Smart maintenance: freshen up for Industry 4.0

The rapid expansion of devices is allowing businesses to gain faster insights by connecting the unconnected

As such, IoT has become a cornerstone of business growth in industries such as manufacturing. With the rise of Industry 4.0, the adoption and usage of IoT shows no signs of slowing. In fact, the use of IoT is being accelerated by COVID-19. Whether it’s connecting equipment in manufacturing, improving supply chains, or leveraging data insights that drive critical business decisions, business leaders are using it to reduce operational risks, improve their uptime and even positively impact their bottom lines.

One of the biggest operational risks across industries, and specifically manufacturing, is downtime. And it’s a costly affair. For manufacturers, for example, the cost of downtime can reach an estimated $50 billion each year, according to Deloitte. Business leaders in manufacturing therefore need to have plans in place to prevent it. Thankfully, with IoT, manufacturers and other business leaders can use new predictive maintenance tools to remain one step ahead. It allows systems and machines to be monitored and maintained before failure, running at optimum levels without causing unplanned downtime. 

Yet, while many businesses attempt to implement as much predictive maintenance as possible, they are not fully leveraging and benefitting from convergence of technologies like IoT, Analytics and AR. It’s time that leaders take matters into their own hands. They must ensure the business is equipped to optimise its operational efficiency, and overcome the obstacles that are preventing the organisation from maximising the results of predictive maintenance. A key part of this is building a completely transformed maintenance process, from identification to resolution. 

Introducing Maintenance 4.0.

Maintenance in industries is undergoing a transformational shift. COVID-19 has not only accelerated the need to remain stable, but the fundamental business benefits are clear to the adopters. The key capabilities that underscore this transformation are prediction, automation, remote connectivity and AR.

Predictive maintenance is a key advancement in maintenance models. In previous models, such as reactive or preventative maintenance, monitoring machines was more difficult as they were still largely manual and data was unconnected. Companies struggled to respond quickly enough to prevent failures, or downtime. Predictive maintenance on the other hand relies on connecting live streaming machine and sensor data with predictive algorithms. This allows systems and machines to be monitored and maintained before issues occur.

Yet, predictive maintenance still needs to be integrated into all aspects of the workflow to bring benefits such as automating processes, preventing unplanned downtime, and reducing on-site maintenance costs. With Gartner predicting that more than one million IoT devices are expected to go into operation every hour in 2021, leaders in manufacturing must plan how to overcome these challenges that may arise. The Maintenance 4.0 approach is to view a process holistically and include the best of IoT interventions and human interventions.

It’s as simple as C, P, A, S

For many industries, and specifically within manufacturing, challenges around using IoT could involve integrating legacy systems, security concerns and demonstrate immediate positive bottom-line impact. To maximise the benefits of predictive maintenance, leaders should follow four simple steps. Here are my tips on how to: Connect, Predict, Automate, and therefore Support (CPAS). 

Connect: IoT sensors to all infrastructure used in the workflow. This is especially important if leaders want to enable real-time monitoring of legacy equipment. Streaming this data is critical for visualisations of their current operating status

Predict: Analysis and modelling of the actual equipment’s data and sensor data will ensure anomalies and faults are easier to detect. Apply predictive analytics to diagnose the condition of infrastructure and you will get a full assessment of the state of the equipment, its faults or failures. You can then understand the probability of failures to occur and act to prevent them.

Automate: By leveraging the intelligence of the predictive models and analytics from the previous stage, automation can find the best method to optimise current processes. It can also find the best person to resolve the issues. Optimisation areas could include classification of failure modes to trigger standard operating procedures and work order assignments directly, instead of lining up in a queue waiting to be allocated manually. 

Support: A key differentiator to realising a higher value with predictive insights and early warnings is to make them actionable.

Using remote capabilities, or AR, there are 3 clear setups that emerge:

You can remotely access, support and fix the machines, for all parameters that can be accessed, this is a typical IoT use case made available for example with OTA for software configuration or calibration type resolutions

Utilise AR to remotely assist and effectively instruct and guide operators through maintenance workflows, this tremendously speeds up turnaround times with high reliability and accuracy and maintains high confidence among operators and support technicians

Plan unavoidable site/field visits with minimum disruptions. If the above 2 methods have not already resolved a maintenance scenario and a specific job must be completed on site, set predictive alerts in advance to accommodate scheduled repairs give customers sufficient time to plan for any downtime

Reap the rewards, remotely

The benefits of following these steps are huge for businesses. Real-time, and around the clock, monitoring with predictive analytics can be used to understand and create maintenance and performance forecasts. This means that when issues are detected, technicians can immediately access infrastructure or machines remotely to adjust configurations, trigger restarts, or change any settings to fix problems faster. As a result of predicting failures and prescribing solutions remotely, rather than maintaining on a reactive basis, entire industries can run at a higher capacity. This approach will significantly reduce the need for on-site maintenance and repairs in the pandemic and beyond — without the fear of costly downtime. 

There are other clear benefits, such as increased productivity of support technicians, cost savings with travel, and increased customer happiness thanks to more reliable and error free procedures. An amazing by-product of such setup also compensates for any bottlenecks caused by technical skills gaps. It can step in where originally only technicians that are very experienced could, and helps to ease the time pressures they face as it speeds up resolving one issue at a time.

The reality is that consistent growth of Industry 4.0, and IoT, will continue to impact industries around the world. The pandemic has been a catalyst for many companies to implement the technologies that they have always planned to adopt. And, as the economy recovers and businesses look to grow again, they can be assured with the right technology they will be able to realise their ambitions. The adoption of predictive maintenance, to optimise workflows, is therefore critical for businesses to recover and thrive in the wake of the global pandemic.

About the Author

Hendrik Witt is EVP of Augmented Reality Solutions at TeamViewer. As a leading global provider of remote connectivity solutions, TeamViewer empowers users to connect anything, anywhere, anytime. By innovating with cutting-edge yet easy-to-deploy Augmented Reality (AR) and Internet of Things (IoT) implementations, the company enables businesses of all sizes to tap into their full digital potential.

Featured image: ©Pongob