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Optimizing Staffing Levels for Heavy Maintenance Operations

Industry: Transportation, Logistics and Services

Service: Operations Strategy and Transformation

Service: People and Organization

Parallaxe

Our Client

A large metropolitan rail operator’s heavy maintenance facility in Chicago sought approval to increase staffing to support a new weekly target of 30 passenger car inspections, up from a historical average of 23.

Parallaxe

The Challenge

Leadership requested an independent assessment to determine the minimum staffing required and to identify operational improvement opportunities. Key challenges included:

  • Uncertainty around actual labor needs for increased inspection volume.
  • Inconsistent use of standard labor hours across car types and maintenance tasks.
  • Lack of cleaning labor standards and unclear bad order repair projections.
  • Potential overstaffing in a mixed-craft environment without efficiency validation.

Real Results Achieved Together

EFESO optimized rail maintenance staffing through productivity analysis, reducing workforce by 59% while meeting increased inspection targets.

59%

Reduction in FTE to meet demand requirements

Transformation Impact

  • Recommended reduction in mixed-craft staffing from 136 to 81
Parallaxe

Our Approach

EFESO conducted a detailed productivity analysis to determine optimal staffing levels:

Standard Hour Benchmarking

  • Collected and compared standard labor hours across various car types; validated against foreman estimates.

Craft-Level Workload Modeling

  • Calculated required labor hours by craft for maintaining 30 cars per week.

On-site Process Observations

  • Assessed actual work hours and validated operational flow through time studies.

Adjustment for Unscheduled Work

  • Incorporated bad order repair hours based on historical performance data.

Staffing Optimization

  • Modeled FTE requirements to align workforce levels with forecasted workload.

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Facing Similar Challenges?

  • Conduct detailed productivity analyses to determine optimal staffing levels.
  • Utilize historical performance data to adjust for unscheduled work.
  • Model FTE requirements to align workforce levels with workload.
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