As the year draws to a close, most organisations will embark on the annual appraisal process. A process which most managers view as time consuming, demotivating, and often unhelpful. Yet, nearly nine out of ten companies employ the year-end ratings and bell curve to score employee performance. Realizing the futility of the exercise, some organisations have taken a step in the right direction by testing new ideas such as continual feedback and coaching. Performance assessment approaches continue to evolve with focus shifting between
Our approach to machine performance and maintenance has also evolved along similar lines. While a parallel between people and machines would look odd in many cases, it is interesting to see how machine maintenance is evolving along the lines of performance evaluation processes.
It all started with breakdown maintenance, in an era when machines were constantly undergoing repairs and improvement. In the early 1900s production capacity was small, most components were built within the factory (very small supply chain) and labour was not expensive. In this era, machine reliability was often related to the quality of the mechanic who worked on it.
With the improvement in technology we reached a state where breakdowns were not frequent. In the 1970s the preventive maintenance era was in full swing. Organizations were now drawing up operating and maintenance procedures for their critical equipment. Preventive maintenance also allowed organizations to effectively utilize / schedule their resources, thereby reducing operating costs. However, over a period of time, industry experts started questioning the preventive maintenance practices. Preventive maintenance was often associated with over maintenance and excessive use of spares.
In the last decade, the focus has shifted to predictive maintenance. The 2000s brought in a technology revolution which saw the advent of smart devices and robust communication networks, which could continuously provide feedback on the status of machines. The maintenance manager now had a two pronged strategy of gaining insights from the data produced by a machine to formulate a maintenance strategy and also rely on preventive maintenance practices.
A number of changes in the technology landscape are strengthening the move toward predictive maintenance. The ability to process big and diverse data is one of the overriding themes. Let’s look at some examples and where systems have reached
With the focus now shifting on optimizing the performance of existing assets, systems will move (or plan to move) from level I to level IV. In the next few years, we are going to see immense changes in the way we leverage machine learning and big data analytics for predictive maintenance. We also believe a similar approach of leveraging data analytics will be used in the appraisal process as well…