Currently the vast majority of maintenance professionals seek to combine all available initiatives, whether quantitative or qualitative, in order to gain control over the chronic failure of their equipment and mitigate the lost time of production assets. A wrong maintenance strategy can reduce the overall productive capacity of a plant considerably, to such an extent that it can mean its failure in the market.
Thanks to the rise of Industry 4.0 with its new technologies is making it easier to face this challenge by allowing assets to do these tasks for them, maximizing the useful life of asset components, ensuring greater availability for production, optimizing the use of allocated resources and additionally gaining greater control of allocated budgets.
Currently, it can be difficult for a maintenance engineer to determine how often a piece of equipment should be shut down for overhaul, as well as to visualize the risk of lost production time due to a sudden breakdown. Traditionally, this dilemma forces most organizations to make a decision between possible scenarios, maximize the life of a part by risking machine downtime, try to maximize production time with early replacement of potentially good parts or, in some cases, use past experience to try to anticipate when breakdowns might occur and address them proactively. Either of these options has one aspect in common; uncertainty and a financial component as a result of a decision.
It is here when it becomes relevant to venture into predictive maintenance, as an alternative to strengthen the current strategy, which already includes corrective, preventive or proactive approaches with a condition monitoring system. The idea of including predictive basically allows to manage uncertainty with a higher degree of control, by capturing in real time equipment operation variables. Previously this was limiting for many organizations due to the high costs of data management, the lack of adequate platforms and the lack of concepts such as machine learning. The investment needed to handle the huge volumes of data required often limited its implementation for organizations.
Today, the high capacity and low cost of digital technologies, coupled with the rise of machine learning technology providers, has made it possible for predictive maintenance to spread across a wide range of industries. This combination of operations and information technologies can enable deeper analysis of data from equipment sensors to facilitate and provide certainty in decision making. Data collected from machines and equipment processed through these software applications and their algorithms can predict with a very high degree of certainty when and where failures might occur, potentially maximizing part efficiency and minimizing unnecessary downtime.
However, before doing so, it is very important to consider the process of information creation, analysis and action as a continuous cycle that is at the heart of how these technologies can create value. The integration of digital information from different sources and locations drives the physical act of maintenance, however, the quality of information and the clarity of purpose of the measurement and its understanding are critical success factors in implementation.
The information management cycle has, in essence, three fundamental aspects.
- Establish a digital record, capturing information from the physical world to create a digital record of the physical operation.
- Analyze and visualize, the connectivity of equipment as they communicate with each other to share information, enabling advanced analysis and visualization of real-time data from multiple sources.
- To generate knowledge by applying algorithms and automation to translate decisions and actions in the digital world into decisions and actions in the physical world.
To ensure that the transition to predictive maintenance is a success, a very well defined roadmap must be defined, where there is a very clear strategy, with a logical rational, we usually recommend our clients to start small and replicate fast. Not everything ends here, designing a change management scheme helps to explore the impacts or challenges of the implementation within the organization, this is vital and can be the difference between success or failure. Last but not least, is the management of expectations in terms of tangible deliverables for the organization, preferably with a return on investment analysis.
Success with your implementation.