ARCHIVE FOR THE ‘artificial-intelligence’ CATEGORY
Mar 21, 2019 • News • Artifical intellignece • Artificial intelligence • Future of FIeld Service
A study by OnBuy.com, conducted by YouGov, showed British people would be reluctant to form a work relationship with a robot.
When asked how they feel about having a robot as a manager, 66% of men and 75% of women said they would feel "uncomfortable" with such an arrangement.
The survey questioned 2,041 people and looked to gauge the British public's attitude to robots, given the rise of Artificial Intelligence smart-devices such as Amazon's Alexa appearing in UK homes.
"Whilst the idea seems far-fetched," said the accompanying press release, "the idea that robots could become part of everyday life has become a topic of conversation. Robot-human relationships has become a concept many have begun to form an opinion on."
Mar 21, 2019 • Features • analytics • Artificial intelligence • Cognito iQ • Laurent Othacéhé • Machine Learning • management • Digitialisation • Strategy
Field service is undergoing what is in my opinion the biggest change the industry has seen in the last 25 years.
All of our customers, across a range of industries, want to talk to us about Digital Transformation, and how they can use digital technology to fundamentally transform the way they interact with their customers, and not just about the operational ‘nuts and bolts’ of delivering a service to them.
Some customers are only at the beginning, taking small steps towards transformation by, for example, moving away from traditional software ownership models towards cloud-based products and services, such as MS Office 365. Others are further along, with strategies that embrace technologies such as IoT, big data and AI.
But regardless of their progress, at the heart of all of these conversations is the recognition that Digital Transformation will bring them closer to the goal of providing exceptional field service.
The Art Of Field Service Ops
I often think that the role of a Field Service Manager is a complex mix of art and science, with a bit of magic thrown in for good luck.
Decision making needs to adjust constantly to changes in conditions – a sudden unseasonable cold snap, for example, or a contract with a new customer. Just as service delivery metrics point to success, something changes, and there is a whole new dynamic.
Without knowing what combination of factors triggered the change, it’s hard to know how best to respond.
Get the reaction to an emerging threat wrong – too great or too small a response – and the complex balance of the operational ‘ecosystem’ can be thrown out.
Recovering that balance and restoring the conditions required for ‘flawless’ field service can prove costly and time consuming.
Data doesn’t drive decisions
Most organisations capture a range of sources and types of data - workload planning, resource availability, schedule efficiency, service outcomes, customer satisfaction levels, asset profitability – and many are integrating new types, such as that offered by IoT.
However, this data is rarely delivered in the right form to support decision making, meaning that managers spend too much time aligning and manipulating data from disparate sources. Even then, many are frustrated to find that the root cause of issues is still unclear and the likely outcome of any decision is still uncertain.
AI, machine learning and predictive analytics
This is where the latest technologies, such as AI, machine learning and predictive analytics come in.
Valuable insights into the performance of an operation often lie at the intersections of these various datasets; these technologies can enable decision support applications to identify underlying patterns of performance in the Field Service operation, including long and short term trends, that were simply too complex for traditional applications to uncover. This is increasingly true as much larger data sets such as IoT have come online in recent years.
"Field service is undergoing what is in my opinion the biggest change the industry has seen in the last 25 years..."
This deep understanding of performance, combined with the power to highlight exceptions in real-time, enables the operations team to see the correct course of action to address each challenge as it arises. And beyond simple advice, these technologies make it possible for applications to automate ‘learned’ responses to common patterns of exceptions that occur.
The next generation of decision support
This next generation of applications will be used strategically to analyse, for example, which factors within a field service operation make engineers productive, and which inhibit productivity. Some of these factors will be within the control of the engineer, in which case performance can be addressed with initiatives such as better training or incentives.
Others will relate to company processes, in which case the applications will suggest tactical improvements, the impact of which can also be measured. Others still will be external factors which can’t be changed, but can be allowed for in planning and scheduling.
Such applications will be programmed with a knowledge base, but will be learning all the time, as the outcome of each decision is fed back into the performance data, effectively automating the process of continuous incremental improvement. This will take some of the challenge of blending art and science out of the hands of the Field Service Manager, leaving them free to concentrate on other activities.
Not just software suppliers
It is clear that this massive change in the industry requires those of us who supply and partner with field service companies to change too. We can’t just be technology suppliers.
We have to embrace our customers’ goals and work with them to add value; to weave their transformation strategies into the fabric of our products and services and to bring to the table our own blend of art, science and, yes, a little magic too.
Laurent Othacéhé is CEO at Cognito iQ.
Mar 20, 2019 • News • Artificial intelligence • Augmented Reality • Future of FIeld Service • Survey
Over half of the US Government workforce feel comfortable with technology such as artificial intelligence but want to see accompanying training and guidance readily available.
Over half of the US Government workforce feel comfortable with technology such as artificial intelligence but want to see accompanying training and guidance readily available.
The survey by Government Business Council, canvassed nearly 500 US Federal employees across more than 30 civil and defence agencies sought opinion on smart technologies such as Augmented Reality and Artificial Intelligence.
Over half (51%) understood that artificial intelligence will evolve "slightly or considerably" in the next three years and will have an impact on their role in the future, however only 26% said their respected agencies had communicated the impact of AI to them either "adequately, well or very well."
The survey also revealed that workers felt confident in adapting to this change but were concerned that there was a lack of training around the technology, with 61% expressing worry at the absence of technical support.
“AI is one of the most engaging topics we are seeing unfold in the federal government right now,” said Daniel Thomas, Research Manager at GBC and the study's author. “These findings show that there is a significant appetite for continued education around the opportunities that intelligent technologies like AI present to the federal employee."
You can read the full report here.
Mar 14, 2019 • News • Artificial intelligence • Future of FIeld Service • GDPR • Cyber Security • Security
Cyber security revenues in 2018 were $160.2 billion and will jump $11.2 billion during 2019, as the focus moves to GDPR compliance. Growth will slow to around $9.8 billion per annum, spiking once a in 2023/4 as AI based Cybersecurity escalates, reaching $223.7 billion, says the report from Rethink Technology Research.
The European Union’s GDPR (General Data Protection Registrar) has set the agenda for legislation over data privacy and protection worldwide and that is generating a spike in spending on security measures that ensure compliance. This will continue to ripple around the world between 2019 and 2021.
North America is expected to continue to spend the most on security (27%), but both Europe (22%) and China (20%) which are rapidly accelerating their spend, with the rest of Asia following closely behind on 16%. North America is expected to lead on almost every market with the exceptions of Industrial and Automotive, where China leads, by a small margin.
You can read the full report here.
Mar 13, 2019 • Features • Artificial intelligence • Future of FIeld Service • Gig Economy • KPIs • click software • Employee Satisfaction • Customer Satisfaction and Expectations
Ten years is a long time in field service. Trends come thick and fast with some trends thicker than others, attaching like coral onto the industry and becoming an integral part of service progress. The worldwide web and mobile technology are probably the two best examples of this; both have been essential in pushing the industry forward. Would we cope without them today?
It’s fair to label these movements as revolutions; their impact has been immense but smaller changes while not as monumental are just as significant. Today though, focus is swinging from technology enablers and back to customer service.
“Mobile was many years ago, everyone expects to have it,” says Hilla Karni, VP of Product and Customer Marketing at Click Software. Karni has just finished hosting a roundtable at Field Service Europe and we’ve managed to find a quiet dining room post-lunch to talk. I settle my dictaphone among skewed butter knives and bread crumbs. Sipping coffee, Karli continues: “In recent years, the shift has moved from a service operation that is a cost-centre, to a service operation that is an opportunity to impact customer service.”
The roundtable titled: The Science Behind Service: Metrics that Matter, centred on KPIs affecting customer service. The fact such a round table was taking place affirms how the industry is focusing on the end-user. “Before you would never hear of this,” she says. “KPIs were always around productivity, travel cost, overtime; it was always cost.”
But what about those enablers such as AI, IoT or specifically Augmented Reality (AR)? What role does AR play in the new customer focus? “Everyone talks about AR. But why are they using it?” She asks, pausing slightly. “It’s for the remote diagnostics which enables a better first-time fix. A first-time fix rate is the metric that combines efficiency, productivity with customer experience.”
In order to achieve customer focus KPIs, Karni tells me, smaller trends such as employee wellbeing are taking on a greater significance. “There is a very clear correlation between employee engagement and customer satisfaction,” she
says. “When an employer is happy with his or her job then he or she will deliver excellent service. Now we are seeing different investments around making your employees happier. There is a very clear correlation between happy and engaged employees with customer satisfaction.”
This, refreshingly, ties in with a general shift in occupational wellbeing and a positive approach to mental health in general. From a business point of view, work-related stress affects staff absenteeism; in turn affecting productivity. One thread of wellbeing, prevalent in field service is the time an engineer might spend on the road. Tools around scheduling play an important part in employee engagement and buy-in. Some firms, Karni says are handing autonomy to their engineers to create their own timetable. “Some of our
customers like their technicians to make more decisions by themselves.” The increase in wellbeing can be loosely attributed to the flexible nature of the modern workforce.
“When an employer is happy with his or her job then he or she will deliver excellent service..."
Today, freelancers choose their workdays and hours to fit their lifestyle. The typical nine-to-five day still exists but the gig
economy – so-called as each piece of work being akin to a ‘gig’ - represents another shift in efficiency and cost. Karni suggests large contractors, with their large overheads, can fail to deliver the required standard of customer service, paving the way for freelancers. “This is where the workforce trend is to have more freelancers, the uber-like model, offering a better service but it must be connected, ultimately, to a better customer service.”
So, if customer focus is the new trends in field service what technology revolution does Karni see to compliment it? Firstly, she is keen to re-label the progress. “I think the next evolution – and it is an evolution, not a revolution – is more focused around prediction,” she affirms. “Having prediction within the service delivery life cycle changes a lot of things because it makes for more
accuracy and real-time decision making.
“Previously, we still made decisions, many decisions. Then we got mobile so were able to streamline the process. Then we had more optimisation and got artificial intelligence to improve productivity and efficiency. Now we are taking it to the next level and saying, ‘Okay, how can I predict better to ensure I make faster, smarter decisions on the day of service, on the minute of service?’”
Despite the influx of new disruptive technologies – such as AR – Karni is aware that the main beneficiary has to be the end-user, the customer. “Everyone talks about the current trend in field service, which is AR. But if you ask ‘why are we using this remote technology’, it is ultimately to create a better first-time fix. A first-time fix rate is the metric that combines efficiency and productivity with customer experience. “You’re not adopting something for the sake of the technology. You need to have a very strong business case with savings. This is what is unique about field service management applications is that it needs to find the balance between time and cost savings while creating better customer service. If it was only a one-way thing it would not be such a valuable asset,” she says.
I push Karni on the role of the asset: the wind turbine, the air conditioning unit, the washing machine. When does it become more important than the engineer? “There is no replacement for the human touch,” she pauses again. “There is, however, a replacement for the process.
“If you can fix something remotely and it’s not a problem and it will smoothly recover, then I don’t see why the customer wouldn’t be happy because the washing machine is fixed. Having said that, if you fix something sophisticated and there is a break-down, I believe there is no replacement for human experience.”
Finally, as waiters circle impatiently around us to prepare the table for the next coffee break, I ask Karni, who has been with Click Software over ten years, why she enjoys working in the field service sector. “As I said, everyone talks about machine learning and AR but,” she says. “But when it comes to field service it’s real. It’s actual technology that serves a use-case and a business value.”
She finishes her cappuccino. “We make a difference I think, and this is what I like about what I do.”
Mar 01, 2019 • Features • Artificial intelligence • Augmented Reality • copperberg • Workforce • Jim Baston • Survey • Video collaboration
The survey gathered comment from over 125 Field Service Directors from global manufacturing firms, and revealed visual-based technologies including VR, AR, Visual Assistance and Video Calling could address the growing disparity between mature field service engineers and less experienced workers.
Commenting, Field Service News contributor and BBA Consulting President Jim Baston said: ‘‘As they [experienced engineers] rely more on their tools to troubleshoot and repair and less on their experience, it opens up the door for less qualified individuals who will be able to give comparable levels of technical service.’’
The survey also identified the need for an open digital eco-system between partners, suppliers and customers to encourage collaboration.
You can download the report's findings here.
Feb 27, 2019 • Features • Artificial intelligence • Gig Economy • Oneserve • Blockchain • Chris Proctor
As a recent guest on the Field Service Podcast, Oneserve’s CEO Chris Proctor shared thoughts on innovation, servitization and the worrying discrepancy between new and retiring engineers. Field Service News Deputy Editor Mark Glover looks back on a...
As a recent guest on the Field Service Podcast, Oneserve’s CEO Chris Proctor shared thoughts on innovation, servitization and the worrying discrepancy between new and retiring engineers. Field Service News Deputy Editor Mark Glover looks back on a conversation that asked uncomfortable yet insightful questions of the sector.
What is digital? “It’s about bringing people together in one global location; a world without boundaries; a world where ideas are born and developed; where people live, and most importantly; where people transact.”
These are the words on Chris Proctor’s Linkedin profile. If it is his mantra it’s a good one. It’s refreshing to get such a holistic view of digital. Do we label it a platform or technology? Who really knows? However it’s useful – and perhaps essential - to try and frame its creative potential and understand how it can seed ideas and drive innovation.
I was fortunate enough to record a podcast with Proctor towards the end of 2018 and among other threads of conversation (including the definition of digital) I asked about the industry’s progression in adopting new technologies. “I might be lambasted for saying this but I don’t think there has been much innovation,” he said, aware of the statement’s brevity. “It’s disappointing that the last real innovation in field service management was moving to the cloud and even then, I don’t think everyone is fully there yet.”
It’s a bold claim, yet one that has substance. The industry has been accused before of lagging when it comes to embracing disruptive, digital technology. In fact, writing for this magazine moreMomentun’s Jan Van Veen, suggested a knowledge gap around its definition was contributing to the malaise. “Too often, I see misconceptions about disruption and disruptive innovation and a lack of clarity on what needs to change and too slow a pace of change,” he wrote, “by consequence, manufacturers tend to make inadequate assessments and develop inadequate strategies, allowing leading competitors and new entrants into the industry to take the lead.”
“I might be lambasted for saying this but I don’t think there has been much innovation...“
To halt this inadequacy, Proctor thinks a further disruptive approach is needed to jolt the sector from its lethargy, encased in an attitude he phrases as “okay is okay”. He uses the utility sector as to expand his point. “You only have to look back at the gas or water boards where there was very little competition with very little incentive to be a lot better and I think we’ve lived with that legacy for quite a long time. “Look at what’s happened in some of the other sectors and see how much things can be disrupted when someone comes in and says ‘We don’t believe ‘okay is okay’ and we’re going to offer a very different service proposition,’” he urged.
To reach this level, servitization and the gig economy, he predicts, will have a fundamental impact on the way services are delivered. Most likely through a subscription-based model, complimented by disruptive innovations and delivered by freelancers who, in order to maintain their personal brand, deliver consistent service excellence. “I can see a world where most of your services are consumed on a subscription-type basis. You contact your service provider who then uses technology similar to programmatic advertising whereby contracts are tended and bid for and secured within seconds, all underpinned by blockchain. You then have the real emphasis of an individual providing a service on behalf of a company, then what you then see is that you don’t get the overheads that come with large contractors, but you do see service excellence from an individual who is trying to make a name for themselves and secure their own future,” Proctor said.
This small-task employment model could be the remedy for the alarming disparity between new field technicians coming into the industry and those retiring, however despite the need for a swift solution Proctor feels the time frame for such a movement is ambiguous. “Whether it’s five years, whether it’s ten years, I think a move towards that model is irrefutable” he says, “I also think the timings are completely debatable.”
I enjoyed the conversation, and I suggest you listen to the full podcast on our website. If the point of digital, as Proctor says, is to create a world where ideas are born and then developed then we need to hit reset, disrupt and collaborate. Perhaps then can innovation thrive.
You can listen to the Field Service Podcast with OneServe's Chris Proctor here.
Feb 26, 2019 • Features • Artificial intelligence • Future of FIeld Service • Machine Learning • Emily Hackman
In the the final article in our current series of articles from field service solution provider Astea focussing Artificial Intelligence and Machine Learning, we see how applying the Machine Learning can lead to better service resolutions...
In the the final article in our current series of articles from field service solution provider Astea focussing Artificial Intelligence and Machine Learning, we see how applying the Machine Learning can lead to better service resolutions...
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.
Congratulations, if you have been following this series of articles across the last few weeks you are now a cognitive technology guru! (If you haven't don't fret! we've written a whole white paper on the topic which is available to fieldservicenews.com subscribers.)
In the series we've tried to give you an understanding of how and why the field service industry is applying these new technologies, and you see the writing on the wall: positive results for your customers, your employees and your financial success.
Now it’s time for you to go through the machine learning process yourself in order to answer the most important question ever asked in a field service scenario: “What is the best resolution to a given problem?”
If we break this question down into smaller operational components, we first need to maintain a set of wide range problems represented in groups which share similar characteristics. We then label these groups. Let's say, in our case, how the problems were fixed or repaired. Then, we need to create a learning process that labels a new problem based on its characteristics.
The Learning Process
The learning process requires “real world” raw data and actual business experts. In our example, this data will be historical incidents and service requests that could most likely be predictive. The business experts are the people with extensive knowledge of hands-on technical support and service (e.g. field service managers with years of service and product experience.)
The next step is called data preparation. This step is required to screen the raw data, remove duplicate or irrelevant records, handle missing data attributes, and extract indicators and features that are needed in the learning process.
"Free text", such as the problem description of these examples, is then extracted into a bag-of-words model. This is a representation of the description text with meaningful information (disregarding semantic relationships in the sentences).
The next step is called the training process. This is where the actual learning happens. Different types of learning algorithms are applied to a data training set (a subset of the entire data to learn from).
The learning process requires “real world” raw data and business experts. In our example, this data will be historical incidents and service requests that could most likely be predictive.
The choice of those metrics influences how the performance of the machine learning algorithms is measured and compared. Accuracy is one of the metrics to evaluate and classify models. It calculates the number of correct predictions made by our training model over all kinds of predictions being made. Another metric, called the confusion or error matrix, is a two dimensional table that visualizes “actual” versus “predicted” results for different questions, called the “classes”. It shows if a model is confusing two classes (e.g. commonly mislabeling one as another).
Aptly named, the confusion matrix visualizes “actual” versus “predicted” results for different questions, called the "classes". It shows if a model is confusing two classes.
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.
Feb 19, 2019 • Features • Artificial intelligence • Future of FIeld Service • Machine Learning • Emily Hackman
In the third article in our current series of articles from field service solution provider Astea focussing on Artificial Intelligence and Machine Learning we now take a look at some of the practical applications...
In the third article in our current series of articles from field service solution provider Astea focussing on Artificial Intelligence and Machine Learning we now take a look at some of the practical applications...
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.
Thanks to early adopters and industry analysts, you know that augmenting your service business with intelligent systems can be useful in enhancing organisational performance. Now let’s review four examples of how field service companies can apply these technologies in order to gain insight and improve performance.
1. Dynamic Scheduling
Dynamic scheduling provides optimised schedule and planning of field service agents. It’s constantly updating in response to events in real-time.For example, when a new high priority service ticket is received, it needs to be scheduled for immediate execution and all other tasks must be postponed.
There’s an element of AI in this process because the scheduling system must perform its task of scheduling this service order intelligently. In other words, it must think and thus deduce that simply prioritising this service ticket is not enough. All other tickets must be postponed in order to ensure that the high priority one is resolved first.
For example, let’s say a call has arrived in the system and agents Steve and Karen have the same skills and are both available. The system decides to assign Steve as the agent for the call by considering the nearest agent rule. However, by looking back at history and identifying patterns based on call attributes and agent performance, we find that while Steve is located closer to the customer site and is more experienced, Karen is the right agent to dispatched.
Why? The pattern shows that Karen has a higher probability to resolve this call on the first visit as compared to Steve. This prediction can be done only if the scheduling system reviews historical data and learns from it. We can even find that having more experience and arriving earlier to the site doesn’t necessarily deliver better results.
2. Customer Contact Center
Call center agents play a key role in customer satisfaction. They are usually the front line of the customer’s experience, so it’s crucial to invest in systems that improve the way call centers are evaluated.
Two of the important indicators to assess the efficiency of call centers are “first call resolution” and “average time in queue”. Calls are typically analyzed manually by the agent who is listening to the call and determining the context. Agents also have access to a knowledge base to help them.
Unfortunately, having a human evaluating each call to determine the course of action is time-consuming and does not guarantee the best solution.
3. Service Contract Renewal
Service contracts are one of the most important parts that a field service organisation can ensure ongoing revenue from customers. However, these services are provided for a limited timeframe
only, after which customers have to renew their contract in order to continue receiving the services. It is very critical for the businesses to understand the underlying reasons that impact contract renewal. Traditional ways to evaluate the chance of renewal are based mostly on client satisfaction survey results. However, a lot of important information is captured as free text from emails, notes, problem descriptions, etc. while call center and field service agents interact with customers in trying to resolve service requests.
Using this unstructured textual information, with the aid of machine learning algorithms, we can perform a sentiment analysis to detect a customer’s emotions about the service or specific product. This allows companies to classify the root cause of problems that lead to a customer not renewing a contract.
Machine learning algorithms can perform a sentiment analysis to detect a customer’s emotions about the service or specific product. This allows companies to learn why a customer does not renew a contract.
EXAMPLE: “Your customer support is great, however battery only lasts for 30 minutes.”
When analyzing the sentiments in this sentence, we can classify that the cause that could potentially lead to contract non-renewal is not related to the service, but rather is based on the product itself. As a corrective action, we can propose an upgrade to a newer model in which the battery life has been significantly improved.
With sentiment analysis, companies can respond quicker to signals, track changes in customer sentiment over time, determine if particular customers feel more strongly about their services, and predict the likelihood of contract renewal.
4. Inventory Management
Efficient inventory management revolves around knowing your demands. On one hand, companies must ensure that they have enough stock in their warehouses to meet the ongoing demands. On the other hand, they must preserve a small quantity of slow-moving items. Traditional solutions rely on formulas to reach static sets between pessimistic and optimistic values, and because of that, the chosen buffer level does not guarantee that the stock level fully satisfies the demand.
The way in which data can be incorporated from different external sources, including non-business data, helps machine learning models to find a correlation between the different variable factors and to optimize stock levels dynamically.
For example, feeding weather conditions and inventory data into a machine learning model can identify that specific parts tend to fail when the temperature outside is above 90 degrees Fahrenheit. In case the weather forecast shows that a heat wave is approaching, several proactive actions can be initiated, like ordering a sufficient quantity of items that are likely to break down so that those items can be used for predictive maintenance visits. Essentially, the machine learning model predicts the necessary stock level and determines how much inventory to order by using the past records of weather and failures.
The machine learning model can predict the necessary stock level and determine how much inventory to order by using the past records of weather and failures.
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.
Field Service News is published by 1927 Media Ltd, an independent publisher whose sole focus is on the field service sector. As such our entire resources are focused on helping drive the field service sector forwards and aiming to best serve our industry through honest, incisive and innovative media coverage of the global field service sector.
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