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How hidden assumptions affect Field Service operations (Part 1)

How hidden assumptions affect Field Service operations (Part 1)

How hidden assumptions affect Field Service operations (Part 1)

November 9, 2011 ClickSoftware 0 Comments

If you are in any way involved with field service operations, here’s an exercise that never failsto surprise:

Step 1: Describe a scenario that requires a scheduling decision.

Example: You have two available engineers for four jobs which you have committed to perform today. The only way you can get all jobs done in time is to assign two jobs to each engineer. Unfortunately, three of the jobs require skills that only the first engineer has. What should do?

A. Assign two jobs to each engineer, knowing that one of the jobs is assigned to an engineer lacking the required skills. This way you respond to all jobs within the time window you committed to, but it is likely that one job that will take far longer than expected, or will require an additional appointment to complete (which will increase costs and reduce customer satisfaction).

B. Reschedule one of the jobs for tomorrow. Find other work for the engineer who got just one out of the four jobs we started with. Customer satisfaction will be lower, and the KPI (Key Performance Indicator) for meeting time commitments will go down, but you are assured of having the right skills for the jobs you did schedule.

C. Ask the engineer who has the required skills for the three jobs to work overtime and deliver all three. You’ll miss the time window for one of the jobs, albeit not by much; and you’ll have to pay more for the work done during overtime. Also, the engineer who is asked to work overtime might not appreciate it, potentially harming employee satisfaction.

D. Try to find an engineer from another district to take one of the jobs: All jobs will be performed on time, but at higher travel costs and with lower productivity due to long travel time.

That’s just one example. You may want to look at some more, including planning decisions which avoid the situation described in the example; decisions which highlight the conflict between spending more time at each customer location (raising satisfaction and upsell) vs. completing more jobs per day (see “Can slower service be better service?”); probing the tension between scheduling the engineer preferred by the customer vs. achieving higher productivity; and any other of the many business decisions you face every day.

Step 2: Pick several people, in various roles from your organization– e.g. dispatchers, managers, service engineers – and ask them to indicate what they would do in each situation.

Step 3: Analyze the responses: Are they aligned? If not, is there a pattern? For example, you might find that most of the engineers agree on one answer while most of the dispatchers agree on another; or maybe the consensus in one region differs from the consensus as another.

At ClickSoftware, we regularly perform such fact-finding. We have to: after all, we help organizations configure field service management and optimization software to automate such decisions, so we need to know what the organization wants the software to do. Almost invariably, we find that there is at least some degree of disagreement. If not brought to the surface, such disagreement will cause at least some people to believe the optimization software is flawed, since its decisions differ from what these people would have preferred. However, that’s only a symptom of a deeper problem. When people disagree about what needs to be done in such scenarios, they’re disagreeing about the organization’s values.

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