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

In the third of our four-part series, our industry experts Bill Pollock, Strategies for Growth, Marc Tatarsky at FieldAware, and Richard Pratley from SimPRO, identify key areas of focus for field service managers in 2019.

In The Big Discussion 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. This week, the panel pick out trends for field service managers to look out for in 2019, including AI, data integration and connectivity.

What do you think should be the key areas of focus for field service managers across the next twelve months?

BILL POLLOCK, PRESIDENT, STRATEGIES FOR GROWTH
The next most important areas of focus for field service managers in the coming 12 months will likely be among the following three items:

(1) embracing the “new” technologies to support an expanded and enhanced capability to deliver their respective service offerings. Artificial Intelligence (AI) and Machine Learning have been around for more than 50 years, but are still relatively new to the services segment – but, it’s time to build them into your service operations!

(2) Changing the way in which you deliver – and price – your service offerings. Traditional break/fix service is essentially “dead”. Long live predictive diagnostics and predictive maintenance! Have you spoken to any chat bots lately? Well, you will!

(3) Re-engineering the way you measure performance metrics, or KPIs. MeanTime Between Failures (MTBF) and Mean-Time-to-Repair (MTTR) will not mean anything in an environment where services are being performed remotely on an ongoing basis. It will be time to replace some of the old “tried and true” KPIs with new ones that can measure systemic productivity, rather than merely individual field technician productivity. It’s time to rethink the entire service delivery process – and adjust to it!

MARC TATARSKY, SVP MARKETING, FIELD AWARE
Integration capability tops software selection criteria consistently for field service leaders. Even those who have been hesitant to integrate in the past, can now see that integration capabilities are far more advanced. Working with the right FSM software creates rapid time to value and ensures minimal risk.

Essentially the integration of FSM solutions into existing business systems of record means there is no disruption to ERP, CRM and accounting systems. Ultimately the real value is delivered through synchronized workflows, enhanced reporting and extending results beyond the current systems. Data integration yields actionable outcomes and connectivity to the wider business.

Field service has long been seen as simply a business cost, but leaders now recognise that integration can elevate their service operation, transforming it to a value-driving organisation that delivers broader business results. The evolution of field service through integration should be a focus to unlock this business value.

RICHARD PRATLEY, MANAGING DIRECTOR UK, SIMPRO
We continue to see businesses turning to software and technology to improve the efficiency of their workforce and to support customer service.

Smart connected products and IoT technology is transforming field service operations and we’ll see more adoption of this over the next twelve months. The predictive model not only reduces the cost of reactive maintenance but it addresses any issues before they become critical. For the customer, they won’t ever need to worry about needing to deal with a broken asset ever again.

We already have a number of customers running trials of our IoT technology. Not only are they winning new contracts off the back of it but it’s helping to increase the lifetime value of their existing customers.


The final part of The Big Discussion. You can read the first instalment here, and the second here. 

The future of field service will see location based services play a dominant role as autonomous vehicles take to the road says Chris Ruff CEO at Location-based technology firm Glympse, who is the latest guest on The Field Service Podcast.

 

The future of field service will see location based services play a dominant role as autonomous vehicles take to the road...In this episode of the Field Service Podcast, fieldservicenews.com Deputy Editor Mark Glover sits down to talk with Chris Ruff, CEO at Glympse, about why he sees autonomous vehicles as playing a significant role in the future of field service delivery and how all of this needs to be underpinned by robust and efficient location based services focused technology. 

In the second of our four-part series, our industry experts Bill Pollock, Strategies for Growth, Marc Tatarsky at FieldAware, and Richard Pratley from SimPRO, tackle the area of IoT and its role in field service.

In The Big Discussion 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. This week, the panel look at the impact of IoT in field service and whether it will become a necessity for firms to embrace if they are to keep ahead of the service curve.

IoT has become an increasingly key discussion amongst field service companies in recent years - do you think it will soon be essential for field service companies to embrace IoT?

BILL POLLOCK, PRESIDENT, STRATEGIES FOR GROWTH
I believe it is already essential for field service companies to embrace the IoT. That ship has already sailed – and those FSOs that run their services operations on an IoT platform are already beginning to see the return on their investment.

The enormous amount – and wealth – of data that is now being generated through the use of an IoT platform is turning many of the traditional ways of thinking upside-down. For example, it has created an environment where the “old” (i.e., last year’s) way of measuring performance is becoming almost instantly outdated. For example, last year, an FSO might have been assessing its service delivery performance on the basis of asset uptime or SLA compliance, etc. However, this year, they may need to gauge their performance via an entirely “new” set of KPIs!

Measuring your performance in providing “power by the hour” or “airplanes in the air” is quite a bit different than measuring on the basis of the number of monthly site visits, PM calls and asset uptime.


MARC TATARSKY, SVP MARKETING, FIELD AWARE
Undoubtedly, IoT has the potential to revolutionise field service in terms of moving to a predictive model of service, increasing efficiency, reducing cost while improving customer service. But any move to IoT is dependent on various operational factors. These include the prevalence of assets and existing IoT sensors, the ability to add IoT sensors to new sources to collect meaningful data and the technology maturity of the organisation and the FSM platform.

These factors can be dictated by numerous elements – company size, the industry and type of clients they serve, the complexity of the work, the value of assets and equipment they supply and service, and their leadership.

It is important for organisations to establish where they need to be in their operational and technological maturity to help the business thrive. For some companies transformative technologies, like IoT, are a key element of their planning and for others they may not figure at all.

RICHARD PRATLEY, MANAGING DIRECTOR UK, SIMPRO
IoT is fast become an essential service offering that customers will ultimately demand of their service providers and it  helps meet the requirement to deliver more for less for customers particularly for those installing and maintaining high-value assets!

When connecting these ‘Things’ to the Internet, using the live operational data and machine learning to analyse performance, it opens up the possibility of variable-based services based on machine condition and utilisation, rather than prescriptive frequency based visits and reactive calls.

This proactive approach can help lower TCO and increase uptime for customers and asset owners, whilst lowering the cost of operation and providing differentiation for the Service Provider. If you want to stay relevant in a competitive evolving market, now is the time to be considering how this technology can be incorporated into your own processes and service offerings.


The third part of The Big Discussion will be published next week. You can read the first instalment here.

In the latest Field Service Podcast, Fujitsu's Head of Delivery Management and Service, Martin Summerhayes, suggests we should be framing the service industry in a different way to encourage young people to the sector.

In this episode, fieldservicenews.com Deputy Editor Mark Glover sits down with Martin Summerhayes, and explains why we need to approach sector in a different way to become an attractive sector for young people to enter. He also recalls his first role in service in the mid-1980s and why employee wellbeing is paramount to delivering great customer service. 
Read our profile of Martin Summerhayes here.

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

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

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

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

 

The Learning Process

 

 

 

 

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

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

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

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

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

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

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

 

 

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Do you want to know more?!

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

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Sponsored by: 

 

 

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

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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... 

---

Sponsored by: 

 

 

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

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

 

1. Dynamic Scheduling

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

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

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

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

 

2. Customer Contact Center

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

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

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

 

3. Service Contract Renewal

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

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

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

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

 

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

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

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

4. Inventory Management

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

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

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

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

 

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Do you want to know more?!

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

---

Sponsored by: 

 

 

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

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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.”