A Guide to Managing the Unexpected with Smarter Insights

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How to Optimize Planning with Predictive Field Service

How to optimize planning witH predictive Field Service

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Field service delivery always starts with a plan, but it’s never set in stone. Factors beyond our control often spoil the best of plans. Not to mention, service teams already have lots to keep track of—employees, skill sets, vehicles, equipment, and parts. Once you throw emergency jobs and cancellations into the mix, the difference between great and not-so-great service operations is the ability to manage the unexpected and adjust around same-day disruptions.

Fortunately, with the right data and intelligent technology, it’s possible to forecast disruptions and delays before they happen. We call this predictive field service.

WHAT IS PREDICTIVE FIELD SERVICE? Predictive field service management is the practice of using artificial intelligence (AI), machine learning, and data science to forecast service fluctuations and automatically adjust processes to achieve desired outcomes.

It works by using machine learning to identify patterns in historical data and make crucial predictions. Armed with this technology, service organizations can plan and schedule more accurately, and improve customer satisfaction .

DATA: THE FUEL THAT DRIVES PREDICTIVE FIELD SERVICE In a fantasy novel or movie, it’s magic or an innate “gift” that allows people to see the future. In reality, it’s data (and lots of it) that powers predictions. With machine learning to quickly process vast amounts of data, field service providers can predict the likelihood of a disruption in seconds and start preparing for it.

Before you can get predictive with field service, you need the right data. Here’s what you should be tracking, and how you can use each in your field service strategy:

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Historical Job Data Historical job data is any information that was significant to a service visit completed in the past. This includes the task type and duration, success rate, and parts and tools used. It also means looking at both the successes and failures of a particular job. Here are some things to consider:

• Was it a first-time fix? Or were there repeat visits?

• What kinds of problems came up during the job? (Missing parts, misdiagnosis, etc.)

• What factors were consistent in the most/least successful visits?

This data is key for creating an accurate and optimized schedule. A predictive field service management solution can analyze what worked well in past visits, and what caused problems. For example, you might find that there’s a history of customers misdiagnosing a particular problem and, as a result, technicians showing up with the wrong parts. With this data, the solution could automatically assign multiple parts for the visit, so your engineer is prepared no matter what.

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How to optimize planning witH predictive Field Service

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Field Resource Data Each engineer has their own skills, specialties, and personalities that affect how they perform during a particular task or with a particular customer. When assigning jobs, it’s important to consider everything about the resource. This means everything from their role and experience level, to their skills and past performance history. This also includes:

• How long they’ve been with your organization or in that role

• How many times they’ve completed a particular job

• Customer complaints or praises

With this data, you can ensure you assign the engineer that’s best-suited for a particular job. This person will fix the problem quickly, on the first visit, and make the customer happy. Imagine that a customer’s heating system breaks down in the middle of winter. With an urgent problem like this, you don’t want to send someone who has never worked on a heater before.

Being strategic about who you assign to a job helps your organization maximize efficiency and complete more jobs per day, without exhausting your resources.

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Customer Data Your customers are just as diverse as your engineers, and each must be treated as individuals when scheduling. Track individual customer data, including anything from service history to demographics like age, occupation, and location. It means noting things like:

• Types of service calls the customer made in the past

• Who their technician was and how they rated their experience

• When (and why) they’ve cancelled or rescheduled an appointment

Customer data can be especially useful in predicting the likelihood that a customer will cancel, allowing you to avoid the added costs and productivity loss that comes with cancellations and no shows.

Environmental Data Insight into things like historic traffic and weather patterns can help you plan your schedule far in advance, while cutting the cost of fuel, maximizing productivity, and reducing the risk of missing SLAs.

Traffic It’s no secret that traffic impacts field service delivery. Morning rush hour traffic can make a technician late for an appointment, and put them behind schedule for the rest of the day. Predictive travel uses historical time-of-day and day-of-week traffic data to estimate future travel times, allowing you to plan ahead for it.

An intelligent field service management solution can also determine in advance the most efficient route on a particular day. Finding the route with the least amount of traffic and quickest travel times leads to more job completion, happier customers, and reduced travel costs.

Weather Extreme weather like snowstorms or hurricanes can be detrimental to service delivery. But this weather also means an abundance of emergency jobs that need to be addressed. A predictive field service solution can proactively add more flexibility into the schedule when there’s a higher chance of a storm. This means scheduling the right mix of low and high priority jobs to you can easily reshuffle the schedule to meet emergency jobs and SLA requirements.

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ESSENTIAL TECHNOLOGIES FOR PREDICTIVE FIELD SERVICE While data is the fuel behind predictive field service, technology is what ignites it. Here’s what your organization needs to get smarter and more predictive with service.

Analytics for Fleet, Parts, Customer Satisfaction In order to use any of the subsequent technologies, managers must tap into the field service data around things like scheduling, fleet performance, parts fulfillment, technician experience, and success rate. Once your organization is tracking the appropriate analytics, you can make use of big data, the massive amount of information available to your organization.

Without data and analytics, machines wouldn’t have the basic building blocks necessary for artificial anything, much less assigning the technician with the most relevant experience to a job or scheduling for greater flexibility in anticipation of extreme weather.

Internet of Things (IoT) Instrumental in connecting troves of data, the Internet of Things keeps the devices within your service organization connected. As they collect and exchange data, you can analyze it to streamline issue detection, troubleshooting, and resolution.

With predictive maintenance, you can prevent failures from occurring in the first place, while further increasing efficiencies across the service chain. That’s because the sensors and chips in your equipment account for the current condition of your equipment when scheduling for repair or replacement, only dispatching a technician when performance drops below a certain threshold.

The applications for IoT in field service don’t start or end there. Your equipment can help you uncover parts or models that consistently cause downtime, predict when they’ll need service or how often they’ll fail, and even trigger automatic alerts to speed up response time.

Machine Learning and AI Machine learning allows you to optimize every aspect of service delivery by applying AI algorithms across the service chain to find hidden patterns in your data. When it detects service fluctuations and new opportunities, it adjusts your business processes accordingly.

One of the most common applications for machine learning in field service is in scheduling and dispatch optimization. Machine learning draws conclusions from data to predict job duration, tools and parts needed, the best technician for the job, and the fastest route—quicker and much more accurately than a human ever could.

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PREDICTIVE FIELD SERVICE. AT WORK. As far as examples of predictive field service go, you’ve probably heard a lot about predictive travel and predictive maintenance. But there are several other applications you can take advantage of. Here are a few to keep in mind:

Predictive Job Duration Though you may schedule an hour to complete a repair, odds are the job will take longer or shorter, depending on the job type or skill level of the resource. For instance, replacing a utility meter may average an hour, but a more experienced engineer might be able to finish the job in half the time. This leads to unnecessary idle time when that same resource could be completing twice as many jobs that day.

Predictive job duration calculates the most accurate time it will take for a particular technician to complete a specific job type, based on all relevant job details and resource information. Having this information while scheduling can help you maximize the productivity of your workforce, without over or underutilizing resources. And you can count on precision because you’re relying on real data and not averages.

Predictive Customer Cancellation No matter how well you plan the schedule, it’s always possible that a customer could cancel. With the right data, your scheduling solution can accurately predict the likelihood that a customer will cancel or not be home when their engineer shows up, and then plan around it. This lessens potential impact of a cancellation, which includes wasted resources, unnecessary travel and fuel costs, and productivity loss.

Predictive customer cancellation considers both structured data (weather patterns, time of day, and customer demographics) and unstructured data (dispatcher notes and customer history). With this data, your solution can automatically avoid scheduling certain jobs when a customer is most likely to cancel. Let’s say a meter replacement is scheduled at an office during business hours. The business owner might not have been aware that power needs to be shut off during the replacement, and cancel as soon as the resource arrives and informs him. Predictive technology would consider this factor ahead of time and only offer time slots outside of office hours.

Predictive First-time Fix No customer wants to hear “Sorry, we can’t fix your problem today; you’ll have to make another appointment”— especially if they already cancelled their plans. Of course, sometimes the problem ends up being more complicated then you originally thought, or the assigned engineer didn’t have the right skills to do the job.

With predictive first-time fix, you can calculate the probability that a particular job will be fixed the first time, based on the technician assigned, by factoring in skills and job data. Reducing repeat visits is crucial in increasing customer lifetime value. It validates to your customers that they can always count on you for a quick, seamless solutions to their problems.

CONCLUSION With the power of predictive field service on your side, it’s possible to keep your business running smoothly, despite any obstacles. This intelligent technology can automate and optimize business and scheduling decisions to reduce the efforts of your dispatch team, and free them up for more important tasks. It can also help you plan more accurately in advance, minimizing the impact of disruptions on the day of service. As long as you consistently track relevant data and take advantage machine learning technology, you can meet customer expectations without overspending or sacrificing your business goals.

For more information contact us at www.clicksoftware.com.

You can assign each available technician a first-time fix rate, and the AI-driven solution can automate assignments in a way that maintains customer satisfaction and decreases the risk of missing an SLA. For instance, the solution can automatically assign technicians with the highest first-time fix rates to higher priority jobs, such as for VIP clients.

Predictive Parts Management No matter how skilled a technician is, without the right parts or tools, a first-time fix is impossible. Organizations need to track parts data to ensure that resources are always fully prepared before heading to a job site.

Sometimes a customer might describe a problem in one way, and it turns out to be a completely different issue when the engineer arrives onsite. The engineer might have the parts to fix the problem described, but not the actual problem, and have to come back another time. This is neither the customer’s nor the resource’s fault—customers might not have the insight to diagnose a problem, just as the resource can’t help that the problem was misdiagnosed.

But by tracking both parts data and job history, predictive technology can ensure you are prepared for anything. If there’s a history of customers calling in with a particular problem and misdiagnosing it, the solution recognizes this and ensures that the engineer has the right tools either way.