an introduction to essential technologies and practical ......4 gartner top 10 strategic technology...

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Demystifying Predictive Field ServiceAn Introduction to Essential Technologies and Practical Applications

an ebook by

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Table of ContentsIntroduction 3

Introducing Predictive Field Service 4What is Predictive Field Service? 5Success Metrics 5The Service Lifecycle 6

Before the Day of Service 7Service Planning 8Predictive Demand Forecasting Model 9Predictive Failure 10Appointment Booking 11Scheduling Complexity 14

The Day of Service 15Travel to Task 16Task Time 17Task Execution 18

After the Day of Service 20Analysis 21Optimize to Goals 22

Predicting Your Field Service Future 24

Key Takeaways 25

Additional Resources 25

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Introduction

© ClickSoftware 2018

Field service management solution vendors have been using the term artificial intelligence loosely for a number of years, but several trends are driving interest and adoption amongst users, along with the ability for vendors to deliver AI and machine learning in a packaged way. In particular, the ever-increasing rate of data generation, falling storage and processing costs, and advances in modeling this data mean that companies have more opportunities to mine their data and extract insights that will provide a competitive advantage.

A Narrative Science survey found 38% of enterprises were using AI in 2016, and that number had grown to 61% by 2017, and continues to increase year over year1. And Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 20162, while IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 20203. Still, many businesses remain bearish on AI.

A 2017 Gartner survey found 59% of organizations are still gathering information to build their AI strategies, while the rest have already made progress in piloting or adopting AI solutions4. For the organizations that have chosen to wait and see how the technology evolves, all is not lost.

Recognizing there is a gap between demand for and supply of data science talent in the market place (According to data collected by Gartner, more than 40% of organizations practicing advanced analytics say “the lack of adequate skills” is a challenge),5 and many companies do not know where to start, software vendors are building applications leveraging machine learning technology to solve specific business problems. For those of you in field service who focus on operations, there is a solution.

What is predictive field service?

TRADITIONAL FIELD SERVICE

PREDICTIVE FIELD SERVICE

HOW IS PREDICTIVE FIELD SERVICE DIFFERENT?

Look for these captions throughout the book to quickly understand how traditional and predictive field service approaches differ.

1 Outlook on Artificial Intelligence in the Enterprise, 2018, https://narrativescience.com/Portals/0/Images/PDFs/OutlookOnAI2018_NarrativeScience.pdf2 https://www.forbes.com/sites/gilpress/2016/11/01/forrester-predicts-investment-in-artificial-intelligence-will-grow-300-in-2017/#6fe2e66655093 https://www.forbes.com/sites/louiscolumbus/2017/06/11/how-artificial-intelligence-is-revolutionizing-enterprise-software-in-2017/#2b24b3ba24634 Gartner Top 10 Strategic Technology Trends for 2018, David W. Cearley, Brian Burke, Samantha Searle, Mike J. Walker, October 3, 20175 Gartner Doing Machine Learning Without Hiring Data Scientists, Alexander Linden, Lisa Kart, Alan D. Duncan, Cindi Howson, June 20,2016

https://narrativescience.com/Portals/0/Images/PDFs/OutlookOnAI2018_NarrativeScience.pdf

https://www.forbes.com/sites/gilpress/2016/11/01/forrester-predicts-investment-in-artificial-intelligence-will-grow-300-in-2017/#6fe2e6665509

https://www.forbes.com/sites/louiscolumbus/2017/06/11/how-artificial-intelligence-is-revolutionizing-enterprise-software-in-2017/#2b24b3ba2463

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IntroducingPredictive Field Service

In this section:What Is Predictive Field Service?

Success Metrics

The Service Lifecycle

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Introducing Predictive Field Service


Predictive field service is the practice of using AI and machine learning technology to anticipate service fluctuations and automatically adjust business processes to achieve desired outcomes.

Let’s consider some of the business KPIs most service businesses strive to improve, and then apply predictive field service to the picture to take these improvements to the next level.

According to The Service Council’s 2017 Field Service Benchmark Study, the top three metrics service organizations are trying to improve are workforce productivity, customer satisfaction, and first-time fix. With current field service management solutions, there are a multitude of ways you can impact these metrics across the service lifecycle:

Productivity: schedule optimization, street-level routing, truth-based appointments, mobility, capacity planning

Customer Satisfaction: narrow appointment windows, real-time status update messaging

First-time fix: augmented reality, knowledge management tools, analytics

These metrics are not new, but when you add predictive field service to the mix, you get an extra boost, or more impactful improvements than once thought possible.

Fig. 1: Field service metrics in focus

Success Metrics

What is Predictive Field Service?

WorkforceProductivity

Customer SatisfactionRate

First-Time FixRate

Service Revenue

Service Cost

Customers UnderService Contract

47%

41%

38%

35%

27%

21%

Source: The Service Council Data June 2017

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Introducing Predictive Field Service


Let’s look at a few examples of artificial intelligence and machine learning applied across the service lifecycle to enable predictive field service:

Before the Day of Service The Day of Service After the Day of Service

Predictive Failure Travel to Job Job Execution

Service Planning Appointment Booking Job time Analysis

Fig. 2: The intersection of the service lifecycle and artificial intelligence

The Service Lifecycle

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Before the Day of Service:Predicting Demand andCapacity

In this section:Service Planning Predictive Demand Forecasting Model Predictive Failure Appointment BookingScheduling Complexity

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Before the Day of Service: Predicting Demand and Capacity


Service PlanningThe foundations of successful customer experience are laid long before the day of service. Organizations need a solid understanding of the likely demands for service, so that they can provide the necessary resources to meet these demands.

Service demand is calculated based on many different inputs, such as seasonality (e.g. HVAC service demands vary greatly by season), customer numbers (new product launches and marketing campaigns may drive service demands in different areas), and product characteristics (what are the expected points of failure? what is the mean time to failure?), to name a few. On the other side of the equation, the capacity to meet this demand is driven by the number of suitably skilled field service technicians, as well as the ability to increase capacity on demand such as deploying contractors.

Ideally of course, the service demands should be fulfilled by the capacity required to meet the service organization’s business objectives, be that customer satisfaction, SLA compliance and/or cost of service. With the number of variables involved in matching capacity to demand, AI and machine learning play a significant role.

For both demand forecasting and capacity planning, artificial intelligence uses a combination of heuristics and predictive techniques to establish which underlying variables have historically been the most important factors in predicting actual demand, and what that means for the future.

Using these techniques, and intuitive user interfaces to visualize the information, organizations can effectively plan the appropriate level of resources, with the right skills to meet demand on the day of service. These plans can be created with enough foresight to avoid paying costly overtime or reaching out to contractors at the last minute and paying higher rates.

Perhaps one outcome of the insights derived from demand forecasting and capacity planning is that the existing territories need to be re-aligned, temporarily or permanently. Machine learning can be applied to geo-plan and best align coverage with demand.


Uses averages from past activities to broadly estimate demand and required capacity.


Machine learning enabled solution crunches mountains of data to provide accurate forecasts that improve over time as new data is added.

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Predictive Demand Forecasting Model

Scheduling &Dispatch

Execution data

Resource Allocation• Allocation

• Capacity limits

Long-term capacity planning• Demand forecast

• Planned capacity

Mid/short-termcapacity planning• Demand forecast

• Planned capacity

• Available capacity

• Used capacity

• Capacity limitation

HistoricalData

Demand Forecasting

1 - 3 yearsDay of service

1 - 3 months3

wee

ks

Fig. 3: Long term and short-term models, fed by forecasts and actual data, ensure supply matches demand

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Predictive Failure In any asset-centric industry, preventive maintenance has historically been part of the job, and is not normally factored into a forecast, but already planned and accounted for in the schedule. Proactively maintaining a piece of equipment extends its lifespan, and is a safeguard against unplanned downtime.

Preventive maintenance considers expected deterioration, age of equipment, anticipated lifespan, and other external factors, such as weather or facility conditions, into an agreed upon schedule for performing the work.

The problem with preventive maintenance is that it’s often inefficient. Because it follows a regular maintenance schedule, it doesn’t take into account actual usage or flag for machine defects. It might mean replacing parts that don’t need to be replaced, or rolling a truck to a highly functioning, underutilized piece of equipment. It’s an inconvenience when one technician has to put effort into an unnecessary fix. But when this happens regularly across multiple tasks, jobs, and technicians, costs begin to add up.

Predictive maintenance takes the actual condition of the equipment into account when determining schedules for repairing or replacing. The condition and performance of the equipment is monitored via many sensors that are connected via the Internet of Things (IoT) to a data processing and analytics tool that monitors and identifies abnormalities in performance. When performance falls below a set threshold, a maintenance request is initiated, and a work order is automatically created for a field resource to respond before the system fails.

The advantage to this approach is that maintenance is more likely to be performed when it is actually needed. This avoids unplanned downtime, and can save the cost of rolling a truck when everything is in great working order.

Predictive failure is the next step in the evolution. It goes beyond reading sensor data and setting thresholds to predict the time of failure to make sure there isn’t a fatal error. Advanced machine learning models are required to identify patterns in equipment performance sensor data to automatically detect and set an appropriate cadence for maintenance.


Accommodates fixed preventive maintenance schedules and assigns jobs to meet a certain SLA.


Prevents service providers from performing unnecessary maintenance, saving time and money.

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Appointment BookingWith capacity in place to meet predicted demand, let’s move to the first customer touchpoint in the service lifecycle: an issue arises and the customer contacts the service organization for an appointment with a field service professional.

In a limited scheduling system, an organization may assign a field service technician who covers the appropriate geographic region and is available to the customer requesting service. Perhaps the technicians have four two-hour windows per day, which are filled up on a first-come, first-served basis.

All this is reasonable and the approach that many service organizations take. However, by using artificial intelligence there is an enormous opportunity to improve the efficiency and effectiveness of the service response, and radically improve customer satisfaction and cost of service at the same time.

In the context of the standard two hour service windows above, consider the following:

» Some tasks will take longer than others » Some field service professionals will be more efficient than others

» Travel time can vary significantly from task to task, but also by time of day

» Customers, whether businesses or consumers want as much precision as possible around their appointment time

» Some customers will have more flexible scheduling preferences

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While allocating a handful of tasks among a handful of workers may not represent a particularly impressive feat, the ability to solve this challenge as the number of tasks and the number of resources increases illustrates the power of AI. When an organization has three technicians to schedule for three work orders, the number of options for assigning the work orders is six.

When six technicians each do six work orders per day the number of scheduling options jumps to around 720,000. When the organization wants to schedule 15 technicians to 15 jobs, the number of permutations becomes astronomical (see figure 4 on page 14). You can imagine the number of scheduling options when the organization has a few thousand technicians. Even when the invalid scheduling options (e.g., attending work orders during non-working hours) are removed, the amount of valid combinations remains unrealistically large, but still a tractable problem for AI.

Using a uniform two-hour window derived from historical averages of service times does not address the realities of the service requests on the day of service. It doesn’t address the vagaries of travel time, the quirks of a particular task, or, importantly, the cost of service delivery. However, artificial intelligence provides a compelling solution to enable higher utilization rates and SLA compliance, more precise appoint-ment windows, and higher customer satisfaction.

In contrast to using averages and pre-determined buckets of time to plan schedules, predictive field service enables the rapid analysis of multiple alternative approaches to meeting a service request, and produces an optimal result for the service organization.

By looking at the specifics of a service appointment in a granular way, especially by looking at travel time and task duration separately, as we will see below, AI and machine learning can produce much more precise appointment windows that are based not on averages but on reality. “Truth-based appointment booking” incorporates predicted travel times and predicted task durations to construct schedules that accommodate customer preferences and allow service organizations to optimize schedules based on their business imperatives.

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An additional aspect of truth-based appointment booking approach is grading appointments identifying which appointment windows, when offered to a customer, will result in the most favorable outcome for the service organization, whether that be, for example, high customer satisfaction, minimal travel time, SLA compliance, or an optimal combination as defined by the company.

Offering truth-based appointments and understanding the implications of all the possible permutations on the service organization’s key performance indicators requires highly sophisticated artificial intelligence. This becomes evident not just when one considers the scale of such a problem when there are many possible field service technicians, as well as the speed required for delivering actionable decisions. For appointment booking scenarios, the AI system must consider, for the task in question:

» All the possible field resources (e.g. given skills, tools, parts) who can address the service request

» The time that it would take for that resource to get to the task given the current schedule, and the time taken to undertake the task

» The implications, for each resource undertaking that task, on the KPIs of the organization

» Whether there is any other approach to scheduling (including rescheduling other appointments as necessary) that would result in a more favorable impact on the organization’s KPIs

...all of this with an interactive response time, so a customer service representative, or web application, is able to present the optimal option to the customer in a matter of seconds.

The ability to deliver against this promise is a triumph of artificial intelligence that can provide field service organizations with a significant competitive advantage.


Applies averages and buckets of time to deliver unreliable appointment windows. Doesn’t factor in real travel time and distance or job duration.


Accounts for actual travel time and predicted job duration for a specific resource to ensure a more accurate and deliverable schedule.

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Fig. 4: The challenge service businesses face as more resources and jobs are added to the schedule. It is impossible for a human to consider all the factors inherent in creating the ideal schedule.

Schedule Complexity increases with number of jobs and resources

With 3 techniciansand 3 jobs there

are 6 possible ways toschedule the work.

There are

1,307,674,368,000different ways to dispatch 15 techniciansto do 15 jobs.

(Or more permutations than there are stars in the Milky Way!)

There are

720different ways to dispatch

6 technicians to do 6 jobs.

The number of possibleways to dispatch techniciansgrows exponentially—andthis represents only

1 job per technician!

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The Day of Service:Predicting Travel Timeand Job Duration

In this section:Travel to TaskTask TimeTask Execution

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The Day of Service: Predicting Travel Time and Job Duration


Travel to TaskAs noted in the earlier section, accurately predicting travel time is a key part of being able to develop a realistic schedule and in turn enable an organization to make firm customer commitments and meet SLAs. To get to and from a task obviously involves two trips—from a previous task and to the next task. In putting together a schedule, making sure that the overall trip sequencing makes sense and doesn’t lead to unacceptable travel times and associated costs is a challenge well suited to artificial intelligence. Predictive algorithms hone in on routes with acceptable cost profiles, even while many other considerations are taken into account to produce the optimal approach.

Of course, the best laid plans can easily unravel at rush hour, and incorporating the realities of traffic is essential in any field service management solution. AI offers a solution. By efficiently analyzing massive amounts of historical data, it’s possible to achieve predictive insight into the road conditions in the future, how those translate into travel times, and to accommodate this additional factor into the service delivery process. Additionally, as changes are made to the schedule throughout the course of a normal day, real time travel estimates are incorporated into the system’s decision-making process when reassigning the work.


Does not account for travel time when creating the schedule, and uses approximate travel times from point A to point B once the job has been assigned to a field resource.


Leverages historical data to predict with a high degree of accuracy how long it will take to get from Point A to Point B and applies the travel time when the task is created in the schedule through to job completion.

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Task TimeFor short-cycle work in particular, accurately predicting task time, rather than using default service windows based on averages, is a major opportunity to improve field service utilization rates and undertake more jobs per day. Machine learning can deliver real business value here by identifying the components of a task that are predictive of the duration.

The technician, the skills and training utilized, granular details of the task performed, weather, time of day—all of these can influence the length of time a task will take. Machine learning identifies to what extent each plays a part, so the scheduling system can predict the most likely task duration for a future service request—and continually improve as more and more data is processed.


Uses a fixed task time per task type, regardless of who is performing the work.


Estimates the most precise duration for each job, based on all relevant task and engineer properties.

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Task ExecutionAs with task duration, the answers to what makes a successful service engagement, and what factors influence a high first-time fix rate, are also available in the data and can be unlocked by artificial intelligence. Historical data indicates which parts, which tools, and which skills combined to produce successful outcomes—and can be used to determine what scheduling decisions are most likely to produce the desired business impact.

Taken a step further, it’s possible to analyze task data to understand the impact that, say, some specific training would have. For example with the current level of skills, the workforce may have achieved a first-time fix rate of 87%, because only 90% of the technicians have the appropriate training, but in order to meet response time SLAs the service organization sometimes is obliged to send a non-trained employee to the service request. If the workforce is trained to a 95% level, artificial intelligence provides the means to model the positive impact on the first-time fix rate, given a wider pool of candidate field service technicians who can respond to the service request.

While AI can illustrate the value of investing in training, the approach of modeling the impact of different variables in the service lifecycle (such as more skilled technicians in the above example) can be applied in a variety of areas for business advantage.

Consider the following approaches that can optimize service execution, that take advantage of the ability to run parallelized simulations, leveraging AI and cloud computing power to identify optimal delivery decisions:

» Territory Planning: Modeling the optimal way to group service requests such that field technician territories are dynamically created on the day of service, rather than using static, and inherently inefficient geographic boundaries

» Parts Distribution: Modeling the ideal distribution and volume of parts, such that working capital tied up in stock is minimized but business objectives are still met

» Predicting Customer Cancellations: Using historical data around service cancellations to establish what characteristics correlate to a high probability of cancellation; being able to predict high probability of cancellation enables the service organization to mitigate that risk (for example, by sending service reminders) or scheduling work such that if a job in canceled there is other work nearby that can be undertaken instead

» Increasing Automation of Scheduling Decisions: Leveraging historical information from the field service management system around manual dispatch decisions to identify the underlying heuristics and automate routine decisions

» Predicting First Time Fix: Identifying patterns that are present in the current service queue, how similar patterns have been handled previously, and if there are better, alternative decisions given the previous outcomes.

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The combination of data and AI can lead to improvements in many aspects of service delivery. While we have seen that resource planning in advance of the day of service can improve the chances of successful delivery there is also great power in measuring the results of previous actions in terms of the KPIs that are important to the business.

Artificial intelligence is, after all, only a means to an end, and the ability to adjust an approach, especially in the context of changes in the business goals is a necessary part of any robust field service management solution as we will now see.


Matches skills and parts to jobs, but does not account for customer availability, or other extenuating factors.


Uses machine learning models to predict whether the job will be completed on the first attempt based on a myriad of data points including necessary parts, customer availability, technician skills, etc.

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After the Day of Service:Analyzing Data to Improve Predictions

In this section:AnalysisOptimize to GoalsTask Execution

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After the Day of Service: Analyzing Data to Improve Predictions


AnalysisField service management vendors talk a lot about optimization. Often lost in the mix, however, is what this term really means in the context of a particular organization’s business. The fundamental requirement is to understand what to optimize for, and it’s the trade-offs inherent to field service that make this a more challenging question than at first glance.

By way of example, an organization may decide that minimizing field service technician overtime is important because of budget constraints. If this is the only thing that is taken into account, and technicians no longer work at all after their regular hours, then this will automatically reduce the number of jobs that can be undertaken, especially because jobs that would finish a few minutes past regular hours will now be moved to the next business day.

Naturally this has implications for other service metrics. For example, utilization rates will fall, customer satisfaction will fall because time to service increases, but, on the other hand, field service employee satisfaction may improve.

All of these may be acceptable trade-offs to the business, but in addressing this problem AI is not a panacea. The opportunity for software vendors is to convey the implications of these trade-offs in a way that the service organization can operate with a business focus, and let the artificial intelligence translate these business imperatives into the “optimal” schedule.

This representation of the business drivers in a meaningful way not only allows the appropriate business control, but also enables users to communicate the implications of alternative approaches, which can be particularly useful when there are conflicting stakeholder interests.

http://www.fieldservicematters.com/5-keys-aligning-conflicting-stakeholder-interests/

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Figure 5: User interface that abstracts the algorithms and schedule logic and enables fine grained business control.

In this image the service manager is keen to reduce travel time due to soaring fuel costs and employee dissatisfaction. Using the sliders, the acceptable trade-off to achieve the desired 20% travel reduction can be easily indicated. Here we see it’s acceptable for the business to take a 20% reduction in response time and increase overtime utilization by 25%.

Optimize to Goals

Travel Time

Goal

20%30m 0m

Response Time5d 10h 1d 2h

pay up to

20%

Preferred Resource0 % 47 %

pay up to

0%

Appointments At Risk100 % 12 %

pay up to

0%

Overtime Utilization100 % 32 %

pay up to

25%

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Once these desired parameters have been established, artificial intelligence undertakes a massively parallelized computation that leverages cloud computing to establish scheduling logic that will meet the goals.

With the combination of business strategy and artificial intelligence, service organizations are able to achieve the optimal schedule for their current requirements. The word “current” is important. Being able to respond rapidly to changing business requirements, as well as being able to specify different levels of granularity (e.g. to reflect regional variation) is essential in today’s fast-moving business environment and so being able to leverage artificial intelligence to abstract the complexity and empower business users with this kind of agility can be a major source of competitive advantage.


Analytics are static, and monitor only historical performance. Changes to what you measure typically require IT intervention.


Monitors current and historical performance, and enables different KPI trade-offs to be considered in order to arrive at the best possible schedule for your business at any given time.

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Demystifying Predictive Field Service


Predictive field service combines the use of artificial intelligence and machine learning with business processes to improve the most important service business KPIs.

Think about what you’d most like to improve: See more customers in a day? Improve the quality of those customer interactions? Ensure you are decreasing repeat visits to customers for the same problem? Or maybe some combination of the three? Then work backwards to determine how predictive field service can have the biggest impact. You don’t have to reinvent the wheel or employ an arsenal of data scientists because insights-driven technology designed to solve these field service challenges already exists.

Not sure where to begin? Whit Andrews, vice president and distinguished analyst at Gartner advises, “Look at how you are using technology today during critical interactions with customers—business moments—and consider how the value of that moment could be increased. Then apply AI to those points for additional business value 1.”

Predicting Your Field Service Future

1 Gartner, https://www.gartner.com/smarterwithgartner/the-cios-guide-to-artificial-intelligence/

https://www.gartner.com/smarterwithgartner/the-cios-guide-to-artificial-intelligence/



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Key Takeaways » Predictive field service is bolstering time-tested service KPIs such as first-time fix and customer satisfaction

» There are several points in the service lifecycle that benefit from the application of AI and machine learning, each corresponding to the appropriate stage

» Traditional field service solutions do not deliver as much ROI as predictive field service solutions because they cannot account for as many variables and data sources

» The sky is the limit for service operations and predictive field service as the technology gets more automated and insights-driven

Additional ResourcesRecommended reading about machine learning, artificial intelligence, and predictive field service.

» Trends in FSM You Can’t Ignore » Briefing Report: Disruption, Development and Diversity in Field Service

» Empower Field Service Decisions with Machine Learning

» Down, Set, Hike! Predictive Playbook for Scheduling, Travel and Dispatch

About ClickSoftwareClickSoftware is a global leader in field service management solutions, delivering value through improved efficiency, effectiveness, and enhanced customer experiences. ClickSoftware blends unparalleled industry expertise and state-of-the-art computer science to deliver meaningful, measurable business value—optimizing critical business processes and delighting customers. Click Field Service Edge arms field service leaders with the smartest technologies and best practices from around the globe to deliver real-world results, real-time recommendations, and real operational intelligence.

For more information, please visit https://www.clicksoftware.com/. Follow us on Twitter.

Click. Actual intelligence. At work.

Contact UsNorth America +1 (888) 438-3308 , Western Europe +44 (0) 1628 607000 , Central and Eastern Europe +49 (0) 69 489813-0 ,

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https://go.clicksoftware.com/rs/697-ZGS-962/images/TrendsinFSM.pdf

https://www.clicksoftware.com/wp-content/uploads/2017/12/Think-Tank-Disruption-Development-and-Diversity-in-Field-Service1.pdf

https://www.clicksoftware.com/wp-content/uploads/2017/12/Think-Tank-Disruption-Development-and-Diversity-in-Field-Service1.pdf

http://www.fieldservicematters.com/machine-learning-powered-field-service/

http://www.fieldservicematters.com/machine-learning-powered-field-service/

http://www.fieldservicematters.com/predictive-maintenance-playbook/

http://www.fieldservicematters.com/predictive-maintenance-playbook/

https://www.clicksoftware.com/

https://www.clicksoftware.com/

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an introduction to essential technologies and practical ......4 gartner top 10 strategic technology...

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