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How Industry 4.0 is changing manufacturing

How Industry 4.0 is effecting asset management

It won’t have escaped your notice that everyone’s rather excited about the Internet of Things (IoT), smart technology, disruptive innovation and machine learning. They’re just a few of the factors contributing to the Fourth Industrial Revolution, a justifiably grand term given the effect it will have on manufacturing.

Advances in connectivity are at the heart of Industry 4.0, with wide-ranging benefits for the way businesses are run. For manufacturing, one of the most important areas is asset management, in particular, how to create efficiencies and provide insight into the whole production chain.

Asset tracking is well established for a range of mature products. There has always been value in being able to locate individual assets, but older, passive systems using RFID and barcodes are not without their limitations. And the more recent addition of GPS is only good for outdoors.

More recently we’ve seen the emergence of Real Time Location Systems (RTLS) such as Pathfindr Locate, which take advantage of new technologies and standards such as Bluetooth. Moving from passive to active technologies such as Bluetooth improves asset visibility and provides more accurate and reliable location information.

RTLS means we can get a single view of work in progress and immediately locate any asset across multiple facilities; indoors and outdoors if the right combination of technologies is deployed. Reducing waiting and motion waste improves efficiency as the time it takes to find parts, fixtures and tooling is decreased.

In addition, RTLS can dramatically reduce overall asset loss and scrappage due to missing paperwork, as products such as smart-folders can be added to the connected ecosystem.

While modern RTLS has taken asset tracking to the next level, location information should be the minimum functionality you expect from such a system. IoT connectivity and advances in chip and sensor technology has seen the emergence of Real Time Asset Intelligence Systems providing insight into much more than just location.

Asset Intelligence is made possible by embedding a range of sensors in the tracking and monitoring hardware, so, in addition to location you can also collect data on temperature, humidity, air quality, pressure, vibration, acidity and pretty much any environmental measure.

The benefits gained from this intelligence will differ from one business to the next, depending on their processes, materials and manufactured product. However, one common application is the optimisation and smoothing of asset utilisation, ensuring you have the correct number of any duplicate assets and tools and that they are receiving equal usage. It can also provide predictive maintenance alerts, enabling correct servicing before any failure or loss of efficiency.

But it’s not enough to just capture environmental data. It’s only useful if it’s accessible and used to make manufacturing decisions in real time. A criticism often levelled at ‘Big Data’ is that it’s just being stored and businesses don’t know how to make sense of it. Asset Intelligence becomes useful when it includes effective integrations, alerts and communications between devices, systems and humans. The ultimate aim for Asset Intelligence is to support fully automated real-time optimisation of processes, something that is not too far into the future.

One example of what is possible already is Predictive Asset Supply. Advanced Asset Intelligence can use an upcoming MES schedule to ensure all assets and resources are assembled in the relevant location required for a certain task ahead of time. They may well be spread out across multiple units and stored off site, so this takes us beyond the efficiency benefits of asset tracking and reducing time to find, and truly optimise the process. The predictive approach can also flag pressure points for the use of specific assets as well as alerts for supply levels, and also spot the most suitable time for maintenance.

Health and safety can also benefit from effective Asset Intelligence. The same trackers used for location and environmental data could be attached to containers of chemicals and programmed to ensure potentially volatile materials don’t come within a minimum distance of each other. If they do, then relevant warning alerts can be raised in facility systems. Trackers can also monitor personal safety equipment to ensure it is being used correctly, thus minimising workplace accidents. For any tracking data associated with the location and movement of humans, you must ensure that all relevant privacy conditions are met and communicated to the workforce.

Some key areas to consider when looking to implement an Asset Intelligence system:

  • Infrastructure. New networking to support Asset Intelligence can be costly, disruptive and make the system unviable. Choose systems that require minimal to zero infrastructure in order to work. These are easiest to retrofit into any facility.
  • Data. Identify what insight you need in order to inform decisions and bring efficiencies, what frequency you need to monitor, and which alerts and actions apply to each scenario.
  • Security. Ensure suppliers have been penetration-tested and adhere to your internal data security policies.
  • Futureproofing. Choose a system that is scalable and can monitor a large range of environmental, location and usage information. Ensure it has open integrations and will work well with your existing systems. Ask the supplier what is on their product roadmap
  • Return on Investment. How will you measure the impact of the system and know if it is having a demonstrably positive effect?

 

The pace of change in IoT and connected factories is fast and will continue to accelerate. The opportunity to optimise processes and efficiencies is huge, yet only a small proportion of manufacturing companies have implemented Asset Intelligence systems. The Fourth Industrial Revolution is well under way, but there’s still a lot more disruptive innovation and smart technology to come for manufacturing.

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