ARCHIVE FOR THE ‘artificial-intelligence’ CATEGORY

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... 

---

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... 

---

Sponsored by: 

 

 

---

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.   

---

 

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.

 

---

 

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.

 

---

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... 

---

Sponsored by: 

 

 

---

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.   

---

 

Artificial intelligence can be applied to enable the better detection of road traffic disturbances in real-time, according to research by the Finnish Transport Agency and Tieto.

The findings were the result of a Proof of Concept experiment conducted by the two organisations in the spring of 2018 that combined LiDAR (Light Detection and Ranging) measurement technology with sensor fusion and artificial intelligence techniques to analyse traffic flows.

Traffic situations deviating from the usual constitute a significant road safety risk. At present, the automatic monitoring of traffic anomalies tends to be based on camera surveillance, and the most sophisticated solutions focus mainly on the security of tunnels. Currently, artificial intelligence and sensor data systems are not widely used for real-time monitoring of traffic disturbances. Data analysis systems using sensor fusion and artificial intelligence can now provide new opportunities for traffic management centers to obtain a richer real-time view of road conditions and disruptions as they occur.

The experiment was carried out at the Mestarintunneli tunnel in Leppävaara, Espoo. Alongside camera surveillance, LiDAR sensors were installed in the tunnel. Compared to traffic camera footage, LiDAR technology has the advantage of being reliable in different lighting conditions, including low light environments. The desired solution was to detect, for example, stalled vehicles and other disturbances such as people or animals on the road.

The project had a lot of data at its disposal, collected from the normal traffic flow. However, the number of abnormal situations during the measurement period was small, which made it more difficult to develop an artificial intelligence solution.

“In order to model traffic flows, we decided to build a tailored machine learning model based on sensor fusion, and one that also recognises traffic anomalies by comparing them with the normal traffic model. This approach shows that even a smaller set of observations can be used to build virtually functional artificial intelligence solutions,” says Ari Rantanen, Chief Data Scientist, Data Driven Businesses at Tieto.

“Automatic recognition of traffic disturbances is a key requirement for a secure road network. Based on current functional requirements, the most cost-effective system has been traffic camera surveillance with a built-in disturbance detection system. However, we are constantly monitoring the market and introducing new technologies to seek new opportunities and cost-effectiveness,” says Senior Officer Kalle Ruottinen from the Finnish Transport Agency.

The project showed that automated analysis of traffic flows can produce new, near real-time information for different stakeholder needs without significant investments in sensors. In addition to analysing traffic flows, sensor fusion intelligence enables a number of other uses, such as forecasting traffic disruptions and assisting traffic management.

---

In a new series of articles from field service solution provider Astea, we tackle one of the burning questions being discussed amongst field service companies across the globe - what is Machine Learning and more importantly how can it be leveraged to improve service delivery? 

---

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... 

---

Sponsored by: 

 

 

---

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.   

---

 

There's probably no singular definition that would be accepted universally, but there are certainly some basic concepts. To understand those concepts, think about what machine learning actually does. The machine finds patterns in the data and uses these patterns to predict the future.

For instance, suppose that we have a data-set of one million emails that were tagged as spam. We could find patterns in that data-set that characterize spam, for example emails with all caps or with exclamation points, especially when they’re in the subject line.

Then, we can use those patterns to predict whether or not an email is spam.

 

Is Machine Learning the Same as Artificial Intelligence (AI)?

These two terms are often used interchangeably, but technically they do not mean the same thing, nor are they used for the same purposes.

We will start by defining artificial intelligence (AI) since machine learning has developed as a result of breakthroughs in the AI field. AI means that machines can perform tasks in “intelligent” ways instead of just being programmed to do a single task over and over. Performing a task intelligently implies that machines can adapt to different situations. Machine learning is considered to be a branch of AI, and it means that machines can be built to learn on their own and automatically improve their decision making through experience, all without human supervision.(1)

If you’re still a little fuzzy on the differences, we will define both concepts more simply:

• Machine learning consists of technology applications that learn by analyzing a pattern of historical and recent data.
• Artificial intelligence consists of technology applications that provide automated reasoning and decision-making capabilities. (2)

 

Origins of Machine Learning and AI

If you are wondering when these concepts were created, you can look all the way back to the original logical machines--computers. The computer’s end goal was that it could eventually function like a human brain. As we learn more about how the human brain works, we build that knowledge into artificial intelligence. And when you couple our deeper understanding of how the brain works with the massive amounts of data that the internet provides, you understand why AI and machine learning have grown so much in the last few years. 

According to AI expert Terrence Mills, “These two breakthroughs made it clear that instead of teaching machines to do things, a better goal was to design them to "think" for themselves and then allow them access to the mass of data available online so they could learn.” (1)

 

Why is Machine Learning so Popular These Days?

It’s all about data. Today, data is all around us. We are living in a data-driven world that’s only going to produce more and more data as time goes on. Humans and machines have become “data generators” that produce a tremendous amount of data every second. The amount of data created in the past few years is more than ever in the history of mankind and it is growing at a rapid pace.

The digital universe doubles in size every 2 years. By 2020, it will contain nearly as many digital bits as there are stars in the universe. 

In fact, the IDC estimates that by the year 2020, the accumulated volume of data will increase to roughly 44 trillion GB.(3)

---

References::

1. Robinson, Jim et a “Critical Capabilities for Field Service Management.” Gartner, 27 March 2018.
2. Minkara, Omer. “Cognitive Customer Experience: The Future is Here.” Aberdeen Group, April 2017
3. Turner, Vernon. “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things.” EMC Digital Universe with Research & Analysis by IDC, April 2014.

 

---

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... 

---

Sponsored by: 

 

 

---

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.   

---

 

Traditionally at this time of year, leading industry experts and analysts like to give their views and opinions on what they see as the leading technology trends for the forthcoming year.

For the first time ever, global business intelligence and research firm AMPLYFI has applied its leading AI-based technology, DataVoyant, to identify and statistically quantify mega technology trends out to 2050 across a range of industries and sectors. In finding, harvesting, and reading over 1,000,000 open-source documents, such as academic papers, patents, journals, news items and government white papers, located on both the Surface and the Deep Web, the machine-driven analysis projects likely future trends and timescales to next expected peaks in technologies’ maturities. 

Of the 2,639 broad themes identified, the top mega tech trends and where they are likely to really start impacting our everyday lives are readily identified. Top technologies to watch out for in 2019 are, unsurprisingly, artificial intelligence, the internet of things, and distributed ledger technologies. Looking beyond this, the realms of energy, mobility, medicine, and our domestic lives appear strong candidates for technology-led disruption on multiple fronts.

 

Projected Mega Tech Trends 

2019	Next 5 Years	5 to 10 Years	Beyond 10 Years
AI data collection and analysis	Graphene-based TVs	Solid State Batteries	Implantable Biofuel Cells
3D Printers in Manufacturing	Energy Harvesting Wearable Technologies	High Temperature Super Conductors	Fully Autonomous Cars
Growth in Connected Devices	Robotic Process Automation	AI-driven Medical Diagnosis	Smart Cities
Distributed Ledger Technologies to Improve Digital Security	Universal Personalised Digital Assistants	Bio-printed Organ Transplants	Cybernetic Technologies
Voice-first Machine Interaction	Remote Patient Monitoring	Personalised Medicine	Quantum Computing

 

The process of generating the projections required DataVoyant’s proprietary AI algorithms to first identify key technologies and quantify their relevance based on a number of factors, including frequency, inter-connectivity to related topics, and importance within each document that they feature in. Extracting date stamps from websites and from within documents, the machine then automatically generated historic trends for each technology. From here, deep learning algorithms enabled the machine to learn the “historic context” i.e. the changing interconnectedness and dependencies across hundreds of thousands of variables to create a basis to project forward into the future.

Chris Ganje, CEO and co-founder of AMPLYFI, said: “The power of AI to capture, analyse, and make sense of huge datasets is enabling levels of insight that were previously impossible under traditional business intelligence and research techniques. Our analysis of mega tech trends is just one example of where AI can either affirm or challenge the opinions and perceived wisdom of a minority of often highly influential commentators. The richness and detail lying behind the results allow us to spot early-stage convergence between innovative technologies and business models, as well as track the development of enabling technologies that will ultimately underpin future phenomena such as Smart Cities. Crucially, the machine is able to simultaneously monitor all of this globally, continuously tracking whether context changes sufficiently to materially alter its projections.” 

“Where the deployment or commercial application of technologies is likely to be hampered by regulatory hurdles or public opinion, such as home deliveries by drones or selective human gene editing, AMPLYFI’s DataVoyant technology enables us to monitor developments in these fields globally too. Conversely, it enables us to monitor where regulation is evolving to select and incentivise specific technologies.”

 “In a world where there are more questions than answers, we believe it is better to base business decisions from a position of informed knowledge than from mere opinion. AMPLYFI’s business intelligence platforms, driven by AI and machine learning technology, enable our clients to unlock the entire internet for themselves, both the Surface and Deep Web, in order to generate insights that their competitors are not seeing and to provide quantifiable windows into the future.”

AMPLYFI’s unique DataVoyant platform uses cutting-edge technology to locate, harvest, and analyse data from the Surface and Deep Web held across academic papers, patents, government reports, databases, journals, or news items, to find early warning signals and quantify trends that can help businesses make smarter, faster decisions.

---

 

Be social and share..

The Field Service Podcast returns for series three with a brand new host Mark Glover who speaks to Oneserve's CEO Chris Proctor. 

in this edition of the podcast fieldservicenews.com Deputy Editor, Mark Glover talks to the ever insightful and engaging Chris Proctor, CEO with Oneserve where they discuss why robots won't be taking over field service operations (just yet) and how OK should no longer be good enough for field service companies that want to excel.

---

 

Be social and share...

Manuel Grenacher, CEO, Coresystems, reflects back on some of the big predictions he made earlier this year and reflects on the progress made in interweaving the Internet of Things, Artificial Intelligence and Augmented Reality into the fabric of field service delivery across the last 12 months...

 

The days are getting shorter and colder, which means the holidays are approaching and 2019 is just around the corner (believe it or not!). So now is the perfect time to take a look back at 2018 and take stock of the advancements the field service industry made this year.

Back in March, we highlighted three trends we predicted would have major impacts on the field service sector in 2018. We noted that while 2017 introduced innovative new technology-based trends with the likes of artificial intelligence (AI) and augmented reality (AR), 2018 would bring real-world applications that put those buzzwords into practice.

So let’s review the progress of each of those three trends this year – after all, predictions don’t mean much if you don’t evaluate how accurate they were, right…?

 

The IoT Drives Proactive Device Maintenance, Service and Repair

Since the Internet of Things (IoT) became an integral part of almost every business’ technology mix midway through this decade, field service innovators have been finding ways to use the increased connectivity of the IoT to gain a competitive advantage. We predicted that in 2018, further innovation would allow field service technicians to utilize the IoT and automation in today’s devices – with the goal of providing service in real-time to meet (and exceed) customer expectations.

As is often the case in the field service industry, supply chain and manufacturing organizations were at the front of the line when it came to utilizing IoT-enabled and supported field service. Toward the end of this interview between SupplyChainBrain and various supply chain executives, the benefits of the predictive maintenance that the IoT enables become clear. Regarding sensor-equipped motors in warehouses, automation solutions provider Knapp noted:

“A motor might transmit information about vibration or heat, for example. It could indicate it needs potential maintenance services, and that's important because that would be predictive maintenance as opposed to breakdown maintenance, which is much more costly and can severely impact service levels.”

We’re seeing this focus on IoT-enabled predictive maintenance across the board with our manufacturing customers, so we can confirm that it definitely became a major focus in 2018 – and will continue to do so in 2019.

 

Artificial Intelligence Simplifies and Automates Service Appointments

Although artificial intelligence (AI) is in danger of becoming a somewhat empty buzzword in many industries, it’s here to stay – indeed, Gartner forecasts that 85 percent of customer interactions will be managed by AI by the year 2020.

The field service industry is applying AI in very meaningful ways as we speak, and it’s the concept of predictive maintenance that is driving the adoption of AI. For example, a recent study of original equipment manufacturers (OEMs) in the supply chain sector found that most OEMs are gathering data from sensor-equipped products in the field—a key requirement for predictive maintenance. In addition, more than half of OEMs plan to make AI and machine learning a major investment, while 90 percent intend to invest in predictive analytics within the next 12 months.

Beyond predictive maintenance (but related to it), AI can streamline the field service technician dispatching process – which crucially means customers can get their equipment serviced faster. Influential software authority Capterra highlighted how design consultancy Philosophie, using AI, developed a field service program that handed 90-95 percent of the technician dispatching duties to an AI system – which enabled the field service team to dedicate its human talent to the more difficult field service jobs.

AI most definitely made its stamp on the field service industry in 2018, and the innovation is expected to continue next year and beyond.

 

Augmented Reality Provides Unprecedented Visibility into Worksites

Back in March, we noted that the increased connectivity that the IoT brings will continue to propel the application of augmented reality (AR) in the field service sector. In 2018, we saw AR applied by companies aiming to improve their first-time fix rate on service calls, as well as other vital field service functions.

ZDNet detailed several highly-recognizable brands using AR for service calls, including BP's U.S. Lower 48 onshore oil and natural gas business, which has been equipping field service technicians with AR platforms to assist with repairs, and Caterpillar, which recently tested an AR solution for the technicians who service a line of its onsite portable generators. Caterpillar provides field personnel with an internally-developed iPad app that interfaces with IoT sensors on the generator to provide real-time diagnostics and repair protocols.

While AR is in its infancy relative to the IoT and AI, we’ve seen our own customers' leverage AR to make better use of their field service resources – including servicing their customers’ equipment remotely through AR glasses. We’re very likely to see the usage of AR expand in the field service industry in 2019.

It certainly appears that the trends we highlighted earlier this year continued to gain significant traction in the field service industry in 2018, and we fully expect IoT, AI and AR technologies to continue to drive a wide range of innovative projects and initiatives in 2019. And once the calendar turns to 2019, look out for our predictions on the developments to look forward to next year!

---

 

Be social and share... 

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...

 

---

Is Artificial Intelligence a key topic for you?! The full white paper on this topic available to fieldservicenews.com subscribers. Click the button below to get fully up to speed!

---

Sponsored by:

---

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.  

---

 

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.

 

---

Is Artificial Intelligence a key topic for you?! The full white paper on this topic available to fieldservicenews.com subscribers. Click the button below to get fully up to speed!

---

Sponsored by:

---

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.  

---

 
Be social and share

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... 

 

 

---

Is AI a key topic for you?! There is a full white paper on this topic available to fieldservicenews.com subscribers. Click the button below to get fully up to speed!

---

Sponsored by: 

 

 

---

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.  

---

 

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.

 

---

Want to know more? There is a full white paper on this topic available to fieldservicenews.com subscribers. Click the button below to access it instantly...

---

Sponsored by: 

 

 

---

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.  

---

 Be social and share... 

In an age of servitization and advanced services, spare parts management has become something of a difficult beast to fully grasp for many companies who are offer aftermarket services.

For example, in a world of guaranteed up-times, the cost of failure to keep an asset running can often far outweigh the lost revenue from the sale of the replacement part needed to get the asset back up and running and fully functional again.

Yet, the path to servitization is not an easy one to tread - so is it worth cannibalising what for many service companies is a reliable, consistent and strong revenue stream in its pursuit?

Whichever route companies ultimately turn to, one thing is certain, spare parts management is going to be a crucial aspect within the service delivery sector and as with mobile workforce management, there are a number of technologies and innovations that are emerging that could change the way we approach parts management in the future.

Therefore it was with great interest that we took a look at the insights from a recent research project undertaken by Copperberg. The research was conducted online across the last month primarily to Copperberg’s own audience of conference delegates.

In total the there were 65 responses to the survey and these representatives were all professionals within the sector ranging in seniority from parts managers through to Managing Directors - although the main body of respondents were at the division head/director level on a national scale.

The majority of respondents were from Europe although other regions, including China, were represented. The respondents were largely from manufacturing verticals, which would be anticipated given Copperberg’s flagship event the Aftermarket Business Platform is also a manufacturing dominated event. However, there were a number verticals within the manufacturing sector represented including heavy machinery, medical and automotive.

So let us take a brief look at what trends the research revealed...

---

Want to know more? Click here to Visit Copperberg's website to register for an exclusive white paper based on this research!

---

 

Inventory Management:

Inventory Management sits at the heart of good parts management as without the ability to track components and parts at any given time as they move from depot to the field (and potentially back again depending on a companies approach to repair and reverse logistics) everything else within in the equation becomes open to inaccuracies and subject to guesswork.

Indeed, the importance of inventory management appears to be hugely important within the organisations represented within Copperberg’s research with 91% of the respondents ranking it as being either four, five or six on a scale on to six with six being very important. In fact, almost half of the respondents (43%) listed Inventory Management as very important (6) - further emphasising the significance of inventory management in the context of spare parts management.

So it is absolutely shown to be clear in the research that the focus on inventory management remains one of utmost importance for the vast majority of companies.

 

Parts Pricing and eCommerce:

Parts pricing is also another area that was unanimously outlined as being important to the survey respondents.

This is particularly interesting as the fact that so many companies still view parts pricing as being highly important to them could be viewed as an indicator that the revenue streams that come from spare parts sales is still very much a critical part of the aftermarket landscape.

In fact, 86% of respondents stated that they felt parts pricing was at least a four on the same scale as listed above, however, here it was just under a third of respondents (32%) that felt this issue was very important.

eCommerce is of course another area that is heavily linked with parts pricing and there are indeed some correlations between the two areas, yet in terms of responses, eCommerce remains somewhat less of a priority than pricing.

With regards to eCommerce, exactly two-thirds of the respondents (66%) listed it as a four, five or six with only 16% seeing it as being very important (6).

This is quite an interesting difference between the two as we might have anticipated these results being more closely aligned.

One assumption, however, may be that with regards to eCommerce the solutions have now matured and so most manufacturers in 2018 may have at least some form of eCommerce solution in place - perhaps this explains why it is viewed as less of a priority?

This is certainly though an area for further discussion - something that will be surely had at the Copperberg Spare Parts Business Platforms which are running in Q1 next year.

 

Digitalisation:

Digitalisation is the key buzzword of the last few years although given that it encompasses a number of important shifts within the current evolution of business processes this is perhaps to be expected and there is no denying the importance of digitalisation within the field service sector and it is also a major consideration within the closely related function of parts management as the research reveals.

Digitalisation was ranked was 71% of the respondents to the Copperberg survey as being listed as either a four, five or six on their scale of importance, with 22% of respondents listing it as a six i.e. very important.

This places digitalisation as being deemed to be not quite as important to the respondent base as Inventory Management and Parts Pricing but more important than eCommerce.

What is interesting to note here is that these two very specific niche challenges seem to be in some-ways the eternal, perennial headaches of the sector, whilst broader, business-wide concerns such as digitalisation are possibly more likely to appear as an issue to overcome in the short-term which in themselves could lead to improvements in other areas - such as improved inventory management for example.

Which leads us neatly into...

 

3D Printing & Artificial Intelligence:

Two perfect examples of exciting new technologies that are emerging would be 3D Printing and Artificial Intelligence (AI) - with one set to play a hugely significant role in the niche of spare parts management, whilst the other will play a broad role in almost all sectors, including spare parts management.

So how do the industry experts who made up the Copperberg respondent base see each of these exciting technologies impacting the spare parts management sector?

With regards to AI just over a third of respondents (35% ) thought it would be important to some degree (again listing it as either a four, five or six).

However, less than a tenth of the respondents (9%) felt that AI was currently very important for them.

In terms of 3D printing, surprisingly the numbers were even lower.

In fact, less than a third of companies listed 3D Printing at a four or higher and only 8% of respondents felt that 3D Printing was very important in the sector currently.

 

Parts Logistics:

One area, however, that was overwhelmingly listed as being important within the field of spare parts management across the next 12 months was that of parts logistics.

94% of respondents listed parts logistics as being at least a four in the scale of importance with over a third (35%) going on to state that they felt parts logistics was important enough to warrant being listed as a six.

This makes parts logistics one of the most important areas in the spare parts sector across the next twelve months according to this respondent base, although Inventory Management is very important to more companies.

 

Conclusions:

The results of the survey bring us some interesting conclusions - particularly when we stand them alongside the trends we are seeing from within the field service sector.

Of course, field service and parts management are two leaves on the same branch with deeply symbiotic relationships between the two.

Yet, from this research at least, it does seem that many of the forward-looking discussions we have been having within the field service sector, particularly around emerging technologies such as AI, IoT and Augmented Reality as well as the wider topic of servitization as a strategy for business growth - may be further down the line than their equivalent discussions with our spare parts colleagues - and in some companies that may be significantly so.

Perhaps, part of the reason for this is that parts management is a highly complex beast with a huge amount of moving parts (literally) and even if solutions such as inventory management systems have been put in place it may take time for the benefits there to be truly felt.

However, the simple fact is that no matter how efficient field service management is - it all falls out of the window if parts management is poor - and this is perhaps the greatest learning from the research - that the focus of professionals within the parts management sector currently remains on efficiencies - and for that, we in field service should be hugely grateful.

 

---

Want to know more? Click here to Visit Copperberg's website to register for an exclusive white paper based on this research!

 

---

Be social and share...