Here in the U.S., today’s jobs report tells us that the month of May has seen the largest jump in employment in history. This is not particularly surprising (though apparently many economists were surprised), as people are re-hired or released from furlough in response to states gradually easing COVID-19 restrictions. Nevertheless, this surge is further evidence that we’re nearing something that we’ve been considering since this crisis began: Service is going to approach a bottleneck.

We’ve been talking about how planning optimization will help organizations tackle these challenges, and that remains true, but I figured it’d be worth taking that a step further, because this recovery is going to happen in waves over the course of, perhaps, the next two to five years. Manufacturers will come online, traffic will slowly increase, then the capacity of public transit riders will increase, then the capacity of restaurant machinery will increase, then there may be a setback, perhaps another outbreak, then airline traffic might increase, and so on. It’ll be a long process with lots of pumps on the breaks.

For that reason it might seem reasonable to move forward with a day-by-day plan for scheduling: tread water until you have a better idea of what the future holds. That’s fine in theory, but such a binary strategy has potentially catastrophic implications for your ability to meet your customers’ needs.

It really all comes down to capacity. You may not need the hours, parts, vans, and appointment windows that you did in January right now, but unless you have plans for scaling up—and I mean plans, plural—then you’ll be reacting to market changes rather than meeting them. That’s where a PSO system that allows for multi-time horizon planning comes in.

Let’s talk about how multi-time horizon planning actually works. It’s pretty much what it says on the package: Planning and scheduling optimization across multiple time domains. Let’s break it down into a few different categories:

Real-time Daily Planning
This is the most tangible form of optimization—take scheduled appointments and provide the best schedule for the parts and labor available, with the ability to optimize in real-time in order to keep things moving effectively as jobs are cancelled, schedules change, and emergencies arise. This is the day-to-day of scaling up; the baseline of successful service delivery.

Weekly Operational Planning
These functions look at appointment booking, but importantly, also considers scheduling. The best systems have triggers in place for exceptions, and furthermore allow you to set thresholds for commissioning contracted labor, where appropriate.

Monthly Capacity Planning
Looking ahead, planning at this level is when we start to get into hiring and staffing, skill and parts planning, all key functions of best-in-class optimization engines. Leading systems build into this level “what-if” planning. For instance—what if you can only bring back 30% of your workforce? What if travel restrictions limit service deployment? This is when we move from the baseline into true strategic planning.

Long-term Strategic Planning
This, of course, is true planning—looking at models to not only set staffing levels, but also set KPIs and define the terms of outcomes-based service. This is the complete picture of service—not just looking at the historical data from yesterday, but using that data to plan for tomorrow.

In March, it seemed like every day we were playing out a slightly different scenario. Sometimes, based on the available data, those scenarios changed from hour to hour. I think that, fundamentally, we have a much better picture than before about what tomorrow holds, but that’s not to say that the future is written. With smart multi-phased horizon planning, you can plan your own future.

Tom Paquin
Author

Contributor, Future of Field Service