Industrial IoT

Over the past several decades, we’ve seen consistent and strong growth in the number of Internet of Things (IoT) applications being used in the market – and this growth is poised to take off even more as IoT becomes increasingly crucial to business operations and as IoT technology continues to evolve. The rise of the internet, followed by WiFi, smartphones, the cloud – all of these have combined with other technologies to transform the way we work and live.

The Industrial Internet of Things (IIoT) is also moving into the mainstream, driving disruption and opening up significant opportunities in everything from manufacturing and mining to logistics, giving businesses the ability to capture real-time insights from historically low-tech assets such as factory machinery, raw materials, tractors, and even livestock.

In the highly competitive business world of today, manufacturers must respond swiftly to fluctuating market trends and fickle consumer demands. When combined with a broader digital transformation strategy, IIoT is a key factor in enabling the creation of new business models, improving operational efficiencies, and driving innovation within products and services, all of which contribute to not just keeping up with the competition but staying one step ahead.

IIoT offers a more comprehensive, interlinked, and holistic approach to manufacturing, connecting the physical and digital worlds in a way that enables superior collaboration and access across departments, vendors, products, partners, and people. Ultimately, IIoT – aka industry 4.0 – offers business owners the ability to have better control and gain deeper insights into nearly every aspect of their operation, which in turn boosts productivity, improves processes, and drives growth.

What is Industrial IoT?

While the IoT applications most of us know lean towards the consumer end of things, Industrial IoT (IIoT) is a subcategory of IoT that uses IoT technologies in an industrial context, bringing together key technologies such as machine learning, automated interrelated sensors and devices, and M2M computing in an orchestrated way within manufacturing operations. IIoT focuses on improving efficiencies in manufacturing, supply chain, and management areas, and unlocks access to unprecedented amounts of data.

By implementing IIoT, smart factories and other industrial or manufacturing environments are able to improve productivity and quality, as well as identify challenges and improve operational capabilities of connected assets. They do this by marrying physical production and operations with smart technology and big data to create a coherent connected ecosystem. Everything from inventory tracking and predictive maintenance to AR devices and collaborative robots are among the many industrial-grade applications.

Implementing IIoT enables shorter production cycles, optimized supply chain management, more timely filling of orders, and the ability to respond quickly to market shifts.

In other words, IIoT is a game-changer for manufacturing due to continued advances in IoT technology. Connected machinery and other assets are able to capture and communicate real-time data more accurately and consistently than previously possible. Additionally, data silos are opened up, allowing access to information at every level.

All of this enables machine operators, supervisors, engineers, and other interested parties to gain crucial insights and visibility into production, allowing them to combine data with other information to achieve continuous improvement and efficiencies on the factory floor. Additionally, management decisions are data-driven, and personnel at all levels are able to detect and identify problems, challenges, and inefficiencies much more quickly.

Additionally, in industrial environments, the continuity, safety, and security of production are crucial, so it is paramount that connectivity does not compromise any of these factors, and that it does not overwhelm users with too much raw data.

And while Covid has left the world facing unprecedented health, social, and economic crises, even before the global pandemic hit, manufacturing was facing volatile market conditions, such as demands for better and faster procedures, as well as decreasing margins, and fierce global competition. What this means is that companies need to take the necessary steps to stave off the competition while also dealing with a rapidly changing world.

From Industry 1.0 to Industry 4.0

The First Industrial Revolution saw animal-assisted manual labor shift to a more optimized form of manual labor that used water and steam power to mechanize production. The Second Industrial Revolution introduced electric power to create mass production, which increased efficiency, particularly through new concepts such as the assembly line. The Third Industrial Revolution, which kicked off in the 1950s, is when the transformation from analog and mechanical technologies to digital systems, communication, and rapid advances in computing power began. And it’s there that the groundwork for new ways of generation, processing, and sharing information was first laid.

Now a Fourth Industrial Revolution is building directly on the Third, taking digital technology to new heights through the use of connectivity, real-time data, and blurring the lines between the physical, digital, and biological worlds through the fusion of advanced technologies.

Some might argue that the Fourth Industrial Revolution is simply a prolongation of the Third, but the speed of current technology breakthroughs is unprecedented in history. When compared with previous industrial revolutions, the Fourth is not just evolving linearly, it is also disrupting nearly every industry in every country on earth – and it is doing this in a way that will mean the transformation of entire systems of production, management, and governance.

It’s important to remember, though, that while the Fourth Industrial Revolution is already underway in the first world, many parts of the world have yet to experience some aspects of the Third or even Second Industrial Revolutions. The good news is that some new technologies are leapfrogging older ones, and adoption of Fourth Revolution technologies offers the opportunity for latecomer economies to accelerate development by skipping some of the intermediate stages of industrialization. For example, developing countries that have not made significant investment in earlier technologies may be looking at a golden opportunity with the Fourth Industrial Revolution – instead of overhauling old factories, new manufacturing installations can be built from the ground up using smart factory principles.

How IIoT improves operational efficiencies

In order to improve operational efficiencies, organizations will rely on IIoT solutions deployed within smart factories. Process improvement will not be driven by human initiatives alone, nor by linear surges within specific fields. Instead, they will be driven by the integration of computer and machine throughout the entire operation. IIoT technologies will enable decentralized decision-making to an extent by allowing autonomous or semi-autonomous decisions, and systems will be able to process and analyze data in order to ‘spot’ trends and patterns that would not be so easily identified by humans.

There are a number of operational efficiencies to be gained through the implementation of IIoT, including:

Transforming legacy systems

The ability to transform and modernize legacy systems is one of the biggest benefits of IIoT.

In the US alone, the average age of manufacturing assets and equipment currently in operation is close to 20 years.

Manufacturing plants are often operating disconnected machines, which makes it challenging to implement monitoring and control systems across the organization.

Implementing IIoT, though, doesn’t necessarily mean replacing all your machinery with new, high-tech models. Integration platforms allow manufacturers to retrofit older equipment and machinery with connected sensors, a cost-effective way of optimizing existing assets. In fact, retrofitting existing equipment presents a host of benefits and offers an excellent return on investment.

According to a survey from an industrial maintenance industry leader, 76% of manufacturing facilities follow a preventive maintenance strategy, while 60% use a run-to-failure method. Retrofitting machines with smart sensors instead allows for condition monitoring, which in turn gives your business the ability to gain deep insight into the state of your machines and a greater understanding into what is happening on your production lines. Additionally, analysis of smart sensor data directly enables two powerful capabilities: continuous improvement loops and predictive maintenance.

Data analysis creates an overview of your plant by comparing old, new, and hypothetical production cycles. This overview allows you to improve plant efficiency by fine-tuning the system while it is in operation, creating a continuous improvement loop. Data also allows for an accurate picture of machine lifecycles by comparing the current status of machinery to overall lifecycle models, which allows you to predict when a piece of equipment or a particular component is likely to break down. Sensors can also send alerts when a problem is detected in real time. Having an accurate prediction of when a component in a machine is about to fail or being alerted that a breakdown is imminent means you can order parts as needed, or even have the machine notify your supplier that a particular part is needed. This provides fast and reliable delivery, streamlines maintenance, reduces downtime, and saves both money and manpower.

Smarter data analytics

Data analytics are a key component for any business that wants to remain competitive in a post-Brexit, Covid world. A new wave of advanced analytics software is removing data siloes and collecting real-time data from all connected systems in one place, uncovering fresh insights into how to increase efficiency, optimize business operations, and build more effective processes. In other words, being able to gather data from a wide range of touch points and get a holistic view of how your business is performing allows you to use data-driven insights for everything from strategy to decision making. In other words, smart data is quickly becoming one of the key components in keeping the manufacturing engine ticking, giving you a wide range of opportunities to implement better processes and gather actionable insights.

Smart analytics allow you to zoom in on every area in your operation – production, supply chains, inventory, etc. – giving you the information you need to understand what is working well and what is underperforming, which in turn allows you to implement alternative strategies.

Without smart data, much of your decision-making may be based on cleaning up a mess rather than anticipating it. Increased visibility and real-time data allow you to be proactive, rather than reactive, so rather than drawing up and analyzing reports after an event, you can view information in the moment. Smart data also illuminates every aspect of your business, from the shop floor to inventory to supply chains and all of that information helps you draw informed conclusions and take the most appropriate actions, both in real time and over the long term, often before any problem becomes a crisis.

While globalization offers myriad benefits, the interconnectivity of the modern, global business world means processes and supply chains have become increasingly complex. Streamlining and optimizing your business requires the kind of insight you can only get from data, which allows you to objectively consider each process and make evidence-based decisions.

With all of that said, some businesses, particularly small and medium enterprises (SME), are finding data to be something of a stumbling block, mainly because specialist expertise is often required to turn data into actionable insights. In an ideal world, you wouldn’t need any specially trained experts to handle your data.

The good news is that data tools are becoming much easier to use, and their interoperability with modern Enterprise Resource Planning (ERP) systems has made them more accessible than ever. Pre-built analytics solutions are available, while they offer great benefits, it’s important to ensure there is the flexibility required to customize, build your own views, reports, and dashboards.

The key is to use analytics to move gradually from information to optimization, all while increasing the value brought to your business. In practical terms, this means starting with basic operational reports that shine a light on business functions and tell you what is happening. The next step is descriptive and diagnostic analytics that will help reveal why a problem or challenge is happening.

Ultimately, though, you want to reach the stage of predictive analysis, where you can predict what is going to happen through machine learning and greater insights, and then act upon that information.

Energy Efficiency

Like in most other industries, manufacturing is eagerly seeking to reduce energy consumption, for both business and societal benefit reasons – and it’s clear that IIoT will play a role in helping organizations meet their reduction targets. IIoT gives businesses unprecedented visibility into energy consumption through the use of sensors, whether that is the individual usage of a machine or energy consumption in the factory overall. Sensors can track things like off-hours consumption, wasteful behaviors, and unusual usage patterns and this information can be then used to set predictive maintenance schedules or optimize production processes.

There are three areas that are low-hanging fruit when it comes to IIoT and energy efficiency: lighting, temperature controls, and equipment power-use levels. And while moving to a more energy efficient operation requires planning and capital, there are a number of IIoT solutions that can be deployed with minimal outlay.

Installing a network of wireless devices will cost about one-tenth of a comparable hardwired system and will have the added advantage of being able to monitor equipment in areas where wired systems can’t reach, due to distance or concerns about wiring installation safety. These devices can’t, as mentioned, be retrofitted on older machines and monitor them for energy usage, and when it’s time to invest in new IIoT-enabled equipment, the efficiency gains will be even greater.

The network of devices will allow specific real-time monitoring and analytics of equipment and building systems’ temperatures, peak usage times, and overall time in use, which helps companies identify areas for improvement.

Cobots

Spending on robotics by industrial companies will continue to rise over the next few years. In fact, investment in cobots – collaborative robots – is expected to hit more than $12 billion by 2025. These smaller, less costly, and highly adaptive collaborative robots are finally having their well-deserved time in the spotlight.

While traditional robots still dominate the market, they are bulky, expensive, and potentially dangerous. Cobots, on the other hand, are lightweight, designed with human safety in mind, and to work alongside human workers. Additionally, just like other IoT technologies, they are equipped with sensors, which allow them to be aware of location, people, and the context within which they are operating.

Traditional industrial robots will faithfully carry out the task they have been assigned, but they are not smart – they simply perform specific actions repeatedly with a high degree of accuracy but no variation. This can be bad news for workers who get in their way, which is why they are often kept caged and away from human workers. Cobots, though, are highly adaptable, which is why they are playing an increasingly important role at industrial companies of all sizes – although small and medium sized enterprises, which provide between 40 and 80 percent of all manufacturing jobs in OECD (Organization for Economic Cooperation and Development) countries, may be the big winners with cobots.

What makes cobots so attractive in an industrial setting is their use of sensors and processing power, which is getting cheaper every day and contributes to their ‘smarts’. Through this technology, cobots can sense the presence of a human colleague and adjust to avoid collisions. And thanks to machine learning, they are easy to train in new tasks. And because they are mobile they can be easily deployed to different areas across the operation.

Timesaving is another big advantage with cobots. It might take months to program a traditional robot to get it up and running. On the other hand, a cobot with all that smart technology can be up and running in as little as a week.

Connected cobots are ideally suited to a diverse range of manufacturing activities, including:

• Machine tending
• Pick-and-place
• Packaging and palletizing
• Process tasks (gluing, dispensing, welding)
• Finishing tasks (polishing, grinding, deburring)
• Quality inspection

Of course, the introduction of cobots does not mean the end of traditional robots, nor will traditional systems and collaborative systems compete. Instead, the two are complementary, although due to advancements in computer vision, motion sensing, AI, and other capabilities, cobots are ready for center stage.

Predictive maintenance

The ultimate goal of predictive maintenance is to ensure better productivity, fewer equipment breakdowns, an increase in uptimes, better use of maintenance staff, and cost savings.

Studies have shown that enterprises who have implemented predictive maintenance technologies have reduced their maintenance costs by roughly 25% while increasing productivity by around the same percentage.

Here’s how it happens: whether your equipment has been retrofitted with sensors or arrived on the shop floor with already embedded IIoT sensors, monitoring devices gather a wealth of data that can be used to both predict failures before action needs to be taken, as well as allow for a better estimation of the remaining lifetime of an asset.

IIoT devices allow you to monitor your equipment’s condition in real time. They track the performance of equipment during idle, normal, and peak operation times in order to detect and prevent problems. The real-time data can be analyzed against a number of factors, including historical performance and machine condition to develop predictive actions which will go a long way towards minimizing unplanned downtime.

That real time data also means maintenance strategies and predictive models can be developed to recognize warning signs or anomalies, while a machine-learning system can ‘learn’ to recognize and send alerts of new events or potential failures before they occur. Actions can be automated for minor fixes so that the machine can continue to operate until a repair can be made at the next scheduled downtime, allowing you to proactively schedule maintenance for when it will be most cost-effective. Unplanned maintenance often leads to substantial downtimes, which jeopardizes productivity. By monitoring and analyzing the health of your assets through monitoring devices you can uncover issues and repair or replace them before you experience failure, minimizing the production hours lost to maintenance.

Supply chain management

With the global pandemic disrupting supply chains, the question of what the supply chain of the future needs to look like is more important than ever. Basically, supply chains need to be resilient and sustainable and in order for that to happen businesses need to become more agile to sense, predict, and respond to disruptions.

IoT devices have already had a big impact on nearly every aspect of supply chains, from manufacturing to packaging, shipping, and delivery to an end point. Real-time track and trace of products and shipments is standard stuff these days, allowing businesses to know the exact location, status, and even the condition of goods at any given time.

IoT can reveal supply chain inefficiencies by eliminating blind spots from logistics practices.

But while the impact of IoT on the supply chain may have started with track and trace, it is now increasingly pervasive throughout the entire chain, with crucial information at your fingertips with just a few clicks of the mouse. One example is using sensors to detect product flaws during the production process, which leads to higher quality due to flawed products being discarded long before they ever make it to the end user. And once products reach their destination, smart devices let you see how your product’s services and features are being used, which allows for both product enhancement and development, as well as new revenue streams.

IIoT also brings transparency to manufacturing. Devices can monitor every stage of production, from sourcing of materials to packaging and shipping, which makes it possible to verify the authenticity of the product, as well as making it easier to identify where in the production run something went wrong.

Inventory management is another area where IIoT brings value. Warehoused goods can be tracked around the clock to ensure optimal storage conditions without anyone having to physically make the rounds to check on things. If there is a problem, such as temperature fluctuations or quality control, sensors can send automatic alerts, allowing for challenges to be addressed before they become real problems.

Digital twins are, put simply, virtual copies of real-world objects. In manufacturing, a digital twin is a virtual representation of an as-designed, as-built, as-maintained physical product. While the application and purpose of digital twins differs from one application to the next, data generated by sensors are most typically used to map and analyze how the object responds to the physical world, and modeling can be used to simulate, monitor, diagnose, predict and recalibrate everything from a jet engine to a physical plant to a beating heart.

The idea of digital twins is to offer a way for designers, manufacturers and operators of equipment to turn real-world data into accurate predictions and simulations of what might happen in various use cases. The systems being twinned can be as simple as a small pump or as complicated as a model of an entire factory’s setup. The digital twin is augmented by real-time process data and analytics based on accurate configurations of the physical object, production systems, or equipment. While virtual models are conceptual, the real-time and operational data are a digital representation of physical events.

Digital twins

Digital twins are, put simply, virtual copies of real-world objects. In manufacturing, a digital twin is a virtual representation of an as-designed, as-built, as-maintained physical product. While the application and purpose of digital twins differs from one application to the next, data generated by sensors are most typically used to map and analyze how the object responds to the physical world, and modeling can be used to simulate, monitor, diagnose, predict and recalibrate everything from a jet engine to a physical plant to a beating heart.

The idea of digital twins is to offer a way for designers, manufacturers and operators of equipment to turn real-world data into accurate predictions and simulations of what might happen in various use cases. The systems being twinned can be as simple as a small pump or as complicated as a model of an entire factory’s setup.

The digital twin is augmented by real-time process data and analytics based on accurate configurations of the physical object, production systems, or equipment.

While virtual models are conceptual, the real-time and operational data are a digital representation of physical events.

Digital twins also allow new business models to emerge, such as selling a service as a product. In this case, the digital twin allows the manufacturer to offer use of the product with services such as maintenance, optimization, updates, etc., all based on the capabilities of the digital twin. The manufacturer retains ownership of the product while providing the services, creating a new and possibly more profitable business model.

Digital twins also allow you to simulate the lifespan of the machine, check updates, and predict potential issues and challenges. You can also replicate equipment and/or goods and monitor them in a virtual environment before releasing the product onto the market, which improves product quality, enhances efficient supply and delivery chains, and opens up new business opportunities.

Challenges

The opportunities that IIoT offers are many, but any change to your setup is going to mean challenges. Data security, data management, interoperability of machines and systems – there are a number of things you need to take into account to ensure the success of your solution. But with PWC reporting that 91% of industrial companies are already investing in digitalization, it’s clear that embracing IIoT will be crucial in both the short and the long term, and not embracing digitalization can lead to falling behind the competition.

These are some of the challenges you may face and how to address them:

Legacy & outdated systems

Most advanced manufacturers are already dealing with multiple legacy data systems and their concern about adding even more systems into the mix – ones that often are not flexible enough in terms of integration – is valid. The thought of eating up even more resources and facing increased spending is daunting, as is the prospect of continually trying to coordinate or close the gap between systems.

But here’s the thing: you don’t need to replace your entire infrastructure in order to implement IIoT. In fact, there is often no reason to worry about integration. Many IIoT solutions offer the ability to make small adjustments, adding intelligence, sensors, and automation on top of existing infrastructure. It goes back to ‘crawl, walk, run’: add new technology layers to legacy systems that continue working as they always have done.

Determining ROI

What is the best way to measure success and what can be expected from the results? While the promise of IIoT is decreased costs through, among other things, better asset management, enhanced data-driven decision making, and productivity gains, digitalization and adoption of IIoT comes with a cost. It can be difficult for organizations to justify the cost when they are not entirely clear on the ROI, particularly if they don’t have previous experience implementing connected systems. In fact, many executives report unclear ROI as one of the top reasons for lack of action when it comes to IIoT.

Determining ROI when it comes to IIoT can be challenging, because rolling out an effective IIoT solution is a marathon, not a sprint. ROI will not happen immediately and if there are people in the organization pushing back on investing in IIoT it’s important to be clear on this point. You can also bring in outside experts who can give an informed perspective on how adding IIoT to your setup will help the bottom line in myriad ways, both short and long-term. This can be particularly helpful when it comes to understanding the benefit to energy costs, something that many leaders put high on their list of challenges they want to address.

Taking the first steps

IIoT is transforming manufacturing (and many other industries) in ways we have never seen before, which is as daunting as it is exciting. And it’s not always clear how to take the first steps.

• Do you want to replace existing systems completely or start layering IIoT solutions on them – retrofit them, in other words?
• What kind of budget are you going to need?
• Which members of your team should be involved, and do you need to supplement with outside consultants?
• Which areas do you want to focus on first? Material management and savings? Inventory management? Quality control and process optimization?

The best way to get started is to define the main business challenge you are facing and wish to address and go from there. You also want to understand how you will manage the project in terms of cost, expectations, success measures, training, etc. And remember, IIoT is not a one-size-fits-all solution. It is vital to assess the needs of your particular business before looking at solutions.

You can learn more about IoT strategy and implementation here.

Data security

IIoT generates a lot of data, and this data has to be processed extremely quickly in order to detect patterns in real-time. All of that data and related technologies demand a high level of security, so it’s imperative that organizations have a strategy in place that addresses streamlining data monitoring, management, and storage, while allowing for fast response time to incoming threats. This means having both a short-term storage solution (such as edge computing) and a longer-term solution (such as cloud or data centers).

So, how do you guarantee the security of your data? How do you prevent third parties from gaining unauthorized access? Industrial espionage, system and/or equipment manipulation, and even violation of data protection regulations (such as GDPR) can mean serious consequences, and any security gap can provide an entry point for cyber-attacks.

Risk mitigation

Organizations often don’t have the know-how needed to understand complex security principles. IT security specialists are not always familiar with IoT, and as a result there can be a lack of coherent strategy when it comes to risk management.

Addressing multiple vulnerabilities

Most industrial systems were not traditionally designed with cybersecurity in mind, although that is changing somewhat with IIoT-enabled machinery now available. The main culprit is that most systems were meant to be used only in closed networks and the shift to open networks has left companies exposed and vulnerable to threats they have never had to deal with previously.

Security updates

Applying security updates can be challenging since they usually need to be performed during downtimes. Additionally, traditional update methodologies, such as OTA (Over-the-Air) are not always feasible in industrial environments, although 5G and new IoT technologies such as LTE-M and NB-IoT will have a positive impact.

Not having the proper security protocols in place can have catastrophic results: loss of profits, data theft, major equipment damage, and even injuries. In some mission-critical scenarios, cyber-attacks can compromise power, gas, or water distribution.

You can learn more about security and IoT here.

Conclusion

Digital transformation doesn’t happen overnight: it’s a journey – and most companies that are planning their IIoT journey or that have already embarked understand that this is a marathon, rather than a sprint.

When implementing any IoT solution it’s important to have a strategy, get the right experts on the team, understand the importance of security, and know what you want to do with your data. And most importantly, know what challenges you are trying to address. Ultimately, the goal of IIoT is to ensure that all of your technologies work together seamlessly to give you a unified picture of your business’s well-being. As with everything around IoT, crawl, walk, run – and make sure you know where you’re going.

Is it time for you to implement IIoT?

How do you know when or if the time is right to invest in your IIoT solution? This checklist will help you evaluate your readiness.

• Are you in a competitive industry with many tech-savvy players?
• Do you want better visibility across your supply chain?
• Do you want to identify challenges and problems early on?
• Do you want to boost efficiency and profitability across your entire organization?
• Do you want informed, up-to-date, relevant views of production and business processes?
• Do you want to improve product quality or maintain current standards?
• Do you want a consistent and flexible view of production and business operations?
• Do you want to tailor production and operations to specific areas or users?
• Do you want richer and more timely analytics?
• Do you want to improve customer experience and satisfaction?
• Do you want a more integrated system that includes inventory, planning, financials, customer relationships, supply chain management, and manufacturing?

If you have answered yes to most or all of the above questions it is probably time to start speaking with the experts on how to best procure and implement your IIoT solution.

If you would like to learn more about how IoT can enable your business, please get in touch.