What is an IIoT Platform?

Manufacturers need to easily access data from their production processes to take advantage of the new inroads available with Industry 4.0. Driven by the need to identify, analyze, and optimize industrial data, many manufacturers are considering (or have already adopted!) an Industrial Internet of Things (IIoT) platform to leverage the power of Machine Learning, AI, and Big Data discoveries.

Digital transformation is on all manufacturers’ minds as they aim to navigate the challenges today and of the future. With access to data limited, the appeal of an Industrial Internet of Things platform is catching many organizations’ attention.

An IIoT platform aggregates real-time data from hardware, software systems, sensors, and other data points into a centralized environment, which can usually be accessed by a wide group of users. It bridges the gap between systems, people, and machines by pulling that data into a centralized system, usually in a cloud, but sometimes also on prem or on edge.

IIoT platforms are used for decision making in industrial environments by improving connectivity, control, forecasting, and data analysis. Manufacturers typically seek an IIoT platform when they are looking to gain insights into the factors that impact quality control, downtime, production (output), and waste. Additionally, an IIoT platform is often used to bring visibility cross-departmentally into what’s going on in your facility.

Benefits of an IIoT Platform

There are many benefits to adopting an IIoT Platform—some of which are easy to measure and some that offer more of a soft ROI. First and foremost, this platform will bring together data into a centralized location from various production systems like Historians, SCADAs, PLCs, LIMS, and more.

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Teams are able to more easily access production data whether from their home office or at corporate headquarters. With access to data, you have more visibility into what’s happening on your production lines and can often react faster to problems or even progress on your short- and long-term studies. By having more democratized data, teams may feel more efficient and benefit from an automated data collection tool.

You can tap into third-party systems to build algorithms, models, and more to better understand the data. With better access to all of your data, you can begin to make comparisons between top performing plants and underperforming plants, and get a better holistic view of your operations. It can feel like a big win to achieve this!

Words of Caution with IIoT Platform Vendors

With an IIoT platform, you’re putting together raw data from a plethora of sources. However, there will likely be a need to either cleanse data manually or tap into another software, algorithm, or specialist to make the data usable. Data analysis takes time and requires specific skills such as Python expertise or deep data mining skills—and can still leave many findings left to individual interpretation and bias.

In this instance, you can remain stuck in the reactive state of ‘what happened when?’. For example, what happened in the hours, minutes, or days before your boiler malfunctioned? Sure, you have the historical data in your IIoT platform that you can then extract and troubleshoot, but by the time you may find the Root Cause Analysis, you may get pulled into another fire. Further, you are limited to time-based association, linking data to an approximate period of time that the boiler malfunctioned. Yet, what if it was actually a valve that caused the malfunction upstream? How can you isolate variables in a way to see how they are interrelated to not just determine what happened when, but rather what would happen if, or even better, how can we prevent this instance from recurring?

The Power of Artificial Intelligence (AI)

To truly transform your operations, there is opportunity even beyond an IIoT platform in the form of AI. The human mind can interpret a set number of variables, which can be limiting, yet AI can help drastically scale the breadth and depth of analysis. Even the best data scientists may only be able to interrelate 60 or so variables, while AI can have endless possibilities.  

Though not all AI is created equal and there are several cautions to consider when choosing the ‘right’ AI, in the midst of a changing workforce, AI may be more important than ever before: especially when humans drive the AI, and not the other way around. The human element—almost completely absent in Industry 3.0 strategies—is the cornerstone of today’s digital transformation to Industry 4.0.

The next 4.0 production systems are autonomous cyber-physical systems. Cyber-physical systems enable intelligent machines to learn our human environment while simultaneously providing humans with a higher quality of life. These systems “integrate sensing, computation, control, and networking into physical objects and infrastructure, connecting them to the Internet and to each other” (National Science Foundation). 

AI technology, deep data analysis, and a connected IIoT platform offer tools to empower your employees to solve the complex challenges hindering your business.

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Cloud or Edge IIoT Platform

Oftentimes companies seek a cloud or edge solution in their pursuit of Industry 4.0. With so many IIoT platform options on the market, it’s important to first understand your current state, your future goals, and your timeline. Ultimately, the solution is not either cloud or edge—it is based on your goals.

IIoT Platform Considerations

While there are plenty of examples of best practices for implementing an IIoT platform, these five less-obvious approaches may be the key to helping drive a successful implementation. From choosing the right platform to multi-facility rollouts, these considerations can help you transition from a successful implementation to a successful Industry 4.0.

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Scaling Your IIoT Platform

Implementing an IIoT platform doesn’t stop with the first production line, business unit, or facility. Scaling your IIoT solution across departments, regions, or even product lines is the key to reaching optimal efficiency. These considerations for your corporate rollout are important to evaluate even before you begin your digital transformation.

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