We’ve all heard the saying that the only constant is change. Nowhere is this more apparent than in technology. Markets are being disrupted in healthy ways by innovation. More than 29 billion devices will connect to the Industrial Internet of Things (IIoT) by 2020, according to market researchers IDC, generating data that will drive economic value of more than $11 trillion by 2025.
The Industry 4.0 era is built on interconnectivity, automation, machine learning and real-time data. With such massive amounts of data generated, companies are now grappling with the challenges of how to extrapolate, interpret and act upon the most useful data. How can data be best leveraged to optimize manufacturing operations to avoid production interruptions? How can it predict preventive maintenance requirements and ensure facility uptime to maximize the bottom line? With the modern digital technology advancements made possible by the IIoT, manufacturers can help overcome changing workforce dynamics and assure plant reliability.
The compressed air marketplace is no different from any other industry in that it is evolving and embracing the IIoT. There are a wealth of applications and connected product strategies in compressed air that allow users to customize how they gather, deploy and act on IIoT analytics.
Incumbent challenges
With every technical advance, there come inherent problems. New tools, new ways of working and new knowledge centers present new elements to understand and accommodate. In this age of big data, there can be a tendency to have everything generating data, which can overwhelm and paralyze a team.
Sometimes data is being collected just for the sake of collecting it. Disparate information that teams have collected over time through various systems does not in itself solve problems. The industry went from having very little data to having too much data, a significant amount of which is superfluous. There’s a compelling business case around the importance of developing a connected strategy that not only filters the data but also provides meaningful and tangible insights that can be leveraged toward positive outcomes and profitability.
Understanding is the key
Companies that are already heavily invested in generating data to become more efficient and cost-effective still face significant hurdles. They may well have the infrastructure in place to gather data, but still encounter problems understanding what it all means. Because of the loss of knowledgeable workers, many companies no longer have experts in-house to analyze the data they have in meaningful ways.
Every manufacturer used to have an employee with holistic legacy knowledge they gained from the first-hand experience with manufacturing processes. But now those people are retiring as part of an aging workforce. The innate ability to look at information and interpret what it meant drove many competencies.
In this new big data paradigm, we can have all the data in the world. But if we don't know how to interpret it, it has zero value. The loss of knowledgeable workers from the air source industry is no different from the problems that people are facing in industry in general. So, how do we add knowledge back into the decision-making process?
As companies take this next step in their digital transformation, it is important to embrace outcome-based problem solving through data extrapolation and interpretation. The ability to integrate remote monitoring capability with anomaly detection is a robust method for solving problems differently. At the start of this process is a need to address some essential elements, for example:
- What is the data we really need?
- How do we best mine that?
- What is the best way to interpret and visualize that data?
- How do we make the outputs accessible easily and rapidly to those who need it?
- How easily actionable are the subsequent necessary procedures?
What underpins everything relating to the boom in big data is that the data sought either has to make you more productive, save you money or help you grow your business. Ideally, it does all three, which is why the key is connecting all these factors in a workable way. Prioritizing the data based on what problem you're solving and what is the value of solving that problem tends not to be easy or fun. This is why programs fail — because outcomes were not established first.
From data to insight to action
When you start a data-gathering project, there is a need to get specific about what the exact problem is that you are solving. Prove that you can solve that problem. Implement an application that will do it right. We are increasingly focused on problem-solving at Ingersoll Rand, and now offer integrated, digital applications focused on outcome-based solutions.
Because of the IIoT, many Ingersoll Rand compressors can now be connected to the cloud and communicate data 24/7. But we’re not just providing customers with information. We’re going beyond that to use decades worth of application knowledge across many industries to help customers solve problems. Our team uses machine learning and remote monitoring to translate the meaning of data and turn it into actionable insights.
The Ingersoll Rand PackageCARE is a service agreement that uses and analyzes data to mitigate risk for customers. Data is gathered from connected compressors to determine preventative maintenance measures, rapid repairs that are needed and even identify risks that may occur in the future but are avoidable.
Assets under the PackageCARE plan are monitored 24/7 by a team of experts who quickly detect when a problem or anomaly exists and start to take action to resolve it. Real-time data analytics available for the assets indicate when replacement parts are needed to prevent downtime. Using primary data, we can diagnose and address issues remotely and dispatch a technician with the right parts and the correct information to solve the problem.
Maintenance routines can also be planned based on real-time data, with engineers and technicians optimized according to the machine schedules, to prevent unnecessary downtime. This predictive maintenance is a preventative approach as opposed to a reactive one, and much more easily managed.
The other advantage of connected compressors are the intelligent alerts it makes possible. Alerts delivered via e-mail, text, apps or on the controller screen are increasingly part of overall compressed air system management. Whichever way they are delivered, alerts communicate the urgency and severity of alarms followed by remote visualization of any issues. Augmented Reality (AR) is also a significant driver moving forward. Service engineers can use AR to virtually see components and visually walk through common issues to troubleshoot. AR can also enable support team members to patch in remotely to pool their expertise to solve a problem.
Security in manufacturing
Industry 4.0 has had some adverse side effects on manufacturing. Hackers have shifted from focusing their mayhem on consumer technologies to targeting the manufacturing sector. In the last five years, there have been various high-profile hacking events where hackers accessed utility systems, for example. Hackers have found their efforts have a higher value for enterprise organizations because of its impact on production.
Manufacturers must beware. Introducing mobile and web technologies into the manufacturing environment can present security risks if strict protocols and standards are not in place. Digital applications and tools designed for the consumer space do not have the required cybersecurity controls the manufacturing space needs. Introducing consumer applications creates an opportunity for hackers and criminals to access your systems.
There’s also the misconception that you can combat cyber threats with inexpensive, off-the-shelf technologies from the consumer world. Although a tempting idea for its perceived cost-savings, that's a false understanding as these products are not made for manufacturing and industrial settings. The price you’ll pay if your system is hacked will be much greater. You are better off to consult with a security expert and select products that meet all the organizational needs.
In today’s era of big data, it’s mission-critical for organizations to design a comprehensive data gathering and analysis strategy that meets their individual business needs. Effective data insights can pinpoint areas to identify operational improvements and optimize the lifecycle cost of your equipment while helping to predict future problems. With greater power comes greater security stakes, so investing in a robust system that is aware and operates in real-time is a key building block in developing a solid data strategy.
