Field Service Management: The Importance Of A Solid Foundation

By Nicolas Grondin, RedbackWMS

I recently read a few articles about the future of field service management. The things predicted to be important for the industry in the next 5 years are consistent. In one way or another, they revolve around the following 4 points:

Automatic or AI based scheduling: this is the ability for a system to automatically allocate work to technicians based on skills, availability and proximity to name of few, and without the involvement of users. This has been around for a while but according to Gartner, only 14% of FSM businesses have implemented this technology whereas 51% intend to implement it in the next 2 years.

Real-time tracking: this is the ability of back-office staff and managers to be informed of things occurring in field such as agent movements, work status updates, levels of inventory and other job outcomes.

Integration with IoT for automatic demand management: this refers to the customer’s assets automatically notifying field service providers that something is wrong or that an incident has occurred.  Consequently, a relevant job is raised for an agent to go investigate and repair.

Integrated field support, video collaboration or augmented reality: this refers to any functionality that improves connection between back-office expertise and field agents. More than rudimentary attached documents, this is often done with live, interactive in-field support through video chats or augmented reality.

Connectivity

To me, these capabilities make a lot of sense as they aim to reduce back-office costs by automating where possible.  They focus on bringing added-value to the field, closer to the most important asset: the customer.

The common theme around these points is connectivity. Back-office support experts are connected to field agents, customer assets to service providers, and field situations to the office tracking displays. 

But connectivity between people and things also implies that the workflow should be connected, that it should -as the name says- flow naturally from end to end.

For example, having an automatic scheduling “bot” which is not aware of the current situation in field – where agents are located, when jobs are finished – means that it won’t be able to react to that situation and customize assignments accordingly.

Another example is receiving direct asset alerts, but not being able to relate the latter to resource requirements – like agent expertise or required spare parts.  This may result in failing to identify and send the right technician with the right equipment to do the job.

The same goes for integrated field support. In the absence of a clean foundation with historical failures and problem comments, trying to automate artificial intelligence to pre-empt in-field knowledge is sure to produce sub-optimal results. 

The Foundation

These four future key functionalities are actually part of a larger, fully integrated “elephant”. They can’t exist in isolation or be deployed as the first step of a field service management journey. They need to be built onto a solid foundation of field service enablement functionality.

These foundational components contain the following 5 elements:

Demand Management: this typically refers to the ability to capture new service requests, validate them and then decide (automatically or not) what to do with them. Resolution actions will mostly involve generating field work but may also include a simple status response (if the work is already done) or a grouping of requests (if part of a larger failure).  

It is often onto this functionality that IoT integration is bolted. In other words, validation, bundling or any other resolution decision needs to be part of the picture when an asset automatically sends alert(s).

Resource Planning: this is where the resource required to carry out the work in field is planned, ahead of time, in order to make sure the right resource, with the right equipment and material is sent to the job. The importance of this functionality is underscored by the old adage: if you fail to plan, you plan to fail.

A lot of AI automation takes place here. It aims to relieve work planners from manually writing out all possible work plans.  It does so from past learnings by looking at which resource was used in successful resolutions or figuring out resolution patterns and trying to apply them at the right time.

Scheduling: this is often described as the crux of field service management as it has historically been to biggest problem to solve: matching the right resource for the job at the right time and with the right equipment.

One of the reasons why a large proportion of field service businesses today still rely on people to accomplish this is the necessity for reactiveness. When a situation in field changes, the schedule must adapt, and people are usually good at that. Therefore, the foundation needs to give users the visual ability to quickly understand the situation and to react appropriately. 

But this is also where real-time tracking comes in. The latter feeds the scheduling AI with real-time information, enabling the system to be as reactive as possible.

In-Field enablement: This typically refers to field mobility, which enables 2 things: in-field allocation of work and in-field data capture.

These two, working together, are fundamental to allow field force to be comfortable with a united communication platform. Only when this happens does it become possible to add elements of interactive support and enhanced reality.

The foundation is about having a straightforward, user-friendly, mobile application that is easily embraced by any type of field force.

Work Closeout: this is where completed work is converted into receivables to allow service providers to get paid and where inventory levels are adjusted based on materials used.

Importantly, it’s also where all in-field key learnings are made. By recording work results, captured data, time spent, etc we can feed learning algorithms that will allow us to automate resource planning, scheduling and future in-field support.

Thus the importance of having a foundation that enables this and puts the emphasis on the review process.

The Roadmap

Each of these 5 capabilities need to be in place and working well before starting to increase the maturity level of any field service management system.

Getting real-time location and status updates from the field but lacking proper visual scheduling tools to put this information into context of re-scheduling decisions is wasteful.

Similarly, receiving high volumes of automatic asset alerts without having the capability to automatically triage and bundle them into appropriate pieces of work may lead to wasted truck rolls.

The key is to first ensure you have a full end-to-end field service management system that supports the foundation. Secondly, to build on this foundation and continue to evolve, prefer a product that is as complete as possible and designed on a single, flexible platform. This will avoid integration headaches that come with having to glue multiple products together and will ensure that you have the springboard to grow when need be.

Lastly, depending on your field service model (asset centric, outcome centric, knowledge centric) you should make the decision as to which higher level maturity function will initially give you the best payoff.

If you have the right foundation, it’s a no-brainer.

Well, do you?

About the author

Nicolas Grondin is the founder of RedbackWMS which aims at helping high volume field service organisations work more efficiently by adopting industry best practices and leveraging system automations.