Here’s an attention-grabbing idea: Deploying cellular-enabled Industry 4.0 solutions can generate a 10-20x operational cost-savings ROI (return on investment) over the course of five years. This is according to a joint research study from ABI Research and Ericsson. The research also suggests Industry 4.0 solutions can generate up to 8.5% in operational cost savings, which, for a factory or industrial site, can equate to an operational cost savings of up to $600 per square meter per year.
These are compelling figures. Industry 4.0, also known as the fourth industrial revolution, is the idea that connectivity, automation technologies, and digitization are creating the fourth major revolution in the business of manufacturing. Thanks to trends like leveraging the IoT (Internet of Things), including wireless networking and sensors to collect machine data and enable predictive maintenance, as well as 3D printing, robots and cobots on the factory floor, machine learning and AI (artificial intelligence), 5G, and digital twins, among other trends, the Industry 4.0 market is projected by MarketsandMarkets to reach almost $157 billion by 2024.
A big part of Industry 4.0 is the use of AI technologies to enable smarter machines that can take on tasks like self-monitoring and diagnosis autonomously. AI’s ability to help manufacturers and other industrial businesses predict their maintenance needs and reduce downtime is also becoming critical, especially as older, knowledgeable workers retire and leave a skills gap behind. Companies like IBM, with its Watson IoT solutions for industrial equipment, are helping technicians of all experience levels leverage IoT and AI solutions to boost productivity in the age of Industry 4.0. IBM’s AI-powered repair assistant, for instance, provides guidance in the form of smart suggestions based on actual data, as well as access to expert help across multiple sources in realtime.
AI is also revolutionizing process monitoring and process control in the industrial realm. But implementing AI can be easier said than done, and many AI projects fail. To address this reality, DataProphet, an AI-as-a-service solution provider for manufacturing, suggests three best practices for manufacturers implementing AI for process control: select a proactive and holistic AI solution, augment human expertise with machine learning, and optimize continuously.
The first best practice—select a proactive and holistic AI solution—encourages manufacturers to consider the benefits of a proactive model of assessing process conditions versus a reactive one. Second, augmenting human expertise with machine learning is an example of “human in the loop” design, which can not only lead to better outcomes but also help build trust in the AI system. Finally, DataProphet suggests continuous optimization of factory processes as a third critical best practice. Because a factory is a dynamic environment, an AI optimization system must also be dynamic by design.
While every manufacturing process is different, AI can help manufacturers improve quality, reduce cost, and, in general, optimize processes across the board. AI technologies are part of the revolution that is Industry 4.0, which holds such promise in terms of operational cost savings for factories and other industrial sites. By seeking out best practices for IoT and AI implementation, manufacturers can get an even better head start on that all-important ROI.
Want to tweet about this article? Use hashtags #IoT #sustainability #AI #5G #cloud #edge #digitaltransformation #machinelearning #futureofwork #manufacturing #IIoT #industrial