The Unrealized Benefits of Predictive Maintenance in Industry 4.0
Time is valuable. That is made clear not least in manufacturing. Availability and utilization are key concerns in any facility that relies on machinery and industrial equipment — or even in more delicate environments, such as biomedical laboratories. In short, if there are routines, there are usually efficiencies to gain as well. Predictive maintenance, utilizing industrial IoT technologies, is an Industry 4.0 staple and targets what may be the most essential efficiency metric of them all: time.
Time matters because equipment downtime and maintenance come with both large direct and indirect costs. Lost production, wasted labor, depleted inventory, as well as opportunity cost; these are all results of downtime. Furthermore, the amount of downtime is often underestimated, which results in costly reactive maintenance practices. Still, even if you do not scramble for reparations but maintain equipment according to the manufacturer’s recommendations – even if you are carefully investing in preventive maintenance – there is room for optimization.
What is predictive maintenance, and what are the benefits?
Predictive maintenance using IIoT promises to help you avoid spending time putting out (metaphorical, we hope) fires. With the help of improving sensor technology and connectivity, predictive maintenance has moved from constituting a competitive advantage to being an Industry 4.0 must-have. IoT and cloud solutions are some of the pillar stones of Industry 4.0 – and guess what, predictive maintenance is one of the applications with the largest financial implications. McKinsey Global Institute has estimated that predictive maintenance could reduce maintenance costs by 10 to 40 percent. Industrial IoT applications in total, may have an impact of 1,210 to 3,700 billion USD.
The concept is to use on-premise and cloud computing to assess the condition of industrial equipment not just centrally and in near real-time, but predictively. Instead of losing valuable time putting out those fires, there is now the option to predict machinery and equipment lifecycles, which widens the window in which maintenance can be scheduled. The results? Minimized lost production, no more wasted labor, and you are freeing up valuable time for yourself and others. The potential for optimization-led cost savings promises redirection of maintenance budgets toward research, marketing, and innovation. In short, the potential economic impact is significant.
How does predictive maintenance work in Industry 4.0?
Delving deeper into the technicalities of predictive maintenance, it offers data-driven understanding of failures. Anomaly detection – a machine learning technique – is used to identify failures and predict the likelihood of future failures. Based either on static rules or more dynamic approaches and using data on critical health values, risk predictions, outage costs, and maintenance costs, analytics can trigger alerts for actions to be taken.
The technology is easiest to implement when it does not have to account for a large amount of variables. In most cases, this is not a hinderance. Consider that equipment often breaks in the same places; in points exposed and vulnerable to increasing pressure, vibration, or temperature – conditions that may be assessed with tools for oil, ultrasound and current analysis as well as infrared thermography. This approach applies well in any type of standardized work environment. (Of course, this leaves the question – for another time – of how to handle breakdowns that are less easy to predict, such as damage to a transformer due to power fluctuation or vegetation, as well as operations that are less standardized.)
The hurdles of implementation
As cloud computing, remote sensors and connectivity improve it is becoming easier for manufacturers to integrate legacy systems and once siloed operational equipment. These are premises for the broad adoption of predictive maintenance in Industry 4.0.
However, although the opportunities seem endless, the implementation may not be without its hurdles. Suffice to say, siloed legacy protocols and processes are definitely some of the hurdles that create headaches for Digital Transformation teams all over the world. In short, data needs to be ready for analysis, consolidated into a unified and reliable layer, before the benefits of interoperability can be realised.
One challenge that many manufacturers will have to deal with is combining on-premises and cloud functionalities. It is also likely that many will find a solution in hybrid data center models. This goes especially for manufacturers with significant on-premises platforms, as systems and data that are in frequent use are more likely to be cloudified. The challenge is likely to impact the speed with which industrial IoT and predictive maintenance technologies are adopted.
The opportunities – future unreaped benefits of investing in cloud and IIoT
On the bright side – with sufficient investment in cloud and industrial IoT solutions, manufacturers will soon be able to take advantage of some of the “as a service” and “pay-per-X” offerings currently in development. Service offerings to the manufacturing sector have the possibility to increase capacity for agility and reliable on-time delivery, much in line with the developing sharing economy. The result: increased flexibility, enabled by for example iPaaS solutions such as dizmo.
Interestingly, predictive maintenance may be a case of Peer’s law – that the solution to a problem changes the problem. Before, the issue was optimizing maintenance schedules. Now, as a result of smart factory development, predictive maintenance in Industry 4.0 raises the stakes by being part of a larger trend towards increased speed and customization in manufacturing. In fact, some managers view Industry 4.0 as a sort of Lean 2.0. Throwing a glance at what is happening in the industry at large, customers will be able to benefit from increased agility in terms of faster and more cost-effective production. In the future, manufacturing will become more customer-centric and more service-centric. The new problem is open-ended: how do we get there?
Leaving current times behind us and exploring industry beyond predictive maintenance, futurists are exploring prescriptive maintenance. This technology aspires for a completely automated approach and uses more advanced machine-to-machine (M2M) techniques to allow machines to schedule maintenance for themselves. However, while prescriptive maintenance is definitely not off the charts impossible, manufacturers with expensive equipment might want the option of human input as well.
To fully capture the value of digital transformation, companies must look end-to-end for technical solutions that are human-compatible and keep up with evolving technologies and business trends. The roadmap to implement predictive maintenance does not just require appropriate data management. In complex system integration, there is also a need for flexible platforms that pull together the full range of data sources, tools, systems, microservices, and eventually, allow for seamless human-data interaction.
In conclusion, at the moment, predictive maintenance is a piece of the Industry 4.0 puzzle, and it is transforming manufacturing. However, the implicated investments in cloud technology and IoT have not yet come into their full force. One thing is clear: that when they do, they hold the keys to unlock more than time-efficiencies.
Author: Lovisa van Heijne, Marketing Assistant at dizmo
Make sure to check out some of the uses that customers have made of dizmo’s patented technology. Or read more about dizmo’s Smart Building solution that was awarded by Atos and Siemens with the Digital Industry Award.