Manufacturing Industry has been in constant lookout for technology advancements that would eventually help increase operational efficiency, better quality control, lower production costs, lower maintenance costs, increase revenue, etc,. With adoption of Industry 4.0 principles, the industry has been addressing challenges like having to deal with complex processes, high costs due to legacy tools and technologies.
Some of the key assessments required to be done before the tech shift include 1) the equipment readiness to be connected to a central data system, 2) Software and Hardware required for addressing loads of data being generated 3) Capacity of the IT systems like storage, processing, etc, 3) Readiness for Smart Operations and 4) Change Resistance in People
One of the key technology shifts in the manufacturing space is deploying technology solutions driven by Artificial Intelligence. Some of these AI driven use cases include: 1) Detection of anomalies and perform predictive maintenance to save huge repair costs. IIOT devices help with collection of relevant data sets 2) Improve quality control by statistical process control 3) Inspection of products/components using AI based visual tech and thereby reduce human errors
Another key AI use case is Digital Twin technology where in a physical object, process or a whole factory for example is represented digitally. Some industry examples include engines, wind farms, buildings, cities, etc,. The digital twin technology can be used to collect data, create simulations that can predict how a product or process will perform