Preventing Unplanned Downtime with Industrial IoT

Preventing Unplanned Downtime with Industrial IoT

Many manufacturers operate at high volumes, and unplanned production downtime is costly. Often these line down situations are the result of production machine failures that could be avoided if data from the machine was available to anticipate the failure so a planned repair could take place in a standard maintenance window. 

The answer to prevention of unplanned downtime is condition-based maintenance (CBM). CBM can help prevent some downtime, or at least just lessen its impact. In legacy models, the cost of preventative maintenance is high enough to warrant applying a rubric. If the cost of disruption is high enough, then traditional monitoring solutions may be brought to bear to prevent (or more likely reduce) downtime. These features are important because of the following benefits:

1. Monitoring and analysis are more ubiquitous. IIoT platforms can substantially reduce the cost of monitoring, therefore expanding the range of equipment that qualifies for predictive maintenance. This allows the previously mentioned rubric to be changed, lowering the bar for the application of analytics to machine components.
2. Modeling is more accurate and more up-to-date. Unlike legacy EAM and MES applications, which often employ static data clusters and models for algorithms to monitor and manage equipment, IIoT platforms are more flexible. Analytics models can be changed; the entire underlying analytics engine itself can even be replaced. As a result, it is cheaper, easier, and more likely that manufacturers will update their analytics for increasingly accurate failure predictions, and to accommodate changes in physical hardware—including new hardware and the aforementioned inclusion of more assets being measured.
3. Monitoring is more flexible throughout the distributed value chain. Manufacturing is typically highly distributed; equipment and machinery in use in a plant are made by another manufacturer; the components in that machinery may be produced by several other manufacturers; some of those components may even include subcomponent originating from multiple vendors. Collectively, it can be difficult to accurately measure the right assets. IIoT platforms provide an opportunity for vendors across a value chain to work together to create complex machinery that is much more transparent and measurable.
4. Computing centers are more customisable. Much has been made about the impact of cloud computing on manufacturing, and there’s certainly potential benefits—with some serious caveats. Depending on the type of data being captured, its volume, frequency, sensitivity, and point of origin, cloud may not be a good (or even legal) solution. At the same time, purely on-premise solutions can be costly and less effective. An IIoT platform provides manufacturers with the ability to choose—and when needed, blend—on-premise, cloud and edge computing to meet specific needs; needs that will often include asset and performance KPIs.

5. First-time fix rates and overall repair efficiency are improved. While much of IIoT’s benefits for asset management and productivity can be attributed to preventing downtime, we’ll likely never realize downtime-free manufacturing. IIoT platforms can integrate and utilise dashboards, diagnostics and even augmented reality (AR) solutions to improve speed of repair. After all, if you can’t always prevent downtime, the next-best option is reducing its duration and cost.

Bottom Line

IIoT platforms still represent an emerging market, and manufacturers should approach them judiciously. However, good judgement shouldn’t be confused with overabundant caution. Given that asset management is one of a myriad of ways that market-leaders are using IIoT to separate themselves from their competitors, manufacturers should understand that the ground between adopters and laggards is only going to expand. That’s why Gartner’s first Magic Quadrant for Industrial IoT is so invaluable, and why we’re making it available to you.

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