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Phase 01 · Data platform
Phase 02 | Power BI · DAX · Databricks

FreightFlow — Operations & Commercial Analytics

FreightFlow brings delivery reliability, revenue and fuel spending into one interactive Power BI report. The report draws on the data platform built in Phase 01.

4 Report Pages + TooltipDatabricksDAX MeasuresPBIR / TMDLOn-Time Delivery Rate

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Behind the report

01 ·Business Context

Freight operators need to compare delivery failures with commercial performance and identify facilities or accounts for further investigation.

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  • The published view reports 21,557 loads and a 33.6% On-Time Delivery Rate for April–December 2024.
  • A network-wide view leads to facility, customer and fuel-purchasing detail.
  • The pages are organized for executive monitoring, operational review and account analysis.

02 ·Analytical Scope

Four report pages separate network health from delivery reliability, commercial performance and fuel purchasing.

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  • Overview pairs load volume and On-Time Delivery Rate with revenue and fuel spending.
  • Reliability tracks delivery breaches, detention and facility patterns; Facility Risk adds contextual detail in a tooltip.
  • Commercial shows customer revenue concentration, segments and route-corridor yield.
  • Fuel compares spending, gallons and unit price over time, by state and by vehicle.

03 ·Technical Decisions

The report uses the Databricks reporting layer built in Phase 01, with a Power BI semantic model and reusable DAX measures.

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  • Loads, delivery events and fuel purchases are modeled at distinct fact grains.
  • A shared measures table organizes reusable DAX calculations used across report pages.
  • PBIR and TMDL make report and model definitions reviewable in version control.
  • Date and time-view controls support period comparisons.

04 ·UX & Design Choices

Each page has one main line of investigation, while shared controls let readers carry context between views.

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  • The sidebar moves from Overview to Reliability, Commercial and Fuel.
  • Date, time view, load type, booking type, facility state and customer filters are grouped in one place, with a reset control.
  • KPI cards show current values, comparisons and compact trend lines.
  • Facility and customer views offer a path from network totals to a specific exception or account.

05 ·Deliverable & Evidence

The published report can be explored directly on this page or opened in Power BI.

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  • The default published view covers April–December 2024; filters and time selection change the displayed figures.
  • On-Time Delivery Rate measures punctuality, not On-Time In-Full delivery.
  • The underlying Phase 01 case study documents the Databricks, dbt and Airflow data pipeline.
  • The Phase 03 AI agent has been built; its portfolio page has not yet been published.

Project continuity

Phase 01: Data Platform (Databricks, dbt, Airflow) → Phase 02: Power BI → Phase 03: AI Agent (built; portfolio page pending).

Phase 01

Data Platform

Databricks · dbt Core · Airflow lakehouse architecture.

Phase 02Current

Power BI Reporting

Interactive executive analytics and operational views.

Phase 03Built

AI Agent

Portfolio page pending.