With AI, industries that are blue collar driven have an ability to setup processes that drive operational efficiency. Service workers diagnose and solve problems. Replacing parts and fixing large machines. Not all parts of the repair process are tracked so information loss prevents solving problems quicker. With AI tools now, the ability to capture what went wrong now takes minimal time through dictation and automated transcription. Combine the service records of different technicians and now you can identify common problem faster and build processes to prevent problems before they arise.
The aviation industry is well known for tracking all aspects of flights. With planes flying 60% of the time in air, an unexpected issue brings severe headaches. Airplane engines have sensors that continually track key components of engine operations (temperature, oil flow etc) and stream live data so operators can know when they are out of alignment.
For oil companies, they will have similar monitoring of real time data for flow of gas or oil through pipelines. Tracking the different components for variation from the norm allows them to fix problems before disasters occur that take down a pipeline. In both industries, unexpected downtime costs in the millions.
For smaller operators that may not have the ability to implement real time data capture and analysis, one can use the service technician’s expertise to provide insight into what went wrong and how issue was fixed. In itself, one report is an outlier, but as you gather across the service visits and compile the data to find commonality, you can identify the pattern to prevent future problems.
How AI improves service visits
The data layer is the most crucial layer as no data means no model. The data can be built from historical records that may capture what was fixed in terms of parts. But the reality is, like with airplane engines, understanding the operational environment is necessary to prevent problems before they arise.
From tracking service visits, the age of equipment, operating condition, and service records one can start to model the conditions necessary to offer preventative maintenance solutions. For service technicians, entering all the data may be time consuming, especially if the data they need to enter does not align with the existing fields.
Instead, with AI and qualitative analysis, the company can give the technician the script of the areas to cover and let them describe in detail the problem areas, the analysis process, and the solution they implemented. The details around the service record that does not exist today, serves as the bed rock for simplifying work tomorrow.
The benefits of the solution for a firm are numerous:
- A solution manual for quick checks to perform to gauge complexity of problem.
- Determine replace vs repair decision based on age of equipment and how previous fixes have lasted
- Identify parts or suppliers that end up lower on the quality or duration scale compared to competitors and make changes.
AI is currently transforming the world through the automation of workflows. There are opportunities for companies and industries that do not think they need AI to see the future of work differently. AI is utilized for operational efficiency and in service work, solving problems may be how they get reviewed well, but removing emergency visits in peak season would be unseen benefit for both the firm and the customer. Instead focusing on preventative visits with key data in hand to make part changes to ensure a continuous run through the worst of winter or summer temperatures makes for satisfied customers.