How Power BI Cut Production Reporting From 3 Days to 15 Minutes Across 14 Plants?
Microsoft Fabric + Power BI | Centralized Data Models | Real-Time Operational Dashboards
At a Glance
| Industry | Industrial Manufacturing |
| Service | Microsoft Fabric Consulting · Power BI Consulting · Data Engineering |
| Challenge | 120+ Excel-based reports across 14 plants with no centralized model, causing 3-day reporting cycles and inconsistent production data |
| Solution | Microsoft Fabric and Power BI analytics platform with centralized semantic models and plant-level operational dashboards |
| Key Result | Reporting time reduced from 3 days to 15 minutes · Real-time operational visibility across all 14 plants |
About the Client
Our client is a large international manufacturer operating through 14 plants in several geographies around the world. Thousands of data points are generated by them every day, including production outputs, machine performance, defective units, downtime incidents, shift performance, and many others. Getting timely insights into these data points was not just important for them. It was vital.
The situation they presented to us was that none of this data was integrated and automated. All plants used their own Excel sheets. More than 120 sheets in total.
The Challenge
The current reporting environment was not built; it evolved from a series of ad hoc changes. Each factory developed its templates, processes for data extraction, and its own formulas. These processes were unique to every single plant and known only by a couple of employees.
Consolidation of these data for monthly group-level reports consumed three days each month. As a result, managers got outdated information by the time it was ready for decision-making. There were four major issues affecting them:
- Reporting was always late: Only after three days following the end of a reporting cycle did managers get visibility into their operations based on stale data that no longer depicted reality.
- The same metrics meant different things in different plants: Metrics like OEE, downtime rate, or yield were calculated using unique methods that differed from factory to factory.
- Nobody had a live view of anything: Shift supervisors found out about production issues at the end of the day or the end of the week. There was no way to catch a problem while there was still time to do something about it.
- Analysts were buried in data prep: Analysts' productivity was extremely low since roughly 70% of each analyst's daily working time was spent on data extraction and preparation rather than analysis.
And with two more plants planned within 18 months, none of this had a path to scale. Adding a 15th plant meant adding another Excel file and another manual consolidation step.
What We Built?
An entirely new Microsoft Fabric + Power BI solution that replaces the previous, Excel-based reporting environment.
The Architecture
The solution leverages the Lakehouse architecture of Microsoft Fabric. Data goes from source systems at the plant level (MES, SCADA, ERP) through a bronze-silver-gold pipeline into certified Power BI semantic models.
OneLake serves as the sole data storage. Data from all 14 plants is ingested into OneLake using Azure Data Factory.
One Semantic Model. One Source of Truth.
One semantic model has been developed to cover all 14 plants. Plant management sees the relevant data based on Row Level Security rules. Senior management sees it all.
Each metric (OEE, downtime percentage, yield, first-pass quality ) is now represented by a certified DAX calculation in the semantic model. No more disputes caused by Plant A calculating OEE differently from Plant B. Now all reports, no matter where, refer to the same definition.
Three Tiers of Dashboards
- Shift-level dashboards update every 15 minutes. Supervisors get an immediate view of output rate, machine availability and quality during a shift, not the next morning.
- Plant-level dashboards offer daily or weekly views of performance with drill-through to specific production lines and comparison of shifts.
- The group executive dashboard consolidates the overall performance view of all 14 plants. Trend analysis, plant benchmarking, and plant-level exceptions are all covered.
The Cutover
We didn't switch off the old system overnight. For four weeks, the new Microsoft Fabric and Power BI solution ran in parallel with the existing Excel reports. Analysts at each plant compared results and found any discrepancies, which invariably stemmed from the formula inconsistency in the old Excel files.
How We Delivered It?
- Discovery and data audit:
Interviewing stakeholders consisting of group analytics leadership, plant managers, and analysts. Inventory all 120+ Excel reports, their data sources, and calculation logic. Identify the 15 critical KPIs that need to be defined at the group level. - Architecture and semantic model design:
Design of Fabric Lakehouse architecture, OneLake storage approach, pipeline designs for each plant's source system, and semantic modeling including metric standardization and RLS implementation. - Dashboard development and pilot:
Dashboard development at shift, plant, and executive levels. Two plants' pilots run with dashboard testing. Analytic training with feedback incorporation. - Full rollout and parallel run:
Rollout to another 12 plants. Four-week parallel run comparing results against Excel reports. Resolving differences and refining models based on results. - Handover and knowledge transfer:
Documentation of the platform and analyst runbook, training for the group BI team. Retirement of legacy Excel process post-approval by plant managers.
The Results
| Metric | Before | After |
|---|---|---|
| Reporting cycle time | 3 days | 15 minutes |
| Data freshness | End of day / end of week | 15-minute refresh |
| Metric consistency | 14 local definitions | Single standardized model |
| Analyst time on data prep | ~70% of working time | ~20% of working time |
| Plants on platform | 0 | 14 |
| New plant onboarding | No viable path | Under 2 weeks |
What Made the Difference?
Standardize before you visualize.
Lack of standardization would render the dashboards no less suspect than the Excel reports. By defining each KPI in the semantic layer first, the issue could be solved upfront, without having to create even one dashboard.
Parallel runs build real adoption.
It is unrealistic to ask plant analysts to adopt a new system immediately. Four weeks running both systems in parallel ensures that any implementation receives more than just approval – it earns acceptance.
Governance is what makes scale possible.
With one standardized semantic layer with RLS in place, onboarding a new plant involves simply connecting to the data and adding an RLS row. No template development, no consolidation step required, nor yet more analyst time buried in spreadsheets.
Real-time changes how people work.
Switching from reports to dashboards in 15-minute intervals does not just save time. It changes the actions that supervisors can take. Issues that might emerge in the report are now identified during the shift.
Technology Stack
- - Microsoft Fabric (Lakehouse, OneLake, Fabric Pipelines)
- - Power BI (Semantic Models, Power BI Service, Row-Level Security)
- - Azure Data Factory
- - DAX (Certified measure development)
- - Source systems: MES, SCADA, ERP (plant-level)
We also work on .NET, .NET Core, and Azure Synapse. Explore these technologies to see how we can support your full data and application ecosystem.
Working on a Manufacturing Analytics Programme?
Whether you are consolidating plant reporting, building real-time operational dashboards, or preparing for Microsoft Fabric adoption, Aegis Softtech brings data engineering, semantic modeling, and manufacturing domain expertise to deliver results.
*Client identity is confidential. Project details verified through internal delivery records. Reference available on request.*