Understanding the technology stack underpinning remote service

Apr 13, 2021 • Features • Digital Transformation

In the fourth article in a series of excerpts from a brand new white paper authored by Kris Oldland, Editor-in-Chief, Field Service News published in partnership with PTC, we now look at some of the core technologies that are required for effective remote service...

This feature is just one short excerpt from an exclusive Field Service News White Paper published in partnership with PTC

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Remote-service is not dependent on any one given technology but rather a technology stack. Here we offer a brief glimpse into what that stack comprises of.

So far in this series, we have put forward the case for remote service delivery being viewed much more holistically as part of a broader service portfolio than it being seen as an alternative to traditional methods of service delivery.

We have asserted that in terms of service being a key differentiator between competitors, we must consider that how we deliver service remotely will become increasingly important as the adoption of such approaches matures.

We have also looked at how this could be applied across multiple different service strategy approaches from transactional service approaches to outcome-based-service models.

Having outlined the above, let us now take a look at some of the core technologies that must be incorporated alongside an existing field service management (FSM) system to introduce remote service capabilities that can give you a competitive edge against your peers and help drive efficiency within your service operation.

The Fundamentals of Field Service Management (FSM) Technology

Before we move onto the new technologies that should be added to your current systems, first let us look at some of the core foundational tools that should be in place for standard field service operations.

It is likely that your organisation will have most of these tools in place. Still, it is important to understand the bedrock of an FSM system whether it be a stack of complementary best-in-class solutions or a dedicated platform before we begin to look at the additional elements required for effective remote-service delivery.

The core solutions that any service operation requires for effective service delivery include:

FSM/Workforce Management Solution
Scheduling
Dedicated parts/inventory management solution
Mobile capabilities to put the above in the hands of the engineer

Depending on the solution you already have in place to manage your mobile workforce it may be that you currently have one solution that meets all of the above requirements or it may be a selection of separate connected components.

However, what we are looking at here is the technology to empower field service management companies to achieve the long-standing mantra of every field service operation of getting the right person, who has the right skills, with the right skills, and the right parts to the right place at the right time.

As all field service management professionals will attest, field service operations are complex with many moving parts, so you will likely already be very familiar with general FSM technology.

Additional technologies that empower effective remote service delivery

When we begin to look at each of the specific technologies listed below, there are enough discussion points on each of the technologies to merit a dedicated paper per technology.

However, that is not the intention or purpose of this paper. Our intention is to provide you with a broader understanding of the questions you need to ask about your own operations as you prepare to move forward into the post-pandemic environment.

Similarly, it may be that not every technology in the list below is required in every use case. In some cases, other additional technologies may also need to be added into the mix. Every company is unique, and every approach may differ slightly. However, the following is certainly a good springboard to start your own exploration into the solutions that your organisation may need to invest in.

Internet of Things:

One of the most critical elements of remote service delivery is remotely monitoring assets in the field. To do this you will need to get access to equipment, get it connected, and then gain access to equipment data – generally, this connectivity is what is known as IoT or the Internet of Things.

The first step in this process is to identify what data will impact your long-term service goals, which is why it is essential to have a firm definition of your service value proposition as we discussed in an earlier segment of this paper.

Once these data sources are identified, the next step is to establish the connections to devices or assets in your fleet that holds them.

These connections may include a combination of wired, wireless, cellular, or indirect connections via gateway devices or control systems. It is important to bear in mind that any IoT platform should have multiple methods to join these connections.

While the type of data collected will vary depending on the use case, some commonly tracked data sources include energy usage, machine settings, hours of operation, or temperatures fed back to the business to gain service intelligence. For newer smart equipment, field service organisations can gain further insight by capturing error codes or alerts, log files, software versions, and configurations.

This data alone can play a massive role in improving service efficiency both by helping field service organisations pro actively identify faults as well as offering crucial insight that can speed up triage of issues ensuring if an engineer does need to visit the site, they are only doing so

a) when absolutely necessary and
b) they are arriving on-site aware of the issue and with the right parts and tools to ensure a first-time fix.

However, communication between the asset and back-office must be bi-directional when it comes to the remote resolution of a problem.

Artificial Intelligence Diagnostic Tools:

Having too much data though can be an issue, particularly in the field service sector where an organisation will often have hundreds of thousands of data sources across an install base. Data ends up in vast data lakes, and the problems of a data lake is that there is always the risk of drowning in a data deluge. A step in the right direction is a data warehouse. Still, to continue the metaphor, the problem with ‘things in warehouses’, is unless there is an intelligent indexing system things can get invariably dusty and forgotten.

Perhaps the biggest hurdle to effectively utilising data is that all too often companies become overwhelmed by irrelevant information. It can even be that they are simply receiving too much data, too quickly.

To put the amount of data generated into context a modern aircraft flying from London to New York generates the same amount of data as 78 years’ worth of mp3s played back-to-back. That is a lot of albums!

It simply isn’t possible or feasible for a human to sift through such vast amounts of data. An average piece of equipment on the IoT provides data from multiple sensors that may take readings every millisecond. Multiply this across an entire install base and we can soon see the volume of data even a small organisation might face coming in.

Even when we are only tracking the data related to core KPIs, it is easy to see how such vast volumes of data can overwhelm an organisation.

This is where Artificial Intelligence becomes critical.

By introducing AI to filter the data using rules designed to surface the right information an organisation needs to service the equipment they have in the field, companies can take a raw commodity (data) and turn it into something valuable (insight). Such tools also allow us to interpret the data to seek out anomalies and areas where the data falls outside of acceptable parameters that could indicate an imminent fault ahead of failure.

When we then begin to take this equipment data and integrate it with other data sources such as CRM, service records, weather data, configurations, customer data, geospatial information, or additional information we can then aggregate, contextualise, and analyse the whole data set utilising AI to create valuable service intelligence that we can action.

With bi-directional communication with the asset in the field, such a wealth of data can indicate whether a solution can be fixed remotely or whether an on-site intervention is required and even how far away the asset is from failure. This level of insight can be critical for capacity planning of on-site engineers, which would be under more significant pressure in a hybrid service portfolio such as we have discussed in earlier segments of this paper.

Robotic Process Automation:

So far we have looked at IoT as the key to getting data from assets in the field and then outlined why AI is crucial to bring value to that data by surfacing the relevant information and thus generating insight.

However, while insight is more valuable than data, it is action that is where both the customer and field service organisation find true value. While that action could and often is triggered manually, for example by a dispatch team with AI-powered, data-driven dashboards to help them make intelligent and informed decisions, the next step is to introduce automation into the action taking process. This is done by introducing Robotic Process Automation (RPA)

We mentioned earlier that by outlining parameters that specific data must stay within we can use AI to highlight when an asset is potentially set to fail. RPA allows the system itself to go one step further and identify the action that needs to be undertaken and either trigger that action directly or push it forward for a human’s attention at the end of the change.

By introducing RPAs, we can further drive efficiency when it comes to resolving our customers problems.

When we look at remote service delivery, RPA could, upon diagnosis of a forthcoming failure, proactively run a check to ensure all firmware on the asset is updated. It could run a self-healing patch, connect directly with the customer with a request for further information or guidance on self-maintenance, automatically dispatch the required part to the customer or schedule an engineer to dial in remotely to run further analysis or offer remote advice to the customer directly.

All of this could be automated via RPA, driven by AI and fed by IoT data, and it can all be achieved remotely.

Augmented Reality

This brings us to the final element of the remote service technology stack, Augmented Reality (AR). Ultimately, AR is the endpoint, yet is also perhaps the tool that dominates the headlines the most as it is the most visible.

However, as with all of the other elements mentioned in this section of the white paper, AR is keenly linked to the broader technology stack and is an integral part of the mix of remote service delivery.

Ultimately, AR is the interface of the future. Many studies have shown that visual communication is far more effective than verbal communication. By allowing annotations and overlaid visuals, AR is the most effective means of visual communication available.

Whether it be guiding a less qualified engineer on-site with a challenging fix, a third-party worker in a blended workforce model or the customer themselves as they undertake self-help, AR remains simply the single most effective means of transmitting subject matter expertise and knowledge quickly and effectively to the site via a remote expert.

This feature is just one short excerpt from an exclusive Field Service News White Paper published in partnership with PTC

www.fieldservicenews.com subscribers can read the full white paper now by hitting the button below.

If you are yet to subscribe you can do so for free by hitting the button and registering for our complimentary subscription tier FSN Standard on a dedicated page that provides you instant access to this white paper PLUS you will also be able to access our monthly selection of premium resources as soo as you are registered.