The Predictive Analytics Market is highly competitive, with the prominent presence of numerous players in different phases of the ecosystem. According to MnM’s market evaluation framework, most companies are adopting organic growth strategies to strengthen their market position. The number of product launches and enhancements increased by around 50% in 2021 compared with 2020. Microsoft, IBM, SAP SE, Oracle, SAS Institute, Google, Salesforce, AWS, HPE, Teradata, Alteryx, FICO, and Altair are a few key market players offering innovative predictive analytics solutions and services.
Predictive maintenance helps reduce costly downtime by detecting manufacturing processes and equipment defects.
A major challenge witnessed by manufacturing firms is an unexpected machinery breakdown, which results in unplanned downtime and high operational and maintenance costs. It occurs due to the extreme pressure on machines due to automated operations and uncontrolled temperatures in a manufacturing facility. According to the study conducted by the Wall Street Journal, an American business-focused, international daily newspaper, and Emerson, an American multinational corporation, unplanned downtime costs USD 50 billion annually to industrial manufacturers, and 42% of this downtime is caused by equipment failure. Moreover, according to another study, After the Fall, the average cost for each downtime instance is USD 2 million. Many manufacturers often fail to notice the benefits of advanced tools and technologies, such as IoT, sensors, vibration analysis, and asset maintenance strategies, which can improve operational performance. This results in frequent system failures and a decrease in performance, which, in turn, causes supply chain disruptions.
Predictive analytics includes statistical study and modeling techniques to determine the future performance of the equipment based on current and historical data. This technique indicates machine operators about asset performance and asset condition, enabling them to address potential issues and conduct maintenance activities before the occurrence of a failure. Using this approach, organizations can maximize asset uptime, improve their reliability, minimize the number of system failures, as well as reduce operational and maintenance costs by conducting maintenance only when necessary. This has boosted the adoption of predictive analytics solutions. For example, a natural gas company approached Kalypso to develop a solution to predict downtime events in natural gas compressors before they occur. Similarly, Duke Energy Renewables started using analytics solutions to predict a turbine failure.
Apart from forecasting asset performance and health issues, other activities including data collection, churn rate prediction, and quality control process automation can also be conducted using predictive models. Moreover, AI and IoT-based predictive maintenance solutions statistically analyze maintenance schedules across the manufacturing plant. This reduces the cost of spare parts and supplies and production hours lost in maintenance, increases machine uptime, and improves safety by eliminating accidents that take place due to faulty machines in a production plant.
Organizations with advanced predictive maintenance strategies would reap significant benefits by getting insights about their valuable assets and achieving optimum operational performance, which may help them to stay ahead of the competition.
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