Top 5 industrial automation trends in 2019
By Kristina Urquhart
Industrial automation experts offer their predictions on what trends and technologies may impact your plant this year
By Kristina Urquhart
January 18, 2019 – If you’ve been reading Manufacturing AUTOMATION over the past few years, you’ve come across concepts such as digital twins, augmented reality, the Industrial Internet of Things (IIoT) and digitalization.
In 2019, these are no longer buzzwords – they’re important technologies that, if implemented correctly, will allow your plant to position itself as a leader for the next decade.
Here, in our annual trends roundup, we’ve consulted numerous automation experts to let us know what manufacturers should be honing in on in 2019. The start of a new year is always a good time to take stock about what is and isn’t working for your operation. And it’s a good time to reflect on the past, too – to look back on prior predictions and see how we did, check out our digital archive.
Muthuraman “Ram” Ramasamy is an industry expert at Frost & Sullivan with over 14 years of manufacturing operations management and strategy consulting experience. He is passionate about creating growth opportunities for clients and tracks horizontal markets such as digital industrial platforms, industrial IoT, analytics (artificial intelligence, machine learning), drones, services 2.0 and ecosystem partnerships. Ramasamy graduated with a degree in mechanical engineering from PSG College of Technology in India.
1. The intelligent edge will augment the cloud, but not displace it
Edge is the next big thing across industrial markets. As customers adopt digital to drive capital, resource and asset efficiencies, computing becomes more distributed and converged at source to build in resiliency and responsiveness. Frost & Sullivan predicts that 30 per cent of all industrial applications will shift to the edge technology and will have better computing horsepower. While this is one aspect of the edge, we also expect the emergence of intelligent field devices, which will have two primary characteristics: a) Edge devices will be intrinsically intelligent, as asset/function specific algorithms will be ported/swapped based on requirements. We will also see the convergence of field devices with artificial intelligence, which will help customers unlock previously untapped levels of efficiencies; and b) They’ll have native integration capabilities with the cloud and use communication protocols such as MQTT, AMQP, LoRaWAN, etc.
2. Digitalization and the emergence of affordable hardware
Industrial markets (process, discrete and hybrid) have historically digitized/sensorized their processes. However, the industry over the next decade will become more focused on closing the loop between data extraction and value creation. In order to achieve this, digitalization – taking action on data captured in an automated manner – will become paramount. At the same time, digitization will be enabled by very affordable hardware, driven by low-cost/self-serve software. A case in point – IIoT-enabled sensors for basic asset monitoring are available for as low as $250/sensor and self-serve algorithms at $1/day rates. Disruption by digital is inevitable, but customers appreciate the cost of digital being low. Clearly, the money is not in selling widgets, but in selling packaged solutions and service offerings – and it’s all about scale. Google speeds, at Amazon prices!
3. Connected products will drive customers to have negative latency operations
As connected products emerge (the industry is already seeing this in likes of steam traps, valves, compressors, turbines, etc.), the often-underemphasized aspect is the tie back to lifecycle services. Customers will be able to accurately predict asset failures before they happen and take actions to prevent the failure from occurring in the first place. This is what we call negative latency in operations. In essence, in closing the loop between connected products and lifecycle services, customers will have better predictability over operations and management (O&M) spend, uptime, production, and process flow.
4. End-of-asset ownership and emergence of partial asset subscribership
Industrial customers are becoming asset light, as they shed heavy asset ownership and transfer them over to OEMs. This trend started in jet engines and is progressively filtering to industrial class assets. As customers are often in the business of producing oil, chemicals, life science drugs, they are not in the business of maintaining/managing assets. This is prompting them to outsource non-core activities. While asset subscribership may not happen fully, we have begun to observe customers own the assets but transfer the maintenance aspects of those assets to OEMs.
5. The emergence of new business models
Technology convergences will lead to a creative destruction and expansion of traditional business models. Frost & Sullivan has identified nine unique business models that are practiced within industrial markets. There is a spectrum on these business models – at one end are models as common as SaaS agreements and at the other end the models are as unique as zero-cost and gain-share–based contractual agreements. Like in trend number four, customers are constantly pushing the envelope to minimize the cost of O&M on assets in order to improve bottom-line benefits. Today, digital pioneers are leveraging digital and new business models to continuously push down O&M costs to less than one per cent of their capital expenditures. Digital is not about technology adoption, but the ability to achieve interesting outcomes. Digital is all about sustained, new value creation across the enterprise.
Craig Resnick, vice-president at ARC Advisory Group, supports both automation supplier and financial clients. He has more than 30 years of hands-on experience in marketing, business development and strategic planning. Resnick graduated from Northeastern University with an MBA and BS in Electrical Engineering.
1. Augmented reality (AR) as a tool for assembly and maintenance
As baby boomers retire and are replaced by millennials, knowledge transfer is a major challenge. One solution is to deploy augmented reality (AR) technology, where the user sees the real world with information digitally overlaid. AR devices “sense” what the worker is looking at and display only the data needed for the operation at hand. This is accomplished with video-see-through technology using tablets or smartphones, or with optical-see-through technology, using smart glasses or wearable computers.
For example, in product assembly operations, the AR device prompts an operator with work instructions as augmented reality overlays physical and digital twin models, monitors progress, provides feedback and incorporates automated inspection for quality control. In another example, for maintenance and service operations, AR devices provide maintenance and service technicians with detailed workflows and procedures, such as asset diagnostics, work order information, recording capabilities, and a platform to contact remote experts for assistance. AR users can share their video feed with a mentor and the remote expert can overlay annotations or feed the user with manufacturing/maintenance details for better contextualization. Companies that employ AR achieve faster throughput, reduce rework and lower downtime.
2. Virtual reality (VR) as a tool for training and simulation
Workforce changes also create a major challenge for training that goes beyond YouTube videos, on-line or classrooms. One solution is to deploy virtual reality (VR) technology, where the user is fully immersed in a virtual world presented through a head-mounted device. Eye- and head-tracking sensors synchronize the virtual display with the user’s motion. VR is a powerful tool for creating immersive experiences and lends itself to applications, such as product and process design or training simulations. VR can provide a highly realistic virtual training environment with contextualized, real-time data overlaid. This enables operators, maintenance technicians, and plant engineers to explore a variety of plant and field scenarios in a safe, off-line environment and prepare for the real-world environment with reduced unknowns. VR enables near-limitless creation of training scenarios with zero risk of disrupted operations. The VR training method is gaining traction in the process industries, where competency requires familiarity with equipment and operational and maintenance procedures. It is often challenging for millennials to acquire this familiarity, particularly for sophisticated and/or rarely executed tasks. VR provides these workers with a repeatable, low-stress learning environment in which to master these skills.
3. Simultaneous deployment of Cloud and Edge solutions
Given the increasing convergence of information technology (IT) and operational technology (OT) and today’s emphasis on digital transformation, manufacturers must focus on deploying computing resources where it makes the most sense to do so on an application-to-application basis. A simultaneous approach that uses both Cloud and Edge solutions has emerged to enable industrial organizations to distribute computing resources more broadly.
In industrial environments, edge technology is used to get the right device data in near real-time to drive better decisions and even control industrial processes. Then that analyzed and processed data is sent to the cloud, enabling this critical business information to be leveraged by IT. Taking a simultaneous approach entails deploying edge devices with embedded analytics, edge servers, gateways and cloud infrastructure, which all must deliver industrial-grade availability and performance. Synchronicity will enable manufacturers to provide actionable information to support real-time business decisions, leveraging asset monitoring, analytics, machine learning and artificial intelligence (AI) to make sense of and act on complex data patterns. This will help manufacturers to better identify production inefficiencies, compare product quality against manufacturing conditions, and pinpoint potential safety, production or environmental issues.
4. IT/OT cybersecurity converging to address manufacturers’ greatest challenge
Many industrial organizations often consider cybersecurity their greatest threat. Reports on industrial cyber incidents show that attackers cross IT/OT boundaries and exploit gaps in security responsibilities. Organizational silos also complicate efforts to pool resources to help alleviate cybersecurity talent shortages plaguing both IT and OT groups. Industrial IoT devices and network edge equipment expand an already challenging attack surface. Integrating information from sensors within and outside control systems creates more confusion in IT/OT responsibilities. Adding more suppliers further complicates enforcement of security requirements for new assets. To help combat this, companies will converge their IT and OT cybersecurity efforts, which will help to clarify responsibilities and remove security gaps. It will also help ensure more consistent security levels across entire organizations. Combined, this will help to reduce the organization’s overall cyber risk.
5. More assets will deploy Digital Twin technology
More and more plant assets will come with digital twins that provide a virtual representation of the asset. These digital twins contain an archive of asset-related information, such as drawings, models, bills of material, engineering analysis, dimensional analysis, manufacturing data and operational history. This historical information can be used as a baseline when benchmarking asset performance. The digital twin will also have an archive of real-time data acquired via integrated sensors or external sources that can be used for condition monitoring, failure diagnostics, and both predictive and prescriptive analytics. Any knowledge gained will add value to the service life of the asset, such as improving efficiency, reducing downtime, anticipating failures and providing insight for continuous improvement. The digital twin can also be deployed to provide plant personnel with operational intelligence. By bringing together big data, statistical sciences, rules-based logic, AI and machine learning, manufacturers and other industrial organizations can use these digital twins to help discover origins of complex problems and determine options for resolving. As assets increase in complexity, demand for assets with digital twins will continue to grow rapidly.
Ruban Phukan is the co-founder and chief product and analytics officer at DataRPM (acquired by Progress), where he leads product and the data science for the flagship Cognitive Predictive Maintenance product, which solves the complex business problems of minimizing asset failures, unplanned downtimes and maximizing yield/efficiency/quality in IIoT. Phukan is a serial entrepreneur and technologist with rich and diverse experience in machine learning, natural language question answering, data science, product, technology and business. He holds multiple patents.
1. Artificial intelligence becomes king
Applications of AI and machine learning will start playing a leading role in the digital transformation of manufacturing. Data science will move from research labs to the production line and begin to have a tangible impact on how the day-to-day business is run. We will start to see technologies like AI/ML, AR/VR and blockchain converging to drive new use cases. As an example, the field service management industry will use these technologies to predict machine health (AI/ML), remotely inspect and perform maintenance (AR/VR) and identify the root cause of faulty parts by looking at the full lifecycle of raw materials (blockchain).
2. IIoT extends its reach
More manufacturers will move from condition-based maintenance to predictive maintenance by embracing the Industrial Internet of Things (IIoT). This shift will significantly minimize unplanned downtime, quality issues, maintenance costs and risks. IIoT will not just transform maintenance and field services, but also play an important role in the evolution of other aspects of the manufacturing lifecycle, such as inventory management, supply chain optimization and managing bottlenecks.
3. OEMs redefining the “as-a-service” model
More original equipment manufacturers (OEMs), especially the business-critical and expensive equipment manufacturers, will offer uptime-based services to their customers. This will require OEMs to provide new sales/service models such as “asset management as a service” or “machine as a service” for their products.
4. Now is the time for apps
As digital initiatives continue to shape the manufacturing industry, there will be a growing need for more industrial applications. As an example, hpaPaaS (high-productivity application platform as a service) will play a crucial role in helping manufacturers rapidly build apps with improved UI/UX for both internal use as well as for their customers.
5. Digital transformation becomes prevalent
The concept of using cutting-edge technology to drive profound operational and organizational change across the enterprise is not new. It’s called “digital transformation” and it has quickly become the business world’s loudest buzzword. Despite many industry experts criticizing the ambiguity of digital transformation, the strategy and technologies behind it still ring true. To be successful in today’s climate, modern manufacturers must embrace the ongoing shifts in technology and adapt in real-time to fight back the growing number of more agile and digitally empowered competitors. In the year ahead, this trend will only continue to play out more rapidly, which is why now is the time for manufacturers to start acting on their digital future instead of simply planning for it.
Olivier Cousseau is the industry vice-president at Schneider Electric Canada. With over 20 years of experience in international aftermarket sales, offer management and business development, Cousseau takes pride in being a corporate ambassador accomplished in global marketing. Some of Cousseau’s key roles as a leader include anticipating change, uncovering opportunities, driving growth and maintaining the standard of excellence Schneider Electric strives to achieve.
1. Artificial intelligence gaining more functionality
Artificial intelligence is becoming a ubiquitous tool in the world of automation, including in quality assurance, predictive analytics for maintenance, operations and design, resulting in improved profitability, optimized assets and a better-informed workforce. Over the last few years, we have seen AI take on many different forms and functions across many different industries, from monitoring to manufacturing.
In the coming year, AI will strengthen industries relying on automation with an expanding network of functionality. Production-line machinery and equipment represent massive investments for companies the world over, and unplanned downtime costs manufacturers approximately $50 billion yearly. Using AI in predictive maintenance, manufacturers can prevent this downtime by knowing ahead of time which part or system in the supply chain is close to failure, allowing for faster response and better-equipped technicians to tackle the problem.
Further, AI allows for a process known as generative design, which supports creation based on goals. Designers and engineers input desired outcomes for a system or mechanism, and from there software runs through all the possible solutions, generating alternative designs, learning to test and gather information on what worked and didn’t work in each iteration. This eliminates expensive real-world testing and provides higher performance products and tools from initial implementation.
2. Digital twins for maintenance and modelling
Digital twins augment AI, machine learning and software analytics to create functional digital simulation models that reproduce physical assets and systems in a virtual space within which designers can model the behaviour and various processes the item will undergo.
In the world of automation, the use of a digital twin system will provide and perfect two high-value solutions in the coming year. First, it allows technicians a clearer line of sight towards predictive maintenance, augmenting one of the functions of the aforementioned AI systems. By modelling the lifespan of a piece of equipment or system with a digital twin, stress points can be identified and addressed before they are encountered in the physical process.
Second, manufacturers have the ability to mirror and model entire supply chains with a digital twin. This will provide an opportunity to increase efficiency and output from production lines and other automated systems by identifying and addressing potential bottlenecks and stress points in the system before they impact the real-world supply chain.
3. Blockchain for safeguarding product traceability
Since blockchain technology made its first widespread appearance with the cryptocurrency boom in 2008, its usefulness as a highly secure record-keeping system has become more apparent. Now, blockchain’s uses are expanding beyond tracking currency in high-value transactions, moving in to multiple industrial sectors as a means of enhanced security and automation for supply chains.
Blockchain is resistant to data modification, allowing for a transparent, consistent record of transactions or uses of an item. In the industrial world, this feature provides an opportunity. With the use of blockchain, manufacturers will have the ability to easily and securely track and trace an item or system from the moment it enters production, to sale, to its on-site use and eventual end-of-life processes.
The digital blockchain system further maintains the integrity of the constantly growing number of transactions by self-auditing and notifying all involved parties of data changes. This acts as an added layer of security for any information shared via the chain, and provides an efficient solution to track the use of systems and machinery.
4. Connectivity everywhere with 5G and mobile satellite systems
5G network technology is spreading and advancing every day, bringing the next generation of mobile internet connectivity to the world and delivering faster, more reliable connection with download speeds averaging 1Gbps or more. In the coming year, we can expect to see 5G networks on factory floors and in homes across Canada providing hyper-fast, low-latency connectivity for improved communication and performance from any system using the network.
IoT connectivity is also reaching remote areas not covered through cellphone networks thanks to satellite infrastructure – constellations of mini satellites orbiting the earth picking up signals from tiny ground-based transmitters, which then relay data to antennas on the ground. From there, the data are uploaded to a cloud-based analytics platform, allowing users to gain better insights and make better decisions based on the data.
With 5G and satellite connectivity speeds, AI will learn faster and augmented reality tools will provide better and more accurate views of real-world systems. Meanwhile, laying a highly responsive and lightning-quick groundwork for numerous powerful systems will allow for companies to embrace an ongoing digital transformation and jump in to the fourth industrial revolution confidently.
5. The further advancement of the IIoT
The above systems feed into Industrial Internet of Things (IIoT) systems, an overarching technology that will become more prevalent through 2019. IoT is already implemented in many homes – for example, a smart thermostat. In manufacturing, IIoT can come into play among complex systems monitoring and maintaining large-scale production lines through machine-to-machine communication to improve safety, production time and efficiency.
With IoT connectivity almost everywhere, improving the connection between integrated IIoT components will allow for faster communication and response to change. Meanwhile, advancements in AI and machine learning will allow IIoT systems to more effectively monitor, predict and react to events on production lines and in factory environments, which will improve safety on the floor and plant ROI.
This article was originally published in the January/February 2019 issue of Manufacturing AUTOMATION. Read the digital edition here.