Detecting hidden optimization potential in a field services company

The director of field services was skeptical.

How could there be any optimization potential – hidden or otherwise – when the company already had an impressive utilization rate?

I was in a meeting with the CEO and director of services of a company with a large mobile workforce and an international client roster. Their workforce was both highly skilled and diverse: each of their field service engineers had a specific type of technical expertise for a particular kind of assignment. To add to the complexity, some clients had strong preferences for certain engineers, and many of the tasks could only be assigned to personnel with the right certification.

I didn’t know if there was hidden optimization potential either, so I did what I always do in such circumstances: I listened and asked some questions.

“Please tell me how you plan your operations now.”

“We have a system that supports our planners by highlighting the engineer with the right skills who is nearest the client’s site.”

But what if you have an assignment that can be completed by a junior engineer when the engineer who’s nearest is relatively senior? And what if there’s another task – a little further away – where the skills of a senior engineer are required?”

I pressed on.

“What if you have two senior engineers available and one of them is 200 miles east of the task while the other is 100 miles west. Which one are you going to assign? The one who’s nearest? But suppose there’s also another assignment that’s 200 miles west of the first task. Now the engineer who’s still available has to travel 400 miles to get to the assignment, making the total distance traveled by both engineers 500 miles when it could have been just 300.”

“Well, perhaps our planners catch these things.”

“But in your system, your planners only see the nearest qualified engineer. They’re not just assigning a couple of engineers to a few tasks; they’re dealing with hundreds of engineers and tasks every day. How often do you think your planners miss opportunities like these?

“Can’t say. No idea really.”

Those of you who’ve read my previous posts have probably spotted the ‘coping mechanism’ behind this company’s hidden optimization potential. The question now was, What would happen if we replaced that sub-optimal ‘rule of thumb’ (nearest qualified engineer) and applied intelligent decision support to a representative day?

The results surprised even me:

  • Time lost by field service engineers (for example by waiting and travelling) was reduced by 18%
  • The number of occasions when an over-qualified engineer was assigned to a task dropped by 20%

Making allowances for factors that weren’t captured in the data (such as traffic jams and engineers who took longer breaks because they were feeling unwell), a conservative estimate indicated that at least half those savings were possible.

And here’s the interesting part.

It turned out that the impressive utilization rate that was quoted to me at the beginning of the conversation included the time engineers spent travelling to an assignment.

Yes, they were utilized but not in the service of a paying customer – which, after all, is the only kind of utilization that counts.