Case study

Context-Adjusted Driver Performance Reporting

A fairer way to measure operational performance.

DisclosureThe portfolio version uses fully synthetic employee, route, payroll and performance data to protect confidential information.

A fairer way to measure performance

Raw productivity metrics can be misleading when employees perform different types of work.

This project created a performance-reporting model that combines route activity, payroll data and historical results to compare drivers against similar work rather than a single fleet-wide average.

The model architecture

The model brings together route or load activity, payroll hours and driver identity translation before creating standardized driver-day records.

It then classifies work, matches historical baselines, adjusts for workload context, checks outliers and data quality, and produces period-, work-type- and day-level reporting.

Context-adjusted driver performance reporting architecture showing route and load activity, payroll hours, and driver identity translation flowing through data preparation, classification, baseline matching, workload adjustment, quality checks, metric scoring, and reporting. — The reporting workflow standardizes records, establishes comparable conditions and makes the resulting score traceable.

How a comparable score is built

Historical results establish an expected baseline for each group, while workload characteristics — including delivery size and physical handling requirements — adjust expectations further.

The model reconciles employee records across different systems, separates daily overtime from payroll-driven weekly overtime, handles employees and owner-operators differently where appropriate, and flags abnormal or incomplete records so they do not distort results.

Score-construction diagram showing observed productivity metrics combined with comparable historical baselines and workload context to produce a normalized 100-based result. — Observed performance is measured against an expected result for comparable work, then combined into a normalized score.

A view for team-level reporting

Each driver receives a normalized score centred on 100, representing expected performance for comparable work.

Supervisors can move from a high-level team scorecard into work-type and individual-day detail to understand exactly what contributed to a result.

Synthetic team scorecard ranking drivers by overall score, with 100 marked as the comparable-work baseline. — A team scorecard makes variation visible while retaining a common comparable-work baseline.

Trace a result into its operating context

Supervisors can move from a high-level team scorecard into work-type and individual-day detail to understand exactly what contributed to a result.

The model is designed to distinguish strong performance, normal variation and results that warrant further review.

Synthetic driver-day drill-down chart showing daily scores for an individual driver against a 100-point expected-performance baseline. — Day-level detail gives supervisors a clear path from a score to the underlying operating context.

A more defensible framework

The result is a more transparent and defensible framework for identifying strong performance, normal variation and results that warrant further review.