We help manufacturers turn production, quality, inventory and planning data into reporting people can act on. We connect the data in your ERP, production systems and spreadsheets, build Power BI dashboards on top of it, and automate the repetitive reporting and approval steps around it.
Power BI Power Automate Microsoft Fabric SQL & Data Engineering
OEETarget 80%
Illustrative dashboard with sample data, not client figures.
Problems manufacturers commonly bring to us. Few plants face all of them, but most recognise two or three, and each one shows up in cost, throughput, delivery reliability or quality.
Breakdowns and long changeovers stop lines, reduce throughput and push orders late. When downtime is logged on paper, or not at all, the largest causes stay hidden.
How data helps: Downtime by line, machine and reason, so the biggest losses are tackled first.
Without agreed availability, performance and quality figures, it is hard to say which line is underperforming, or by how much.
How data helps: One agreed OEE definition, shown by line, shift and product.
Scrap and rework consume material, labour and capacity. If defects are only counted at month end, patterns are found too late to act on.
How data helps: Defect, scrap and rework trends by product, line and cause.
Orders sit in the ERP, production counts in an MES or machine logs, and supplier data in spreadsheets, each with its own codes and update cycle.
How data helps: Data engineering that maps codes and joins sources into one model.
Customer forecasts, stock levels and orders change constantly. A large change spotted late means expediting costs, shortages or excess stock.
How data helps: Forecast comparisons and inventory reports that flag large changes early.
Daily production and shift reports are often built by downloading, pasting and reformatting data. It is slow, depends on a few people and invites errors.
How data helps: Scheduled data collection and refreshes, so the report builds itself.
Approvals, report distribution and copying data between systems take time away from planners, supervisors and engineers.
How data helps: Power Automate flows and desktop RPA for the repeatable steps.
Supervisors and managers often see yesterday's or last week's figures when the decision has to be made today.
How data helps: Dashboards refreshed as often as the source systems allow.
Machines and sensors record a great deal of data that is never summarised into something a manager can act on.
How data helps: Shift- and machine-level summaries sent to the people who need them.
Four services we deliver today, applied to manufacturing data analytics. The fifth area depends on the data you have and is scoped case by case.
Manufacturing KPI dashboards on a clean data model, from a plant overview down to a single line or shift.
We connect ERP, MES, SQL databases, machine or sensor exports, Excel files and SharePoint, then clean and map the data into analysis-ready tables. What we can connect depends on whether each system offers exports, APIs or database access.
Power Automate and desktop RPA handle the steps around the data: collecting files, sending reports, routing approvals and alerting the right people.
When data volumes or the number of sources grow, a Fabric lakehouse or warehouse keeps reporting fast and consistent. For smaller estates, a simpler setup is often enough, and we will say so.
Scoped case by case
These use cases are possible when enough reliable history exists, such as several months of machine readings, maintenance records or order data. We do not sell them as ready-made products. We build the data foundation and reporting first, check whether your data can support a dependable model, and tell you plainly if it cannot yet.
Two published projects for manufacturing clients, each written up in detail with the architecture, the data model and the automation behind it. Client names are withheld for confidentiality.
Challenge: vehicle makers sent rolling forecasts in different layouts and with their own part numbers, and planners compared each new version with the last by hand.
Approach: Python standardisation into SharePoint, a Power BI model that compares forecast versions over 30, 90 and 120 days, and Power Automate alert emails for large changes and unmapped part numbers.
Reported outcome: a forecast cycle went from almost five days to less than one day, as reported by the project team.
Power BIPower AutomateSharePointPythonDAX
Read Case StudyChallenge: sensor data on engine time and arm movement was downloaded and processed by hand, and every vendor needed a separate daily report.
Approach: Power Automate collects the data into SharePoint, Power BI calculates engine, idle and productive hours for every shift, and a paginated report emails each vendor and yard a PDF with only its own machines.
Outcome: the daily reporting cycle runs with no manual steps.
Power BIReport BuilderPower AutomateSharePoint
Read Case StudyGeneral guides to the tools used in the projects above. None is written only for manufacturers, but each covers techniques that apply to production and supply-chain reporting.
Power BI is read-only by default. See how to combine Power BI, Power Apps, and Power Automate into a closed-loop, editable dashboard that refreshes itself automatically.
Includes inventory and stock adjustments as a use case.
Learn how Power BI reports, dashboards, paginated reports and alerts help organisations improve profitability, reduce manual work and make faster, data-driven business decisions.
Covers paginated reports, alerts and inventory optimisation.
Learn how Microsoft Power Automate helps businesses automate approvals, email workflows, reporting, SharePoint processes, and repetitive tasks across Microsoft 365.
Covers report automation and approvals, as used in both case studies.
Potential benefits, not guarantees. What you see depends on your data, systems and processes.
Output, OEE and downtime by line and shift in one place, instead of in separate files.
Figures ready at the start of the shift or meeting, rather than days after the period ends.
Drill-through from a plant total to the line, machine or reason behind a loss.
Recurring reports that collect, refresh and distribute themselves, where the source systems allow it.
The same definitions and checks applied every day, so changes stand out early.
Manufacturers use Power BI to report production output against plan, OEE, downtime, quality, inventory and delivery performance, using data from ERP, production systems and spreadsheets. Reports refresh on a schedule and can be shared in Teams, in SharePoint or by email.
Yes, if the underlying data exists. OEE combines availability, performance and quality, so it needs planned and actual run time, downtime, an ideal cycle time or rate, and good and rejected counts. We agree the definition with your team first, because small differences in how downtime is counted change the result.
Usually, yes. Power BI and Microsoft Fabric can read from SQL databases, many ERP and MES systems, files and SharePoint. What we can connect, and how often it can refresh, depends on whether each system provides exports, APIs or database access, so we check this during the assessment.
We scope these case by case. They need enough reliable history, such as machine readings, maintenance records or order data. We build the data foundation and reporting first, then assess whether a forecasting or anomaly detection model would be dependable. If the data is not ready yet, we will say so.
Not always. Many manufacturing reporting needs can be met with Power BI on a SQL database or SharePoint. Fabric helps when data volumes, the number of sources or refresh requirements grow. We recommend the simplest setup that fits.
Tell us about the reports you build by hand, the systems that do not share data or the KPIs you cannot see yet. We will suggest practical next steps, starting with the data you already have.
Microsoft-certified team: PL-300 · DP-600 · DP-700. Serving businesses since 2020 from our India-based team.