Connected Field Service: IoT and Improving Asset Management

Feb 07, 2022 • News • Digital Transformation • IoT • servicenow

There are several areas in which connected field service can change asset management for field service organizations in the not too distant future. In this first feature in a series of excerpts from the latest white paper from Field Service News, we shall explore some of the potential benefits of IoT enabled assets...

Asset Self-Monitoring:

The traditional paradigm of field service has relied on an asset breaking down, a service request being submitted, an engineer being dispatched, and in an ideal world, resolving the issue in a first-time fix. Even when this process is undertaken with optimum efficiency, when everything goes perfectly, there is still a significant amount of downtime between asset failure and issue resolution.

Of course, many field service organizations introduced planned preventative maintenance (PPM) to help their customers avoid unnecessary downtime. However, helping in this first issue of reducing unnecessary downtime, a PPM based-approach results in unnecessary service calls, which can become an additional cost-line either for the service provider or the customer.

Despite PPM being often viewed as an improvement on the break-fix approach, the truth is that depending on the importance of the asset in the customers own revenue generation, either a break-fix or PPM approach may have been the best option within a service portfolio. However, ultimately neither are optimum.

In a world of connected field service, we can move beyond PPM and introduce true pro-active maintenance into a service portfolio.

To illustrate this, let us take a simple example of a hot drink vending machine with a failing boiling mechanism.

In the old break-fix model, the issue would be eventually noticed by a customer, who would report their cup of hot morning coffee wasn’t piping hot. Eventually, a customer complaint would lead to the arrangement of a service call; the vending machine would be turned off, so no longer generating any revenue until an engineer could be sent to resolve the issue.

"IoT offers a far greater workflow by allowing the asset itself to, in effect, undertake that inspection requirement and then the service company only need schedule necessary maintenance as and when it is required..."

In a PPM environment, this fault may be picked up before it was reported by a customer, which could help mitigate unplanned downtime. However, it is not cost-effective to send a technician skilled enough to resolve the issue on an inspection call.

The alternative is for the field service organization to commit to sending a skilled resource to a series of calls where their skill-set is not being used to maximum effect (i.e. those calls where there is no fault to be resolved). Yet, of course, there is no benefit to the customer to pay for one set of technicians to undertake inspection calls if they cannot resolve any issues they may find.

While offering an enhanced layer of service to the customer, such an approach ultimately results in a bloated cost base for the service provider. However, IoT offers a far greater workflow by allowing the asset itself to, in effect, undertake that inspection requirement and then the service company only need schedule necessary maintenance as and when it is required.

In the example of our vending machine, the machine could have a sensor that monitors the temperature of the water when it reaches boiling point in the delivery of the coffee. If the average temperature of the water begins to hit outside of an acceptable parameter that indicates an imminent failure, with an IoT connected asset, an alert could be triggered automatically.

What Comes Next?

The above example allows us to see how even in just a very simple situation how having just one data feed on an asset could have a significant benefit for both service provider and their customer alike. The customer benefits from proactive maintenance that can be primarily scheduled in off-peak hours where the impact of downtime is minimized. The service provider has a considerable reduction in costs as truck-rolls are minimized to effective work.

However, we could also expand on this simple example in several ways to further leverage this IoT data feed to enhance the service delivery process.

Connection to Engineer and Parts Scheduling

The most obvious first step would be to schedule an engineer directly if sensor data crosses a threshold to trigger an action within an organization’s field service management solution. The reality is that this chain of processes is precisely the type of mundane process automation that Robotic Process Automation (RPA) is designed for.

The rules could be fairly straightforward to make this happen. If the water temperature moves outside of acceptable parameters by two degrees, then historic data can interpret that this is an indication of failure. To keep the example simple, let us assume a direct correlation between the degrees outside the defined parameters and the mean-time-to-failure. In a real-life scenario, the modelling may be more complex, but for clarity of the concept, let us just assume that as the water falls a couple of degrees below the accepted parameters, this is an indication of the asset falling within the next three weeks.

This provides us with a fairly routine set of requirements that we would want to automate. By leveraging the IoT asset data and data within our workforce management tools, we can begin to map this out reasonably simply.

If the water temperature falls outside of standard parameters by 2 degrees, an engineer needs to be scheduled to provide a proactive repair within three weeks. This is asset data provided by IoT.
Identify potential fault cause ahead of the engineer visit. This is where the asset data can be filtered through a knowledge bank via Artificial Intelligence (AI).
Identify which technicians are local to this area, have the required skills-set and availability within the timeframe. This is workforce data held within the relevant system of record.
Identify potential spare parts required and arrange for them to be delivered to the client site, engineer or drop off location ahead of a service call to allow the best opportunity for a first-time fix. This is a trigger based on the output of the AI triage being fed into either a dedicated parts and inventory or FSM solution.

All of this can be done and fed either directly to the customer themselves or potentially a customer service agent who can contact the customer with a list of potential solutions and times that a field technician can be dispatched to resolve the problem in the absolute minimum impact to the customer's business.

Facilitation of Self-Service Solutions

The above allows us to see how the use of RPA being fed by IoT data with a layer of AI can dramatically change how we approach field service delivery. However, in the above example, we could even take this a step further and look to utilize the same tools to facilitate a self-service approach to maintenance.

Let us for a moment consider once more the vending machine in our example and take into account another potential development into this mix, modular design. If the customer could easily replace the failing part, the process could be slightly reworked to allow for self-service.

The exact same processes could be leveraged to facilitate such an approach.

However, in the final stage of this process, the customer being contacted directly via automation or via a customer service agent could be given the opportunity to undertake the maintenance themselves.

"The question of utmost importance in this instance is what is in it for the customer? The answer would be in two of the most precious commodities of the twenty-first century; time and convenience..."

Clear instructions on the maintenance could be sent to the customer via the knowledge base digitally, and the required parts could be identified and dispatched to the customer directly.

Suppose we include a remote service support solution within the technology stack of the field service provider. In that case, the customer could even be given a link for real-time support as they undertake the maintenance. The question of utmost importance in this instance is what is in it for the customer? The answer would be in 2 of the most precious commodities of the 21st century; time and convenience.

Self-service allows the customer to have their asset up and running without the wait for an available engineer. Self-service allows the customer to ensure the maintenance is undertaken when their own customers are not present.

Of course, such an approach may not be suitable for every situation. However, it is certainly something that is capturing the attention of many in the field service sector. Such an approach is made infinitely more seamless as we look to build upon the current use of IoT-based asset data.

The Question of Data Ownership

While there are undoubtedly as we have seen, many opportunities for IoT to drive consistent improvement in asset management, the one area we must also address in this conversation is the challenge of data ownership.

This has been something of a critical and often contentious topic of conversation as we move into a new era of field service and indeed business at large that is driven by data - the great resource of the twenty-first century.

The conversation is particularly complex - does data generated by an asset fall within the protection of patents so remain with the manufacturer? Or is the data created through use so generated by the customer and as such the output of their own work and something they own?

It is one of the big discussions that we will collectively need to resolve as we move ever onwards in our data-driven digital transformation journey. Yet, in some ways a focus on the granular debate of data ownership should and must make way for the macro discussion of how we can leverage the data for the benefit of the customer.

As Tasker Generes, Global Head – Strategy and Information, ServiceNow explained on the Field Service News Digital Symposium “The question of who owns what data is a thorny issue that we are currently in the middle of trying to really get to grips with as an industry.

“However, I believe the discussion ultimately comes down to the fact that customers want it to be really easy to engage with any company they are doing business with. Frankly, if it is not easy to engage they are going to leave because they have options. When you are looking at data, it is really necessary for those who need to know to understand the inner workings of what goes wrong, but customers more often than not just want the service or product they use to work.

"We have to understand as an industry that going forward this market is massively evolving. These changes we are moving through currently will bring with them complex questions over privacy, data and ownership and data stewardship..."

- Tasker Generes, ServiceNow

“The real challenge is determining what is and what isn’t needed within the data set. It is identifying how do we really become proactive and help prevent the issue. In order to do this we cannot look at IoT as a standalone; it has to be viewed with additional context of other variables relevant to the specific situation. It is imperative that the service provider is able to get the data into a position where it offers actionable insight. IoT can offer us so much data, but to be able to have those brilliant insights, it has to be tied to the action.

“We have to understand as an industry that going forward this market is massively evolving. These changes we are moving through currently will bring with them complex questions over privacy, data and ownership and data stewardship. With Industry 4.0 and the current digital trends, access to data is going to be required to deliver the customer the great service they expect.

“However, as service providers, we now must understand that customers whether it is in the B2B or consumer verticals, don’t want to be ‘on the grid’ and let people learn everything about them through their data.

“So the critical question service providers must consider is ‘how do we create the ability to learn from the data in a set rather than for the individual?'”

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