How are evolving maintenance solutions into companies Industry 4.0 roadmaps?

Industry 4.0
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Ideally, predictive maintenance allows the maintenance frequency to be as low as possible to prevent unplanned reactive maintenance, without incurring costs associated with doing too much preventive maintenance. Predictive maintenance is a technique that uses data analysis tools and techniques to detect anomalies in your operation and possible defects in equipment and processes so you can fix them before they result in failure.

Predictive maintenance uses historical and real-time data from various parts of your operation to anticipate problems before they happen. There are several key elements to predictive maintenance with technology and software being one of these critical pieces. Namely, the Internet of Things (IoT), artificial intelligence, and integrated systems allow for different assets and systems to connect, work together and share, analyze, and action data [1].

When implemented properly, predictive maintenance programs can greatly reduce maintenance costs, decrease downtime and increase asset life cycle. Here are a few of the main benefits of predictive maintenance [2]:

  1. Reduces Spending: predictive maintenance is a cost-effective maintenance solution because maintenance is only performed when it is required, eliminating unnecessary maintenance costs associated with preventative maintenance. Because predictive maintenance addresses problems before total equipment failure occurs, the costs of predictive maintenance tasks are often lower than reactive maintenance expenses. This reduces long-term maintenance spending, as well as reducing time spent performing maintenance tasks. This can also reduce overtime hours and associated labor costs. Predictive maintenance can also increase the lifespan of equipment, which reduces expenses associated with purchasing new machinery.
  2. Decreases Downtime: downtime caused by equipment failure can be long and costly. When machinery runs until it breaks down, the necessary repairs are often more expensive and extensive than if small repairs and tune-ups are made before equipment failure. Repairing or replacing essential equipment after a failure can lead to excessive downtime, which results in more overtime hours to make up for the loss in productivity. With predictive maintenance, costly downtime from equipment failure can be avoided by performing maintenance tasks before a breakdown. Downtime only occurs before equipment failure is imminent to maximize equipment productivity and facility uptime.
  3. Protects Assets: equipment is often a company’s greatest asset but by running machinery until it breaks, valuable assets are put at risk. Through predictive maintenance, manufacturers can protect their equipment from failure and constantly monitor the condition of vital assets. With full visibility of the condition of machinery, companies can create the best maintenance plan to keep their equipment in peak operating condition.
  4. Increases Life Cycle: when equipment is carefully maintained through predictive maintenance, it can run longer before it needs to be replaced. This increased life cycle of equipment can greatly lower long-term equipment costs. Replacing equipment also requires significant downtime, so by increasing the lifespan of equipment, companies can also reduce downtime and its associated expenses.
  5. Improves Product Quality: for manufacturers, predictive maintenance can also improve the overall quality of the final product. When machines are not operating properly, this can lead to defects or inconsistencies in the final product. Continuous monitoring through predictive maintenance allows manufacturers to ensure their equipment is running smoothly and producing high-quality and consistent products.

As it can be appreciated, with the adoption of predictive maintenance solutions, managers and technicians are able to anticipate to potential defects and fix them. In fact, 91% of manufacturers who deployed a predictive maintenance program saw a reduction in repair time. As well as a 9% increase in equipment uptime and a 20% extension in the life cycle of ageing assets. Predictive maintenance programs have been shown to lead to a tenfold increase in ROI, a 25%-30% reduction in maintenance costs, a 70%-75% decrease of breakdowns and a 35%-45% reduction in downtime.

Despite of all the advantages of the predictive maintenance solutions, manufacturers are also starting to talk about proactive or prescriptive maintenance, which is a more specialized form of predictive maintenance that looks at known failure models and prescribe proactive actions to optimize desired outcomes. Prescriptive maintenance takes things a step further to not only predict the potential issue, but also to offer information regarding potential causes before failure occurs. While the two terms are spokes of the same wheel, the latter’s goal is to provide recommendations that apply the anticipated outcome using analytics with greater lead time and cast a broader net on overall facility impacts and causes [3].

AI-based solutions will continue transforming the maintenance applications the future of the failure detection, root cause analysis, remaining useful life estimation, anticipation and smart-repair recommendations to improve machine operations, reduce unplanning downtime and optimize maintenance scheduling.

In future posts, we will be continue writing about technology and business trends for enterprises. The objective of this blog is to provide a personal vision of how digital transformation trends will be impacting in our daily activities, businesses and lifestyle.

By Gerardo Beruvides twitterlinkedin2

Industry 4.0 and Smart-mobility expert, his research interest includes Industry 4.0, Smart-Maintenance, Process Optimization, Machine Learning, AI engineering and Cloud-based solutions.





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