KPI#3: MEAN TIME BETWEEN FAILURES

 

What is it?

Mean time between failures (MTBF) measures the average time between customer issues. The goal for service organizations is to maximize this metric because a high time between failures represents high service quality and maximum uptime.

How it measures workforce performance

MTBF is less about resolving the single issue that prompted a failure, and more about a service professional’s ability to holistically maintain the health of the machine. This is a skill that is typically difficult to measure. Seasoned service heroes know how to take advantage of time in front of an asset to ensure that everything is working properly. They’ll work to proactively maintain or replace parts before a failure occurs, and understand how to keep failures from happening in the future.

How it measures customer experience

The less you notice this measurement creeping up, the better. Tracking MTBF is a great way to measure customer uptime and service reliability — ultimately resulting in happier customers. While service is about providing a great experience in each customer interaction, a better outcome is preventing the failing in the first place.

Challenges to measuring mean-between-failures

• Measuring mean time between failures requires long intervals of data because it’s a lagging indicator: MTBF is a big picture measurement. It requires a sustained ability to measure the time difference between failures. Quite often, organizations may only look at a quarter’s worth of data, and that’s not enough time to accurately measure MTBF because a good indicator of health means that failure may only happen every few quarters. 
• Attribution of mean time between failure to service professionals is up for debate: What is the definition of failure? Is it when different, unrelated issues arise in the same asset? Or can it also be a problem that (unknowingly) is not fixed correctly the first time, and subsequent service calls were needed to address the actual problem? Because multiple service technicians are usually contributing to service across one asset or customer, it’s difficult to attribute a long or short MTBF to a single individual.

How to measure mean-time-between-failure:

It’s straightforward with the exception of these scenarios. When dealing with long MTBF intervals (6 months or longer) or an extremely short average asset lifecycle, using the standard approach to MTBF might not be suitable. Instead, using MTBE (mean time between events) might be a better measurement. When using MTBE as a metric it’s less about whether a particular interaction with a customer constitutes a failure and more about measuring how many times you had to interact with that customer. Fewer interactions signal fewer service issues.

 

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KPI#4: SERVICE COST PER WORK ORDER

 

What is it?

Service cost per work order (or per case) is an effective measurement of the total cost of each work order or case created. It’s also known as the average cost per truck roll.

How it measures workforce performance

The cost per work order provides a measurement of the duration and/or parts usage a technician uses, on average, to resolve an issue. Higher performing individuals innately know how to solve problems without shotgunning expensive parts.

How it Impacts Customer Experience

While customers aren’t concerned with how much it costs to resolve each problem, the bigger quality issues remain true here. A lower cost per work order generally highlights a higher quality of service -- and that’s what customers notice.

Challenges of measuring mean time to resolution

• Service cost per work order might be a misleading indicator of performance: Context is key, especially when a few experts are regularly assigned complex jobs. These are cases when top employees may have a higher average service cost per work order. If yours is an organization that saves the most difficult cases for workforce heroes, know that the more complex the cases, (typically) the more expensive the service costs. ○
• Service costs can have an inverse relationship to customer satisfaction: You likely get requests from service teams for additional help because more resources (and faster response times from those with lighter workloads) typically result in better service performance. So don’t look at this metric without regard to the customer experience component. ○
• Service cost per work order doesn’t differentiate poor experiences and repeat visits: After scouring data from hundreds of organizations, it’s clear that one of the most difficult challenges in measuring service costs hinges on the ability to identify when consecutive work orders are related to the same issue. Organizations that carefully track when cases that were previously closed, were closed in error -- because the root issue wasn’t resolved properly the first time -- are rare.

How to measure mean time to resolution:

One of the best ways to track cost per work order is to use the metric to measure service cost per success. By flipping to the positive outcome, your organization will have a better understanding of who within your workforce are the heroes of your organization (those who resolve issues with minimal parts and visits). The difficulty with measuring each success is understanding whether a case is isolated or related to another case. Depending on how you capture data, you can use different methods to measure successful events, including the method describing how to measure repeat visits previously.

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KPI#5: NET PROMOTER SCORE

 

What is it?

Net promoter score (NPS) is one of the most widely used methods to identify customer satisfaction and loyalty. Simply put, it measures the number of promoters and detractors for the service you provide

How it measures workforce performance

There is no other metric that reinforces the statement “customer is king” more than NPS. Happy customers overwhelmingly signal strong workforce performance. Typically, NPS measures a qualitative experience (technical workforce skills), such as how quickly a problem is resolved, but sometimes, NPS reflects a customer’s opinion about how they feel they’ve been treated (soft workforce skills).

How it measures Customer Experience

This is one of the most straightforward measurements possible to gauge customer satisfaction.

Challenges of measuring mean time to resolution

• Net promoter score alone is not a true measure of workforce performance: While customer satisfaction is a critical component in delivering great customer service, NPS alone is an incomplete measure of the performance of your service workforce. In service organizations where NPS is the measurement used, service costs skyrocket. Customer service professionals provide endless perks and free services in order to create customer loyalty. Unless this is part of your organization’s objectives, be careful when using NPS alone. 
• Net promoter score is difficult to attribute to any one service factor: While NPS is a comprehensive measure of a customer’s experience, it’s difficult to use it to clearly identify any one specific factor or event that impacts score.

 

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In the final feature in this series we will look at the Aquant Workforce Performance Index and how field service organisations like yours are leveraging this to improve their customer satisfaction ratings. Look out for this article in a weeks time!

Alternatively, subscribers to www.fieldservicenews.com can access the white paper on the button above and the rest of our premium content library! Join 30K of your field service management peers and subscribe @   www.fieldservicenews.com/subscribe

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Further Reading:

• Read more about Leadership and Strategy @ www.fieldservicenews.com/blog/tag/leadership-and-strategy
• Read more about Managing the Mobile Workforce @ www.fieldservicenews.com/blog/tag/managing-the-mobile-workforce
• Read more news and articles featuring Aquant @ www.fieldservicenews.com/hs-search-results?term=aquant
• Connect with Edwin Pahk on LinkedIN @ https://www.linkedin.com/in/edwin-pahk-8a066515/
• Find out more about the solutions Aquant offer to help field service companies @ www.aquant.io/
• Follow Aquant on Twitter @ twitter.com/Aquant_io

Service operations capabilities are at the heart of successful transformations. IDC believes that much of the focus in recent years has been on providing staff with good tools to deliver basic services, rather than creating new services and updating the flows to support those.

 

One example is mobile access. The first chart in Figure 9 shows how the spread in choice between good performers (Stage 3 and 4) and subpar performers (Stage 1 and 2) is very thin. Most companies across all sizes have managed to provide their field workers with decent capabilities, even if only providing augmented features.

 

 

On the other hand, innovation on service portfolio is overall a far more immature topic. Approximately half of the companies surveyed offer maintenance contracts at best. Another 30% augment this with financing, which means barely 20% are focused on innovating services. On top of that, there is a very large rift in regard to service creation between good performers (Stage 3 and 4) and subpar performers (Stage 1 and 2), as shown in the second chart in Figure 10. 70% of the slow performers have haphazard approaches to service creation, the complete reverse of what Borderless and Joined-Up enterprises do.

Another key discerning element in service operations excellence is the ability to have a two-way process and data flows between service management and back-office functions. 40% of the organizations surveyed have zero interlink of key systems (ERP, FSM, supply chain), suggesting a fully fragmented value chain, and just 15% have two-way links, meaning manual or sensor-based input from field service processes triggers information updates and actions in ERP and supply chain systems.

The final key success factor is related to skillsets, both in the field worker group and in the leadership team.

• Field workers need to be not only apt at using and understanding technology, but also enthusiastic about expanding their knowledge and abilities at a much faster pace. Annual training and “experience” are not enough when product updates happen monthly over-the-air and digital applications are part of the portfolio.
• Lack of know-how or internal capabilities to run a services business is ranked as the number one hurdle to servitization. IDC believes it is very hard for companies in production-oriented industries to hire executives that are capable of innovating on the service side of the house.

Eyes on the Prize — What Borderless Enterprises Teach Us

Assessing the status quo is important. But what should companies aim for? What are the benefits of becoming a Borderless enterprise and the traits defining one? The in-depth Barometer research allows us to start answering those questions:

• Companies should start investing in a long-term service vision. 90% of the best performing companies expect business model revolutions in three years with pay-per-use, outcome-based services, and ecosystem monetization all playing a role. This compares to 15% in the sample average. They also already generate 12% of their revenue from services, versus an 8% average.
• Front-end capabilities must be proactively built. The ability to immediately route calls from the contact center to engineering support is a strong indicator of leading organizations. Similarly, Borderless companies have often managed to build fully automated troubleshooting capabilities for end customers. Data is automatically synced from in-field products to the cloud, issues are logged, and solutions proactively offered.
• Strategic partnerships are critical drivers to success in servitization. Becoming a Borderless organization requires an ecosystem approach to the development of data-driven digital services. Organizations at the most advanced stage of servitization maturity are monetizing co-creation initiatives to enrich their services portfolio by leveraging the capabilities of partners in adjacent industries.

 

Figure 11 (below) shows other traits as well as the immediate benefits of being a Borderless enterprise. Much higher profitability and above-average revenue growth are both enjoyed by these types of companies.

 

 

While it is true that real Borderless enterprises are relatively hard to come by, it is imperative that businesses look to them as examples and start acting now. At the Joined-Up stage (Stage 3), roughly one-third of companies in the market are within striking distance. IDC believes many members of this group are very likely to “up-rank” in a two to three-year horizon. Remaining in the bottom ranks would mean becoming pigeonholed in a low-margin, low-growth mode for the foreseeable future.

 

Getting the Low-Hanging Fruit

IDC maintains that some low-hanging fruits can be achieved even by moving up from the lower stages of servitization. Figure 12 provides a summary of the potential gains and likely roadblocks on the path towards a Borderless enterprise. While there are multiple ways to move along the servitization journey, some common patterns start to appear. In particular:

• Strong profitability and sustained revenue acceleration are visible from Stage 3 onwards. As almost 50% of buyers sit in Stage 2, it is recommended that they build expectations around those KPIs as they connect their whole value chain. Failing KPIs would be a sign that the journey is not progressing
• The last, trickiest roadblock is linked to decision making. Transformation towards a service-first business model will eventually pass through the leadership. In the Barometer survey, 85% of respondents said they were part of a decision-making group that typically involves Head of Products, Head of Services, and often Head of Operations, plus the IT side of the organization. Building the right “dream team” dynamics will be crucial to ensure success in the final stages.

 

IDC Advice to End Buyers in Production-Oriented Industries

On a backdrop of subsiding demand for traditional products, IDC sees a clear trend emerging towards augmenting products with services, and ultimately transforming traditional physical supply chains into open ecosystem value chains. This transformation process is called servitization, and it is to be understood as a subset to digital initiatives.

While only a few have achieved the servitization “nirvana” of the Borderless enterprise, more than a third of companies globally are already Joined-Up, poised to get within striking distance of that vision soon. Those fast movers already have proof-points to show for it, including:

• Service revenues that are on average one-third larger than their peers’ as a proportion of total revenue
• 5X more chance of accelerating top line growth above 5% yearly
• Much greater likelihood of showing profitability improvements thanks to DX initiatives

IDC recommends every company in all physical value chains get moving as soon as possible. Laggards are likely to find themselves pigeonholed in low-profit niches within the next two years if they fail to do so. Based on our research, IDC recommends the following strategic and tactical best practices.

 

Strategic Best Practices

1. Work with the leadership to build a “dream team” involving Head of Products, Head of Services, Head of Operations and CIO/CDO that aligns early on strategic objectives for servitization. This transformation process will impact all those departments (and more). While initial attempts can work without coordination, data shows that internal conflicts are the biggest obstacle in Stage 3 and beyond.
2. Be proactive in customer-centricity. No servitization can happen without a strong, ultra-connected customer engagement function. Even good performers appear to struggle to go beyond basic tools and processes. Combining customer metrics with operational scores and above all being ready to invest in staff and technology from day one is highly recommended.
3. Set key performance indicators and make them public. IDC research shows that revenue growth accelerates when crossing the chasm to servitization. Financials can be volatile, though, especially on a quarterly basis and even more so when business model change hits. Measuring other items such as the percentage of service revenue in each division, field service worker satisfaction, and net promoter score is recommended to keep a steady pace and not lose sight of the end goals.

Tactical Best Practices

1. Put together a small “Services Tiger Team” as soon as possible. The team should be comprised of mid-senior members across the “dream team” divisions, including IT. Its key objectives should be to review and classify existing services, related processes, and profitability (Phase 1) and to ideate new offerings to test in the market within twelve months (Phase 2). For the sake of iteration, multi-year launches are not recommended.
2. Perform a complete review of your data flows end-to-end. To test how well connected your backoffice and front-desk are, first pick your best-selling product. Then analyze whether contact center and field service agents have consistent information about parts availability, whether they can directly augment or modify information in back-office systems, whether potential IoT or third-party component supplier data is consistent, etc. This will quickly highlight bottlenecks in process and information.
3. 3. Kill all of your IoT projects that don’t generate events in either service management or ERP/ supply chain systems. Focusing efforts on actionable data is a must. Chances are you already have IoT data collection from end products or the supply chain in place — often without a production use case. IDC recommends ruthlessly eliminating those to make space for IoT projects subordinated to the launch of new service offerings.

 

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Further Reading:

• Read more about Servitization and Advanced Services @ www.fieldservicenews.com/blog/tag/servitization-and-advanced-services
• Read more about Digital Transformation @ www.fieldservicenews.com/blog/tag/digital-transformation
• Read exclusive FSN features from IDC's Aly Pinder @ www.fieldservicenews.com/blog/author/aly-pinder
• Read FSN Features and News from the IFS team @ www.fieldservicenews.com/hs-search-results?term=IFS
• Find out more about the solutions IFS offer to help field service companies @ www.ifs.com
• Follow IFS on Twitter @ twitter.com/ifsuk

 

KPI#1 FIRST TIME FIX RATE

 

What is it?

First time fix rate (FTF) is one of the most popular metrics for workforce measurement. It’s simply how often someone is able to fix the issue in question on the first try. This is typically referenced in both a contact center and field service scenario.

How it measures workforce performance

Generally, it’s assumed that the higher the first time fix rate, the more competent or skilled the technician is.

How it impacts customer experience

FTF makes a big impact on great customer experience. Customers want resolution quickly, and they don’t want to have to place a service ticket two days later because the issue was never properly resolved. Solving issues correctly the first time boosts a brand’s value and acts as a competitive differentiator, making it just as important as the quality of the initial product or service.

Challenges to measuring first time fix rates

• Service costs can increase: Service organizations often throw a lot of money at the problem to increase first time fix rates. The problem? First time fix rates alone do not represent the skill level of your workers. For example, a technician tends to swap costly parts for a new one every call instead of digging deeper into how to fix the root issue. Stats will show a high FTF, but doesn’t take into account that a cheaper option could likely have fixed the problem
• First time fix doesn’t reflect technician skill: Not all issues resolved on the second attempt reflect technician error. Often, failure to achieve FTF happens in tandem with an accurate diagnosis. If the dispatcher didn’t provide insight into the situation, a tech may arrive onsite, quickly diagnose, but need to come back later that day or days later with the right part. Understanding the context of what the metric represents is just as important as measuring first time fix rates.
• Properly defining and tracking first time fix is a challenge: This is especially problematic depending on how an organization tracks the KPI. If each new ticket is measured in a vacuum, a first time fix rate may be high. But what if tech thinks they fix the issue on the first visit, only to be called back a week later because of a different problem with the same machine? If the tech makes another quick fix, you record that as another FTF. Go team!
  But wait a minute. What if a third ticket is issued a week later and a different tech arrives on-site to realize the first tech was simply putting a band-aid on a more complex root issue? A deeper analysis of these common miss measurements finds that service organizations really have more chronic repeat visit problem than they realize.

How to measure first time fix:

It’s not an exact science, but a much more accurate approach is to measure whether there was a visit for that same asset or issue that occurred within X number of days of the previous visit. While this isn’t a complete solution, it addresses the major fallacy of the first time fix rate.

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KPI#2 MEAN TIME TO RESOLUTION:

 

What is it?

Mean time to resolution (MTTR) refers to the time it takes to resolve a customer issue. This is typically defined as the time between the case creation date and its closure date. Similar to the pain of staying on hold when trying to resolve a personal issue, minimizing MTTR is a key factor in increasing positive customer experiences and reducing costs. Organizations with high MTTR often have techs who find themselves in escalation black holes.

How it measures workforce performance

MTTR has an inverse relationship to first time fix rate. As your FTF rate goes up, MTTR should go down. How so? That’s usually related to service heroes resolving issues (really resolving the root cause) on the first visit, with the right parts and tools to make quick work of the problem.

How it Impacts Customer Experience

Consumers and B2B clients want immediate service. Amazon Prime, overnight shipping, Netflix, and more all represent the demand for immediacy. MTTR is a critical part of that customer service experience, and if your customers don’t receive the attention and quick resolution they want, they’ll seek out a competitor.

Challenges of measuring mean time to resolution

• Poor data collection or lack of uniform data: This is the biggest issue related to measuring MTTR. While case creation/creation date is a fairly consistent data point across organizations, case resolution time or date is much less reliable due to poor data collection. The biggest issue here is a lack of compliance among users -- technicians often forget to close out cases until days after the problem is resolved.
• Dependence on first time fix measurement: MTTR is highly dependent on how FTF is measured. Failed visits have a profound impact on MTTR, and the stats are grim. Issues not resolved the first time require, on average, another 14 days to resolution. The reason is often attributed to the need to order additional parts, scheduling issues with customers. MTTR suffers from the same challenge as FTF if the root causes of failure aren’t addressed.
• Mean time to resolution is a measure of process and people: It’s tough to separate the two. MTTR can be used to measure workforce productivity, but it’s also a measure of capacity and process. Sometimes when MTTR slumps, that’s the result of lack of enough headcount versus workforce performance.

 

How to measure mean time to resolution:

There are several approaches to mitigate some of the challenges faced when measuring MTTR.

• Measure the difference in mean time to resolution rather than overall rate. Instead of looking at MTTR as a single unit, focus on the aspects of MTTR that reflect workforce performance. For example:
  • This can be identified in the difference in MTTR between individuals when a failed visit occurs. Service professionals who often require multiple customer visits will generally have greater MTTR rates versus your best experts who seldom make repeat visits.
• Use only open dates to measure mean time to resolution. Given the lack of quality in measuring accurate resolution dates, using open dates and visit dates to define the MTTR threshold is a way to identify MTTR. This eliminates the inconsistency in resolution or close dates, and will only work on customer issues with failed visits.

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In the next feature in this series we will look at three more crucial KPIs, Mean Time between Failure, Cost Per Service Per Technician and Net Promoter scores. Alternatively, subscribed now for access to the white paper above and the rest of our premium content library @  www.fieldservicenews.com/subscribe

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Further Reading:

• Read more about Leadership and Strategy @ www.fieldservicenews.com/blog/tag/leadership-and-strategy
• Read more about Managing the Mobile Workforce @ www.fieldservicenews.com/blog/tag/managing-the-mobile-workforce
• Read more news and articles featuring Aquant @ www.fieldservicenews.com/hs-search-results?term=aquant
• Connect with Edwin Pahk on LinkedIN @ https://www.linkedin.com/in/edwin-pahk-8a066515/
• Find out more about the solutions Aquant offer to help field service companies @ www.aquant.io/
• Follow Aquant on Twitter @ twitter.com/Aquant_io