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Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful twenty-first-century technology? In the part one of this two-part feature Marne Martin, President of Service Management, IFS outlined why AI in field service is about far more than chatbots, now in the concluding part, she outlines how AI can bring a touch of genius to your field service operations...

 

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Solving Problems When One Isn't Albert Einstein

Human agents are capable of optimally dealing with a customer, and AI can free them up for the most interesting and demanding tasks. In the case of scheduling technicians in the field, humans are just not up to the numerical challenge of adjusting a schedule in an optimal fashion as humans typically focus in on an aspect of a problem to solve rather than finding the best solution overall.

A dynamic scheduling engine (DSE) driven by AI algorithms is designed to solve complex scheduling problems in real time—problems much too complex for any human dispatcher or customer service agent to handle, especially when at times individuals will act myopically based on their area rather than for the greater good of the company and its customers.

 

"Even a static service schedule can be handled in myriad different ways and decisions regarding which technician to send to which of several jobs in what order are often made based on suboptimal heuristics..."

 

Even a static service schedule can be handled in myriad different ways and decisions regarding which technician to send to which of several jobs in what order are often made based on suboptimal heuristics.

“Steve’s son is in daycare in this part of town, so I will schedule this appointment last, so he will be close by.” Sometimes jobs are scheduled based on first-in, first scheduled, regardless of the actual urgency of requests that come later.

Manual or traditional software-based scheduling may be a workable solution for service organizations with a very small number of technicians each engaged in a small number of jobs during a day. But it does not take many technicians or jobs for the number of possible solutions to outstrip human computation capabilities either individually or as a group.

Even at the low end of the spectrum, a human dispatcher cannot quickly identify all the possible solutions and pick the best one. With two technicians and four service calls there are already 120 possible solutions— different combinations of technician, job and order. Two technicians, and five service calls yields 720 possible solutions. Four technicians and 10 service calls present a dispatcher with 1,037,836,800 possible solutions.

But the time you get to five technicians that must complete six calls each—a total of 30 calls, you have 12,301,367,000,000,000,000,000, 000,000,000,000,000 possible solutions.

Finding the optimal solution becomes even more complex as additional and rapidly-changing factors are added into the mix:

• Emergent jobs come in that must take precedence over those already scheduled
• SLAs and other contractual requirements demand that some jobs be completed within a given timeframe
• Technician skill sets that influence which tech is sent to which job
• Tools and materials currently in stock on each service vehicle
• The current location of a technician in proximity to each job and to drop locations for inventory that may be required for a job
• The duration of each service call, both in terms of estimated time required to complete the call and whether a current job is running over the estimated time, resulting in knock-on effect on subsequent jobs

Former world chess champion Garry Kasparov, in his book Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, makes clear that even his mind is not capable of computing possible solutions and outcomes as rapidly or effectively as an AI algorithm.

 

"Automating the schedule through AI not only enables a much higher level of service but frees up dispatchers to handle those “beautiful or paradoxical moves” that may delight a customer or solve a tough problem...“

 

The human mind isn’t a computer; it cannot progress in an orderly fashion down a list of candidate moves and rank them by a score down to the hundredth of a pawn the way a chess machine does,” Kasparov writes. “Even the most disciplined human mind wanders in the heat of competition. This is both a weakness and a strength of human cognition. Sometimes these undisciplined wanderings only weaken your analysis. Other times they lead to inspiration, to beautiful or paradoxical moves that were not on your initial list of candidates.”

Automating the schedule through AI not only enables a much higher level of service but frees up dispatchers to handle those “beautiful or paradoxical moves” that may delight a customer or solve a tough problem.

In the end, collaborating with intelligent machines will get us further faster than going it alone. According to Kasparov, the best chess is now played as grandmasters use computers to analyze positions, opponents’ games and their own games—elevating the level of play. In an interview with the Financial Times, Kasparov, who famously had matches against an early chess supercomputer, described how the best chess is now played by combining “human intuition and understanding of the game of chess with a computer’s brute force of calculation and memory.”

“I introduced what is called advanced chess; human plus machine against another human plus machine,” Kasparov said. “A human plus machine will always beat a super machine. The computer will compensate for our human weaknesses and guarantee we are not making mistakes under pressure … the most important thing is not the strengths of the human player. It is not the power of the computer. But it is the interface. It is the corporation.”

 

Legacy Approach to Inventory Logistics

Service management for many businesses relies on inventory … if completion of a service call requires inventory and you are out of stock, you cannot meet your commitment to the customer. When a service request cannot be closed on the first visit, it is often because the right part is not on the truck or immediately available.

So, service management software should encompass inventory management functionality, and that functionality should include automated reorder points for each part. The ability to take parts availability into consideration is a critical data set for AI to work on as parts are a critical determinant in first-time fix and job completion where parts are a factor. It also is a key aspect to successful SLA and outcomes-based commercial relationships.

Once inventory data is available and integrated, a powerful DSE may also be configured to influence inventory logistics so parts and materials are housed in warehouses, satellite offices or inventory drop locations closer to anticipated demand, with inventory matched to jobs in a forward or current day schedule. In one very large implementation of IFS Planning and Scheduling™ Optimization—in the London underground transit system—inventory and tools are dropped ahead of each service visit so technicians who ride the subway to the service site can pick them up.

This is only possible with a high degree of coordination between the service schedule, inventory logistics and an AI-driven scheduling tool.

 

Conclusion

Service organisations should recognise the tremendous potential AI holds—they can harness it to transform their operations, outflank their competitors and disrupt their markets. We are only starting to tap into the different ways AI can be used to better solve the problem of delivering optimal service in a rapidly changing environment as adoption is still lagging despite the real benefits AI brings. The good news is there are several straightforward and easily accessible ways service executives can harness AI technology right now, today.

 

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Adopting IoT as part of the greater service and business environment involves keeping up with industry changes as they take place. That means incorporating better measures when needs arise in any business area and keeping cost-effective solutions in mind for the future progress of the company as a whole...

Already, 76% of companies are using IoT data analytics to establish product and/or process quality imperatives. Their decision makers can analyze IoT data to improve solution recommendations, feedback on installations, demonstrations, specific services, and others.

IoT also serves as a signifier for opportunities to improve more processes, such as identifying popular products and managing inventory.

Respondents to a recent research project undertaken by WBR and commisioned by Astea believe data should be usable in decision making at a variety of business levels. In every case, a majority of companies have either adopted IoT for specific business functions or plan to do so in the next 24 months. But companies prioritize customer-facing initiatives—service, products, and satisfaction—over internal functions such as business projections and aligning service data with financials.

 

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Customer Satisfaction & Loyalty:

73% of companies have incorporated IoT (42%) or plan to do so within 24 months (31%) for the purpose of customer satisfaction and loyalty. More companies have incorporated IoT for this purpose than for any other measured in the study.

With connected data, companies are able to understand and fulfil customer demands better thanks to improved communication. In this way, minor technological improvements can be made without delay or other consequences.

 

Service Processes & Optimization

Respondents agree that connected data and IoT have helped streamline processes across departments. By leveraging IoT data, they can measure efforts for overall growth through set channels, be they internal or service-driven.

Now, 41% of companies have incorporated IoT for process optimization, a close second to customer satisfaction and loyalty. Thirty-six percent have already incorporated IoT with service processes; more companies plan to do so within 24 months (37%) than with any other business function measured.

 

Product Uptime

Companies’ attention to customer experiences carries over to product support, where one respondent cites “notable improvements” to uptime in both industrial and consumer-driven channels. One healthcare executive says IoT helps them sustain products “during times of higher demands, especially due to the fact that these are used during medical procedures.”

More than one-third of companies have incorporated IoT for product uptime (34%); more than one-quarter of companies have plans to incorporate IoT with product uptime (30%) within 24 months.

 

Business Projections & Decisions

IoT data can be applied to various business requirements and provide essential statistics to support managerial functions. Derivations from reliable signals allow for better judgements when making business projections and decisions.

Over one-third of companies have incorporated IoT for business projections and decisions (35%); more than one-quarter of companies have plans to incorporate IoT with business projections and decisions (27%) within 24 months.

 

Predictive Maintenance

Respondents’ ambitions for better response to maintenance needs extends to real-time automated reporting, a better understanding of their products’ “general maintenance structure,” and even signals for customers to be proactive—to seek out maintenance themselves.

Several respondents cite their use of predictive reporting for scheduling, sustainability, and research methods, among others. Only 32% of companies have leveraged IoT for predictive maintenance; however, 29% plan to do so within 24 months.

 

Aligning Service Data with Financials

Fewer companies have incorporated IoT to align service data with financials (26%) than any other business function in the study. But the data suggests this is a growth area. More companies (61%) are either planning to incorporate IoT in this way within 24 months or are interested in incorporating IoT in this way than with any other business function.

Despite the prioritization of functions that drive customer success, it is in business projections, business decisions, and aligning service data with financials that companies take an increasing interest in incorporating IoT.

At least one-quarter of companies have already incorporated IoT for each of these purposes. Have you?

 

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Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful twenty-first-century technology? In the first of a two-part feature, Marne Martin, President Service Management IFS, offers her expert insight... 

 

 

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Artificial Intelligence (AI) will impact every industry and every business discipline—including field service management. But how quickly will practical solutions be available that enable the typical medium to large field service organization to take advantage of AI? And by practical solutions, I mean AI that delivers knowledge efficiently, processes solutions to complex data sets, and automates repetitive activities to allow human workers to focus on personalized service, solving complex problems and escalations, i.e. what people do best.

In some cases, these easily applied solutions are still on their way to market. In three specific areas, however, practical AI applications for field service are already commercially available as proven, commercial off-the-shelf software delivering real business value.

 

AI For Customer Interaction

First impressions matter. And unfortunately, the first interaction a customer has with your service organization often involves several missteps. Chief among these are long wait times on hold due to high call volumes. And then, as a customer attempts to reach out through multiple channels including email, chat and phone, the resulting data stream goes into separate siloes that are disconnected from each other, resulting in disjointed communication.

 

"Today, AI solutions can solve both these problems, but it requires more than “just” chatbots..."

 

Today, AI solutions can solve both these problems, but it requires more than “just” chatbots. Commercially available AI software that ties into chatbots is capable of learning which answers posed in a chat are appropriate for each question and automating a significant majority of chat interactions. A chatbot can be taught to answer commonly encountered questions, like inquiries about when a technician is scheduled to arrive. Of course, at some point, the AI chatbot may get stuck when personalized service is required, and a human agent takes over the discussion thread without missing a beat. This should be seamless not only to the customer but for the internal customer service, ticketing and support systems as well. The chatbot—regardless of whether driven at a given moment by AI or a human agent—should update the same customer record as other channels including social media, phone and email.

And from interactions, the AI functionality learns from answers provided by human agents and gets better and better at answering questions through learning processes. A truly advanced AI chatbot will also seamlessly hand off the chat to a human agent when the extent of its learning is overtaken. Only then can the entire customer experience be unified and consistent, even with a static number of agents handling a rapidly growing fluctuating volume of customer interactions.

AI-based chatbots, for instance, can enable a good agent to handle up to five or more chats at a time. It can capture Facebook messages and tweets and direct them to an agent or to AI for intervention. AI alone can handle, typically, between 50 and 60 percent of requests, freeing up human capacity or lowering staffing levels required to handle a given volume of activity.

 

Enables Management By Exception

In the case of AI applications for the service organization, a primary driver for ROI is that it enables humans to manage by exception. A high volume of activity can be automated, and humans intervene primarily when a situation falls outside the business rules or logic built into service management software. AI doesn’t eliminate the need for human interaction—it makes the human interaction more focused on what humans do best—handle escalations and complex decision making for unique cases.

At one IFS customer, an AI chatbot handles about 50 percent of interactions— primarily those reaching out to cancel their service after a free three-month trial period. Interactions cancelling a free subscription are handled entirely through automation. But if a longer-standing customer is cancelling their service, the interaction gets routed to an agent dedicated to saving the account.

Some interactions are by default easily handled by AI. If 30 percent of inbound contacts are requesting information on the arrival time of a field service technician, it may be possible to automate 90 percent of that 30 percent of contacts. But it is also important to consider the demographics of the customer base. Millennials are more likely to communicate via chat or social media, so if a significant percentage of customers are under 40, heavier reliance on chatbots and AI may help you increase engagement by streamlining your customers’ preferred method of interaction.

 

"Management by exception is also more successful when an AI application has access to extensive information about each customer..."

 

Management by exception is also more successful when an AI application has access to extensive information about each customer. So full integration with enterprise resource planning, field service management and other enterprise tools is essential. AI tools can be more effective if they have more rather than less information on the status of the customer’s account, including their maintenance or service history and warranty or service level agreement entitlements.

Integration between an AI chatbot, email, voice, social and enterprise applications is important for another reason. It enables one version of the customer record. Lacking this, a customer can send an email, and get no response. They send a direct message through Twitter. Then call and sit on hold. Then initiate a chat. All these interactions may not appear in a central customer record, but there have been three attempts to contact the company. Right from the first contact by email, the clock started ticking on a service level agreement.

Full integration can also enable a customer service team, once a customer request is resolved, to close off all queuing activations at the same time for the various contact methods associated with a customer case. Failing this, a service organization may spend a significant amount of time chasing customer requests that have already been resolved.

 

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In the age of the industrial internet of things (IIoT), the speed of data analysis is key to effective operation. Edge computing accelerates this process, allowing for industrial data analysis to be performed at the point of collection. 

Here, George Walker, managing director of industrial control and automation provider Novotek UK and Ireland, explains the core benefits of edge computing.

Edge computing is the term for when process data is collected, processed and analysed in a local device, as opposed to being transmitted to a centralised system. Supported by local cloud networks and IIoT platforms like GE Digital’s Predix, systems that support edge computing are proving increasingly popular as a means of streamlining the effectiveness of IIoT networks.

For plant and utility managers, this presents a range of opportunities to not only improve the efficiency of operations but to also overcome some of the limitations of centralised IIoT networks. In fact, there are the three main ways that edge computing drives value in businesses.

 

Greater operational efficiency

Traditional analysis is undergone by transferring data externally, which can delay decision-making as errors take longer to be found. With edge computing capable systems, large parts of the analysis can be carried out by the devices collecting the data.

The benefits of this are two-fold. For one, this can allow plant managers to access partial deep analysis in real time without waiting on lengthy analysis to be carried out externally. This means action can be taken earlier, streamlining the decision-making process.

The second benefit is that the IIoT platform, such as GE digitals Predix, can automatically respond to operational data. The system will be able to automatically adjust processes in real-time. In effect, this would allow for a self-correcting system that is able to maximise uptime and reduce the need for manual maintenance.

 

Overcoming network latency and bottlenecks

Traditionally, data analysis is carried out by having smart sensors send all their data to a remote location where it is analysed and processed. This is data intensive and can create problems if a network is not robust enough.

Channelling large amounts can cause network latency, which interrupts working within the plant as there will be a delay with transferring messages that run through the same network.

This is particularly problematic for applications where a system needs to act rapidly to a problem, such as in an industrial oven control system in a food production plant, where even a temporary dip in the temperature can result in a batch being unsuitable for market.

In addition to this, the sheer volume of raw data that can be generated in an industrial or utility plant is also likely to cause data bottlenecks in the wider network.

By using edge computing systems and a machine-learning IIoT platform, systems can respond to changes in real-time to prevent problems, while also having edge computers in place to compress the data and reduce network impact.

 

Lower operating costs

Due to the amount of information being produced, the cost of data storage is becoming a growing concern for companies. Edge computing and its ability to process data without transmitting it, lightens the load put on the network.

Processed data is also less substantial than raw data as calculations can be made that allow the raw data to be compressed, thus reducing file sizes. As such, industrial companies are able to make more economical use of their cloud servers. By minimising storage requirements and the number of storage upgrades required, edge computing can allow for a lower overall operating cost.

It’s clear that there are many benefits to edge computing, both from a financial and operational perspective. Whether a business is still considering adopting IIoT technology or is already making use of such systems, edge computing marks a step forward for businesses looking to streamline processes for efficiency and effectiveness.

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Fujitsu Limited and Ericsson have entered an agreement to deliver end-to-end 5G network solutions and related services under a strategic partnership. The two companies are joining forces to develop this based on their combined portfolios – spanning radio access and core network – for the dynamic 5G market in Japan, connecting communications service providers to the global 5G ecosystem.

The two companies aim to initially provide systems and solutions for the Japanese market, and seek to further expand their collaboration to other customers globally.

In the 5G era, mobile communications service providers anticipate the ability to provide highly scalable, and intelligent services through open and globally standardised technology for core and radio access network for more efficient network operations.

Ericsson and Fujitsu’s strength in research and development will ensure the best path for bringing global 5G solutions to Japan, as well as exploring a wider global market.[/quote]As a leading network technology provider, Fujitsu is making concerted efforts to support open standards activities driven by major telecommunications providers and aims to achieve broad interoperability for its radio access products in global markets.

As a world leader in 5G, Ericsson has worked closely with mobile operators around the world in the development of 5G, through standardization, trials, and prototyping.

Ericsson and Fujitsu’s strength in research and development will ensure the best path for bringing global 5G solutions to Japan, as well as exploring a wider global market.

Tango Matsumoto, Executive Vice President, Head of Network Business Group at Fujitsu, says: "Through this partnership with Ericsson, we will provide flexible 5G network systems that are open and standard compliant, and will leverage our expertise in wireless technologies and network integration to a wide range of customers in and outside of Japan. From mobile broadband, expected to be the first widespread use case of 5G, to the Internet of Things (IoT) and beyond, this partnership holds out the promise of exciting new business opportunities."

Fredrik Jejdling, Executive Vice President and Head of Business Area Networks at Ericsson says: “Our global expertise in 5G combined with our understanding of the local market puts us in an excellent position to support the introduction of 5G in Japan. By working closely with operators and partners, we are creating solutions that will bring successful use cases and applications to the market. With Fujitsu we get an excellent partner to accelerate this development.”

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In this, the latest edition of the Field Service Podcast, Kris Oldland, Field Service News, Editor-in-Chief, is joined by Marne Martin, CEO of WorkWave and president of Service Management for IFS about her new role with IFS as well as discussing whether the time has come to finally recognised Field Service Management systems as a standalone category such as CRM or ERP [hr]

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We've been asking for some time now how Augmented Reality will fill its potential as a central fulcrum within the future of field service. For Nick Frank the key is for it AR to become entwined with Knowledge Management... 

The English philosopher Francis Bacon once said: “Knowledge is power,” In earlier times, knowledge was usually kept to oneself for personal gain. Today, it is the sharing of knowledge that leads companies to success, especially in times of increasing digitization.

This ‘sharing’ involves collecting data, transforming it into insight and then getting it to a place where people can use it to make a difference. Benefits are only seen when the ‘knowledge chain’ is completed and any break in the chain nullifies our efforts.

So when industry commentators tell you that a particular technology is the “silver bullet” to success, it really is an oversimplification!

The problem is that knowledge is often “hidden” in the various IT systems and applications, or lost in the heads of employees who leave the business. For field service, this problem is particularly severe as the service portfolio is significantly larger than the current product offers due to longer product lifecycles and ever faster new product introductions.

On the other hand, service knowledge must be immediately available, in a distributed fashion, to achieve quick solutions and to ensure customer satisfaction. For service, we should view the challenge as being to provide customers or field technicians with that extra piece of know how that will help them solve problems more efficiently. A kind of “Augmented Knowledge” for expand it and provide it in a targeted manner. Existing information stored in different systems is merged. This can be structured data such as parts lists and unstructured information such as service tickets or service reports.

 

"Unstructured knowledge – text or prose – is analysed using text mining tools and integrated with the structured data. Large amounts of data can then be digitised and used intelligently..."

 

Unstructured knowledge – text or prose – is analysed using text mining tools and integrated with the structured data. Large amounts of data can then be digitised and used intelligently.

Urgently needed information is provided easily and quickly. Being able to network across databases makes it possible to recognize contexts, to analyze causes of failures and to create transparency. By using the system and verifying or excluding results, users continuously enrich it with expert knowledge. The current problem may already be the solution for the next user.

A classic example is finding similar cases (or problems). If an engineer is looking for the cause of a failure, the system looks for similar case and offers potential solutions.

The source for this could be the targeted evaluation of completed service cases (e.g. service tickets). By analysing which solutions were chosen by the engineer, the associated repair instructions, and confirming them as successful (or not successful, as the case may be) after the repair – the system learns through this interaction.

In fact, this process can go further and develop new insights from existing information. By visualizing and recognizing patterns, correlations can be identified, and appropriate measures initiated. For example, as part of a maintenance action or repair, the system can recommend the maintenance or repair of other elements to avoid subsequent failures that have arisen in similar situations.

But how to get that information to the point of need?

Augmented Reality (AR) technology, with its capability to supplement a real object, such as a machine or a component, with additional digital content is an ideal tool for this. It is not just the traditional approach of an expert communicating with a technician, it is extending it to ‘’smart’ databases supplying answers to questions.

 

"There is much to learn about the ergonomics of Augmented Knowledge and how to integrate it into people’s working lives..."

 

For example, in addition to the live video image on a tablet, smartphone or smart glasses, information and instructions can be augmented to the display to help solve the problem. These may be created by an expert remotely or they may be rendered as step by step instructions by the knowledge management system.

The individual steps necessary to solve the problem are now available in the form of AR annotations and can be subsequently edited and saved. This is another advantage of the AR system: The repair process gets documented and can be used again for similar cases.

So, if the engineer encounters this problem again in the future, they can reuse the annotations of the first repair without having to consult the expert. In addition, the solution is also available to all other engineers.

This saves significant time and effort. The caveat is to be able to present information to users such that they can use it. There is much to learn about the ergonomics of Augmented Knowledge and how to integrate it into people’s working lives.

This is a good example of how by turning information into transportable and analysable data (some call this digitisation of their processes), it is possible to accelerate service delivery, saving time and money for both the service provider and the user of machines.

Our experience is that by breaking down Knowledge Management and Augmented Reality into smaller pilot projects, we learn how to provide Augmented Knowledge to the Technician. Not just the technology, but actually how people brains cope with having access to this additional insight.

This may seem as bit ScFi and daunting at first, but you would be surprised how much of this you already do. Our advice is don’t look to anyone technology being pushed at you as the unique solution to your problems. You must develop your Knowledge Management, Augmented Reality and People capability in parallel.

For more information on how to start this digital journey, you can contact authors at peter.maier@si2partners.com or nick.frank@si2partners.com

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Nick Frank, Managing Partner at Si2 Partners 

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John Hunt outlines why when it comes to digitalisation, the focus should be on the end goals, not the technology...

There is a common thread being pulled by executive boards across the globe at the moment both in the field service sector and far beyond.

That is, of course, digitalisation.

In my role here at Astea, it’s a topic that our customers and new prospects are keen to talk to us about – almost every company I’ve spoken to in the last 12 months has broached it in one form or another.

However, there is a fundamental point I believe is often being overlooked by companies as they dive head first into weaving the digitalisation thread into their strategy – that is that digitalisation itself should be seen as an on-going process continuously woven in your field service tapestry rather than a one-time project, or a mere stitch in time.

 

An opportunity to redefine workflows

To start, let’s look at what digitalisation shouldn’t be.

Digitalisation shouldn’t be simply taking all of the previous steps your field service engineers used to undertake manually with good old pen and paper and dumping them onto a mobile device. Digitalising their workflow is an opportunity to re-evaluate some of these processes, re-order some things, maybe even remove others entirely all in the spirit of making your customer ambassadors happier and more efficient and effective. That happiness, efficiency, and effectiveness translates into better top and bottom line performance and most importantly, happier and more loyal customers.

 

"Those companies that get the most success from the implementation of a Field Service Management (FSM) solution are those who bring a selection of their engineers into the implementation process..."

 

Invariably, those companies that get the most success from an implementation of a Field Service Management (FSM) solution are those who bring a selection of their engineers into the implementation process. Just like product marketers use focus groups of prospective customers to fine-tune their product offering to maximise demand, so should you leverage a similar approach with your customer ambassadors, also known as the engineers. For example, by speaking with your engineers to understand what elements of your FSM system’s mobile app they use the most frequently, you can ensure that access to the relevant parts of the solution need are easily surfaced within the app.

The same of course also goes for your scheduling solution – digitalisation should be an opportunity to put the information your team needs at their fingertips, quickly and seamlessly to improve both service triage and first-time-fix rates. So who better to ask what information should be where than the folks on the front line that need access to such information each and every day? The happiness through efficiency and effectiveness you will weave throughout the services organisation will pay big dividends not only in the traditional operational sense but also in employee retention and the all-important increased customer loyalty.

 

A journey of continuous improvement.

The concept of continuous improvement is one that many field service professionals are fully aware of, yet all too often it doesn’t get factored into discussions around digitalisation.

Facebook is famously always in ‘beta’ when it comes to its development, and whilst I wouldn’t recommend such a fluid approach to something as mission-critical as field service operations, digitalisation certainly allows us the opportunity to tweak things here and there to find those sometimes hidden incremental improvements that can yield seemingly small efficiencies that stack up hugely in the overall picture.

For example, I recall speaking with one service director earlier this year who explained to me that by implementing a simple keystroke study of his dispatch staff across 3 months they were able to identify some simple yet effective changes to the menu structure of their system which brought some frequently used options to the front of the solution when they were previously tucked away behind a couple of sub-menus.

On an individual basis these changes sped up the dispatcher’s role by just a few seconds each time. However, the overall net benefit to the service organisation was millions of dollars per year as those seconds began to add up across the entire workforce just like a snowball accumulating more and more snow as it rolls down the mountain.

Digitalisation allows us to not only make these changes quickly and easily across a large user base but also to understand how, why and where these changes should be made.

 

Build processes today with an eye on tomorrow.

One final piece of advice I would give to companies embarking on their own digitalisation journey is remember you don’t always need to boil the ocean; digitalisation should be an iterative process.

For example, IoT is the hot topic in field service right now and rightly so as it is set to play a huge role in the future of service delivery. Yet, for many companies a full IoT rollout is cost prohibitive and requires a gargantuan feat of logistical planning.

 

"I’d suggest you don’t even need one asset connected before you start building in the processes of identifying key data you wish to collect – what is to stop your engineers noting certain key data points when performing maintenance?"

 

However, do you need to have every asset in your install base connected before you can start pulling data for analysis to dig out some key trends and insights that could be of value to your organisation and perhaps even more importantly to your customers? Of course not!

In fact, I’d suggest you don’t even need one asset connected before you start building in the processes of identifying key data you wish to collect – what is to stop your engineers noting certain key data points when performing maintenance? You can build the processes and collect the data that would form the backbone of a digitalisation strategy before a single asset is connected, and then introduce automation for these new processes across a much more manageable timeframe.

In doing so you will have already begun to think beyond the realms of what is possible today and begun to consider what can we build now that will improve our service delivery tomorrow.

And this at its heart is what good digitalisation strategy should be all about. There are already some masterpiece tapestries with shiny digitalisation threads prominently featured in the field service world today reaping big rewards with their own teams and customers; these will continue to grow and outpace the industry. The best time to have begun your work of art was yesterday. The second best time is today, so gather your thread and your team to design it and start weaving!

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John Hunt, is Managing Director, EMEA, Astea, 

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Water and waste company Severn Trent is on the lookout for new and innovative ways to manage its water pipe network, and is now looking to the robotics sector to help provide solutions to some very specific issues.

The company is looking for individuals, companies and others to share their research or practical solutions to issues such as checking on water pipes buried several feet underground or even the automatic repair of pipes that have burst.

Dr Bob Stear, Deputy Chief Engineer at Severn Trent, explains: “Simply put, we want to see what’s out there when it comes to robotics and whether there’s anything that already exists, or which could exist in the near future, to help us with some of the issues that we face today with the thousands of kilometres of water pipes that we own and operate.

“It might be that there are devices, or research, that has a practical application in our industry but which was actually developed for something completely different.

“The beauty of this process is that we have no idea what diverse technologies might come back --but we absolutely do know the areas of our business where we think a technology solution could help us, and therefore our customers.

“We’re really excited to see what’s out there in the world of robotics and look forward to talking to the market about how this fascinating technology can help us to deliver wonderful water to our customers.”

Full details and provisional tender documents are accessible via the Bravo Solutions Tool:

severntrent.bravosolution.co.uk/web/login under the project identifiers;  project_666 - Request for Information Soft Market Test Robotics Market  and rfp_648

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