The Path To Achieve Operational Excellence

As the organizations are adopting AI & IoT technologies to enhance the operational efficiency of the plant, it is essential to understand the critical aspects. Mr. Varadharajan Raman, Principal - Value Engineering, Flutura Decision Sciences & Analytics shares with Pro MFG Media the path to achieve operational excellence.

The path to operational excellence can vary from industry to industry, but the goal is to achieve consistent and reliable execution of the processes in the organization. Even though data is the key enabler of operational excellence, it is essential to realize that data accelerates understanding the process. The data derived from the machines act as a catalyst to appreciate the machine's performance. However, it should not be considered as machine-specific knowledge. It helps to learn the particular asset's overall scope of work effectively.

In the context of machine-specific data, there is an immense availability of headroom to utilize the data for leveraging the capabilities of data science and artificial intelligence, eventually enhancing the overall uptime of the machine. The collection of data plays a significant role in the digital transformation journey of an organization. Every segment of the process has evolved during the transformation, like the scheduled maintenance has migrated from basic calendar time-driven scheduled maintenance to run hours-based maintenance to eventually to stress-based maintenance.

To cite an example - a motor that requires its routine maintenance, scheduled once a month or quarter, is known as calendar time-based maintenance (1.0), which collects data on the number of hours performed by the motor. The data processed from 1.0 can be migrated into the run-hours maintenance based 2.0, wherein the scheduled maintenance can perform based on the number of hours performed by the motor.

To achieve additional visibility, the organization can derive real-time data from the particular machine. Assuming the 100-kilowatt motor performs similarly, the organization needs to decide whether they intervene at the 100th hour or continue it to the 110th hour. In this fashion, the organization progress from calendar-based maintenance to run - hours based maintenance, which further proceeds to stress-based maintenance. The next level of data achieved by the real-time loading of the machines will help in deciding the requirement of the maintenance.

Similarly, the whole maintenance management process could further be elevated to 4.0 levels, wherein adding additional data pertaining to the real-time loading on the machine and a multitude of other sensors i.e. temperature, pressure etc provide more relevant reminders and recommendations.

The process provides a better paradigm to understand the current situation of the machine. It also helps in deciding whether it is worth intervening before the stipulated time or after the stipulated time. The data will keep growing as the organization migrates from various levels, which will enable the industry to leverage the power of data to create an efficient asset.

Asset performance metrics such as Mean Time To Repair (MTTR) and Mean Time Between Failures (MTBF) are essential for any organization with equipment-reliant operations. The organization can maximize uptime and keep disruptions to a minimum by tracking these critical KPIs. To achieve maximum efficiency, an organization must thrive to maintain the Mean Time to Repair (MTTR) the lowest, whereas the Mean Time Between Failures (MTBF) should be the maximum.

A key element for the KPIs to create a difference is to constantly track the growth, compared to its initial baseline value. These KPIs will require additional investment in terms of time and effort from the maintenance team. The just-in-time maintenance concept is all about deploying analytics at the machine and shop floor level. This will enable the management to track efficiency levels on a real-time basis and make changes wherever required.

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