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As a recent guest on the Field Service Podcast, Oneserve’s CEO Chris Proctor shared thoughts on innovation, servitization and the worrying discrepancy between new and retiring engineers. Field Service News Deputy Editor Mark Glover looks back on a conversation that asked uncomfortable yet insightful questions of the sector.

 

What is digital? “It’s about bringing people together in one global location; a world without boundaries; a world where ideas are born and developed; where people live, and most importantly; where people transact.”

These are the words on Chris Proctor’s Linkedin profile. If it is his mantra it’s a good one. It’s refreshing to get such a holistic view of digital. Do we label it a platform or technology? Who really knows? However it’s useful – and perhaps essential - to try and frame its creative potential and understand how it can seed ideas and drive innovation. 

I was fortunate enough to record a podcast with Proctor towards the end of 2018 and among other threads of conversation (including the definition of digital) I asked about the industry’s progression in adopting new technologies.  “I might be lambasted for saying this but I don’t think there has been much innovation,” he said, aware of the statement’s brevity. “It’s disappointing that the last real innovation in field service management was moving to the cloud and even then, I don’t think everyone is fully there yet.”

It’s a bold claim, yet one that has substance. The industry has been accused before of lagging when it comes to embracing disruptive, digital technology. In fact, writing for this magazine moreMomentun’s Jan Van Veen, suggested a knowledge gap around its definition was contributing to the malaise. “Too often, I see misconceptions about disruption and disruptive innovation and a lack of clarity on what needs to change and too slow a pace of change,” he wrote, “by consequence, manufacturers tend to make inadequate assessments and develop inadequate strategies, allowing leading competitors and new entrants into the industry to take the lead.” 

 

 

“I might be lambasted for saying this but I don’t think there has been much innovation...“

 

 

To halt this inadequacy, Proctor thinks a further disruptive approach is needed to jolt the sector from its lethargy, encased in an attitude he phrases as “okay is okay”. He uses the utility sector as to expand his point. “You only have to look back at the gas or water boards where there was very little competition with very little incentive to be a lot better and I think we’ve lived with that legacy for quite a long time. “Look at what’s happened in some of the other sectors and see how much things can be disrupted when someone comes in and says ‘We don’t believe ‘okay is okay’ and we’re going to offer a very different service proposition,’” he urged.  




To reach this level, servitization and the gig economy, he predicts, will have a fundamental impact on the way services are delivered. Most likely through a subscription-based model, complimented by disruptive innovations and delivered by freelancers who, in order to maintain their personal brand, deliver consistent service excellence. “I can see a world where most of your services are consumed on a subscription-type basis. You contact your service provider who then uses technology similar to programmatic advertising whereby contracts are tended and bid for and secured within seconds, all underpinned by blockchain. You then have the real emphasis of an individual providing a service on behalf of a company, then what you then see is that you don’t get the overheads that come with large contractors, but you do see service excellence from an individual who is trying to make a name for themselves and secure their own future,” Proctor said. 

This small-task employment model could be the remedy for the alarming disparity between new field technicians coming into the industry and those retiring, however despite the need for a swift solution Proctor feels the time frame for such a movement is ambiguous. “Whether it’s five years, whether it’s ten years, I think a move towards that model is irrefutable” he says, “I also think the timings are completely debatable.” 

I enjoyed the conversation, and I suggest you listen to the full podcast on our website. If the point of digital, as Proctor says, is to create a world where ideas are born and then developed then we need to hit reset, disrupt and collaborate. Perhaps then can innovation thrive.  
You can listen to the Field Service Podcast with OneServe's Chris Proctor here.

 

In the the final article in our current series of articles from field service solution provider Astea focussing Artificial Intelligence and Machine Learning,  we see how applying the Machine Learning can lead to better service resolutions... 

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Is Machine Learning a key topic for your organisation?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

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Congratulations, if you have been following this series of articles across the last few weeks you are now a cognitive technology guru! (If you haven't don't fret! we've written a whole white paper on the topic which is available to fieldservicenews.com subscribers.)

In the series we've tried to give you an understanding of how and why the field service industry is applying these new technologies, and you see the writing on the wall: positive results for your customers, your employees and your financial success.

Now it’s time for you to go through the machine learning process yourself in order to answer the most important question ever asked in a field service scenario: “What is the best resolution to a given problem?”

If we break this question down into smaller operational components, we first need to maintain a set of wide range problems represented in groups which share similar characteristics. We then label these groups. Let's say, in our case, how the problems were fixed or repaired. Then, we need to create a learning process that labels a new problem based on its characteristics.

 

The Learning Process

 

 

 

 

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The learning process requires “real world” raw data and actual business experts. In our example, this data will be historical incidents and service requests that could most likely be predictive. The business experts are the people with extensive knowledge of hands-on technical support and service (e.g. field service managers with years of service and product experience.)

The next step is called data preparation. This step is required to screen the raw data, remove duplicate or irrelevant records, handle missing data attributes, and extract indicators and features that are needed in the learning process.

"Free text", such as the problem description of these examples, is then extracted into a bag-of-words model. This is a representation of the description text with meaningful information (disregarding semantic relationships in the sentences).

The next step is called the training process. This is where the actual learning happens. Different types of learning algorithms are applied to a data training set (a subset of the entire data to learn from).

The learning process requires “real world” raw data and business experts. In our example, this data will be historical incidents and service requests that could most likely be predictive.

The choice of those metrics influences how the performance of the machine learning algorithms is measured and compared. Accuracy is one of the metrics to evaluate and classify models. It calculates the number of correct predictions made by our training model over all kinds of predictions being made. Another metric, called the confusion or error matrix, is a two dimensional table that visualizes “actual” versus “predicted” results for different questions, called the “classes”. It shows if a model is confusing two classes (e.g. commonly mislabeling one as another).

Aptly named, the confusion matrix visualizes “actual” versus “predicted” results for different questions, called the "classes". It shows if a model is confusing two classes.

 

 

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Do you want to know more?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

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In the first of a four-part series, we bring together three industry experts and put four key questions for them to answer to give us a balanced view of the major trends impacting the field service sector. With responses from Bill Pollock, Strategies for Growth, Marc Tatarsky at FieldAware, and Richard Pratley from SimPRO, we look forward to what we can expect across the next twelve months.

2018 was a year in which we saw a number of significant changes move from the fringes of discussion within our industry to becoming an established part of mainstream discussion and in some cases fundamental parts of common place strategy within the field service sector.

The Internet of Things (IoT) for example, has become a staple part of field service delivery with many organisation having already adopted some layer of IoT technology which they are utilising within service delivery and the majority of those who have yet to take their first steps into connected field service are actively planning to do so in the not too distant future.

And as is often the case with technology in field service, the emergence of IoT in our sector, much as mobile did before it, has resulted in seismic changes into the processes and work-flows that underpin how we define service excellence. For example, we have seen servitization become an increasingly popular over-arching strategy for many manufacturers as they shift towards more customer-centric, service-focussed revenue strategies.

Even amongst those organisations who have yet to commit to a fully servitized business model, there are many who are shifting towards adopting a pro-active approach to service delivery, with increasing operational efficiencies and greater customer satisfaction two of the major benefits being heralded by such developments - which are again enabled and empowered by IoT.

Yet, at the same time other technologies that should be having positive impacts on field service delivery, in particular Augmented Reality (AR), have yet to evolve as rapidly, seemingly stagnating in the early adopter phase. Perhaps, 2019 may be the year we see AR finally emerge from its embryonic potential to also becoming a fully established part of the field service sector?

Or maybe, there will be other key breakthroughs, whether they be technologies, or strategies, that will shape the future of field serviceTo get a flavour of what we may expect across the next twelve months we’ve brought together a panel of experts to get their opinions on what to expect in 2019. We begin this series, however, by taking stock from last year.

Across the last twelve months what do you think has been the biggest shift in how we approach field service delivery?

BILL POLLOCK, PRESIDENT, STRATEGIES FOR GROWTH
The last 12 months have been quite a bit more active among global Field Services Organisations (FSOs) with respect to their acquisition and implementation of new technologies. For example, after having spent a number of years more as a perennial line item on an organisation’s “wish list”, Augmented Reality (AR) has gained a much wider acceptance, and is presently in use by more than twice as many FSOs as just a year earlier. In fact, the trend lines for AR adoption are have begun to increase at an accelerating rate.

We are now also seeing the further incorporation of Artificial Intelligence (AI) and Machine Learning into existing FSM systems.
As a result, many FSOs have already begun the transformation from the traditional break/fix model to the use of predictive diagnostics and AI-powered chatbots to facilitate and expedite.

MARC TATARSKY, SVP MARKETING, FIELD AWARE
We are seeing a convergence of technology capabilities changing how field service operations are being enabled. One of the key drivers of this convergence is analytics and a data platform that is empowering organisations to take insights from various new technologies (IoT, AR eg) and existing data within other Systems of Record to provide context and the ability to make “new” business decisions.

Field service organisations, due to the complexity of the operations, have always embraced technology and were early adopters of analytics. We are seeing an evolution of analytics in field service, moving from a need to turn data into information, to meaningful business insight and then to decision-making capabilities.
Over the past twelve months we are now experiencing a shift to a more strategic approach to business intelligence. Field service leaders are applying analytics to drive value-adding initiatives into the wider business, with customising service and product innovation, for example.

RICHARD PRATLEY, MANAGING DIRECTOR UK, SIMPRO
There are a number of external pressures that are aggregating together forcing business to make a shift and change about how they are approaching field service delivery.

All businesses are looking to do more for less thanks to a skilled labour shortage, pricing pressures on services, travel and resource and compliance cost increases and customer demand for value.

During the last twelve months, we’ve seen more field service businesses looking to streamline and automate their operations to enable them to scale up their workforce without adding in more resources.

The second part of the big discussion will be published next week, when the panel answer questions on IoT.

THE FIELD SERVICE PODCAST: Florian Kriz, Vandelande.
The digitalisation of a spare parts inventory has huge potential but comes with many challenges.

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In this episode of the Field Service Podcast, fieldservicenews.com Deputy Editor talks to Florian Kriz, Manager E-Commerce and Product Management at Vanderlande's Global Spare Parts Division, about the potential of spare parts in the service sector and shares some of the challenges involved with the digitalisation of a spare parts portfolio.

As a mantra, fixing the customer first and the problem second, has served Martin Summerhayes well in his 30-plus years in service profession. Mark Glover, caught up with Fujitsu’s Head of Delivery Strategy and Service Improvement to discuss what it really takes to deliver client satisfaction. 

Let’s go back to the mid-80s, 1985 to be precise. The first of excellent Back to the Future films was released, Nintendo launched its first games console and music was being sold on small, shiny discs called CDs.  

At the same time, Martin Summerhayes was taking his first step on the first rung of the service ladder. And what a tall ladder it turned out to be, for when we speak some 33 years later, Martin is still in the sector and just as wide-eyed and enthusiastic as he was when he stepped out of college with a Higher National Diploma (HND) in Computer Technology. 

His course was sponsored by IT companies including IBM and Hewlett Packard who provided a route into employment for students following their graduation. Martin’s first role was to install and support a dealing room system for Morgan Stanley in the heart of London’s financial district; a fascinating first placement, however Martin fears those opportunities for young engineers just don’t exist anymore, making the industry’s skill-set gap widen further. “When I first started you went to college – not university – and you got a qualification that was equally respected, equally of value,” he says. “Then you got out there and then you went into the workplace. “Then about 10 or 15 years ago, the mindset changed. What we have now is a maturing population of engineers. Most of them are in their 40s or 50s, certainly some of the more experienced ones are in their 50s but then they retire and they leave. “But there isn’t an educational ground that backs this through. Most young people wouldn’t be interested in technology, around computer science or electronics, for example. At the end of the day, most people just don’t get into that,” he says. 

It’s a damning verdict but one that carries weight. The work-place disparity between new technicians coming into the industry and those retiring is vast and has been well commented. But what, if anything, can be done? Martin suggests a re-positioning of what service is could help. “It goes back to when I first started out,” he says. “I think field engineering or field service is as much around customer service as it is technology. “You can bring people into the organisation, who might not have a technology background but have a customer service background but we give them those skills and we cross-train them into the different environment. “Effectively what we want to do is to give this training to the more senior and experienced engineers and you might get three or four juniors working with senior and the whole process can start to work. You start to build up a little network and can start to see results.” 

Martin comes from a place where the customer sits at the heart of all service theory. “You should fix the customer first and the problem second,” he tells me; it’s a mantra he cultivated very early on in his career. Does it still carry weight today? “It is prevalent now as it was then,” he says confidently, “and in fact in some respects more so.  “When you’re visiting the customer, how do you present yourself? You’re the face of the company you’re presenting, how do you talk to the customer? How do you actually let the customer know you’ll deal with the problem they have? Even if you don’t manage to fix the problem you have to give reassurance to the customer that they’re important. At the end of the day the problem will get resolved at some point, even if you don’t fix in on the first visit. “But if you send out someone who doesn’t talk to the customer, or doesn’t acknowledge the issue but goes out to fix the part, even if they fix it first time, the customer will end up with a negative experience of that service interaction,” he warns. 

We now live in an age of ‘keyboard’ warriors, of negative social media reviews that can spread like wildfire across a company’s reputation. “When I first started, we talked about how it takes ten positive interactions to change one negative interaction,” Martin says. “These days, the amount of connections people have on Facebook, Twitter or Instagram could be up to 2,000 people. The fact that we’re more connected today, it means we’re more likely to share those negative experiences. “Customer service is even more critical today than it was 10, 20 or even 30 years ago.”   

 

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"If you send someone out who doesn't talk to the customer, or doesn't acknowledge the issue , even if they fix it first time, the customer will end up with a negative experience..." 

 

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Martin is currently Head of Delivery Strategy and Service Improvement at Fujitsu, a firm he joined in 2008, prior to which he spent nearly 20 years at Hewlett Packard, his first role following his apprenticeship with Data Logic. At HP he sampled an array of various service and operational roles, working his way up to become its EMEA Customer Services Performance Director. Given his years in the industry Martin has witnessed enablers such as connectivity, mobility and the internet come to assist in the engineer’s role, almost as much as a screwdriver and notebook, but does the end-user, the customer care about new technologies such as machine learning and Artificial Intelligence? “Not, really, no,” he says quickly. “When a function doesn’t occur the issue then becomes, how do you as my service provider resolve it as quickly as possible? Whether you use Artificial Intelligence or Augmented Reality or whatever technology platform people are talking about these days, they are enablers.” 
He suggests a future when customers will pick up their i-pads, connect to a portal and are guided through the fixing-process interactively, perhaps live-streaming a remote-service technician for extra support, is on the horizon. As advanced as this sounds, Martin strips back it back to customer empowerment. “All you’re doing is enabling the customer to self-solve that event quicker and more effectively than what you would have done 20 years ago,” he says. “You’re moving the technology closer to the customer.”    

And what about customer satisfaction? What can service professionals do to ensure this most important of factors? Martin outlines five things that every service professional needs to be asking themselves “How do you get the right engineer, with the right skills, with the right parts, to the right call? If you can guarantee those five things,” he says confidently, you’ll end up with really good customer satisfaction.” 

A lot has changed in movies, music and computer since 1985, but Martin’s approach to achieving excellent customer service has not. It’s a career we should all take note of. 

You can listen to the Field Service Podcast with guest Martin Summerhayes here. 

In the third article in our current series of articles from field service solution provider Astea focussing on Artificial Intelligence and Machine Learning we now take a look at some of the practical applications...

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Is Machine Learning a key topic for your organisation?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

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Sponsored by: 

 

 

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Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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Thanks to early adopters and industry analysts, you know that augmenting your service business with intelligent systems can be useful in enhancing organisational performance. Now let’s review four examples of how field service companies can apply these technologies in order to gain insight and improve performance.

 

1. Dynamic Scheduling

Dynamic scheduling provides optimised schedule and planning of field service agents. It’s constantly updating in response to events in real-time.For example, when a new high priority service ticket is received, it needs to be scheduled for immediate execution and all other tasks must be postponed.

There’s an element of AI in this process because the scheduling system must perform its task of scheduling this service order intelligently. In other words, it must think and thus deduce that simply prioritising this service ticket is not enough. All other tickets must be postponed in order to ensure that the high priority one is resolved first.

For example, let’s say a call has arrived in the system and agents Steve and Karen have the same skills and are both available. The system decides to assign Steve as the agent for the call by considering the nearest agent rule. However, by looking back at history and identifying patterns based on call attributes and agent performance, we find that while Steve is located closer to the customer site and is more experienced, Karen is the right agent to dispatched. 

Why? The pattern shows that Karen has a higher probability to resolve this call on the first visit as compared to Steve. This prediction can be done only if the scheduling system reviews historical data and learns from it. We can even find that having more experience and arriving earlier to the site doesn’t necessarily deliver better results.

 

2. Customer Contact Center

Call center agents play a key role in customer satisfaction. They are usually the front line of the customer’s experience, so it’s crucial to invest in systems that improve the way call centers are evaluated.

Two of the important indicators to assess the efficiency of call centers are “first call resolution” and “average time in queue”. Calls are typically analyzed manually by the agent who is listening to the call and determining the context. Agents also have access to a knowledge base to help them.

Unfortunately, having a human evaluating each call to determine the course of action is time-consuming and does not guarantee the best solution.

 

3. Service Contract Renewal

Service contracts are one of the most important parts that a field service organisation can ensure ongoing revenue from customers. However, these services are provided for a limited timeframe

only, after which customers have to renew their contract in order to continue receiving the services. It is very critical for the businesses to understand the underlying reasons that impact contract renewal. Traditional ways to evaluate the chance of renewal are based mostly on client satisfaction survey results. However, a lot of important information is captured as free text from emails, notes, problem descriptions, etc. while call center and field service agents interact with customers in trying to resolve service requests.

Using this unstructured textual information, with the aid of machine learning algorithms, we can perform a sentiment analysis to detect a customer’s emotions about the service or specific product. This allows companies to classify the root cause of problems that lead to a customer not renewing a contract.

Machine learning algorithms can perform a sentiment analysis to detect a customer’s emotions about the service or specific product. This allows companies to learn why a customer does not renew a contract.

 

EXAMPLE: “Your customer support is great, however battery only lasts for 30 minutes.”

When analyzing the sentiments in this sentence, we can classify that the cause that could potentially lead to contract non-renewal is not related to the service, but rather is based on the product itself. As a corrective action, we can propose an upgrade to a newer model in which the battery life has been significantly improved.

With sentiment analysis, companies can respond quicker to signals, track changes in customer sentiment over time, determine if particular customers feel more strongly about their services, and predict the likelihood of contract renewal.

4. Inventory Management

Efficient inventory management revolves around knowing your demands. On one hand, companies must ensure that they have enough stock in their warehouses to meet the ongoing demands. On the other hand, they must preserve a small quantity of slow-moving items. Traditional solutions rely on formulas to reach static sets between pessimistic and optimistic values, and because of that, the chosen buffer level does not guarantee that the stock level fully satisfies the demand.

The way in which data can be incorporated from different external sources, including non-business data, helps machine learning models to find a correlation between the different variable factors and to optimize stock levels dynamically.

For example, feeding weather conditions and inventory data into a machine learning model can identify that specific parts tend to fail when the temperature outside is above 90 degrees Fahrenheit. In case the weather forecast shows that a heat wave is approaching, several proactive actions can be initiated, like ordering a sufficient quantity of items that are likely to break down so that those items can be used for predictive maintenance visits. Essentially, the machine learning model predicts the necessary stock level and determines how much inventory to order by using the past records of weather and failures.

The machine learning model can predict the necessary stock level and determine how much inventory to order by using the past records of weather and failures.

 

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Do you want to know more?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

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Sponsored by: 

 

 

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Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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Customer be central to every organization’s customer support strategy. Every field service repair should be treated as an opportunity to exceed expectations and deliver a truly exceptional customer experience, writes Martin Boggess from Hitachi.

It’s imperative that your organization consistently deliver on the four “R’s” of field service management: delivering to the right person, at the right place, at the right time, with the right tools. Proper field service management ensures that work is completed in accordance with customer expectations — within their budget, timeline, and quality specifications. 

Meeting customer expectations is often easier said than done. Field service technicians are expected to be aware of everything that occurs at each stage of the customer lifecycle, to possess detailed technical knowledge about equipment and regulatory compliance, to execute complex repairs, and to draw upon this vast wealth of information at a moment’s notice — and they need to be able to do everything right the first time to avoid the dreaded callback. The job of a field service technician can be overwhelming, to say the least.

Fortunately, cloud and mobile technology have made it much easier for organizations to adopt field service software to automate and optimize core work processes, such as scheduling, dispatch management, contract, SLA and warranty management, inventory management, and more. Advanced field service solutions include applications tailored to complete specific tasks and resolve specific issues, which makes a field technician’s job more manageable and increases employee productivity in the process. To demonstrate how a field service solution can resolve some common problems, let’s look at a few examples.

As mentioned above, two of the four “R’s” of field service are the right person and the right tools. It’s essential that a field service organization dispatch the technician with the right level of expertise for the job and with the proper tools to complete the repair. Field service software makes it easier to match the right technician to the job, and the technician can then use the software to review service orders and see what parts they need for the job — all prior to meeting with the customer. This foresight drastically reduces the number of unnecessary trips and can increase an organization’s overall first-time fix rate.

"The job of a field service technician can be overwhelming, to say the least..."

Many field service solutions leverage mobile technology. Once a technician is on-site, they can use either a tablet or mobile phone to access the history of a piece of equipment, including previous repairs, previous service tickets, technician’s notes, meter readings, and so on. This information makes it easier for the technician to determine why the equipment is malfunctioning and what the best strategy is to repair it. Technicians can also use mobile field service applications to pull up service contracts while out in the field and present customers with accurate pricing.

The internet of things has also radically changed how field service organizations administer repairs. Put simply, the internet of things (IoT) is a system of connected devices capable of rapidly transferring data via virtual network. By attaching IoT-enabled sensors to equipment, field service organizations can receive real-time diagnostics from anywhere in the world, which enables them to identify and respond to issues before they even arise. This shift from a preventative repair model to a proactive model will be crucial to field service organizations’ success in an increasingly competitive market.

In addition to mobile and cloud technology, augmented and virtual reality are also transforming the way field service sector operates. Organizations can use AR and VR in conjunction with IoT-enabled devices to simplify knowledge transfer via interactive training, while technicians can use it to access equipment repair history and diagnostics without taking it apart and even perform remote fixes. Since AR and VR are still relatively new technology, only certain field solutions will offer AR and VR functionality.

As you can see, field service solutions provide innovative tools and capabilities to reduce costs and increase employee productivity and first-time fix rates. Hitachi Solutions’ Extended Field Service solution is one such solution with extended functionality to optimize scheduling, simplify contract and inventory management, increase mobile productivity, perform remote troubleshooting, deliver an end-to-end customer-centric experience, and more.
Martin Boggess is Industry VP, Manufacturing and Field Service at Hitachi Solutions America. 

 

In the second article in our current series of articles from field service solution provider Astea focussing Artificial Intelligence and Machine Learning we explore how and why field service companies are adopting these emerging tools... 

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Is Machine Learning a key topic for your organisation?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

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Sponsored by: 

 

 

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Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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There is a lot of buzz around machine learning and AI in the field service industry, but are service organizations actually adopting it? How are they applying the concepts to their business operations? And lastly, what results have they experienced thus far? We turn to some of the predominant service management industry analysts to help answer these questions.

Let’s tackle adoption trends first. Gartner predicts that by 2020, 10% of emergency field service work will be both triaged and scheduled by artificial intelligence, up from less than 1% in 2017. (1)

And by 2022, Gartner predicts that one- third of complex field service organizations will utilize machine learning to predict work duration and/or parts requirements, rising from less than 2% today. (2)

In addition to analyst predictions, another way to look at adoption trends is to ask how many companies plan to deploy machine learning in the near future. In a recent Gartner survey of 50 leading field service organizations, over 25% indicated they had artificial intelligence or machine learning projects planned for the next 12 to 18 months. (2)

Aberdeen Group’s research is in alignment with Gartner’s. According to a recent survey of customer experience management leaders by Aberdeen, only 14% of their organizations were currently using machine learning and only 9% were using AI. Yet 40% of the surveyed companies are planning to deploy machine learning and 34% are planning to deploy AI.

 

Adoption Results:

How have service companies applied intelligent systems to their operations? What have their results been thus far? 

Again we turn to the analysts at Gartner who state that field service companies will use AI and machine learning in their customer service channels to gain better insights, increase self-service and improve productivity.

Specific AI applications in customer service include:

• Intelligent case management routing and workflow;
• Robotic process automation (RPA) tooling;
• Knowledge management;
• Chatbots, virtual personal assistants (VPAs) and natural language processing (NLP);
• and finally, sentiment analysis and emotional detection in social media.(2)

Customer service appears to be the most popular application thus far for field service, but what other business functions could AI improve? According to Aberdeen, another application is managing the customer experience. Retaining existing clientele is top-of-mind for almost all companies. In fact, companies using cognitive technologies achieve 6.5 times greater year-over-year increase in customer retention rates, compared to all others.(3)

We’ve seen positive results from AI and machine learning when applied to customer service and the customer experience. But can these technologies improve life for your employees, too? 

Striking a balance between positive results for your customers and your employees is a best practice you’ll hear often when reading about cognitive technologies. The early adopters have proven it’s possible. Aberdeen’s research shows that companies using cognitive technologies enjoy 81% greater year-over-year increase in employee engagement. This is a bit of a paradox since in the short-term, some employees will be replaced by robots. Yet, when looking at the big picture it’s clear that the large scale impact of AI and machine learning will be improving employee productivity.(3)

 

Can this lead to financial success as well?

Thus far we’ve discussed positive results for your customers and your employees, but can cognitive technologies help drive financial success also? Absolutely!

Aberdeen’s data shows that companies using cognitive technologies enjoy 36% greater year-over-year increase in annual company revenue, compared to All Others. They also attain 63% greater annual improvement in customer lifetime value, compared to others.6

Companies using cognitive technologies enjoy 36% greater YoY increase in annual company revenue. They also attain 63% greater annual improvement in customer lifetime value.

 

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References::

1. Robinson, Jim et a “Critical Capabilities for Field Service Management.” Gartner, 27 March 2018.
2. Huang, Olive et a “Predicts 2019: CRM Customer Service and Support.” Gartner, 13 Dec 2018.
3. Minkara, Omer. “Cognitive Customer Experience: The Future is Here.” Aberdeen Group, April 2017.

 

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It’s no secret that data collection is important for generating valuable insight that helps plant managers improve efficiency in industrial systems. However the importance of understanding the context of the data is less well known. George Walker from Novotek explains more.

Tunnel vision is a problem across a variety of fields, from sports to industry. It is easy to get fixated on a single goal and to forget to take a step back for a wider view of a situation. Yet, taking this step back can provide invaluable insight and understanding of the reality of a situation. 

Let’s take a hypothetical scenario. Imagine that a maintenance manager has a machine that is bending wires. These wires have to bend to a very accurate angle to properly work. However, the machine isn’t bending them properly at certain times of the day, causing the business to produce faulty goods that have to be discarded. 

To fix this issue, the maintenance manager brings up the data collected by the device’s onboard software. The manager then analyses the data with a digital twinning platform. Looking at the analysis, it becomes apparent that the machine is vibrating anomalously at certain hours of the day. The manager dismantles the machine, reassembles it and even runs it in an isolated scenario, but is still unable to find the source of the problem.

 

"It is easy to get fixated on a single goal and to forget to take a step back for a wider view of a situation..."

 

In this instance, if the manager had taken a moment to take a step back and looked for context, they may have realised that the anomalous vibrations coincided with the activation period of a nearby piece of heavy machinery. There is nothing wrong with the machine in question, but its surrounding context reveals the cause of the error.

This is why context in data collection is vital.With an array of smart sensors and devices, paired with a digital twinning system like GE Digital’s Predix platform, across the whole production line, the manager could have clearly seen the correlation by viewing the data in context. Another method of achieving contextual understanding would have been by comparing the machine with other similar ones in different plants. 

This shows why digital twinning is such a powerful tool. Being able to recreate an entire plant in a digital model breaks silo mentality. This allows managers to have holistic insight, which reveals issues that were previously not apparent. Whatever the issue, it is evident that context in data collection matters. Being able to analyse systems is now a reality and should be used to the full extent of its potential.

With potential to reduce waste and unnecessary expenditure, digital twins will allow stronger operations. With context being easily achievable in the modern industrial arena, it no longer needs to be a trade secret among industrial businesses. And by sharing this knowledge, more businesses can make their smart networks smarter, their operations more efficient and their production processes more productive. 

George Walker is Managing Director of Novotek UK and Ireland.

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