We talk quite a bit about the importance of planning and scheduling optimization. We know the business value, we know what constitutes best-in-class optimization, and we know that optimization is a lot more that auto-scheduling and recommending a couple parts.
True best-in-class optimization automates repeatable tasks, provide real-time resource adjustments that are scalable to meet the number of technicians in a firm (whatever that number might be), and can provide planning insights for a day, a season, a year, or whatever unit of measurement that your business is in need of.
An automation system, at its best, actually automates activities. And like any AI-powered system, you can’t just provide inputs without matching criteria for how to catalog, rank, and execute those inputs into practical outputs.
So it’s necessary, then, to build a set of criteria that moderates your service system to prioritize outcomes, which will ultimately be passed on to your customers. I’ve written about this previously as a function of AI-based learning. Let’s outline some criteria areas that best-in-class systems can employ in this capacity:
As we frequently discuss, building outcomes-based service scenarios has become an imperative for many service providers. Planning and Scheduling systems are an important (and often overlooked) piece of the outcomes-based service mix. So for that reason, you can build outcomes into the systems to help prioritize jobs to meet SLA expectations. Here are a few examples of criteria:
- Time from ticket-to-invoice
- Downtime expectations
- Dispatch expectations
- Asset value expectations
The list goes on and on, but, as an example, if you promise 2-hour resolution time for a specific asset for a customer (like our friends at Scientific Games), when a ticket is raised, it needs to be appropriately scheduled and prioritized to meet those SLA expectations.
These tend to be what people think about when considering planning and scheduling tools, but as you can see, they’re only one piece of the puzzle. Here are some considerations:
- Cost per truck roll
- Technician schedules
- Time per job based on criteria
And so on. The name of the game here is to build a list that offers businesses the ability to derive the most value out of a day’s worth of technicians. And something to note is that might not be the highest quantity of completed jobs in a day, and, when the criteria is established, you might end up being surprised by what the optimization tells you is the right thing to do.
Finally, and most compellingly, is the ides of using optimization as a launchpad for sustainability. Yes, I wrote about this not long ago, but let’s outline some of the criteria that best-in-class optimization systems can work off of when building and stress-testing schedules:
- Drive time
- Fuel consumption
- Trucks on the road
And so on. And yes, these naturally overlap with what’s going on in the world of operational criteria, but as I said in the article I linked to above, there’s an added value, and added imperative for businesses to focus on it. It might just be what drives a customer to choose you over the competition.