Posted: November 28, 2022
The manufacturing industry faces a plethora of external challenges: supply chain disruptions, labor shortages and vast globalization top the list. Against the backdrop of vast international competition, these external factors have undoubtedly created increased pressures for manufacturers to outperform their counterparts. Colin Crow, managing director of Nexer UK, reports.
Artificial Intelligence and the Internet of Things (IoT) are fundamentally reshaping the manufacturing landscape by allowing manufacturers to re-evaluate their current processes and create long-term competitive advantages that allow them to adapt to current and future challenges. .
When considering potential areas for operational improvement and productive efficiencies, manufacturers should not underestimate the impact of their in-house machinery and equipment, especially with regard to maintenance and malfunctions.
Not only does infrequent routine maintenance and the inability to get operations back online lead to an immediate and frustrating end to productivity, it also creates lengthy and costly delays that allow competitors to bypass the manufacturer in question.
With technicians and repairers tasked with repairing machine failures often being told of the wrong plant location, being moved from site to site, and visiting sites without cause, manufacturers should seek to implement the concept of connected on-site service as a solution to these damages. inconsistencies.
Connected field service
The connected field service is powered by the Internet of Things and the cloud. When manufacturers choose to use the benefits of this technology, connected devices send information about the operating status of machinery and equipment directly to service providers. As a result, technicians are able to determine potential repair needs without the manufacturer having to be contacted.
The benefits of AI and AR
Usually, when a technician arrives on site to perform the relevant machine repairs, they must use outdated, jargon-laden manuals to deduce the information needed to correctly identify the problem. After returning to the depot, attempting to locate critical parts, retrieving equipment, and returning to the customer site to begin repair work, valuable time has been lost – even then the technician may need to review site to complete the job if parts are not available. For a process that could take days or even weeks to resolve, these inefficiencies could create a detrimental impact for manufacturers.
Competitive advantages are paramount, and this is where AI thrives. Unrestricted access to machine details and data shared between connected devices allows field workers to systematically prepare for the necessary course of action before they arrive, providing a welcome exchange to the process usually delayed.
What is predictive maintenance?
Concretely, the connected equipment will be equipped with multiple sensors that will transmit real-time data, in particular on the state of the machine, which can then be connected to work order management software. The algorithms used allow workers in the field to monitor manufacturing equipment and anticipate failures, increasing their productivity and creating what is known as “predictive maintenance”.
Predictive maintenance processes can also be fully automated, so that if an irregularity occurs in the normal operation of a connected machine, the problem can be immediately resolved through the self-healing process. In the context of broader discussions around mass digital transformation, it’s understandable that manufacturing executives haven’t put this technology at the forefront of their minds. However, it is now clear that these incremental, highly intelligent and proactive processes can completely alter a company’s accuracy, time management, productivity and overhead – key factors in creating an advantage over competing manufacturers.
Powered by AI, this data-driven approach reveals a wealth of information that allows technicians to immediately begin working on faulty machines and monitor equipment status, usage and disruptions before manufacturers are aware that there is a problem to report. By focusing the attention of field workers only on the most productive tasks, manufacturers are encouraged to do the same.
Although AI has yet to achieve human-like cognition, artificial neural networks that replicate this technology will only continue to evolve. So not only are the benefits of AI and IoT evident throughout day-to-day manufacturing operations, but they are also a guaranteed ally to external support and competitive pressures, while developing a better return on investment. over time.
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