How Automated Predictive and Preventive Maintenance Solutions provide critical advantages to manufacturers.
Here’s a scene straight from an organisation’s maintenance program put into practice. In preventive maintenance, technicians perform fixed inspections and tests at specific time intervals. However, the challenge they face is that these activities still do not guarantee a seamless operation in the period between the planned checks. Additionally, this mode of working is outdated, costly, and requires a lot of time and efforts.
Predictive maintenance, as the name implies, is an effort towards defect checking early on in a products cycle, to prevent any costly repairs and damages later. This too requires a lot of focus and time for being performed on a continuous basis.
In essence, a lot of precious time of maintenance and reliability teams is thus usually wasted in performing manual tasks that are required to keep the preventive and predictive maintenance programs going.
To overcome this time wastage and to improve the accuracy of the system, organisations need to invest in automation. Implemented properly, automation can help achieve almost 202% RoI in Predictive Maintenance.
The more the organisations can invest in automating their preventive and predictive maintenance plans, the more they can save in terms of effort and time, which will eventually lead to faster RoI. Add to this the benefits of accuracy that come as part of a good maintenance automation plan, and the results only get better.
How does Automation Help in Preventive & Predictive Maintenance?
Organisations equipped with Enterprise Asset Management (EAMs) or Computerised Maintenance Management System (CMMS), enjoy the inherent components necessary to automate the processes. Automated predictive and preventive maintenance solutions provide critical advantages to manufacturers. With these solutions, one can:
- Control most unplanned stoppages due to equipment failure
- Deliver better-customised maintenance services compared to competitors
- Get to know the accurate performance capacities of the equipment and service technicians
- Improve the operating margins by controlling costs usually incurred towards technicians and maintenance
- Predict degradation of equipment and replenish the stock before its performance starts affecting the product quality
- Improve the quality of the product and the performance of the equipment using automated processes that improve, and
- Optimise the work distribution between the workforces, and also make smart adjustments for completing a work order.
These benefits are even more clearly felt in the fields such as building systems, energy, and transportation.
- Buildings: Maintenance teams today take full advantage of in-built sensors and use data captured to assess climate control, lighting, plumbing, surveillance etc. This helps them catch and rectify any potential issues with system performance.
- Energy: Automation helps the responsible operators in the control and management of wind turbines and generators from a central base. It additionally helps in taking corrective measures for components that are overheated and overloaded.
- Transportation: Networked vehicles alert the central information centres and thus save a lot of time and effort of not only commuters, but also operating teams who otherwise have to regularly schedule maintenance runs for trains, planes, and buses.
- Industry: Variable frequency drives (VFDs) run the integral processes in case of compressors, pumps and stamping presses that keep production going. If one VFD goes down, the app goes down. However, VFDs are now connected via networks and can be accessed, analysed, and tracked for performance over time. This also helps collect data related to core components such as bus capacitors, fans, and insulated gate bipolar transistors (IGBTs). The advanced VFDs can monitor the performance of fans and accurately predict their life span based on the actual hours operated, the speed of operation, and temperature. Pneumatics manufacturers, working with automation OEMs, have developed enhanced approaches to both predictive and preventive maintenance practices, as well as enhancing the technical features of their pneumatics systems. These enhancements, including new sensors and other digital capabilities, align with the emerging requirements of the Industrial Internet of Things (IIoT) production systems and enable more data-driven approaches to maintenance.
Who are the Players?
The following table lists a few organisations that are doing a commendable job in the field of providing automation in maintenance.
Software AG is another organisation that is leading the predictive maintenance solutions sector that combines Internet of Things (IoT) with streaming and process analytics, which makes predictive maintenance affordable and easier to manage.
Software AG’s predictive maintenance (PM) solution enables manufacturers to monitor machines with embedded actuators and sensors in the equipment. Streaming analytics continuously analyse the sensor data and then combine it with historical intelligence to accurately predict equipment failures and dispatch maintenance services only when they are needed. It also uses automated intelligence to dispatch a part or schedule a technician, monitoring machine performance and field service technicians’ task lists in real time for more dynamic scheduling. The result is lower technician costs, improved service levels, and greater machine uptime—all contributing to improved profitability and product quality.
Automation thus helps correlate, aggregate, and detects patterns across huge volumes of fast-moving data from multiple sources, that enables organisations to take corrective action at the right time to leverage the data in advanced prediction engines.
What is the Future of this Market?
Two recent reports – one by Markets And Markets, and another by Market Research Future indicate a great future ahead for the Predictive Maintenance market.
The report "Predictive Maintenance Market by Component, Deployment Type (Cloud and on-Premises), Organization Size (SMES and Enterprises), Vertical, and Region - Global Forecast to 2021", by Markets A Markets states that the predictive maintenance market size is estimated to grow from USD 1,404.3 Mn in 2016 to USD 4,904.0 Mn by 2021, at a Compound Annual Growth Rate (CAGR) of 28.4% during the forecast period.
Consistent with the same, the report by Market Research Future on “Predictive Maintenance Market Research Report – Forecast to 2022” – states that the Predictive Maintenance market is set to touch USD 6334 Mn, at 27% of CAGR by 2022. The report is divided into specific areas such as Predictive Maintenance Market by Component (Service, Solution), by Technique (Vibration Monitoring, Thermo-Graphic Inspection, Oil Analysis), by Deployment (Cloud, On Premise), by End-User (Manufacturing, Aerospace & Defence, Healthcare, Automotive, Transportation, Government).
Predictive maintenance solutions are gaining popularity with their increasing usage across major verticals such as aerospace & defense, energy & utilities, government, healthcare, manufacturing, and transportation & logistics. A major reason for the increasing usage of predictive maintenance solutions is the fact that these solutions provide precise predictions for asset failure that enables enterprises to take corrective actions in time. This ensures that there is no setback to the production.
The manufacturing vertical is expected to have the largest market size in the predictive maintenance market because it is a strategic business function that helps lower maintenance costs thereby increasing profitability.
While the major vendors in the PM market include IBM (US), SAP SE (Germany), Software AG (Germany), General Electric (US), Robert Bosch (Germany), Rockwell Automation (US), PTC (US), Warwick Analytics (UK), RapidMiner (US), and SKF (Sweden), the Asia-Pacific (APAC) region offers huge growth potential for the predictive maintenance market because the APAC countries are investing heavily in increasing the efficiency of their production assets.
According to a study by the World Economic Forum and Accenture, following are some great benefits of predictive maintenance:
“Pretty much every asset has some sort of gauge on it to give you critical information. More and more of these assets are feeding that information back into a consolidated location so you can manage it all from your desk,” says Paul Lachance, President and Chief Technology Officer of Smartware Group, provider of Bigfoot CMMS.
Going forward, as the world gets more integrated with the IoT, automated predictive maintenance will eventually end up as the standard way of managing and mitigating errors and failures. The twin benefits are of course performance optimisation and significant reductions in maintenance and inspection costs.
In an environment of interoperable components and devices, across a range of very different scenarios, automation is vital to ensure efficiency and smooth functioning. Manual methods in predictive maintenance will end up far more time-consuming, tedious, and resource-intensive, and therefore detrimental to any business.