Data acquisition
exceed safety and quality thresholds. This affords operators a quicker reaction time, reducing damage and waste and ultimately improving the reliability and utilisation of their assets. Real-time access to this data is also helping prevent toxic leaks and other incidents, critical in industries such as chemicals or oil and gas. Beyond the shop floor, IoT devices advance inventory and distribution management by monitoring finished products in transit. While a growing number of companies are embracing IoT in factory settings, they face some obstacles to implementation. One common pain point is managing the integration of Industrial IoT (IIoT) applications with existing technology; frequently businesses do not establish the basic principles when designing their reference architecture structure. To enable cross-enterprise data integration,
ERP, MES and PLC data must be combined in digital applications such as real-time production debottlenecking and performance dashboards, computer vision-enabled quality management and selected machine learning (ML) control applications. Selecting the right IIoT platform and a corresponding data design, which allows data coming from the various systems to be integrated, is key when maximising IoT’s usefulness.
3. LEVERAGE AI TO PRODUCE FORWARD-LOOKING INTELLIGENCE
Machine learning and other AI-based technologies can help unlock further insights from the growing amounts of data being collected across shop floors and supply chains. While AI refers to a broad set of
computerised capabilities that emulate some of humans’ cognitive abilities, ML is a subset of AI that enables machines to learn from data to deliver forward-looking intelligence, without being directly programmed to do so. Presently, one of the most powerful applications of ML in manufacturing is in the predictive process control space and setting prescription for optimal operation. This is a critical area because machine failures and unplanned equipment downtime can cost manufacturers millions of dollars every year. Predictive maintenance software powered by ML algorithms harness the data collected by machine sensors to monitor performance 24/7 and predict technical faults, avoiding unexpected stoppages or breakages. These solutions have been proven to achieve a reduction in machine downtime and overall
maintenance costs of 10 per cent to 20 per cent. Enhanced Vision System solutions now incorporate AI and ML to provide high levels of automation and increased accuracy in quality control and inspection. The technology enables digital “reading” in manufacturing environments and can support the production process by performing visual tasks traditionally done by humans – for example when selecting parts, performing quality assurance or detecting defects. Vision Systems combined with AI can complete tasks within a shorter time frame and with greater accuracy than human operators, often removing errors and the potential for cognitive bias. The net effect being improved efficiency, higher run rates and reduced costs associated with downstream scrap and rework. In an increasingly challenging market, it is essential that manufacturing organisations realise the full value of shop floor data. Digital and Technology solutions such as those highlighted above, are key to providing manufacturing companies with the opportunity to kick their performance into a higher gear whilst achieving maximum return on investment.
Alvarez & Marsal
www.alvarezandmarsal.com
Instrumentation Monthly May 2023 51
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