At an estimated $3.9 trillion dollars, Industry 4.0 / Manufacturing is widely recognized as the industry with the most to gain from the Industrial Internet of Things (IIoT).
Industrial organizations globally are turning to the IIoT both to drastically improve operational efficiencies and automation and to generate additional sources of revenue with new business models. PR Newswire reports that the IIoT Manufacturing market is growing 27% annually, and will reach $14 billion dollars by 2020. However, IIoT solution development and implementation does have initial challenges:
- Most companies have a limited internal knowledge of how the IIoT works and its specific business value to operations/new revenue streams.
“The majority of senior IT executives are not prepared to handle the volume, velocity and variety of data being generated by the IIoT. 70% have not implemented an IIoT strategy because they are either not prepared, and are currently struggling to effectively manage their data.” - IDG Quick Pulse CIO Survey
- Heavy equipment lifespans average from 30-60 years. Aging industrial equipment is expensive to replace but unexpected outages lead to loss of productivity and profit. How can you get the most out of your existing infrastructure without risking costly downtime?
- Legacy Industrial assets need not only to be smart and connected but have systems in place to collect, store and analyze all the data they can now process, to achieve the desired improvements in OEE (Operational Equipment Efficiency).
Until now, manufacturers have had to rely on preventative maintenance, which relies heavily on compiling previous statistics and making a best guess at how to prevent problems. For example, a car manufacturer might say to change the oil every 16,000 KM. A boat engine manufacturer might say do that after X number of hours. Those numbers are based on statistical models developed by the engineers who build and designed those machines. Those models relate wear to time and cumulative force.
- Statistics related
- Time/Operation count-based
- Performed whether it needs it or not
- Ineffective in identifying problems developed between scheduled inspections
- Not cost-effective.
- Unreliable and time consuming
- Breakdowns or outages are reactive
- Technical resource coordination is difficult
As you can imagine from this list, the ineffectiveness of preventative maintenance results in reduced equipment life which means increased downtime, resulting in lost revenue and productivity. Predictive maintenance uses the real-time, current condition of equipment to determine when to do maintenance. Nobody has to study old data and guess about when service should be performed, with predictive maintenance the machine tells you!
- Based on real-time data and correlations
- Current situation and hints/failures related
- Performed if needed
- Performed when the maintenance activity is most cost-effective
- Performed before the equipment loses performance within a threshold
Predictive maintenance lives up to its name: it can predict machine-specific failures by 70% because it will diagnose problems and deploy maintenance technicians much more quickly – and only when actually needed, reducing costs associated with unplanned downtime. It predicts the likelihood and timing of an event type occurring again, and uses that data to optimize your maintenance schedule and prevent unpredictable breakdowns. Not only are overall costs reduced but your cost control becomes much more transparent; you now know exactly what’s going on, where, and why. Predictive maintenance is much more valuable than preventative maintenance!
Are you wondering if predictive maintenance would work for your company?
Absolutely! Ask yourself:
- What percentage of your machinery experiences unplanned downtime?
- How do you access machine data and analyze patterns?
- What are the top three contributors to unplanned downtime in your facilities?
- How much does unplanned downtime cost you?
- How much do you spend to repair and update existing equipment that could be replaced with newer, modernized machines?
The relayr Smart Manufacturing solution addresses innovation needs specific to the manufacturing industry. Nowhere is more data being generated than in manufacturing, which generates millions of bits of data and is a top market for the advanced - and customizable - analytics we provide. We’ve worked with enterprises around the world, across all verticals (manufacturing, transportation, retail, major cities) and have the success stories and “wins” to show why we are the IoT company for the Digital Transformation of Industries.
One of our innovative solutions helped a global beverage giant gain a 10%+ increase in Efficiency and Output, at just one part of their bottling process. We began by looking at one of their production lines. They knew that three machines in particular were responsible for 90% of downtime, and this unplanned downtime was costing them $35,000 per hour - per hour!
Having identified the troublesome machines, we then looked at which parts of the machines could be best outfitted to measure, identify, and predict the expensive downtime. We were able to retrofit the equipment and develop an application to use the accumulated data on vibration, pressurized air flow, and glue nozzle temperature to detect functional anomalies in advance and integrate maintenance during planned downtime.
Read more of our success stories and contact our team to learn how the many benefits of predictive maintenance can be integrated into your company.