The IIoT-enabled evolution of the industry is imminent. The questions about Industry 4.0 have rapidly changed from “what” to “when”. One of the most promising gifts of Industry 4.0 is the metamorphosis of Predictive Maintenance. The ability to steadily capture and interpret behavioral data generated by machines has made predictive maintenance a crucial tool for all.
The effectiveness of asset health management techniques determines the roadmap of a business. Predictive Management leverages analytics to monitor all functions and sub-functions of the machinery and processes alerts about any sign of degradation. These alerts can then be used to create a dynamic maintenance schedule for parts of machinery.
A majority of the complex and difficult to replace pieces of machinery are costly. As these devices age, keeping them in operation becomes increasingly crucial for the financial health of a business. IIoT sensors can account for the age of a machine and estimate its remaining usable life (RUL), saving valuable capital for the business.
Industry 4.0 era Predictive Maintenance is capable of tracking down the root cause of the failure of a piece of machinery. Artificial Intelligence can be leveraged to study these causes and identify the existence of patterns in them. The data generated and the root causes identified in this process can be used to solve one of the personnel problems in existence. By bringing Mixed Reality into the mix, technicians can be trained to maintain specific assets remotely, without the OEM experts having to be on-site. This can save travel costs, minimize downtime, and make the entire process far more efficient.
It is possible to utilize IIoT and simulate the future performance of a machine as well. This is a potential game-changer because it can play a big role in determining the machine replacement schedule months in advance. The businesses will have ample time to devise alternatives if the machine concerned is a vital one.
Evidently, Predictive Maintenance in Industry 4.0 is full of promises which are in keeping with the continuing information technology boom. Preventive Maintenance was maligned after it was revealed that close to half the capital that is poured into it is wasted. The hype of Predictive maintenance was a breath of fresh air. However, potential alone cannot ensure positive results. Over time, businesses of all scales have stumbled onto some roadblocks when deploying IIoT- based Predictive Maintenance. The uptake of Predictive Maintenance, especially in the light of Industry 4.0, has been slower than expected.
The reality of Predictive Maintenance, no matter how enchanting it sounds, is that it is not a cure-all solution. The cutting edge, bordering on fantastical solutions that seem to be within the grasp of IIoT, requires an immense amount of planning and commitment. Abridged implementation is ineffective and wasteful.
Predictive Maintenance in Industry 4.0 is capable of ingesting copious amounts of data in its attempt to come up with the best possible solution. Insufficient data limits the capability of predictive maintenance to only forecasting age-related failures in parts of the machine. More common and damaging problems like process and personnel-related crashes are well beyond the reach of such a system. These unplanned outages cost a business an average of $2 million.
The solution, of course, is the use of sufficient numbers of IIoT sensors and the application of Big Data analytics. However, this poses another conundrum. For most companies, state-of-the-art smart factories are not affordable. Irrespective of the ROI, the initial investment to furnish all machines and the entire supply line with IoT sensors are considered to be immense by most businesses. Additionally, a leap of faith in such a gargantuan financial measure is not ideal for most businesses.
The cost of installation is accompanied by the cost of maintenance of this network of sensors. This investment cannot be slighted because a few malfunctioning sensors may distort the findings of the system, making the entire exercise thoroughly counterproductive.
The biggest challenge that companies are currently facing as they attempt to transition to predictive maintenance is the need to integrate all internal processes. A robust IIoT-based Predictive Maintenance strategy would require multiple organizational teams to band together and cooperate.
The IT unit has to be in perfect synchronization with the maintenance crew for the predictive maintenance strategy-generated solution to be implemented. Likewise, affiliation will be required if a device needs to replaced or parts of a machine need to be obtained at a certain time. Breaking down the traditional siloed organizational structure is a humongous task and the industry does not seem to be ideally placed to execute it.
A better way of approaching IIoT-enabled Predictive Maintenance is probably scaling up gradually. Market conditions are perpetually volatile and the margin of error is getting narrower by the day. As such, it is wiser to figure out the key areas where predictive maintenance could make a noticeable impact without setting off global changes.
Contact the team of data scientists at Enquete Group to receive assistance with a personalized Predictive Maintenance plan for your business or visit www.enquetegroup.com to explore other services.