As we slowly phase into the 4th wave of the Industrial Revolution, or also known as IIOT 4.0; our tools also need to advance to our needs and requirements. Machinery has evolved from equipment used to do dumb manual labor towards complex and sophisticated beings capable of sensing and making decisions based on its observations (don’t worry, they’re still not sentient).
With the advent of assembly lines and automation, our dependence on manual labor has reduced but our dependence on the reliability of these machines has increased tenfold. Production has become more and more time-critical, even nanoseconds of improvements are seen as groundbreaking and quantifiable in many business fields. Any equipment failure now will be disastrous, and can now result in a complete shutdown of the entire production chain.
In the case of a fabric manufacturer for example, by the time the manufacturer figures out there’s been an anomaly in the production, or if a piece of equipment isn’t behaving as it should be; it’s already too late and almost a complete batch of goods has been affected by it., A problem that could’ve been spotted and fixed in a manner of a few seconds now has caused unplanned losses and downtime throughout the production whole chain.
A joint study by Wall Street Journal and Emerson found that unplanned downtime costs industrial manufacturers an estimated $50 billion per year; with equipment failure being the cause of 42% of this unplanned downtime. These downtimes are costly in both repair costs and opportunity lost. So what can we as a manufacturer do we do here?
Preventive Maintenance
Colloquially, the term ‘preventative maintenance’ has been synonymous with scheduled maintenance. Which roughly translates to periodically servicing a piece of equipment on a time or usage basis. Something similar in concept to how we’re expected to get our car serviced every 10,000 miles, or our heating system serviced every year, right before winter. I think we’re all abundantly clear with the value of maintenance. By preempting equipment failure, scheduled maintenance protects against unwanted downtimes, lost product, and output and reduces the exorbitant costs of frequent and long breakdowns.
It’s been estimated that the true cost of a machine breakdown is between four and fifteen times the preventive maintenance costs, and I think we’re all familiar with the very famous childhood quote, “Prevention is better than Cure”.
But as we’ve come to realize even this system is far from perfect, we can’t possibly predict every single instance of failure just by taking into account only a single factor like time or distance. The costs of excessive maintenance of assets can also be wasteful. Over-investing in maintenance squanders precious resources, which directly impacts cost and profitability.
There’s far far more that goes into scheduling maintenance, for one person living in the city getting their car serviced every 10,000 miles might be too much while for another living in the Snowy Hills getting it serviced even every 5000 miles be too little. As a result, the equipment can often be subjected to too much maintenance or not enough.
In fact, according to a recent paper from IBM, up to 70% of a company’s investment in preventative maintenance does not affect uptime metrics. This is due largely to the fact that only 11% of machine failures follow an age-degradation pattern. A whopping 89% occur at random.
How IoT comes into Play
In a survey carried out by the World Economic Forum (WEF), the most widely cited application of the Industrial IoT is predictive maintenance, and rightly so. Predictive maintenance allows manufacturers to lower maintenance costs, extend equipment life, reduce downtime and improve production quality by addressing problems before they cause equipment failures.
Rather than risk over-servicing or under-servicing a piece of equipment, IoT has enabled us to fine-tune the entire process, so that the moment something falls out of place or any anomalous behavior is detected within the equipment, alerts can be sent out immediately to dictate maintenance.
This way, we aim to turn the idea of predictive maintenance into a more reliable concept of responsive maintenance. No more shooting darts into the air and hoping they land correctly but into real data-backed and intuitive decisions, which maximize your business potential.
Traditionally, corporations charged exorbitantly for these technologies, making them only viable for big-scale deployments. But as the costs of IoT hardware have dramatically dropped down, we aim to implement these ideas into all existing latitudes of businesses, and not just the ones with the deepest pockets.
Today, a simple and cheap microcontroller connected with a few sensors can be programmed to keep eye on your essential equipment. Sending real-time data to your Dashboard enabling constant monitoring of your devices; alerting all necessary personnel about any anomalous behavior within the systems, keeping you a step ahead of any possibilities of breakdown or disruption. All this while with the added benefit of relatively easy implementation, almost all legacy equipment can be retrofitted with these microcontroller devices, meaning no need to change or upgrade your existing equipment.
While this might sound relatively simple and minor, but if implemented at scale and kept a careful eye on can easily result in immense savings and increased reliabilities.
But how does it actually Work?
There are various ways to make use of data. The most ubiquitous will probably be using dashboards. Dashboards enable you to consolidate all your assets and device data streams into a single easy-to-access platform, enabling you or your operators to view their health and status in new and intuitive ways such as in a map, list or hierarchy; exploring device data history or operating conditions with creative visualizations and graphs. Correlate data with multiple factors, such as weather conditions, temperature, etc.
Another way could be by setting Device Data Processing Rules. Notifications or alerts could be sent out, and for particularly high-risk assets, you may even be able to shut them down automatically on observing any anomalous behavior. Digital panic buttons and alert systems have already become commonplace in IoT dashboards to keep employees informed and machines safely operating.
Leveraging the power of Data and Machine learning, Organizations can easily develop models that identify pending asset degradation or failure using past historical and real-time asset performance data.
Conclusion
Drawing insight from all the problems highlighted above, we feel that predictive maintenance might be the thing that changes the face of IoT within the coming decade. Our goal while building axess.ai was to understand the issues from the top down and bring a real product which brings actual change. As the barriers to entry into the world of connectivity continue to fall, so will the losses incurred through breakdowns.
In a recent survey from Aruba Networks, 62% of businesses in the industrial sector reported already using some form of IoT in their operations. This makes it a good time to explore your opportunities to drive costs out of maintenance while mitigating risk to your operations—the time to see what IoT and axess.ai can do for you the health of your assets and your organization.
Your journey towards adopting IoT begins with a better understanding of asset & their health to drive greater levels of efficiency and reliability in your deployments.
“Your assets are talking. Are you listening?”
