Preventive maintenance is the latest buzzword to take over the manufacturing industry. However, preventive maintenance does not operate in a vacuum; it needs support from additional technologies like predictive maintenance. After all, there is no magic formula to the burning question – how often should you take a machine offline and service it?
Predictive maintenance is a bridge that connects smart factories to reliable professionals for maximum asset usage and availability.
On that note, let’s take a look at everything you need to know about predictive maintenance to introduce operational excellence in your manufacturing processes.
What is Predictive Maintenance in Manufacturing?
Preventive maintenance follows round-the-clock condition monitoring complemented with proactive action to mitigate machine breakdowns. However, predictive maintenance takes it up a notch by eliminating any guesswork.
As a product of Industry 4.0, predictive maintenance utilizes data and technology, namely, Industrial Internet of Things (IIoT) and Artificial Intelligence (AI), to realize preventive maintenance. This network of sensors and data analyzers offers an estimate of potential issues, along with a corresponding timeline, so that businesses can intervene at the right moment and minimize losses.
Key Benefits of Predictive Maintenance in Manufacturing
Predictive maintenance can inject operational excellence in the following ways:
- It automates condition monitoring and raises alerts in case of deviations from regular performance.
- It cuts down unplanned downtime by maintaining the optimal health of machinery through preventive maintenance.
- By scheduling repair and maintenance activities, manufacturing units enjoy greater control over strategic sourcing and resource allocation.
- It ensures greater equipment reliability and safety by addressing issues prior to faults and failures. Such actions also lend longevity to the equipment.
- It enhances product quality and adherence to timelines by ensuring that machines always deliver peak performance.
- It optimizes the periodicity or need for preventive maintenance, which will set an efficient cadence for planned downtime.
Primary Challenges in the Way of Predictive Maintenance (And How to Overcome Them)
It may be clear by now that predictive maintenance offers tangible benefits in the form of increased production capacity, higher productivity, and efficient maintenance – all of which will impact the bottom line. That being said, it finds few takers due to the challenges summarized below:
Lack of High-Quality Data
The manufacturing industry has been somewhat of a laggard when it comes to putting data first. In fact, the only semblance of change has been driven by disruptions where businesses are faced with the “pivot or perish” situations.
Some manufacturing units would prefer shifting their operations to geographies where manual labor is cheaper than undertaking the mammoth task of digital transformation. This lack of data literacy is singularly the greatest impediment to come in the way of embracing the predictive maintenance discipline.
However, poor data is worse than no data. As such, the false sense of safety that manufacturing businesses may have while dealing with poor quality data poses even graver challenges.
Fortunately, the COVID-19 pandemic has been an eye-opener for manufacturing industries. So much so that nearly 95% of senior leaders acknowledge that digital transformation is essential to their company’s success in the future.
As a result, most organizations have already embarked on a journey to become more data-driven, and it is only a matter of time until they attain this goal. And once businesses realize the true value and potential of data, they can be more mindful while maintaining data hygiene. Upon implementing a data filtering system, manufacturing units can churn out large volumes of high-quality data that can train predictive maintenance tools to become more accurate.
Infrastructural Setup and Integration
Predictive maintenance rests on the pillars of data generated and transmitted through the network. This pillar is propped by a series of sensors, seamless data flow channels, and a centralized data repository for effective communication. Only once, when such a system is in place that a predictive maintenance setup can continuously monitor variability in indicators like temperature, vibration, equipment sound, etc.
As such, building the infrastructural setup and integrating it throughout the floor can be a tough challenge.
As with any major and disruptive change, elements of predictive maintenance must be introduced gradually and in a phased manner. Businesses can devise a roadmap that prioritizes equipment maintenance and ensures continuity in manufacturing activities during the transition towards the predictive approach.
When it comes to predictive maintenance systems, the greatest relief lies in the fact that you do not have to build them from scratch. Modern-day predictive maintenance solutions come equipped with rich features and functionalities to adapt and cater to varying business needs. With their intuitive dashboards and powerful reporting, the enterprise solutions face zero trade-offs, while the power of customization lends the platform a high degree of flexibility. And most importantly, it comes in plug-and-play packaging to deliver results from day one.
All you have to do is select a reliable predictive maintenance system that delivers to your expectations.
Cost Liability and Justification
Every business decision eventually boils down to the cost implication and its alignment with the budget. Preventive maintenance appears to be more viable considering that its total cost is spread over a period. On the other hand, predictive maintenance calls for immediate upfront expenditure, be it for the sensors or the enterprise solutions that go with it. The uncertainty of whether the predictive maintenance model will take off adds more to the confusion on whether or not one should go ahead with the investment.
While there is no denying that the upfront capital required will be massive, the cost-benefit business analysis must acknowledge the long-term benefits.
As stated previously, predictive maintenance will rake in a considerable ROI through operational excellence and plugging revenue leakages through inordinate preventive maintenance activities. Such considerations, by themselves, offset the initial investment and make a solid case in favor of predictive maintenance, which justifies the business decision. Plus, once a predictive maintenance system is in place, it will deliver results in the long haul and ensure continuity and resilience.
Where Does Eugenie Fit in the Picture?
Eugenie offers an AI-powered enterprise solution that checks all the boxes of the solutions shared above. Apart from inculcating a data-first mindset, it enriches the user experience by combining customization, advanced analytics, and actionable insights to implement predictive maintenance. All the while, it can seamlessly integrate with existing systems to generate valuable insights that can propel businesses to new heights of operational excellence!
The manufacturing sector today is now struggling with sustainability-related problems in addition to the usual operational difficulties of declining margins, rising raw material costs, and supply chain disruptions.
But why is the manufacturing industry particularly under pressure? The simple explanation is – it produces a lot of waste, uses copious amounts of water and energy, and significantly contributes to greenhouse gas emissions. 54 percent of the world’s energy sources and one-fifth of the carbon emissions are produced by the manufacturing and process industries.
Due to increasing environmental awareness and regulations, most manufacturing companies are actively implementing strategies to reduce their carbon footprint. Eugenie’s products have helped several world-class companies in reducing their emissions through the efficient use of resources and processes.
Register for our product demo to learn more about how Eugenie can help you achieve sustainable operations.