Industrial AI, a subset of the broader field of artificial intelligence (AI), refers to the application of AI technologies (including Generative AI) in industrial settings to augment the workforce in pursuit of growth, profitability, more sustainable products and production processes, enhanced customer service, and business outcomes. Industrial AI leverages machine learning, deep learning, neural networks, and other approaches. Some of these techniques have been used for decades to build AI systems using data from various sources within an industrial environment, such as sensors, machinery, industrial engineers, and frontline workers.

It's still all about data, and how to leverage public and enterprise data securely to protect and preserve IP, while garnering new insights, reducing waste, and achieving new levels of productivity and business process optimization by leveraging industrial grade AI solutions. Breakthroughs in Generative AI, and the massive investments being made to drive its mainstream adoption are changing the whole software landscape, creating new alliances and partnerships, and how we interact with software in our daily lives, and on the manufacturing shop floor.

New software design patterns accommodating the desire to leverage Generative AI, are driving new requirements for the Industrial Data Fabric at the core of industrial organizations digital transformation.

This program will:

• Cut through the confusion surrounding AI in the industrial space
• Highlight new AI breakthroughs such as Large Language Models for Generative AI
• Provide a useful conceptual framework for governing and prioritizing Industrial AI use cases
• Provide context for how AI relates to Industrial Metaverse, Industrial Data Fabric, etc.
• Present case studies and examples from early adopters of Industrial AI
• Provide industrial companies the information they need to begin their own Industrial AI journey

Leading industrial companies are actively engaged in transformation programs that will reshape their production operations to be more integrated, responsive, and optimized to meet business and customer needs.  Realizing these innovations requires an understanding of the emerging 21st century operations ecosystem and how it interacts with business, engineering, supply chain, and other organizations.  Today's connected environments surface machine data that was heretofore unavailable, and so enable new business models.   New systems may monitor the assets, and new actors may interact with the assets in new ways.  Industrial plant operations, typically siloed and fairly isolated today, will be reshaped as the core of a 21st century industrial production operations ecosystem.  A similar evolution is taking place for field operations - such as mining or agriculture.  In other cases, such as automotive, where the assets operate in public spaces and both the assets and ecosystems are evolving quickly, other factors come into play. 

In 21st century production operations, work is accomplished with a combination of internal and external actors (such as asset manufacturers, 3rd party machine monitoring services, spare parts suppliers, etc.), putting new demands on data requirements and cybersecurity strategies.  New types of production systems (such as additive manufactuiring) and new types of data are being generated (from wearables, vision systems, machine health sensors, etc.).  And digital twins and machine learning systems can work at various levels to optimize the overall system in synchrony with the needs of customers and business operations. 

An important enabler for this  innovation in industrial companies is the convergence of IT (Information Technology at the enterprise level), OT (Operations Technology, the information and automation technologies employed in the plant), and ET (Engineering Technology, the newer technologies that create virtual models).  IT/OT/ET convergence is among the drivers of the digital transformation that leading companies are embarking upon.  A wide range of technologies, such as Ethernet/Wi-Fi, virtualization, cloud, SaaS, analytics, Big Data, mobile, social, modeling, augmented reality, machine learning, remote monitoring, and digital twin are now being employed in industrial operations to improve operating performance, create a virtual environment, or introduce the Industrial Internet of Things.  But the big payoff comes when companies begin to operate in new, collaborative ways across the whole of the enterprise.

This program features speakers talking about moving to 21st century operating models, and how they utilized the IT/OT/ET enablers of digitization and innovation to improve performance in their production operations and throughout their organizations.

Energy transition and sustainability are now being woven into the core business strategies of industrial companies, who now have the mandate and opportunity to tackle environmental and social challenges. What are the market drivers behind the dramatic uptick in board-level attention - and the ensuing formalization and execution - of environmental, social, and governance (ESG) strategies? How are companies tackling decarbonization, new tracking requirements, and the many, complex threads associated with the energy transition and improved environmental stewardship? How are these efforts being balanced with demands for financial performance, reliability, and ongoing digital transformation?

Companies who are more advanced in their digital journeys are already integrating sustainability and energy transition initiatives, together with competitive excellence, resilience and agility, and workforce goals, into a comprehensive strategy driving towards becoming a focused, integrated, digital organization.  Powered by transformative technologies, innovative processes, and data, they are creating smart integrated systems that can develop new insights and, in some cases, even automate decision making, while optimizing to corporate performance goals and improving competitive excellence. These new systems can also support improved cybersecurity, helping industrial companies protect against a significant and growing risk.

Asset Performance Management incorporates Industrial IoT (IIoT) and new analytics solutions like machine learning.  It uses information from production management, control systems, and asset management applications to provide new opportunities to optimize asset availability and operational performance.  This optimization goes beyond functional silos and occurs between silos where significant inefficiency, waste, and sometimes dysfunction often reside. 

Balancing the objectives of operations for on-time delivery, volume, and quality with those of maintenance for asset availability, longevity, and reliability requires sharing information and harmonizing these objectives with the goals of the enterprise.  New information technologies provide functionality to intensify cross-functional collaboration, business process improvements, and higher levels of performance to achieve asset performance management excellence. 

The ability to interact with equipment – like a variety of devices on a single site, or widely dispersed machines – presents new opportunities for industrial companies, utilities, and equipment suppliers.  These networks provide a new “connected ecosystem” of equipment manufacturers, systems integrators, and end users.  Some manufacturers and utilities have begun adopting these technologies.

Those attending this Asset Performance Management program will learn what was successful, and gain insights into what is next.  The objective is to make fact-based decisions using reliable information that aligns with the organization’s objectives.  An APM strategy helps ensure the best possible returns on capital investments over the lifecycle of the asset.  If you are involved in operations, maintenance, or industrial IT, you will want to attend these program sessions.

We have entered a period of intense innovation in industrial automation.  In the areas of high value-added manufacturing there is growing global competition.  Countries with developed economies want to maintain and grow their existing competitive advantages and support their exporting industries.  Countries with developing economies want to improve their competitiveness as well.

This competition is, in part, taking the form of national initiatives to improve manufacturing competitiveness.  These initiatives are found in Germany, the US, Japan, Korea, and China.  In the US the Industrial Internet Consortium has formed. Platform Industrie 4.0 is the German initiative; the “4.0” referring to a 4th industrial revolution (following steam, mass production, and IT).  This can be thought of as a set of new technologies that are fusing the physical, digital, and biological worlds, and impacting all disciplines, economies and industries.

Some of the topics that will be covered in the sessions for this program include:

Devices that will use the future 2‐wire Ethernet standard now in development by the IEEE 802.3cg committee. This 2019 standard will enable high speed communication to process field devices over distances as long as 1 kM.

The Open Process Automation Forum:  The Latest Update
The Open Group’s Open Process Automation Forum is noteworthy for several reasons.  First, because of its genesis within ExxonMobil, a leading international oil company with a long reputation for operational excellence.  Second, because the products of this program will be technologically quite different from the process automation systems used today.  Third, because the value chain envisioned for this program is also quite different from the way the process automation market works today.  The latest developments in this initiative will be presented and discussed.

Industrial Automation and the Industrie 4.0/Industrial Internet Initiatives
What will be the impact on manufacturing and automation of these major national initiatives?  How will the process industries, discrete manufacturers, and automation suppliers adapt and change their products in response to these programs?  What are thought leaders in Europe and North America doing now and planning for the next few years? End users will present the current programs for rapid integration of skid-mounted process systems, based on the Module Type Package (MTP) initiative of the NAMUR group in Europe. Also, European end users will discuss their pilot work with the future 2‐wire “single pair Ethernet” standard now in development by the IEEE 802.3cg committee. This 2019 standard will enable high speed communication to process field devices over distances as long as 1 kM.

New business processes are driving higher performance and lower costs across the industrial landscape. Companies are leveraging new developments in cloud computing, AI, IoT, edge solutions, and digital twins to identify bottlenecks and opportunities for improvement.  Technological developments are likewise driving improvements in the safety and reliability of critical operations.  Benefits from these developments can be large, but they can be quickly erased unless companies also address the increased risks of serious, costly cyber incidents. 

Industrial cybersecurity has never been more important. New business processes and technologies are rapidly expanding the pathways for attackers to steal information and disrupt operations.  At the same time, industrial security teams are struggling to deal with ransomware and sophisticated nation-state attacks.  This situation is jeopardizing worker safety, delivery of critical services, and the operational continuity of plants and facilities that fuel profitability.

Conventional industrial IT and OT security programs weren’t designed to handle all of these new challenges.  Digitalized organizations need security strategies that monitor, control, and coordinate security across every corporate device, application, system, and network regardless of where it is located.  Security programs also need to ensure consistent management across IT, ET, OT, Cloud, Mobile and IoT environments.  Likewise, security needs to span every business process and worker regardless of where, when, and how they are performed.  Developing a comprehensive cybersecurity strategy that addresses the current and emerging risks of digitalization has never been more urgent. 

The Industrial Cybersecurity program at the 2023 ARC Industry Form Americas addresses these issues with informative workshops, panel discussions, and case study presentations.  It is the ideal venue to learn what others are doing, how they are doing it, and the benefits they are achieving.  Attendees will also have opportunities to discuss their unique challenges with peers, researchers, educators, service providers, and solution providers.

Autonomy and intelligence embedded pervasively in automation equipment is one of the key attributes to realization of connected smart machinery.  Connected smart machinery is important in virtually every application imaginable, but is even more valuable in cases where there is limited communications.  Machinery that analyzes and compresses large data sets are essential to ensuring that the data traffic on the Internet does not overwhelm the system or data can be analyzed local to the device.  The connected smart machine will require not just more sensors, but also more intelligent sensors.  Sensors must perform more sophisticated signal processing “at the edge” to provide accurate signals that filter out the noise before it gets to the automation system. 

In this program, machine builders employing intelligent sensors into the machinery to perform complex condition monitoring algorithms into automation systems will be highlighted.  Key topics for discussion include:

• Creating new maintenance service revenue streams for machine builders
• Development of equipment protection algorithms that increase resilience of machinery to stay operational for much longer periods of time.
• Adaptive control algorithms allowing systems to operate over a wider range.
• Predictive condition monitoring systems that use real-time control algorithms to provide a new level of maintenance information.
• Exploring the potential of the Industrial Internet of Things (IIoT) to combine the benefits of multivariate analysis, predictive modeling, and inferential information to preempt abnormal situations.

The digital transformation of industry, infrastructure, and cities is under way, with new business processes, services, and models being pursued. This energy and investment is a rational response by organizations to digital economies that present new and very real opportunities. When combined appropriately, data and technology can provide competitive advantage, that – in some cases – enables organizations to leapfrog their peers.

Yet, when planning or executing this transformation, what is often missing is a focus on the human element of digital transformation.  Where do people fit in? The changes that digital transformation will have on the workforce are likely to be the most far-reaching and sustained effects.  Not only will digital transformation change the number of people needed to do work, it will rewrite how that work gets. Those going through digital transformation quickly realize that managing the human element can be the most difficult aspect.  However, as they work through their journey, people begin to better understand the human-centric benefits of digital transformation.  Enabled by technology, the workforce will become more empowered to identify challenges, adapt to circumstances, and find new ways to solve problems.

This program will:

• Discuss digital transformation through the lens of people, examining the changes in the workforce as organizations become increasingly data-enabled and service-based.
• Identify the common human-centered challenges to successful digital transformation.
• Provide a framework for creating an organizational strategy and culture for digital change  
• Present case studies of how companies organized people and work cultures around digital-first thinking
• Outline how to evolve customer relationships and engagement for a digital world

Industrial Internet platforms are emerging as pivotal, value-added components of the Industrial Internet of Things (IIoT) architecture.  These platforms add incremental value by functioning not only as the glue linking connected industrial devices to higher level performance-enhancing applications, but also as the execution environment for the applications themselves.

Industrial Internet platforms play an integral role in analytics, big data, remote asset monitoring, performance management, decision support, universal visualization, and the value chains for connected products and products-as-a-service.  The ability of these applications to access, analyze, and process industrial data is central to the IIoT value proposition. 

Industrial Internet platforms architecturally reside between intelligent devices and higher levels of the enterprise architecture.  Device connectivity platforms monitor, collect, process, and transmit data from a variety of intelligent sensors, devices, machines, products, and other assets to higher levels of the architecture, while analytics, big data, machine learning, and numerous other applications that deliver incremental process improvements typically reside in enterprise-level platforms.

The IoT network edge has emerged as a primary vehicle for delivering incremental business value via internet-enabled business strategies such as the Industrial Internet of Things (IIoT), Industrie 4.0 (I4.0), and Smart Cities and Infrastructure. 

Escalating demands to feed information from data-rich intelligent edge devices to the cloud is one of the most pressing issues facing OT and IT professionals in the era of internet-enabled business strategies.  Standard options include support of common API or protocols, but cloud-based agents themselves are migrating into network edge devices.  These agents are increasingly viewed as not only vehicles for closer edge-to-cloud integration, but also as platforms for edge computing applications that execute locally, offloading processing from the cloud and providing enhanced security and closer to real time performance. 

This program will highlight current and prospective demands on both network edge infrastructure, such as gateways, routers, and switches, as well as smart end devices that function as edge nodes in the IoT architecture.  In addition, this program will look at the central role of Industrial Internet platforms in the emerging Industrial Internet of Things and how to use them to achieve incremental business benefit.

Supply Chain and the Attainment of ESG Goals
Companies are under increased pressure to reduce carbon emissions, ensure that they are not trading with bad actors across their extended supply chain, and are treating their employees and subcontractors fairly. Many large companies have announced the goal of becoming Net Zero by 2050. This will involve not just controlling internal carbon emissions, but Scope 3 emissions as well.  Clearly, reaching ESG goals will require not just participation from the supply chain team, but creativity and commitment. How can the supply chain team participate in reaching these goals across the factory floor, warehouses transportation, and importing/exporting? What tools are needed? How do processes, like integrated business planning, need to change? How do procurement, planning, manufacturing, and distribution need to collaborate?

Artificial Intelligence in the Supply Chain
Many large enterprises use one form or another of a supply chain application to help manage their supply chains. Supply chain vendors have been touting their investments in artificial intelligence for the last several years. What artificial intelligence does well is make predictions. But what does the near future hold? Both predictive and generative artificial intelligence play a role in supplying intelligence to the supply chain. Predictive AI is focused on analyzing data and making predictions about future events, and generative AI is focused on creating new content based on existing data. As predictive AI has become more powerful, companies can make hundreds of what-if scenarios in minutes, across transportation, warehouse, and supply chain planning functions. Generative AI is more intuitive and easier to harness. This will enable workers with less experience to operate at higher levels, helping to bridge the talent gap. There is more promise in broader applications such as code generation, knowledge management, and user interfaces.