Downtimes for heavy machinery are inevitable. While planned downtimes are still sustainable, unplanned downtimes are significantly costly and reducing its frequency and the duration of downtime is one of the biggest challenges that many business owners and organizations in the heavy machinery face.
A research by Gartner in 2014 revealed that a single downtime period typically costs a whopping $540,000 per hour lost in assets, workers and infrastructure, and that translates to an astounding $5,600 per minute. Likewise, a recent report by Ponemon Institute in 2016 averaged the cost of downtime on businesses across various industries to have surged to about $9,000 per minute. The research study not only brought to light the nature of this trend to grow and be predictably incremental with time but also uncovered the maximum cost of an unplanned downtime incident in 2016 to be $2,409,991.
It is obvious that the costs of unplanned downtime and the impact on productivity would have drastically progressed since and the traditional Reactive and Time-based Preventive maintenance strategies from one and two decades ago do not adequately serve to reduce these unplanned downtimes. Hence, business owners regardless of industry are enduring the undeniably urgent need for a reliable maintenance strategy in place.
With the advances in technology, words like Machine Learning (ML), Artificial Intelligence (AI) and Industry of Things (IoT) are already predominating news articles, blog posts and search engine suggestions today. They pose to be the next big thing and legions of businesses across many industries actively seek to deploy these applications in one way or another, as an attempt to conveniently supercharge their business and enable increased productivity. As industry trend observers should know, the field of maintenance is also one such area that ML and AI have infiltrated recently. These technology-powered maintenance strategies aim to revolutionise the process of maintenance and tackle the conundrum of unexpected downtime due to breakdowns/ failure of heavy machinery. While AI has surely become a reality outside science fiction in these past few years, AI-powered solutions have proven to be the game-changer to easily achieve near-zero downtime. Hence, organizations are obligated to invest in contemporary data-driven heavy machinery maintenance strategies for hefty cost savings and increased productivity.
We shall explore in detail the prominent heavy machinery maintenance strategies; Reactive, Preventive (Time-based, Condition-based) and Predictive maintenance, and determine the strengths and weaknesses for an unbiased side by side comparison in terms of the influential factors such as the cost of implementation, estimated downtime and overall service life of the equipment, to help with this crucial investment.
The major maintenance strategies for heavy machinery explained
1. Reactive Maintenance
As the name suggests it is a corrective maintenance strategy, whereby maintenance works are performed only after a breakdown. This strategy is acquired by business owners due to its simplicity and advantage of being extremely economical at the initial stages. For instance, it does not require any planning at all, thus saving the operational team the time and hassle from making plans for maintenance works. It also exterminates the maintenance costs of equipment until the event of breakdown or failure.
While this strategy demands minimal cost and effort to implement, it is only practical for businesses with low-value assets whereby the costs of repair or replacement in the event of a breakdown are feasible and the service life is short. It is not at all suitable for large scale businesses where high-value assets are involved and an unplanned breakdown consequently leads to prolonged downtime to perform emergency maintenance consisting of costly repairs and replacements. It also results in a sharp decrease in productivity and overtime labour in a short-notice.
Likewise, this strategy does not guarantee the normal lifespan of the equipment provided that service life reduces with every breakdown coupled with the fact that maintenance efforts only aim to bring the equipment ‘as close to normal functioning as possible’. All of which ultimately transverse to a function of cost. Not to mention the potential risk to safety and slight probability for this remedial maintenance to be ineffective.
2. Preventive Maintenance
This strategy aims to prevent failures/ breakdown through scheduled maintenance tasks routined based on calendar intervals or conditions of the asset.
- Regular maintenance in Intervals – Maintenance scheduled based on calendar-time (for example., weekly, monthly and annually) and usage (for example., every 10,000 hours or 100 cycles).
- Condition-based Intervals – Maintenance scheduled based on alerts and updates about equipment condition (for example., pressure, temperature, vibration, etc.) generated from real-time data from sensors.
Both involve taking the equipment off service for maintenance and have costs for implementation.
This strategy allows business owners and organizations to plan the workflow and budget resources ahead of scheduled downtime, thereby minimizing overtime costs and reducing the duration of downtime. The condition-based preventive maintenance strategy especially allows technicians to track equipment condition with the help of a dashboard and schedule maintenance if at all any component performs outside its normal parameters and ultimately help to reduce the number of unexpected breakdowns. Furthermore, both preventive maintenance strategies aim to optimise the service life of the equipment as an effect of regular maintenance.
However, despite being able to downplay the effects of unexpected downtime and enhancing the service life of the equipment, the regular interval maintenance especially could lead to invasive disruptions to stable equipment due to technicians intervening frequently to perform maintenance tasks just based on calendar time or a phenomenon also known as “PM creep”. This post-maintenance breakdown thus arises since equipment components are regularly taken apart for maintenance activities without any basis. Simply put, these breakdowns are also repercussions of either maintenance tasks performed improperly due to errors in human judgement or unnecessarily for the sake of keeping up with the schedule. Likewise, post-maintenance breakdowns could also occur by the Condition-based preventive maintenance, if maintenance activities based on the alerts or updates are not thorough.
3. Predictive Maintenance
Predictive Maintenance is also a strategy aimed at preventing downtime but utilising AI to accurately predict impending downtimes and schedule maintenance based on prognostics of real-time data combined with an extensive database that includes extensive knowledge about the heavy machinery and its capabilities. The cost and effort of implementation of this strategy is higher upfront. However, it minimises the downtime significantly by necessitating maintenance activities to take place only as needed before the predicted breakdown. This increases runtime/ productivity and cost savings due to reduced frequency and duration of breakdowns thus optimising equipment performance whilst ensuring an enhanced service life.
Compared and Contrasted!
By comparison, both reactive maintenance and regular maintenance for heavy machinery in Interval strategies are pre-emptive in nature thus less effective in easing the problem of unplanned and prolonged downtimes. The Condition-based Preventive maintenance strategy notably fairs better at reducing the likelihood of breakdowns/ failure compared to both formerly mentioned strategies due to proactive maintenance tasks scheduled based on real-time asset condition data or actionable alerts specific to individual parameters. Not only is it better suited for large scale industries and businesses with high-value assets whereby repair/ replacement costs are not easily feasible and downtimes weigh exorbitantly high, but it also reduces the frequency of downtime. Likewise, it also allows planning to ensure resource allocation thus reducing overtime costs of a breakdown while still ensuring the reliability and sustainability of the asset’s lifespan.
Although the Condition-based preventive and predictive maintenance strategies sound similar in terms of technology utilization, they are not on the same level. By contrast, predictive maintenance strategy is the highly advanced being based on extensive data about the equipment itself and being able to provide comprehensive data about its capabilities and functioning thus offering accurate predictions on the top of performance-based real-time data. It also completely bypasses the consequences involved with bias in human judgement and knowledge that lead to unnecessary maintenance and workforce costs to ensure decent upkeep throughout the high-value assets service life comparatively.
The Predictive maintenance strategy allows business owners to anticipate these impending breakdowns in advance rather than receiving alerts as and when individual components of the equipment are not functioning at its normal capacity. This not only minimises the large unnecessary cost and time spent on maintenance successfully, but it also increases efficiency and makes the ideal near-zero downtime in industries achievable. The predictive maintenance strategy also ensures the safety of technicians and enables better decision-making to reduce operational stress in businesses that are often influenced by human errors in knowledge or judgement about equipment capacity/ potential whilst increasing the service life of the equipment.
Nonetheless, both Condition-based preventive maintenance and predictive maintenance strategies are reliable maintenance strategies that utilize technology efficiently to reduce downtime, increase efficiency with the upkeep that saves costs and time with just the initial cost of application. They also ensure safety by reducing the risk of injury.
There are several maintenance strategies out there for businesses in the heavy machinery to choose from today, and while downtimes due to unexpected breakdowns that cost a fortune were inevitable in the past, it has the potential to easily become history with technological advancements.
Implementing a Condition-based preventive maintenance strategy or an AI-powered predictive maintenance strategy in your heavy machinery business smartly steers your business by allowing you to schedule maintenance based on real-time asset condition monitoring and acquire predictions based on extensive equipment data respectively. This allows you to reduce the duration of downtime or even completely avoid it by near-zero downtime as an effect of scheduling maintenance only when needed and allocating repair with the workforce sparingly. It not only increases cost savings and productivity significantly but it also sustainably optimises the service life of your equipment. This strategy, therefore, facilitates businesses to make better decisions about allocating workforce and resources, thus alleviating high operational stress.
However, although Condition-based preventive maintenance and predictive maintenance strategies have both proven to be effective and reliable, they are not exactly a one size fits all solution. Business owners and organisations will need to evaluate based on their companies’ individual needs and goals, which strategy amongst them suits better.
Alternatively, get in touch with us to discuss further and we could explore together the better technology-driven strategy for your business. At groundup.ai, we will support and guide you through the process of implementing the appropriate system into your daily operations. Using our technology, we can efficiently pull data and behaviours from your machinery to create evolutionary changes to your business and use innovative solutions to drive business value.