The Power of New Digital Technology & Maintenance
1.What are the key considerable aspects of predictive maintenance in an organization?
Maintenance has always been a major business headache, however it can be brought to a new level by combining the power of new digital technology with a fair understanding of maintenance. Predictive maintenance being one of the most talked about in maintenance and asset management, it is critical to the success of the business today and a source of new value. In Reactive Maintenance approach, maintaining an asset too late can bring upon significant risk of disrupting business and baring unnecessary and high maintenance costs. In Preventive maintenance, schedule optimization does not take place and unnecessary maintenance downtime add to the overall cost. In such cases many a time failure to detect issues with a critical parts leads to sudden parts failure and downtime. In condition-based monitoring issues are detected when it crosses a threshold and time window to address that sometimes is too short and downtime occurs during critical production time. Predictive maintenance recognizes a pattern highlighting when things may go wrong and having an understanding over TTF (Time to failure) gives sufficient time window to source a critical part and schedule the downtime. The consequences of poor maintenance strategies can substantially affect operational efficiencies and profitability.
To be competitive, companies need to minimize unplanned downtime and optimize maintenance costs. Industry 4.0 capabilities enable companies to monitor their assets in real-time, integrate data from many different sources, analyse and translate that data into meaningful insights and automatically turn those insights into prescriptive actions to optimize maintenance.
McKinsey & Company predicted, $1-4 trillion value creation p.a. in 2025 due to IoT in the factory setting globally. A large portion of that value creation is due to industrial analytics and predictive maintenance. This will impact maintenance costs, asset reliability, productivity, safety, quality, and customer satisfaction.
2.How can companies leverage advanced technology for financial and operational benefits?
Predictive Maintenance has gained traction lately due to exponential growth in IoT devices. Collecting the data is the first step, many companies have a vast amount of data but they don’t do anything with it and don’t know what to do with it. Analysing the data and drawing meaningful insights provides a competitive advantage. The benefits of predictive maintenance are dependent on the industry or the specific process it is applied to.
According to Deloitte Predictive Maintenance increases equipment uptime by 20 percent, increases productivity by 25 percent, reduces breakdowns by 70 percent and lowers maintenance cost by 25 percent. The benefits are significant when you can leverage technology and extract full value from it. This highlights not only how companies use technology but also shows the culture of the company.
Predictive maintenance is automated maintenance that monitors the performance and condition of equipment during normal operation to reduce the likelihood of failures
3.Predictive maintenance plays an important role in enterprises. What is the role of tech providers in supporting business to adopt predictive maintenance?
Predictive maintenance is automated maintenance that monitors the performance and condition of equipment during normal operation to reduce the likelihood of failures. The goal of predictive maintenance is the ability to first predict when equipment failure could occur (based on certain factors), followed by preventing the failure through regularly scheduled and corrective maintenance. AI and machine learning plays a significant role in recognising much in advance patterns which lead to a failure at a later point in time thus helping in optimizing maintenance schedule and many a times the need for a part replacement. While there are several different technologies to aid in your predictive maintenance efforts, it is vital to choose the right one to ensure success.
I would like to share four areas to understand and predict a machine behaviour, they are:-
1)Trend detection – Statistical trend and pattern detection of KPIs
2)Anomalies - Auto detect deviation from normal behaviour
3)Recommendations – Prescriptive actions based on root cause analysis
4)Predictions – Predict future state by learning from the machine data and business context
Machine learning models embedded with cloud solutions create knowledge for a machine behaviour. Future-Ready Predictive Maintenance solution uses predictive analytics to predict asset failure, generate actionable insights in real-time and automatically turn those insights into prescriptive actions to optimize maintenance.
4.What are the key features of predictive maintenance? How can transforming the technology infrastructure improvise operations of the entire supply chain?
Under predictive maintenance, each asset is monitored using condition monitoring sensors to get an accurate representation of what’s happening inside the asset without any kind of productivity disruptions. By using the IoT technology, different sensors can collect and share data which helps identify if any asset that (will) needs attention. Using underlying machine learning models we can recognize patterns leading to failures and thus help in predictive maintenance. We have established digital thread between IoT and Maintenance system to automatically create maintenance work order based on such predictions. The thread may be further leveraged to check similar maintenance need in the past, spare parts requirement and its inventory and whether such need to be proactively ordered from the supplier. The thread gets completed when such predicted downtime can be incorporated into production planning process to adjust the production plan considering the impacted machine availability and its utilization.