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The evolution of field services industry; From ‘react and respond’ to ‘predict and prevent’

(Image credit: Image source: Shutterstock/everything possible)

The Field Services Management industry is expanding at an unprecedented rate. Currently, reactive and preventative forms of upkeep and maintenance are the norm but the sector is undergoing some exciting changes. The next step in the evolution of the industry is predictive maintenance and it is poised to be worth $4,904 million by 2021. 

One of the most common forms of maintenance is reactive repairs. When a system fails an alert is raised and a repair is made - with usually quite a hefty bill. Studies have shown that reactive repairs following machine failure can cost up to 20 times more than maintenance repairs. In addition to the repair bill, a defective machine can rack up expenses in downtime through loss of production – with a cost as high as £18,000 per machine in some industries, for every minute it is inactive. 

The huge repercussions aligned with reactive repairs have led to a rise in preventative maintenance, in which enterprises carry out regular services and replace parts quickly after wear and tear, but crucially before they break. Similar to a car, after it has driven so many miles, the driver is alerted that a service is due. 

Yet, there are still negatives associated with preventative maintenance. When a warning is alerted, it isn’t always clear what has gone wrong and the extent of the damage until a full service has been carried out. This can still allow for the potential of expensive repairs and downtime. 

The next revolution is beginning 

The Field Services Management industry is now shifting towards a predictive model which is revolutionising the way enterprises maintain their machines. With the rise of the Internet of things, IoT sensors can be built into machines to monitor them and anticipate failures before they happen. 

These sensors collect machine data – such as temperature or vibration levels – which is fed into a centralised cloud dashboard, so that when a machine is acting unusually, or is producing data that warrants a closer look, an alert is created. The system then schedules maintenance and assigns it to the most appropriately skilled engineer along with the exact knowledge as to what the fault is and how to correct it. When done effectively, this can in theory have the potential to alleviate machine down time completely. 

Artificial intelligence (AI)

The key to a successful predictive model is Artificial Intelligence, which is now becoming more prevalent in the Predictive Field Services industry. Previously, the use of AI was largely restricted to alerting a human to a machine’s fault, and then the business would call and schedule an engineer. Now, AI can instinctively take a much more holistic approach in preventing large scale losses. 

A faulty machine will stream data highlighting the fault to a predictive system. When the machine notes an abnormality, the AI software kicks in and will schedule the correct engineer to arrive autonomously and automatically before it breaks. This IFTTT trend (if this then that) – being able to intelligently identify an issue (if this) and then act upon that knowledge to solve the issue (then that) – is a real game changer. 

In the best instance, AI also allows the machines to run more efficiently. Should a company only use their air conditioning for 6 hours a week, the Predictive Systems harnessing AI will have learned this overtime and calculate a less frequent service than that of a client that uses theirs 24 hours a day. 

The sheer volume of data within the Field Service Management industry means the opportunities are endless for AI to keep learning and improving. The more data a system has, the more it will learn and the quicker it can predict and prevent faults in machines, and ultimately save money. 

As the saying goes amongst us humans “practice makes perfect!” 

Maximising performance, minimising downtime

Occasionally, there will be instances when a machine breaks down unexpectedly, no matter how many services that have been completed. As well as facing extortionate downtime costs from loss of business, companies can also experience high costs from uninformed engineers which can have a detrimental impact on KPIs, such as First Time Fix.

In the future, this needn’t be the case. The sensors integrated into the machine will transfer continuous streams of data directly back to deep learning algorithms. Through analysing this data stream in real-time, the intelligent software will be able to generate the appropriate action. This could be alerting the engineer with the correct expertise needed to fix the machine which establishes a direct line of communication from the deep learning algorithms with future engineers. Once the machine has been fixed by an engineer, the intelligent software stores the repair process for the next time that machine needs to be fixed. 

A line of communication created between the machine and the engineer will guide them to the most efficient and successful method of repair. The speed and accuracy that is introduced to the work force from this service eliminates the risk of an uninformed repair, which would elongate the downtime and cause prices to escalate. 

Ultimately, with predictive maintenance capabilities, businesses will be able to focus on their KPI uptime success rate, rather than worrying about potential downtime. 

The future is predictably bright 

Saying goodbye to the traditional rigid processes of reactive and preventative management and embracing predictive software solutions will result in a more streamlined approach to businesses and lead to better work flow optimisation.

The cost saving potential is huge - our whitepaper on efficiency in field service management revealed that 38 per cent of organisations have shown they can save more than half an hour a day, per technician using predictive management technologies, averaging a cost saving of £525,000 per year, per company.

Media have reported on the widespread fear of jobs being replaced by AI, but these technologies aren’t necessarily putting workers out of a job. Rather they are maximising the potential of a workforce by clamping down on resource underutilisation. 

The field services industry has come a long way in recent years. With the integration of AI and low code software philosophies, businesses are making proactive, evidence-based decisions reducing the risk of losing precious time and money that comes from uninformed decision making. 

Thanks to field mobility solutions, intelligent appointment scheduling and real-time visibility, predictive service management businesses are making a bold entrance into the field services industry and producing more streamlined workforces. Regardless of their size, they are eclipsing large-scale competitors who only provide reactive measures. 

Chris Proctor, CEO, Chris Proctor, Oneserve
Image source: Shutterstock/everything possible

Chris Proctor
Chris Proctor is a leading figure within the field service management industry. As CEO of Oneserve, an award-winning cloud-based predictive field service management software provider, Chris is pioneering the latest machine learning technology for service management companies.