The Opportunity
- A transportation service company provides maintenance services to very large fleets. They needed to project future maintenance requirements to aid in financial planning, among other operational issues.
Our Approach
- We implemented automated forecasting systems to project the number of repairs expected for each maintenance facility, by month. The models produced these estimates at the job code level, which translates to an associated number of labor hours and bill of materials.
The Impact
- With statistical models, rather than human inputs relied upon previously, greater accuracy was attained, resulting in better utilization of labor and parts resources.
- From a process standpoint, planning time was reduced and mistakes eliminated by moving away from a spreadsheet-driven process.