Fuel Surcharge Impact Model | Deep Analytics & Insights LLC

Logistics & Transportation · Case Study

Fuel Surcharge Impact Model

An interactive Power BI planning tool that turns fuel surcharge exposure from a budget surprise into a negotiable lever.

Surcharge overruns are discovered after the fact.

Fuel prices have risen faster than anticipated, driving up freight surcharges and putting logistics budgets under significant pressure. For most organizations, surcharge costs are tracked reactively, and finance teams only discover the overrun after it has occurred, leaving little room to correct course.

Forward visibility, before the overrun happens.

An interactive Power BI dashboard that gives logistics and procurement teams forward visibility into surcharge exposure before the overrun happens. The tool answers three core business questions:

  • How are we tracking against budget year to date?
  • What does the full year forecast look like if nothing changes?
  • What freight or surcharge rate adjustments would bring the forecast back within budget?

Users enter their annual logistics budget and surcharge table parameters. The dashboard instantly surfaces a year to date actuals estimate alongside a forward projection. When the forecast signals an overrun, a what if negotiation module built into the report lets users make small adjustments to freight rates and surcharge tiers to model their way back under budget, turning a risk signal into a concrete negotiation target.

Test it yourself.

This is the live, interactive Power BI report. Enter a budget, adjust the surcharge parameters, and use the what if module to model your way back under budget. Best experienced on a desktop screen.

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Built independently, end to end.

Built independently as a freelance consulting engagement, end to end:

  • Power BI: interactive dashboards, KPI cards, and scenario modeling visuals
  • Power Query: data shaping, surcharge table logic, and budget variance calculations
  • SQL: sourcing and structuring EIA actuals and forecast data feeds
Power BI Power Query SQL EIA Data Logistics Forecasting

Historical fuel prices: EIA monthly U.S. average retail gasoline prices, available at eia.gov/petroleum/gasdiesel

Forecast fuel prices: EIA Short-Term Energy Outlook (STEO), monthly U.S. average gasoline price forecast, available at eia.gov/outlooks/steo

Forecasts are based on EIA Regular Grade Gasoline, as no forecast specific to fuel grade is published by EIA. Analysis covers 2026 data only; U.S. national averages only.

A number, not a feeling.

Surcharge exposure is often treated as uncontrollable, a market variable that companies simply absorb. This tool reframes it as a negotiable lever. By quantifying the gap between current trajectory and budget, and modeling the precise rate adjustments needed to close it, logistics teams walk into carrier negotiations with a number, not a feeling.

The project reflects my approach to analytics: understanding the business pressure, building something actionable, and making the path from data to decision as short as possible.

Facing a similar challenge? Let's talk.

Whether it's surcharge exposure, spend visibility, or another analytics problem, I'd love to hear what your team is working through.

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