Power BI shows ₦262.53M revenue

A five-page Power BI dashboard posted by data analyst Idowu Akorede Emmanuel offers a compact example of how manufacturing and logistics teams can put commercial and operating metrics on the same screen. The project, shared through Emmanuel’s public portfolio and referenced in a May 22 social post, tracks a trucking operation with revenue, profit, delivery performance and safety measures in one model. The headline figures are ₦262.53 million in revenue, 61% profit margin, 56% on-time delivery and 170 incidents. The setup matters because the dashboard was built on a 14-table logistics database using a star-schema approach, which Microsoft says is the recommended model design for Power BI semantic models. ### Why do those four numbers belong together? ₦262.53 million in revenue and 61% profit margin tell only part of the operating story. Emmanuel’s dashboard also puts a 56% on-time delivery rate and 170 safety incidents beside the financial totals, linking commercial output to service execution and operating risk. That makes the report more than a sales snapshot: it shows whether the business is earning revenue while meeting delivery commitments and controlling field conditions. (datawithakorede.github.io) A logistics dashboard that separates those measures can miss the trade-offs. A month with strong revenue can still mask late deliveries, costly rerouting or incident-heavy operations. By placing the measures together, the design supports the kind of management review where finance, operations and transport teams are looking at the same facts. That use case is consistent with how Power BI is often deployed in supply-chain reporting, where service, cost and throughput need to be read together rather than in isolation. (datawithakorede.github.io) ### What does the 14-table star schema do underneath the report? Microsoft Learn says star schema organizes data around fact tables that store events or observations and dimension tables that describe the business entities around them, such as dates, products, places or people. In practice, that means a logistics report can calculate revenue, trips, incidents or margins from central transaction tables while slicing those measures by route, driver, customer, plant, time period or region. (datawithakorede.github.io) The model choice is not cosmetic. Microsoft says star schema is a mature modeling approach widely adopted by relational data warehouses and relevant to Power BI because it improves performance and usability. For a dashboard with multiple KPI pages and drill-down views, that structure helps keep calculations stable and makes it easier to define reusable DAX measures instead of rebuilding logic visual by visual. (learn.microsoft.com) ### Why would an executive care about on-time delivery next to profit margin? A 56% on-time delivery rate changes how a reader interprets a 61% profit margin. If service is weak, margin may not be durable: expedited freight, customer penalties, lost repeat orders or production disruption can show up later. The same applies to the 170 safety incidents, which can point to insurance exposure, downtime, compliance costs or driver-management issues even when current-period revenue looks strong. (learn.microsoft.com) That is why logistics and manufacturing teams increasingly build “single-pane” dashboards that combine TMS, ERP and operational data. Industry guidance on Power BI for logistics describes the goal as turning large operational data streams into actionable decisions, especially around delay reduction, cost control and service visibility. Emmanuel’s example follows that pattern, but with an executive-facing KPI layer that is easy to scan first. (datawithakorede.github.io) ### What makes this a useful Power BI example rather than just a portfolio piece? The public portfolio entry describes the project as an end-to-end logistics dashboard covering operational, financial and safety metrics across a trucking operation. That gives the example practical value for analysts because it shows how to frame a report around business questions — revenue quality, delivery reliability and operating risk — instead of around source tables alone. (epcgroup.net) Microsoft’s modeling guidance provides the technical backdrop for that design. The company says dimension tables describe entities and fact tables store observations or events, with numeric measures sitting in the fact layer. That is the structure analysts typically need when they want one report to support both headline KPIs and deeper driver analysis. A next step for readers is to compare Emmanuel’s public dashboard example with Microsoft’s star-schema guidance and adapt the pattern to their own route, plant or customer data models. (datawithakorede.github.io) Emmanuel’s portfolio page remains the public reference point for the logistics dashboard, while Microsoft Learn documents the modeling approach behind it. (learn.microsoft.com)