A Simple Approach
Enhancing the serviceability and availability of devices or other assets is a clear and obvious goal which applies to many business situations and industries. The more complex, capital intensive, or mission critical the asset, the more incentive to proactively manage the serviceability. The alternative is to let the device run to failure, and then repair or replace it, which may or may not be viable for a given situation.
We may be blinded by the apparent “shine” of modern digital and IoT methods, and in some cases that technology becomes an end in and of itself.
This short article seeks to break this topic into simple layers and get to the foundational elements in simple-to-understand terms.
IoT and the Industry 4.0 Landscape
We are talking about Internet of Things (IoT), or more currently referred to in this context as Industrial Internet of Things (IIoT). IoT is a significant element of the Industry 4.0 landscape, along with other digitally focused technologies such as advanced sensors, Artificial Intelligence (AI) and cloud computing. Industry 4.0 describes the current generation of industrial technologies and the ongoing evolution and application of these advanced digital methods. It is focused on creating connections between previously stand-alone elements to create a smart manufacturing environment through real-time awareness, efficiency, and predictability. The world-wide investment in Industry 4.0 related technology, which is at double digit annual growth rate, is a clear testimony to the demand and expected returns on investment. These techniques and tenets as applied to devices in-use, and how they can increase the serviceability, is the focus here.
We are talking about using information and IoT methods to increase the serviceability of “things”. This can be abstracted to many different applications, but here we’ll refer to the thing as a “device”.
Devices fail. Analysis of device failure trends can yield insights that inform future design changes which increase the reliability of the device. Reliability engineering applies advanced modeling and analysis to optimize the designed reliability. However, the reliability of a device once deployed is inherent. The Inherent Reliability cannot be increased by any digital magic or methods. However, between this Inherent Reliability and the perceived End User Experience, there are multiple layers and real opportunities to positively affect change.
Let’s peel this onion a bit.
Where is it? How is it being used?
How the device is used, and the environment in which it is used can play a significant role. Usage may be measured in hours or cycles, for example. The environment may be hot or cold, wet or dry, clean or contaminated, etc.. The Utilization and Environment can be understood remotely if there are appropriate sensors, either within the device or in close proximity. Of course all this is predicated on each device of interest having a unique identity and identifier.
There is a growing focus on understanding the real-time location of assets using Real-Time Location Services (RTLS), which opens the door to a wealth of opportunities for management and controlling where items (or people) are, or have been. The three dimensional space can be modeled and the motion through that space and how these items relate to each other can be analyzed and optimized in four dimensions. This is a key enabler of “Industry 4.0" and process optimization.
In short, if you can understand an asset’s location at a given time you can understand much about its utilization and its operating environment. If you can measure a devices usage during and after its manufacture, and throughout its lifecycle, you can develop a strong understanding of its current and future expected life, and the possible need for proactive maintenance.
Maintainability is Key
Maintainability describes the elements which allow a device to be more easily and efficiently maintained. For example, if you have to remove your car’s entire front grill to replace the headlights, that would be very bad maintainability. The physical access and elegance of the installation and removal scheme matter. It is human nature to avoid unpleasant tasks, and maintainers are human. Whether scheduled or unscheduled, on-site or in the back office repair shop, how the maintainer interacts with the device is very important. In modern best practices, design for maintainability is standard.
Much boils down to what information is available to the maintainer at the point of maintenance. In today’s world there is an expectation that the proper and latest maintenance documentation is available at the point of use at all times. Mobile devices have made this somewhat standard. However, the slickest digital delivery is not meaningful if the source documents which guide the maintainer through the diagnostics and maintenance tasks themselves are not clear and trusted.
For more complicated devices there should be built-in diagnostics and Built-In Test (BIT), and these must be meaningful, accurate, and understandable. If not accurate they will be seen as noise to the maintainers and drive unnecessary removals and maintenance (and thus raise costs).
Meaningful data can and should be collected at the point of maintenance. Ideally this is an automated and digital process where the maintainer captures the data in an easy manner that generates codified data rather than free text records. This is a vital step, as accurate and regularly collected maintenance data will accelerate the aforementioned analysis. This will help drive future design changes to increase the reliability of the device, enable document changes to increase maintenance document quality, and enable continuous learning via data analytics to smart digital diagnostics and predictive tools.
On-going investment and focus on maintainability will pay off over the life of a device.
Optimization of Serviceability
Serviceability is the degree to which the device performs its intended function for the end-use application, function or user. Keeping with the car analogy, if I get into my car and it starts right up, and drives well without any warning lights, my car is serviceable.
Obviously, a device with good inherent reliability, that is operated in a benign environment, in a usage profile that is aligned with its intended design, and is easy to maintain, is going to be more serviceable a device than a device where one or more of these are not true. That said, we still have an opportunity for appropriately sensored and connected devices to further manage and increase the serviceability through digital and IoT methods in a very material way. In fact, this is where adept digital and analytics combined with IoT really shines.
We have stated that devices have an inherent reliability, and they may be affected by usage or their operating environment. Also, that devices fail and need maintenance. If we can understand the device’s current serviceability in a remote and real-time manner, we have a significant advantage. We can start the next actions moving immediately.
It’s not enough to know that the device has failed. There is a larger value chain. The right stakeholders need to know the facts in a form which makes sense within their jobs, and have the other information needed to understand the full context of the situation. For example, when a device failure is observed remotely, depending on the situation the device may or may not require immediate maintenance. The right people having the full context and a robust process in place allows for an immediate, informed assessment. An efficient and coordinated plan can then be created and executed. A panicked response is just as likely as not to create new problems. In short, the device failed, but because of proactive diagnostics, responding action is as efficient as possible, and the potential disruption to the end user is minimized. This is at least partial success.
Real success is when we can predict an oncoming failure or performance degradation in advance via prognostic methods (aka predictive maintenance). Obviously this is more complicated and involved than solely the real time awareness of a failure which has occurred. However, the major tenets remain the same: the right stakeholders need to know the facts in a form which makes sense within their jobs, and have the other information needed to understand the full context of the situation. In addition, they must trust the prognostic recommendation, and sometimes be incentivized to act upon it. It’s been said many times: no one ever added value by looking at information on a computer screen. Value is only added by doing something different based on that information.
The value proposition of IoT serviceability ecosystem investment is extensive. Some elements are obvious, such as better performance and reduced disruption. Others are less obvious and indirect. For example, with a full lifecycle approach, designing in IoT and processes may reduce the burden of having to specifically design devices to be “fail-operational”, potentially obviating the need for additional hardware and cost.
The complete value chain required to create and deploy a successful predictive maintenance program is enormously complex, with many different choices regarding sensors, data analytics methods, and information delivery. Moreover, each element requires a return-on-investment trade. There is no one-size-fits-all approach.
What all of the preceding has in common is that there must be, along with the technologies and people, a culture which embraces the proactive and predictive. Again, it’s technology, processes and people (i.e.,culture).
Regardless of the industry, the Serviceability IoT ecosystem moves from the design, to the device itself, to the environment, to its maintenance, to how the serviceability is managed, to the ultimate end-user experience. By deft use of Industry 4.0 methods we can increase the effective serviceability of devices in a meaningful way. Moreover, by looking at the larger value chain we can greatly bolster our chances of success.
Hopefully we sliced the onion without tears.