Technologies

Case study

By the time the report landed, the month was already lost.

A manufacturer asked for real-time production and supply-chain dashboards. The gap was never the dashboard: every decision waited for month-end, so by the time a report showed downtime creeping or a supplier slipping, the loss was already banked.

Faster reporting cycle
~90%Faster reporting cycleFrom a two-to-three-week month-end wait to same-day, live figures (representative)
Less unplanned downtime
~15%Less unplanned downtimeThe top recurring loss causes surfaced to act on (representative)
Decisions, not monthly
DailyDecisions, not monthlyLeaders act on live signals every shift, not twelve times a year

What they asked for

"Build us real-time production and supply-chain dashboards. We're flying blind between monthly reports."

They didn't have a reporting problem. They had a timing problem. A dashboard nobody acts on in time is just a faster monthly report.

The challenge

The plant produced, the numbers were compiled, and two to three weeks after the month closed, leadership finally saw how it had gone. Output against plan, OEE, downtime and supplier performance were all accurate, and all history. By the time a report showed a line running slow, a machine failing intermittently or a supplier slipping, the cost had been incurred and the month was gone.

Decisions ran on a monthly clock. Output, downtime and OEE surfaced weeks after the events, so every corrective action was a post-mortem rather than an intervention.

The data was scattered and un-modelled. Machine logs, MES, ERP and supplier records sat in separate systems. Each report was a manual reconciliation that took days, and nobody fully trusted it.

Supplier risk was invisible until it hit the line. A late or defective delivery only showed once production had already stopped, far too late to switch or expedite.

What we built

We didn't treat this as a dashboard project. We treated it as a decision-speed project.

One source of truth. Machine, MES, ERP and supplier data modelled in Microsoft Fabric: one governed semantic model instead of a dozen conflicting spreadsheets, so Operations and Finance trust the same OEE and downtime figures.

OEE and downtime, live. Power BI dashboards for real-time production monitoring: OEE (availability × performance × quality) and downtime by cause, with drill-down from plant to a single machine in seconds.

Supplier risk, early. A supply-chain visibility layer flags late deliveries, quality slips and at-risk suppliers before they stop the line, so the response is a switch or an expedite, not a shutdown.

We built the analytics and decision layer, not the sensors or the MES. Fabric and Power BI sit on top of the plant's existing machine, ERP and MES systems, integrated with the systems of record rather than replacing them. Built on Microsoft Fabric, OneLake, Power BI and DirectQuery.

How we rolled it out

A live dashboard only matters if it changes what people do. We piloted on one production line and one supplier group, and proved that live OEE and downtime signals changed decisions during the shift rather than after it: a recurring changeover loss that had been invisible in the monthly pack was surfaced and fixed within a fortnight. Then we scaled, with operations leaders and plant managers in the build, so the boards answered the questions they actually ask.

  • Reporting: two to three weeks → same day.
  • OEE visibility: monthly → live.
  • Supplier risk: reactive → flagged ahead.

The win wasn't a dashboard. It was catching the loss while there was still a month left to fix it.

An illustrative engagement. The scenario and figures are representative, drawn from outcomes across comparable Microsoft Fabric and Power BI deployments, not the audited results of a single named client.

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