Automation Column IIoT

Help For Your IIoT Efforts

Gary Parr | January 22, 2018

Unrecognizable corporate manager exchanging data between networked cyber manufacturing assets. IT concept for cyber-physical systems, CPS, cyber-manufacturing, Internet of things, reconfigurability.

Industrial Internet of Things (IIoT) is the topic of the day for enterprises that want to move efficiency/reliability programs toward the predictive and, preferably, prescriptive levels. Organizations seem to be organized in three camps:

• those that have fully implemented IIoT technology and practices and are realizing game-changing results

• those that have made an investment and realized some success, but haven’t found a path that leads to the promised land

• those that know they need to get on board aren’t sure where to start and don’t have the resources to experiment.

For the latter two camps, the overriding question is “How?” I asked Google’s experts for an answer to that question and they came back to me with a white paper from SAS Institute Inc., Cary, NC (sas.com). The publication, “5 Steps for Turning Industrial IoT Data into a Competitive Advantage,” lays out one plan of attack that might help your operations get in the IIoT game. Download the document at efficientplantmag.com/1801sas.

Here are some high points from the plan:

Step 1. Define IIoT Business Goals: This pops up time and time again as the starting point for technology-oriented efforts. Basically, business and technology leaders need to put their heads together and identify areas in which IIoT technology can benefit the company. That calls into play the old saw of finding places where you can realize small victories, then building on them.

Step 2. Define an Analytics Strategy: Once you have your use cases in place, select an analytics platform. According to SAS, assess possible options for how well they deliver a holistic analytical life cycle that:

• efficiently prepares, stores, and transforms data for analytics

• drives discovery from diagnostic, predictive, and prescriptive analytic techniques

• deploys, manages, and monitors analytics in the cloud, the fog, and on the edge.

Step 3. Assess the Need for Edge Analytics: This is a key step. According to the whitepaper, IIoT users must do more than analyze information. They need to turn analyses into action, which requires a management structure designed to operationalize the insights. Edge analytics can capture value in real time, and it deserves special consideration by IIoT planners. Edge analytics processes the data stream close to the source of the data. This allows the analytics system to stem impending problems by shutting down machinery, triggering alerts, or taking other actions. The capability for immediate, automated response is not possible if analysis has to wait until data reaches back-end storage systems.

Edge analytics also filter data at the source so that only relevant data is sent to the cloud. This prevents irrelevant information from overloading networks and keeps the focus on what’s most important to the business.

Step 4. Choose the Right Analytics Solution: Time to go shopping. This is a big hurdle and a key decision point, so let the buyer beware. The white paper offers a good deal of advice to help you.

Step 5. Focus on Continuous Improvement: As with network security, you can’t take a set-it-and-forget-it approach. According to the whitepaper, because IIoT continues to evolve, industrial organizations should regularly assess their use cases and analytics performance, and update these areas as new capabilities and business opportunities arise. At the same time, they should re-examine existing deployments to ensure that analytics continue to achieve use-case goals.”

I can’t say for sure that this is a one-size-fits-all plan. But, if you’re wrestling with IIoT implementation, the whitepaper could provide some food for thought. EP

gparr@efficientplantmag.com

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Gary Parr

Gary Parr

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