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As IoT adoption expands, organizations must approach deployments with the right mindset to realize their goals.
Part of a successful implementation strategy is to create the right culture and adaptability for IoT adoption and remote work; this subject was a reoccurring theme at the IoT World 2020 virtual conference, held Aug. 11 to 13.
IoT devices can create many opportunities for organizations to improve in process visibility, effectiveness and cost, but the technology also challenges traditional approaches to building value, such as implementing technology in a silo, one department at a time.
Plan the correct approach to IoT adoption
Organizations don't all need to start their IoT initiative with the same strategy, but they should understand that, with any approach, the goal is to build capabilities to maximize the value of IoT, said Sean Riley, global industry director of manufacturing and transportation at Software AG, during his session titled "Learnings on How To Realize Scalable IIoT Value."
There are three ways organizations can get started with IoT:
- Rapid experimentation. This approach delivers a platform as soon as possible and users can discover its value.
- Blueprint. This strategy requires a plan for a specific targeted issue and projecting the platform's value, as well as how teams will roll out the software.
- Commitment. Organizations go all-in with this approach to IoT and apply a strict, segmented development and rollout.
The key to any strategy is to stay focused on the value IoT will bring the organization, such as increased productivity or decreased costs through predictive maintenance. An IoT initiative is not just an IT project; every part of the enterprise -- including production, field service and project management -- must be involved and able to use the platform.
Organizations must design the platform so anyone without much coding experience can use it, and it should have an effective UI to make adoption easier. They can also use vendor platforms to simplify IoT adoption, but premade platforms aren't as customizable.
"[Each department is] actually intertwined with each other, and that is key to unlocking value in a scalable manner with IoT projects," Riley said. "Easier said than done, but it's a very different mindset than traditional software or a traditional siloed approach to rolling out objectives."
How to successfully build capabilities into IoT setups
With any IoT approach, those involved must understand what the data from a connected device means. Teams should start to actively monitor IoT data and apply smart rules. For example, if a connected thermometer and pressure sensor on a compressor exceed set parameters, technicians can receive an alert.
As the organization gets more comfortable with IoT data platforms, it can apply more complex analytics. This could mean technicians look at how temperature and pressure relate over time and use that data to determine when the compressor needs maintenance. IoT analytics, with enough continuous data from smart sensors, can predict the useful life of a machine and the most effective maintenance strategy.
"The value out of a dashboard isn't quite as significant as being able to predict failure and automate an action to resolve that particular failure," Riley said. "Or not even failure; be able to predict what the remaining usefulness of life is and being able to schedule maintenance or replacement before there's a quality impact."
Organizations can't just implement IoT analytics and expect to get value immediately, Riley said. IoT adoption is a challenging process that starts with connecting assets, monitoring equipment and understanding the diagnostics.
From that understanding, the organization can expand its IoT analytics to put equipment data in a greater context, create a predictive model and move from condition-based maintenance to predictive maintenance. A digital twin can simulate how potential changes affect a facility's equipment based on collected data.
IoT adoption should follow these overarching steps to create greater value:
- Connect devices and understand what data from equipment means.
- Make sense of data inputs through dashboarding, condition monitoring and analytics.
- Act on suggestions created from analysis of the collected data.
- Automate actions, such as notifying a technician that a machine needs maintenance when certain conditions are met.
Typically, organizations must invest the most capital and effort at the beginning of their IoT journey when they connect entire factories. These costs diminish over time, and the greatest value grows when organizations can move onto more advanced analytics and automation.