The phrase “artificial intelligence” describes the concept of creating a machine capable of acting as a human brain. Many different AI algorithms have been invented to emulate reasoning, represent knowledge, process natural language and even socialize. Further development has divided AI into several branches, such as searching and planning, reasoning and knowledge representation, perception, moving and manipulating objects, natural language processing and finally learning. The last one naturally complements all of the rest, and is called machine learning (ML). The idea behind ML is to create an algorithm that will learn from data and be able to make predictions based on this knowledge. With machine learning you can solve problems requiring reasoning, classification, anomaly detection, etc. ML can also be applied in big data, which is a way of storing and processing huge volumes of unstructured data. Such data are meaningless at first glance, but the right approach can extract an unbelievable number of conclusions.
How can Machine Learning and Artificial Intelligence Advance Field Service Management?In field workforce management, the significant number of tasks to be scheduled and equipment parameters to be taken into consideration can create a volume of data that is impossible for the human brain to process in a reasonable time, and is too expensive to analyze using a dedicated team. A very good example of such a challenge is preventive maintenance. Each machine or equipment part provided by a manufacturer has a recommended date and time for replacements and periodical inspections. These need to be planned, executed and tracked to avoid unexpected breakdowns, associated downtime and resulting costs.
With a proper FSM system capable of storing and processing information efficiently, a field service company can benefit not only from optimal planning but also from advanced problem identification.
AI in Field Service Management as the Next Step in TransformationThanks to machine learning algorithms, a field service management solution can predict possible issues, reasoning from domain knowledge (such as documentation and manufacturer recommendations), historical data stored during everyday tasks (reasons for issues) and data gathered about temperature, fluid levels, telecommunication system indicators and so on by dedicated sensors such as Comarch Smart Devices. Working with this information, an ML algorithm can recognize certain patterns (e.g. possible failure due to overheating) and plan proper tasks for field workers based on previously known solutions. In such a scenario, you can identify and fix the issues faster, and limit serious and expensive failures. For service companies, this means that they can take issue resolution to the next level and introduce a predictive maintenance approach.
Such inference can be carried out in the background of all other everyday actions, providing extra knowledge that can give a service company a competitive advantage over competitors who lack such detailed insight. FSM systems can act more as an assistant, helping to diagnose the reasons for a problem and automating significant parts of the dispatching process, relieving technical staff of the task of identifying a problem, thus helping managers optimize everyday work. All of the above examples of company benefits, directly impact the service quality and can be noticed quickly by end customers.