ARCHIVE FOR THE ‘future-of-field-service’ CATEGORY

Atos has announces an agreement with the Science and Technology Facilities Council’s (STFC) Hartree Centre that will see one of the UK’s leading high-performance computing research facilities take the first UK delivery of an Atos Quantum Learning Machine.

 

This Quantum Learning Machine will be one of the highest-performing ever deployed by Atos and will be used to develop new quantum-based services designed to help researchers and industry prepare for the coming quantum computing revolution. These include quantum algorithm development and the first UK repository for quantum algorithms, collaborative research projects on quantum computing applications and specialist training. 

This new collaboration builds on an established partnership between Atos and the Hartree Centre, which began with the UK’s first Bull Sequana X1000 supercomputer being hosted at the facility in 2017. The Hartree Centre, based at Daresbury Laboratory and part of the Sci-Tech Daresbury Campus in Cheshire, UK, also hosts the JADE national deep learning service. 

Commenting on the partnership announcement, Andy Grant, Vice President, HPC & Big Data, Atos UK and Ireland said: “We are delighted to deepen our existing relationship with the Hartree Centre which we believe will help UK industry future-proof itself for the arrival of quantum computing. Our Quantum Learning Machine as a service will be made available to any organisation wanting to learn about, and experiment with, quantum computing and understand the key opportunities and challenges this technology presents. Quantum is the future of computing and it is crucial that organisations are ready to harness the coming revolution.” 

Alison Kennedy, Director of the STFC Hartree Centre, said: “We’re thrilled to be enabling UK companies to explore and prepare for the future of quantum computing. This collaboration will build on our growing expertise in this exciting area of computing and result in more resilient technology solutions being developed for industry.” 

Atos produces the highest-performing Quantum Learning Machines in the market. In November 2016, Atos launched an ambitious program to anticipate the future of quantum computing and to be prepared for the opportunities as well as the risks that come with it. As a result of this initiative, Atos was the first to successfully model quantum noise. To date, the company has installed Quantum Learning Machines in countries including Austria, Denmark, France, Germany, the Netherlands, and the United States, empowering major research programs in various sectors.

IX Reach, the leading provider of SDN cloud connectivity, remote peering and Ethernet services to more than 170 global locations is proud to announce its network expansion into East Africa in partnership with the Djibouti Data Center.

Djibouti Data Center (DDC) has been selected as the strategic hub for IX Reach’s African expansion owing to its excellent reputation and unique position as the first and only Tier 3 carrier neutral ecosystem in east Africa with direct access to all major international and regional cable systems connecting Africa to Europe, Middle Eastern, and Asian markets.

“We are delighted to call Djibouti Data Center a new Point of Presence (PoP) on our network”, said Simon Vye, CEO at IX Reach. “IX Reach is dedicated to increasing connectivity, collaboration and innovation as well as improving the range of services we provide to our customers.This new partnership with DDC is key in continuing our vision of making our full portfolio of solutions including cloud connectivity just one cross connect away on the IX Reach network.”

DDC tenants will be able to connect directly onto IX Reach’s global network giving access via a single port to over 50 Internet Exchanges and Direct Connect into the industry’s leading Cloud Service Providers. Given the geographical importance of Djibouti, IX Reach will also be able to address markets in other African countries improving global connectivity and reach.

"We are very excited to have IX Reach join the carrier neutral DDC ecosystem, and to be enhancing the DDC’s available network footprint for our global customer base as well as Africa’s growing and emerging markets,” said Anthony Voscarides, CEO of the Djibouti Data Center. “In addition to the DDC’s market leading access in east Africa to international and regional fiber cable systems, the new IX Reach PoP will further enhance connectivity options to a diverse network of major Internet Exchange Points, cloud service providers, and data centers across Europe, North America, and Asia Pacific regions.”

The expansion into a new continent continues to highlight IX Reach’s global ambitions and increased investment into its network and services demonstrating its dedication to meeting the global challenges of increased data use driven by the growth of video streaming, content delivery, IoT, big data and AI technology.

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

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

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

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

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

 

The Learning Process

 

 

 

 

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

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

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

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

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

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

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

 

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Is Machine Learning a key topic for your organisation?!

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

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

 

1. Dynamic Scheduling

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

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

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

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

 

2. Customer Contact Center

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

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

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

 

3. Service Contract Renewal

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

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

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

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

 

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

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

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

4. Inventory Management

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

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

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

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

 

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Do you want to know more?!

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

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Ericsson has launched its critical communications broadband portfolio for service providers, enabling service providers to meet the business-critical and mission-critical needs of industries and public safety agencies as digitalization and modernization of land mobile radio communications increases. 

When communication is disrupted by minutes, seconds, or even milliseconds, it can have huge consequences for business operations, or serious implications for public safety. The need for fast and reliable communication is therefore paramount. Such critical communications are used in many areas: from first responders and nationwide emergency services to workforce safety in enterprises.




There is a growing demand for business- and mission-critical broadband for such use cases. Service providers need to deliver the highest level of availability, reliability and security to meet this demand.To meet critical communications users’ needs, Ericsson has developed a new portfolio comprising three offerings: Critical Network Capabilities; Critical Broadband Applications; and Flexible Deployments for both local private networks, and nationwide mission-critical LTE networks.

Per Narvinger, Head of Product Area Networks, Ericsson, says: “We see growth opportunities for service providers and government operators by addressing new segments with LTE/5G networks. Our critical broadband portfolio will enable our customers to effectively secure the critical communication needs of sectors such as public safety, energy and utilities, transportation, and manufacturing.”

Critical Network Capabilities

This offering includes advanced features for critical network performance and covers the following: high network availability; multi-network operation with spectrum sharing techniques; and coverage and capacity for critical applications. It also includes network security capabilities that ensure network services are maintained even when the infrastructure is under attack. Finally, quality of service, priority and preemption all guarantee latency performance and capacity requirements during high load and congestion. The critical network capabilities include new features that simplify the rollout of broadcasting services across nationwide areas. Another new feature enables radio access sites to operate in fallback mode, should the network connection fail. This offering also includes deployable systems that allow temporary coverage for disaster recovery and operations in rural areas without existing coverage.

Critical Broadband Applications 

This offering covers Ericsson Group-Radio that provides mission-critical push-to-talk, data and video services. This will enable, for example, blue light personnel such as the police to be more effective in performing community services that require advanced mobile broadband.

Flexible Deployments for Private Networks

New business models are emerging for industries. From owning and operating their own networks, critical industries are now procuring private networks and services that leverage service providers’ existing network assets and operations – without compromising required local control. Ericsson’s flexible deployments for private networks range from network slicing to fully dedicated networks, enabling service providers to offer scalable, critical broadband network solutions and services for critical industries.Ericsson also offers Managed Services for private networks, with solutions based on AI and automation that predict and prevent events while reducing OPEX.

These solutions enable service providers to reduce time-to-market and onboard new industries, while securing critical service level agreements.Critical broadband will enable industries to increase efficiency through the following: enhancing workforce productivity and safety; massive onboarding of devices and sensors; real-time location of assets and equipment; and data collection to boost equipment and personnel performance and avoid downtime.


Thomas Lynch, Executive Director at IHS Markit, says: “The critical communications industry is developing ways to deliver critical mobile broadband solutions for professional users, augmenting today’s critical voice communications. Through its new portfolio, Ericsson is empowering service providers to address this growing segment by leveraging their existing LTE infrastructure and operations in an affordable and scalable manner.”

 

IoT connectivity and remote monitoring, used by companies to monitor the safety of their lone workers, straddles into medical sphere to keep tabs on patients' health. 
The number of remotely monitored patients grew by 41% to 16.5 million in 2017 as the market acceptance continues to grow in several key verticals, according to research by Berg Insight.

This number includes all patients enrolled in mHealth care programs in which connected medical devices are used as a part of the care regimen. Connected medical devices used for various forms of personal health tracking are not included in this figure. Berg Insight estimates that the number of remotely monitored patients will grow at a Compound Annual Growth Rate (CAGR) of 31.0% to reach 83.4 million by 2023.   The two main applications are monitoring of patients with sleep therapy devices and monitoring of patients with implantable cardiac rhythm management (CRM) devices. These two segments accounted for 82% of all connected home medical monitoring systems in 2017.






The number of of remotely monitored sleep therapy patients grew by 37% in 2017, mainly driven by Philips and ResMed that together dominate the sleep therapy market. The CRM market is led by companies such as Medtronic, Boston Scientific and Abbott that started to include connectivity in CRM solutions more than a decade ago. Telehealth is the third largest segment with 0.8 million connections at the end of the year.

Leading telehealth hub vendors include Tunstall Healthcare, Resideo (Honeywell), Medtronic, Philips and Qualcomm Life. Other device categories – including ECG, glucose level, medication compliance, blood pressure monitors and others – accounted for just over two million connections. “The most promising segment is medication compliance, which we expect will become the second most connected segment in the next five years”, says Sebastian Hellström, IoT Analyst at Berg Insight.  

More than 60% of all connected medical monitoring devices rely on cellular connectivity today and has become the de-facto standard for most types of connected home medical monitoring devices. The number of mHealth devices with integrated cellular connectivity increased from 7.1 million in 2016 to 10.7 million in 2017.

The use of BYOD connectivity will increase the most during the next six years, with a forecasted CAGR of 48.2%. “BYOD involves low cost and the technology is mostly adopted in patient-centric therapeutic areas such as diabetes and asthma that have younger patient demographics compared to many other chronic diseases. Many of these patients prefer to use their own smartphone as the interface instead of carrying around a dedicated device for remote monitoring”, concluded Mr. Hellström.

The increase of remote monitoring usage across professions, including health and safety, was highlighted in a further report by Berg Insight who found the number of monitored lone workers in Europe and North America reached 900,000 in 2017,

Download report brochure: mHealth and Home Monitoring 

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

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Is Machine Learning a key topic for your organisation?!

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

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Sponsored by: 

 

 

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

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

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

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

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

 

Adoption Results:

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

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

Specific AI applications in customer service include:

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

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

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

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

 

Can this lead to financial success as well?

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

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

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

 

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References::

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

 

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

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

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

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

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

 

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

 

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

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

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

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

George Walker is Managing Director of Novotek UK and Ireland.

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