Industry 4.0, the proclaimed fourth industrial revolution, is unfolding at the moment. It is characterized by interconnectedness and vast amounts of available information. Productivity has risen continuously due to modern machines. With the advancement of technology, maintenance can do much more than merely prevent downtimes of individual assets. Machines are increasingly interconnected along the production chain. One failing machine might halt the whole production process. Today, poor maintenance strategies can reduce the overall productive capacity of a plant by 5 to 20 percent[1]
Long and continuous runtimes of capital-intensive, highly-integrated assets can represent a significant competitive advantage. The same goes for efficient and well-orchestrated maintenance. Knowing well ahead of time when an asset will fail avoids unplanned downtimes and broken assets. On average, predictive maintenance increases productivity by 25%, reduces breakdowns by 70%, and lowers maintenance costs by 25%. It is based on advanced analytics and marks a new way of organizing and implementing maintenance on an industrial scale.
Maintenance Strategies: An Overview
Depending on assets, costs, and technical sophistication, a broad spectrum of maintenance strategies can be applied. These strategies range from mere reaction to failures to highly evolved systems optimizing maintenance efforts for groups of assets.
Reactive Maintenance entails acting when a failure has already occurred. It is the classic form of maintenance, usually being applied when the object is of low value, easy to replace, and might not necessarily have a severe impact on the business process.
Preventive Maintenance attempts to prevent failure by maintaining machines at pre-scheduled time intervals. This approach is usually taken when the cost of maintenance is moderate and when it can be done outside of production hours.
Condition Based Maintenance is similar to preventive maintenance but rather considers the actual use of the object instead of relying on pre-scheduled intervals. An example of this approach is check-ups for airplanes that have traveled a certain distance or hours.
Predictive Maintenance utilizes a wealth of process data and advanced analytical methods to predict failures well before immediate action has to be taken. With the implementation of concepts like Industry 4.0 or Smart Factory additional process data becomes available. This allows for estimating the remaining runtime of assets with increasing accuracy. This maintenance approach is usually taken when high costs are incurred due to downtimes or maintenance. This approach can ease scheduling when maintenance activities are complex.
Building the Business Case for Predictive Maintenance
In the context of Industry 4.0 – increased interconnectedness and new opportunities to collect, process, and analyze information – predictive maintenance can be a very powerful strategy, especially when the potential downtime of capital intensive assets could lead to a massive dent in the revenues.
Reducing Downtime - One of the most obvious benefits of predictive maintenance is that it maximizes runtime. Repairs can be carried out just before a breakdown. That represents a major advantage – studies show that unplanned downtime is costing industrial manufacturers an estimated $50 billion each year[2] indicating an approximate 10-20% increased equipment uptime and availability. Also, necessary maintenance efforts can be orchestrated to minimize system-wide downtimes. Predictive maintenance can further be used to ease logistics by maintaining machines at convenient times – for example outside of production hours or while the needed personnel is close by.
Spare part inventory - Predictive maintenance enables to replace parts when actually needed (not when the calendar says so), the number of spare parts used (and in storage) can be reduced. Additionally, it helps if one can assist purchase departments by predicting which spare parts will be needed at which point in time.
Reducing Costs – As per Deloitte, embracing predictive maintenance helps organizations reduce 5- 10 % of overall maintenance costs and the same amount of savings in operations and MRO material spend. They could be further classified as:
Reduction of unplanned repairs – means more stable production and operations, decreasing costs occurred from lost production. These cost savings primarily arise from 20-50% reduced efforts on maintenance planning time.
Reduced maintenance labor cost – Since predictive maintenance allows maintenance personnel to focus on the machines that actually need it, unnecessary labor costs can be reduced
Reduction of replacement costs – ordering and replacing machinery/components can be expensive and time-consuming, predictive maintenance can prevent machines from reaching failure
Reduction of “over-investments” – keeping a good balance between capital expenses for additional money machines that have to be bought to compensate for the non-availability and operational expenses of the organization.
Revenue Increase - Here are various ways in which the organization could register a significant increase in revenue by implementing Predictive maintenance. These could be classified under the following heads.
Increased equipment availability- increased production and operational efficiency
Increased or accelerated production/sales – more asset uptime means more production resulting in more potential sales.
Increased product quality – less faulty products and the need for re-works, less waste in materials, and production time resulting in increased profitability.
Increased energy efficiency – Wear and tear can cause machinery to consume more energy than it optimally should. Machinery in good condition will use less energy, reducing overhead costs.
Other intangible benefits - There are various other benefits that predictive maintenance would have to your organization. Some of them are listed below:
Effect on the organization – how predictive maintenance improves the overall efficiency of the organization and keeps it competitive among a cut-throat marketplace with companies increasingly looking to deploy technology solutions to lure your customers.
Risk reduction – how predictive maintenance helps to build a safe working environment for your personnel.
Key Considerations
Data is the fuel of any predictive maintenance engine. Its quality and quantity is the limiting factor for analyzing root causes and predicting failures well ahead of time. Therefore, a major consideration inherent to any predictive maintenance program is increasing data quality and coverage. The more information is available on events to be predicted the better predictions become.
Predictive maintenance is an investment, which reveals implicitly the second key consideration: Establishing the needed processes initially creates costs. Businesses need to add sensors to their machines and often set up a wide array of IT infrastructure, processes, and trained personnel. Data from various sources must be integrated and transformed so that it can be made available on a suitable platform. Dashboards, email triggers must be put in place to coordinate the necessary maintenance efforts. Process experts’ and data scientists’ knowledge is needed to build and maintain a functioning predictive model. Also, personnel needs to be trained to handle the information inflow and interpret alerts appropriately.
Needless to suggest, predictive maintenance can help you manage maintenance more efficiently. However, keep in mind that not all enterprises require the same level of reliability from their assets.
A good place to start the assessment for your enterprise is to look at mission-critical requirements and maintenance program maturity.
Ask yourself the following questions:
How reliable do our assets need to be?
What are our availability targets?
What is our machines’ current failure rate?
How high are our current maintenance costs?
Do we have the right spare parts in the right place at the right time?
How do we determine when it is time to replace an asset rather than to maintain it?
What data do we already have that is not being used effectively?
Have we identified the critical assets in our production system?
Are there some critical assets that would benefit from a predictive maintenance program?
If you have answered in affirmative to most of the above questions and understand that you would like to proceed with Predictive maintenance, the next challenge would be to understand whether one would have the needed technological expertise in-house to develop a predictive maintenance program? Additionally, would the organization be able to afford an analytics team that would be able to analyze data and offer insights from the same?
QuickReach No-Code MRO: Predictive Maintenance Done Right
Fortunately, with a platform like QuickReach, you would be able to off-load a majority of the heavy-lifting to the platform itself. The platform would help you identify which parameters indicate imminent failure and would trigger alerts to the concerned teams for necessary action. This would already prevent a large proportion of potential failures from occurring already, saving your organization downtime and revenue losses. All this, without deploying an army of data analysts to churn out insights on when the failure might occur or having to depend on expensive technology to provide you with the solution you need.
Still curious about QuickReach's Predictive Maintenance Solution? Connect with one of our customer success team HERE for a demo to gain a deeper understanding of how QuickReach No-Code MRO solution would be ideal for your company to transition into an organization data-ready to adopt technological change for better business outcomes.
******
References: [1] “IoT Slashes Downtime with predictive maintenance”, Gary Wollenhaupt, ptc.com, March 2016 [2] “How Manufacturers Achieve Top Quartile Performance”, Industry Week & Emerson, Partners.wsj.com.
Comments