Spatial analytics, deeper predictive maintenance capabilities and enhanced security data are on the horizon for the Industrial IoT, members of the Braintrust say.
CTO at PTC
The emergence of scalable, enterprise spatial analytics (both in terms of technique and the continued increase of sensor and other data sources) in industrial environments and contexts will be the last "piece of the puzzle" for understanding complex industrial processes and truly articulating complex digital twins.
The ability of IIoT products and solutions to understand machines has advanced to the point where most data insights are achievable, although with this comes some challenges, such as cost constraints. All the same, it remains within the current technological capabilities of most companies. However, advanced machine intelligence requires articulating, simulating and predicting not just machine performance but complex interaction and movement in space, especially in terms of human machine interaction and complex manufacturing processes.
These processes definitionally require an understanding of humans, machines and the physical environment in which they work and operate. Further, these analytics enable more refined and deeper understanding of overall labor effectiveness (OLE), complex production optimization (via more advanced bottleneck analysis) and optimization across processes as well as machines.
In essence, with refined spatial analytics, true digital twins, analytical representations of machines, processes and people in their specific physical context can become a reality.
Chief Product and Marketing Officer at Pelion
We are only scratching the surface of what's possible with predictive insights. We will continue to see the practice of predictive maintenance being enriched by a broader range of sensor data and analyzed with more complex machine learning (ML) models to produce richer insights.
This next order of insight, derived from a combination of machine and human or machine and environment data, will prove to be a game-changer. For example, in the not-so-distant future, we will be able to learn from an individual's actions over time and derive predictive insight into what a machine operator is likely to do in the event of a particular machine failure or other incidents. By taking the time of day, potentially the weather, level of tiredness and other specific variables into account, and the accumulating learnings, we can identify very specific risk scenarios and implement corresponding mitigation actions.
These insights can improve safety by reducing repetitive stress injuries (RSI), which is the most common injury in many industrial settings. According to the Occupational Safety and Health Administration, RSI affects 1.8 million workers per year and costs up to $20 billion per year. So, these insights can reduce the occurrence of serious incidents and increase the efficiency of incident response with more targeted action, which is vital for in-field industrial settings like agriculture, mining and forestry. Such insights could result in personalized training and the development of adaptive machinery that allows the machine to tailor operations to its users' behavior.
VP, Solution Management for Digital Supply Chain at SAP
The reality is that as the cost of sensors comes down, we are designing smarter products and assets that have embedded intelligence and IIoT data points. This means that everything is "smart" and we are only limited by our imagination for use cases.
Let's take an item like a washing machine. The manufacturer may decide to move to an as-a-service model where they don't sell the washing machine but offer it as a service and generates a monthly bill based on how many loads of washing you do in a given month.
But to do this they have to be able to track several things. Firstly, you must track how many times the washing machine has been used, so that you can bill the customer. But you also want to make sure that the washing machine is working at an optimal level and isn't about to break down in mid-wash. This requires a whole new set of sensors on key components of the machine that can be used to alert the manufacturer that the machine needs maintenance or in the worst case, needs to be replaced. If it breaks down everybody loses.
You may also position your product as the "sustainable washing machine" that can provide the customer information on their mobile device about how much electricity it is using, what its carbon footprint is, how much water it is using per wash and much more. Again, this would mean leveraging IIoT sensors to capture and share this information.
Director, Industrial AI & Analytics, Internet of Things Group at Intel
There will be two data insights capabilities that will significantly change the IIoT game. The first isn't actually a new "insight" per se, but a closing of the gap between available insight and control logic. Bringing together perception and action, in real time, will provide a whole new level of efficiencies to operations. Imagine a robotic weld process with the ability to not just perceive that the weld it's currently applying is going to result in an out-of-specification defect, but also to respond and adjust its actions to avoid creating the defect altogether! This is the same concept enabling the fully autonomous cars navigating around test-friendly cities like Chandler, Arizona. Today, those cars carry miniature data centers in their trunks, which makes cost a barrier to leveraging that capability more broadly.
The second major shift we will see in IIoT data insights will be enabled by 5G. The ability to share information between nodes with only millisecond latency will allow the intelligent, autonomous devices at the edge to operate in concert, delivering flexibility never before seen in IIoT.
Chief Product Officer at Samsara
Every day it seems there's another news article about global supply chain shortages and the impact across sectors: from car manufacturing to housing to personal electronics. These shortages have shown us just how critical — and fragile — "just in time" supply chains are to the global economy, where shortages of $0.10 components can stall production of a $40,000 automobile. Technology has given us far more visibility into supply chains than ever before, but with supply chains becoming more complex, the data is still siloed and incomplete.
With IIoT, we can build toward a future with complete end-to-end visibility that can reduce delays, better match supply with demand and accelerate overall economic growth. Supply chains are diverse and aren't a one-size-fits-all technology solution, but many of the key ingredients are rapidly maturing — from low-cost sensors and RFID to smart cameras, 5G networks and satellite connectivity. And cloud-based systems with open APIs can facilitate the exchange of data between independent nodes.
If used to their potential, these advancements have the potential to improve the efficiency and resilience of global supply chains, and the economies that depend on them.
Chief Product and Platform Officer at AT&T Business
What will be game-changing isn't a specific data insight, but the ability to use more data, faster to drive business outcomes. But scale is only part of the story. Businesses are recognizing the incredible opportunity to materially improve their operations, or create entirely new revenue streams, using 5G and Internet of Things hyperconnectivity.
Think about one of the biggest challenges being tackled right now: automated vehicles and machines, specifically heavy industrial machinery. Safety to operators and surrounding workers, rightly so, are the highest priority, and it remains the largest hurdle.
The challenge is in two equally important parts.
First, the number of sensors and the raw amount data they capture that is necessary to safely deploy automated vehicle of the future will be staggering — optical, telemetry, weather, mechanical condition, etc. You need ultrafast, low-latency connectivity to move the information fast enough to be captured, processed offsite or at the edge and then execute actions in milliseconds.
Second, security plays an extremely important role. With the health and safety of workers at stake, businesses are hesitant to couple their operations to networks where they lack control, visibility and predictability. That's why the interest in private cellular networks is growing right alongside 5G. These private networks will be critical in supporting low-latency, device-rich environments with greater security and control.
We're on the cusp. 5G is just now taking hold across many industries. In the coming years, expect to see adoption rise significantly as well as real innovation in this space.
Business Development Manager and Solutions Architect – Health Care, Life Sciences and IoT at World Wide Technology
From increased energy efficiency to greater cybersecurity controls to improvements in marketing and customer service, the power and potential for data to transform business operations has steadily been driving the adoption of IIoT. But recent events like the power outages in Houston and the Colonial Pipeline cyberattacks illustrate the vulnerabilities that remain within our nation's critical infrastructure.
The next wave of game-changing data insights will focus on addressing these vulnerabilities by truly galvanizing real-time, collaborative decision making. This insight has long been a promise of IIoT, but it requires companies to adopt a view of IT infrastructure that looks less at technology as a tool or process and more as a cultural enabler for overcoming organizational and cultural silos. Imagine the long strings of manual email approvals and dashboard screenshots being transformed into an automated group text, sent via sensor at the edge of the power grid that one day stops a significant power outage in the middle of the night.
Smart networking, multicloud and edge technologies allow us to do much of this today, but it requires enterprises take a strategic and streamlined approach to technology that's rooted in a culture of trust. Organizations that realize the next game-changing insights will be those looking for IIoT to bridge cultural divisions, drive collaboration and empower decision making in real time, when it matters most to the stability of our critical infrastructure.
Kevin McAllister ( @k__mcallister) is a Research Editor at Protocol, leading the development of Braintrust. Prior to joining the team, he was a rankings data reporter at The Wall Street Journal, where he oversaw structured data projects for the Journal's strategy team.
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