| |May 20199PROACTIVE EQUIPMENT MONITORING IS CRUCIAL FOR ASSET-HEAVY MANUFACTURERS TO NEGATE THE UNCERTAINTY AROUND THE RELIABILITY ON MACHINESof that, ineffective or poorly planned maintenance strategy only adds to the monetary woes; for instance, preventive maintenance can cost USD 13 per hour to the company as compared to predictive maintenance which costs manufacturers an average of USD 9 per hour.Manufacturers, therefore, must look beyond the preventive and corrective approach by implementing digitally powered, next-generation, predictive equipment monitoring and maintenance strategy. This analytics-driven proactive approach called Predictive Maintenance or Condition Based Maintenance (CBM) facilitates increased control over the production schedules and minimizes operational ambiguity to a great extent by analyzing real-time data collected from machines to forecast potential breakdowns.Predictive Maintenance uses sensors embedded in machines to capture critical data round the clock regarding various performance and environmental parameters. This information is then transmitted to a central processor which deploys an advanced analytics engine to detect disparities over time. Once any anomaly is identified, the engine raises a flag to notify the factory personnel who can then schedule maintenance before a failure actually occurs minimizing unnecessary wastage of time and efforts. CBM can also help manufacturers keep a stock of the right spare parts thus avoiding unnecessary storage expenses.Advantage AnalyticsThe cost of sensors has come down drastically due to the confluence of new-age technologies in semiconductor manufacturing and digitization-enabled breakthroughs. These advanced sensors are capable of monitoring a wide range of operational parameters of the equipment such as temperature, pressure, work-load and safety among others to generate high volumes of accurate and timely information. This has given a big push to the use of data analytics in manufacturing to prevent downtime of equipments much before the incident occurs.Using data analytics, manufacturers can model their equipment maintenance drive on their real-time conditions rather than assumptions and time the schedule accordingly. This approach will augment maintenance work and take it to a new level of efficiency while reducing the repair induced damages and failures which will result in improved equipment availability and smoother operation for longer time periods. Proper use of analytics will also enable faster and more insightful repair-related decisions which will in turn significantly reduce the workload of the maintenance crew. Another added advantage would be ahead-of-time warning of machine failures or unanticipated breakdowns, allowing more time to be devoted to recognizing areas for bettering machine performance, energy efficiency and output. All the aforementioned advantages are tangible and quite substantial and manufacturing companies can reap these business benefits by leveraging effective predictive maintenance powered by analytics. Overall, CBM can help manufacturers automate the entire arrangement of events­from sensing operational data, updating critical systems and anticipating breakdowns to alerting concerned people in the plant and triggering scheduled repairs. This makes data analytics a compelling proposition for modern manufacturers looking to improve asset productivity and cost competitiveness.The machine condition monitoring market is estimated to grow from USD 2.38 Billion in 2018 to USD 3.50 Billion by 2024 between 2018 and 2024. Factors such as the increased reliance by manufacturers on analytics, preference for secure cloud computing platform, and increased use of wireless communication technology and inclination of toward predictive maintenance are driving the growth of the machine condition monitoring market at present. Additionally, qualitative aspects such as smooth factory operations, product optimization, reduced plant interruptions help companies attain higher levels of customer satisfaction, better capacity management, improved supply chain relationships and boost the manufacturer's trustworthiness. We have entered the era of intelligent machine maintenance wherein equipment has the ability to predict their own failures, enabling organizations to sustain smooth flow of operations. C I
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