TICO, United States. TICO, Savannah, Georgia. Port dispatch runs on timing — trucks against vessel schedules, drivers against deadlines — but the data that describes it lived in the ERP, the system built to run the operation, not to answer questions about it.
TICO runs port dispatch operations out of Savannah, Georgia — an operation where trucks, vessels, drivers and deadlines all move at once. Three different audiences need to see it clearly: operations managers running the day, dispatch coordinators working the queue, and leadership watching the trend line. Each needs a different altitude over the same operation.
The operational record lives in Acumatica ERP — the system built to run dispatch, not to give a comprehensive picture of it. Real-time awareness and historical analysis are different questions, and an ERP on its own answers neither well: the moment-to-moment state of the operation and the patterns behind driver performance and late deliveries both stayed harder to see than they should be.
We built the TICO Dispatch Analytics dashboard in Microsoft Power BI, with Azure SQL Server as the data layer between Acumatica ERP and the reporting on top. The dashboard delivers both real-time and historical operational intelligence across four domains: truck dispatch activity, vessel scheduling, driver performance and late delivery tracking.
The design carries the workload, not just the data: multi-level filtering and slicers so each role sees the slice of the operation it needs, dual-layer reporting with drill-through and drill-down from the big picture to the detail, and a consistent visual language so every page reads the same way. On top of that sits natural-language Q&A — a question typed in plain English, answered directly from the data, without waiting for a report to be built.
The hard part of a dashboard is never the visuals — it is trust. Real-time awareness is only useful if the numbers agree with the ERP, and drill-down only helps if every layer reconciles with the one above it. That is why the Azure SQL data layer between Acumatica and Power BI was the real work: model the data properly once, and the filters, the drill-throughs and the Q&A all stand on it.
The figures below describe the engagement as delivered — the shape of what was built, not estimates or projections.
The dashboard itself: real-time and historical views across four operational domains, with filters, slicers, drill-through and drill-down — and natural-language Q&A, so a plain-English question gets an answer straight from the data.
The data layer between the ERP and the reporting: operational data staged and modeled so the dashboard is fast, the layers reconcile, and every page draws on the same version of the numbers.
The system of record, where dispatch actually happens. The dashboard reads from it rather than replacing it — Acumatica keeps running the operation while Power BI explains it.
A dashboard is a promise that the numbers are right. The visuals, the slicers and the Q&A all ride on whether that promise holds.
Where it usually gets harder than expected: the data layer. Getting ERP data into a model that supports real-time awareness, historical analysis and record-level drill-down at the same time is the actual project — the visuals are the last twenty percent. Model it properly between the ERP and the reporting tool, and everything above it gets easier.
We sit with the people doing the work and watch what actually happens, including every workaround. Process documents and reality are rarely the same thing.
We prototype and show you your own records in it, not a demo company, before anything is built.
By site, by practice group or by service line — with the group that wants it most going first, to a fixed price.
We report against the numbers agreed at the start, including where the result fell short of the target.
If your operational data lives in the system that runs the business — and the people running it still can't see it clearly — tell us where it hurts most. We will tell you what a dashboard on your own data could realistically show, what we would measure, and whether we think it is worth doing at all.
Describe the situation in your own words.
