How Utilities Can Improve Overall Field Service Performance by Using Schedule Optimization

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Increase Productivity with Schedule Optimization

Increase ProductIvIty wIth schedule oPtImIzatIon

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How Utilities Can Improve Overall Field Service Performance by Using Schedule Optimization Have you ever heard of the “traveling salesman” problem? It goes like this: If a salesman must visit all 48 capital cities in the continental United States and visit each city only once, what path should he take to yield the shortest overall distance?

Interesting problem isn’t it? It’s simple to state of course, but if you spend a little time with the problem, you’ll quickly see what a brainteaser it is. That’s because there are literally millions of unique travel paths the salesperson can take ... but only one optimal path. Many mathematicians have spent a good part of their careers trying to find the answer. But notice that in the traveling salesman problem, only one factor – distance – must be optimized.

Compare that to field scheduling where you have hundreds of factors – travel distance, travel time, overtime costs, labor costs, customer availability and preferences, contractor availability, and others to deal with. So a multidimensional problem such as yours makes the one- dimensional traveling salesman problem look like child’s play. This is why field service organizations at utilities need a sophisticated schedule optimization solution because calculating optimal decisions overwhelms the capabilities of traditional systems.

Let’s walk through a typical schedule optimization problem so you can appreciate the complexity of the problem – and see why traditional systems have trouble keeping up.

First, a scheduling system calculates job assignments in two distinct phases – job matching and optimization. The job-matching phase is the easy part of the problem. It’s here where you’re comparing your technician list against a set of rules. The rules are the hard-and-fast requirements of the job at hand. For instance, you may have a rule that says, “Does the person have the proper certification to install a smart meter?”

That’s a simple decision to make. If a technician is not certified correctly for the job, does not have the required parts or is too far away, you can safely eliminate these people from the pool of techs you assign to similar jobs. This is straightforward “yes” or “no” decision making. It requires no elaborate calculating. But it’s in the second phase – the optimization phase – where the troubles begin because it’s here where you’re no longer dealing with simple rules, but objectives.

Objectives include things such as reducing travel time, lowering labor costs, balancing workloads, and cutting mileage costs. Unfortunately, when you enter the world of objectives, simple “yes” or “no” answers no longer work. Objectives introduce constraints, so your answer is never black or white like with job matching.

In fact, the only way a scheduling system can mathematically handle objectives is to prioritize the business constraints – weighing the importance of one constraint versus another.

For example, say you have five technicians qualified for a smart meter implementation. “Qualified” means they have the right skills, are available, and meet any other “must-have” criteria. Now if “reducing travel times” is the most important factor on your list of objectives, then the system pares the technician pool down to the three who are closest to the job site. If the next most important constraint is “lowering labor costs,” then you select the one technician out of the remaining three who’s worked the least number of hours this month. This is how a scheduling system optimizes through a process of scoring and top-down elimination.

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And if you consider scheduling decisions in four seconds to be a tough requirement, remember that the number of options that must be evaluated for each scheduling decision has factorial growth. In other words, there are 6 possible schedules for 3 jobs and 3 engineers, while there are 720 possible schedules for 6 jobs and 6 engineers, and there are 3,682,800 possible schedules for 10 jobs and 10 engineers. Optimizing the schedule for an organization with 100’s or 1000’s of engineers is far beyond the capability of simplistic scheduling products even those claiming to offer ‘some’ optimization.

Every optimized decision requires you to do an elaborate scoring calculation – to sift through constraints such as overtime, travel time, mileage – and dozens of others. Now, we know that simple job matching will quickly purge most of those 7,000 field technicians from the job pool. But even still, if only 15 technicians are left in the optimization hopper for scoring, that’s still a mind-boggling number of combinations to compute – something like 1.3 trillion. And that’s for only one job!

By now you can sense the problem: How can a traditional scheduling system keep up with a computational load like that? The answer is, it can’t. Without highly sophisticated algorithms filtering through all the possible combinations, you simply can’t do real-time schedule optimization. In short, it’s a scalability issue. Traditional solutions fail to optimize because they can’t scale high enough to solve the problem.

The good news is that it is possible to master optimized scheduling and minimize the impact of unplanned changes. These four steps, mixing technology and process, will help keep service jobs on track—and your customers happy.

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CLICK’S SCHEDULE OPTIMIZATION AT WORK FOR UTILITIES Click has helped utilities across the globe improve their scheduling and operations.

• Average number of preventative maintenance visits increased from approximately 4.5 to 6.5 per day (an increase of nearly 50%).

• 40% productivity improvement

• Jobbundlingandefficientterritoryplanning reduced unnecessary site revisits, reduced customer outages and minimized costs of customer attrition

• 16% decrease in travel time

• 11% increase of wrench time leading to greater productivity

• 20% Increase in technician productivity

• Greater levels of optimization helping to improve performance against competitors

• Ability to complete more work with thesamestaffinglevels

• Improved customer service through realistic, accurate and narrow appointment slots

• Reduction in fuel costs and carbon emissions

• On-site on time arrivals increased by 20%

• Appointment waiting time were reduced by 45%

• Emergency SLA conformity increased by 97%

• Overtime was reduced by 22%

LEARN HOW ENDEAVOR ENERGY IMPROVED PRODUCTIVITY BY 40% WITH CLICKSOFTWARE’S FIELD SERVICE EDGE.

Increase ProductIvIty wIth schedule oPtImIzatIon

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These smart systems can also record disruption data over time to help with future planning. It can track trends, such as weather conditions throughout the year. In the months when there’s a higher probability for snow, the system can schedule more low-priority jobs in case there’s a storm and the tech needs to cancel. It could also look at typical traffic conditions during this weather and account for potential delays.

Finally, schedule elasticity leaves room for high priority and emergency work, without affecting the overall flow of job assignments. The system can fill a technician’s day with a mix of high and low priority work, so there’s room to be flexible. If an emergency comes up, low priority jobs can be rescheduled without upsetting the customer.

HOPE FOR THE BEST, BUT PLAN FOR THE WORST Regardless of how experienced your dispatchers and technicians, or how sophisticated your workforce management software, both human and technology decisionsshould stem from a clearly defined process.

Accept that factors like surprise cancellations, changing weather conditions, and traffic patterns will always remain out of your control, and will inevitably impact field service operations. But having predictive, flexible policies in place will help prepare your team to manage any disruption. For instance, using predictive travel times, especially for the first job of the day, can eliminate repeated delays throughout the day due to traffic. And scheduling technicians with a mix of high and low priority jobs allows for schedule reshuffling.

Remember that the success of both policies and systems hinges on how well you connect your field technicians to the dispatch team. Mobile devices allow techs to check in with the team back in the office. That way if the tech’s vehicle breaks down and must end the day early, the dispatch team can make other arrangements.

Surprises will always be part of field service delivery. Smart technology solutions and even smarter policies can help you mitigate their impact on each technician, job, and customer–and your bottom line. FOR MORE INFORMATION CONTACT US AT WWW.CLICKSOFTWARE.COM.

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WHEN IN DOUBT, OVER-COMMUNICATE The most effective dispatchers, whether aided by technology or their experience, have access to critical information about every job planned for the day. They can combine intelligent automation with their own insights to adjust on the fly.

It’s also expected that the dispatcher and field service technician will be in regular communication, with the most important information for each party being provided as needed. But that visibility isn’t always extended to the customer.

While customer communication preferences vary by country and region, lack of visibility is a common frustration. Being able to share an accurate arrival times, information about the technician, and their progress towards their destination in real time will reduce customer no-shows and last-minute cancellations. In the absence of these Uber-like capabilities, the customer should still receive phone or SMS notifications and see what’s happening.

In addition, showing up for a job unprepared due to insufficient information and requiring a follow up visit will also frustrate a customer who already took time off for the service. Enabling the customer to proactively share information like photos of broken equipment, environmental notes (e.g. don’t knock, the baby is sleeping), and other insights ensures the technician can be successful the first time around.

GET REAL RESULTS FROM ARTIFICIAL INTELLIGENCE (AI) Having the capacity to automate scheduling decisions is a game changer for field service teams. Artificial intelligence-driven technology can use predictive data to reduce idle time, better anticipate travel time, and allow for back up resources to step in quickly when necessary.

For instance, instead of leaving white space in the schedule when a customer cancels, the idle time can be used for other jobs. The system can take the tech’s location into account and send them to another nearby job, without delaying other scheduled work. This makes for a more productive day and satisfied customers.

Increase ProductIvIty wIth schedule oPtImIzatIon

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