Artificial Intelligence and Machine Learning for Optimized FSM Planning
- 4 min reading
In field service management, careful planning can be the difference between success and failure. Get it right, and processes will run like a well-oiled machine. Get it wrong, and your customers will become exasperated and dissatisfied.
But task planning, scheduling and dispatching technicians can be a huge and complex logistical task. Done manually, this may consume many working hours – and there will always be the risk of mistakes caused by human error. All of this translates to costs, which businesses in all sectors, everywhere in the world, seek to optimize.
The solution is to implement FSM planning assisted by artificial intelligence (AI) and machine learning (ML).
The ultimate goal should be fully automated, “zero-touch” workforce planning. This means software that can pair the best technician to any given job (taking into account skills, availability and location), plan their route (giving consideration to their starting location and other assignments they may have), check the availability and location of any spare parts or specialist tools that may be necessary, and react to changes as they happen. The ideal solution would also be able to interrogate historical data and use the results to predict demand or maintenance issues ahead of time.
It's a complex process, and one which usually begins with a “learning” phase, where the AI software first uses already existing data to make suggestions about optimized planning. Over time, as more data become available, the AI should be able to take over FSM planning completely.
It’s a complex task. For most FSM companies, it will mean partnering with tech experts in the field to help them evaluate requirements, plan how to address them, draw up a roadmap of how to reach goals, and finally implement and manage their FSM solution. There will be costs involved, but these should be viewed as an investment because of the huge benefits of deploying a fully automated, zero-touch FSM planning solution.
Most obvious of these benefits are savings that can be made. That’s not just in the work hours that will no longer be required for manual planning, but also in terms of fuel and transport costs (which can be lowered by automated route planning), expenditure associated with customer care (for example, when the AI engine can monitor KPIs and ensure they are not exceeded), and gains in work efficiency of the technicians on the ground.
To find out more about AI/ML-driven FSM planning, and what to expect in the near future, download and read your free copy of our new ebook, entitled “Comarch 2022 Telco Trends Alert – Predictions for the Telecom Industry in 2022 and Beyond”.
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