Leanburn Internship 2023

The fuel savings were already inside the engines. Fleets just couldn't see them

Leanburn helps fleet managers find the fuel they're wasting: how far each fleet is from its potential fuel economy, which engine settings would close the gap, and whether anyone is acting on it. It's early work from my internship, kept in its original style.

Role
UI/UX Design Intern
Timeline
Jul 2023 to May 2024
Team
Who you worked with
Skills
Dashboard design, Data visualisation, Charts, Design handoff

My role

So my job, as an intern, was to put the savings first, where a fleet manager would see them.

I designed the KPI dashboards and charts: the overview, the fleet table, feature adoption and the fleet drill-down.

Problem
The engine settings that save fuel existed but were rarely switched on, and the data sat in spreadsheets.
What I did
Designed dashboards that lead with gallons saved, rank fleets by the gap to their potential, and show which settings each fleet is missing.
Result
Built so a fleet manager can see where the fuel is going and which setting to change first.

Internship work at The Cloud Company for an engine manufacturer. Names and data are changed.

Problem

Fleets were paying for fuel their own engines could have saved…

Modern engines ship with settings that cut fuel use, like gear-down protection, road speed governors and predictive gear shifting, but they're often left off. Recommendations to turn them on were made in spreadsheets and rarely followed up.

…but the settings that save it were rarely switched on.

Every truck running without them burns fuel it doesn't need to, and nobody could see which fleets had the most to gain.

The overview

Who it’s for

  • Fleet managersFind the fleets and engines with the most fuel to save.
  • EngineersCheck each engine's settings against the suggested and simulated values.
What had to change

From a spreadsheet of recommendations to a list that gets done.

  • Before: Recommendations kept in spreadsheets

    After: Recommendations with a status on each fleet’s page

  • Before: Miles per gallon, as the engine reports it

    After: Gallons saved, which a manager acts on

  • Before: Every fleet looked alike in a table

    After: Fleets ranked by the gap to their potential

  • Before: Follow-ups lost in email threads

    After: Comments that stay with the fleet’s data

Core idea

The opportunity is the gap between what a fleet does and what it could do.

The dashboards rank every fleet by that gap, so the longest bar is also the first job.

The savings gap

Potential economy
Fuel economy with the saving settings switched on.
Current economy
What the engines report today.
The gap
Ranked across fleets, it becomes the to-do list, shown as gallons saved.

Decisions

Three calls shaped the dashboards.

  1. Decision 1: Lead with gallons saved, not miles per gallon

    MPG is what the engine reports, but gallons saved is what a manager acts on. The overview opens with estimated yearly savings, open recommendations and the fleets with the most room to improve.

    Headline KPIs

  2. Decision 2: Current and potential in one chart

    Each fleet's current fuel economy sits against its potential, ranked by the gap. The longest gaps are the best opportunities, so the chart doubles as a to-do list.

    Current against potential, per fleet

  3. Decision 3: Adoption shown against the whole fleet

    Each fuel-saving feature shows its adoption in the filtered fleets next to all fleets, so a manager can see where they lag the average.

    Feature adoption

Solution

A fleet manager can see the opportunity, find the fleet behind it, and drill down to the engines.

Here is the overview, then the fleet drill-down.

End to end

From the overview down to one engine.

  1. 1
    See the opportunityEstimated yearly savings and open recommendations, first.
  2. 2
    Pick the fleetFleets ranked by the gap between current and potential.
  3. 3
    Check the settingsEach fuel-saving feature’s adoption, against all fleets.
  4. 4
    Drill to the engineEngine types and build years, then one row per engine.
  5. 5
    Follow it upComments stay beside the fleet’s data.
The overview dashboard
  1. 1Headline KPIs: estimated yearly savings, open recommendations, fleets with room to improve, latest fuel economy.
  2. 2Current against potential fuel economy, per fleet, ranked by the gap.
  3. 3The fleet table: manager, savings, current and optimal fuel economy, engine count.
  4. 4Feature adoption, filtered fleets against all fleets.
Drill-down

One fleet, down to its engines

Engine types, savings, top recommendations with their status, build years and transmission types, then one row per engine.

Fleet drill-down
Follow-up

Follow-ups stay with the fleet

A comments tab sits beside each fleet's overview, so the follow-up on a recommendation stays with its data instead of in an email thread.

Comments

More screens

From the fleet down to one engine

Every fleet, ranked by savings
One row per engine
A cluster's settings: current, suggested, simulated
One engine against its fleet

Outcome

Built so fuel-saving settings get switched on, not just recommended.

These are the effects the design was built for, not measured results. Next to each is the signal that would show it.

  • Recommendations get followed up

    What would show it: Open recommendations closed each month.

  • More engines run with fuel-saving features on

    What would show it: Feature adoption across fleets, before and after.

  • Fleets move toward their potential

    What would show it: The gap between current and potential fuel economy, per fleet.

Reflection

What did I learn?

This was my first data-heavy product. A dashboard is only as good as the decision it leads to, and the charts that worked best were the ones that ranked something.

What would I change?

Today I'd design the follow-up, not just the insight: who owns a recommendation and what happens after it's made. The dashboards showed the gap; they didn't do enough to close it.

Next case study

Skillset