How a West Midlands manufacturer stopped losing three hours every Monday morning — and found a quality problem hiding in their own data.
When a client like 7-Eleven needed a software upgrade or hardware install across all US stores, work orders had to be created individually. A single national rollout meant thousands of manual entries — each one taking minutes, each one prone to error. With no bulk creation tool, large projects were simply not operationally feasible.
SAP Business One held production and inventory data. A shared drive held quality tracking sheets. Finance kept a manually updated spreadsheet. Nobody had all three in one view at the same time.
The Monday 10am management meeting couldn't start properly until the first two hours were spent answering: what are the actual numbers this week? That's two hours of your plant manager, production supervisor, and finance lead sitting in a room reconciling files.
The first thing we do on any data project is spend time with the actual data before agreeing to a scope. In this case, that meant two days on-site in the West Midlands — one day with the production team, one day with finance.
SAP Business One had solid transaction data going back several years. But the way it was being used meant that several key fields were inconsistently populated. Some production orders had completion timestamps. Some didn't. The quality tracking sheet, maintained on a shared drive, had been through three different formats in four years and had columns that meant different things depending on when the row was entered.
We came back with an honest assessment: a clean Power BI dashboard was achievable, but the data quality issues in SAP needed to be addressed first — otherwise we'd be visualising unreliable numbers more attractively. That conversation took some back and forth. Nobody likes being told their data is messy. But they agreed, and the project was better for it.
Before opening Power BI, we spent the first three weeks on the data pipeline. We built an Azure SQL staging database that pulled from SAP via the Business One Service Layer API, ingested quality sheet data on a schedule, and replaced the manually maintained Friday finance update with a Power Automate flow.
Once the pipeline was stable, we built four dashboards:
We ran the Power BI dashboards in parallel with the existing Excel process for two weeks. Every Monday the team used both and flagged any discrepancies. By the end of week 8, the management team was confident enough to run the Monday meeting from the dashboard alone.
We spent a full day on-site for handover — training the plant manager and SAP administrator on how to modify reports, refresh data manually if the scheduled refresh failed, and add new product types to the tracking.
Not because people were rushing — because the questions that used to take the first hour were already answered before anyone sat down.
The quality dashboard surfaced something the team hadn't seen clearly before: a specific rejection reason — dimensional tolerance failures on a particular product family — had been increasing consistently month on month. It had been in the data all along, buried across different weekly sheets. Visible in a single chart, it was actionable. The production team traced it to a tool wear pattern on Line 2 and corrected it within three weeks of the dashboard going live.