The automation marketplace has changed vastly from what it was 30 years ago, but many trends persist and intensified during this time. The first is the increasing emphasis on software and services. Rapid advances in processing capability launched the decade of industrial, artificial intelligence (AI). Cloud computing, edge data processing, virtualization, containerization, Open Process Automation (OPA) and time-sensitive networks (TSN) have transformed the world of control systems. Suppliers continue to beef up their software capabilities, as we saw in 2022 with Emerson buying a majority stake in AspenTech, and Schneider Electric’s acquisition of industrial software giant Aveva that closed in January 2023. ARC Advisory Group keeps reporting this in just about every Control/ARC Automation Top 50 article in recent memory, but software is the driving force behind today’s market, and AI is now the tip of the spear. Suppliers know this, and will continue expanding and honing their software expertise and industrial AI solutions.
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Approaching the Control/ARC Automation Top 50 global and North American automation suppliers is a unique exercise because we must keep one eye in the rearview mirror and the other on the road ahead. According to our standard methodology, ARC has a strict definition and scope of the automation marketplace that you can read about here. We examine supplier performance based on calendar-year revenues. ARC uses publicly available data in addition to information from our own database that we’ve developed during more than 30 years of following the automation marketplace, the overall operations technology (OT) space, and the key end-user trends that drive them.
Hardware and services
This doesn’t mean hardware isn’t important. The influx of commercial, IT-based solutions began in the automation marketplace more than 30 years ago, too, with the adoption of commercial, off-the-shelf (COTS) operating systems and computing components. The industrial sector debated the benefits of Ethernet for years, until the dam finally burst, and just about every control network available today is based on Ethernet technologies with a few modifications. Today, this trend is accelerating faster than ever, with demand for industrial, edge-based systems and cloud-based automation architectures soaring. Computing requirements for industry will increase exponentially over the next decade, with AI again leading the way. Ethernet Advanced Physical Layer (APL) networking was also introduced in 2023, and will bring the benefits of Ethernet down to the device level and hazardous locations.
Aveva and AspenTech aren’t the only recent software acquisitions, though they are substantial. In 2023 alone, ABB acquired a majority stake in Swedish AI startup Viking Analytics; Emerson acquired National Instruments (NI); PTC announced its acquisition of pure-systems; Belden acquired edge-software provider CloudRail; Rockwell Automation acquired OT cybersecurity provider Verve Industrial; and the list goes on. ARC expects more acquisitions as suppliers continue to strength their AI and cybersecurity capabilities.
However, it’s the services business for all of these automation suppliers that continues to grow at the fastest rate. Almost universally, supplier service revenues are increasing faster than either their hardware or software businesses. Suppliers continue to differentiate themselves based on their deep understanding and engineering knowledge of specific industrial and customer requirements, and are striving to create lifelong partnerships with their clients where they provide a continuous stream of value-added lifecycle services. Many of these services are increasingly tailored to help users get more value out of their sustainability and digital transformation programs.
Supplier performance in 2023
On a year-over-year (YoY) basis, consolidated supplier revenues grew by 8% worldwide and by more than 3% in North America during 2022. This continues the post-COVID-19 growth trend that we’ve seen since 2021, when the market sharply rebounded from the pandemic. In typical fashion, the discrete manufacturing markets were the first to contract and the first to bounce back, while the process industries lagged slightly behind.
In typical fashion, the overall market leaders haven’t changed significantly, though some smaller suppliers gained ground during 2023. It’s worth noting that changes in relative position on this list don’t necessarily indicate the strength or competence of any supplier, and could be due to other factors, the most obvious being merger and acquisition (M&A) activity. This list will give you an idea of who the market leaders are, but any evaluation of suppliers should obviously be more in-depth than their positions in the Control/ARC Top 50 rankings. With the rapid changes taking place in the automation market, it’s also a good time to reevaluate supplier selection criteria.
Big acquisitions fortify positions
Emerson’s acquisition of NI cemented its number-one position in North America with total revenues close to $8 billion. NI’s consolidation into Emerson also removed it from the list, affecting the relative positions of other suppliers.
The same happened with Schneider Electric, which now wholly owns Aveva. It likewise cemented its number-four position worldwide, and moved up the list in North America to the number four position, placing it very close to ABB. In North America, Hexagon gained some ground to reach the eighth slot by acquiring several software suppliers concentrated in the digitalization, optimization and AI spaces.
Automation market hitting a wall?
After robust growth following the COVID-19 pandemic, the global and North American automation markets remain strong, but they’re also facing some serious headwinds and signs of softening in several sectors. We expect an overall slowdown in growth, but not a market contraction. Uncertainty surrounding the U.S. election and mounting geopolitical conflicts and tensions also contribute to future market uncertainty.
For example, ARC’s Automation Index declined in 2Q24, which was the first time since the post-pandemic recovery. The automation markets had experienced growth since 3Q20 (past 12 consecutive quarters). Demand for automation products continued to expand in the U.S. market, but at a slower pace. The YoY growth cycle started showing a decline since late 3Q23, and shows further decline through 1Q24. Many of the leading automation suppliers in North America have also increased prices to keep pace with rising production and distribution costs.
Many of the key automation suppliers in North America are reporting strong backlogs, which they plan to work through in 2024. ARC is seeing a shift in growth from converting backlogs to new orders as distributors and machine builders reduce excess inventory. Supply-chain issues have also eased since our last report, and because of this, we’re witnessing improved lead times.
New energy and decarbonization activities also remain strong, as we’ll discuss later in this report. Aside from obvious investments in renewable energy, sustainability is also driving investments in automation for hydrogen infrastructure and its frequent counterpart, carbon capture and storage. Investment in plastics and other recycling technologies, such as wind turbine blade recycling, are increasing. Electrification, utilities, data centers and infrastructure (rail, port and marine) sectors are all experiencing strong growth.
However, automotive growth remains weak as automotive manufacturers focus on near-term profitability amid a slowdown in demand for electric vehicles. In addition, semiconductor growth remains weak as manufacturers face several different challenges, including excess memory capacity and workforce shortages. Despite these signs of softening, our outlook remains positive for continued growth in both North America and worldwide through the end of 2024.
How AI is transforming industry
AI has revolutionized the way we approach problem-solving and decision-making in various industries. Often described as prediction machines or inference engines, AI systems are designed to analyze data, recognize patterns, and make predictions about future events or outcomes. This predictive capability is central to AI’s value proposition, enabling businesses to anticipate trends, optimize processes, and make informed decisions. For instance, in the industrial sector, the most widely deployed AI use case involves predicting equipment failures before they occur, allowing for proactive maintenance and reducing downtime.
The surge in investment in AI infrastructure is a testament to this technology’s transformative potential. Investment banks like Goldman Sachs have highlighted the rapid growth in AI investment since the breakthrough of Generative AI (Gen AI) with OpenAI’s ChatGPT 3.5, with projections indicating a global spend nearing $200 billion by 2025.
Over the longer-term, AI-related investment could peak as high as 2.5% to 4% of gross domestic product (GDP) in the U.S. and 1.5 to 2.5% of GDP among other major AI leaders, if Goldman Sachs Research’s AI growth projections are fully realized. This investment is driving down the cost of AI solutions, making them more accessible, and enabling broader adoption across industrial sectors. They’re hungry to apply the technology to address growing skills gaps, control spiraling energy costs, and meet demand for more sustainable products and services with more resilient supply chains.
AI and the data explosion
The industrial sector’s challenges aren’t just about volume, but also involve the complexity and fragmentation of data generated by sensors, machines and smart factories. This data is often disconnected and scattered across various applications, making it difficult to harness for insights and decision-making. Many end-users find it necessary to build a better framework for managing data when implementing new technologies like AI, and this is driving growth in industrial IoT platforms like Honeywell’s Forge, Siemens’ MindSphere, ABB’s Ability, Rockwell Automation’s FactoryTalk InnovationSuite, and Schneider Electric’s EcoStruxure. Other suppliers like Yokogawa are implementing their own industrial AI platforms that allow connections between operational assets and the enterprise.
Each industrial AI use case requires specific datasets, and may necessitate different tools and techniques. For instance, predictive maintenance relies on sensor data to forecast equipment failures, while generative design uses parameters like materials and cost constraints to create product designs.
Sustainability drives digital transformation
The real question is how all this technology is applied and what its business case is. Energy transition and sustainability are being woven into the core business strategies of most of the world’s largest industrial companies, which now have a mandate and opportunity to not only to tackle environmental and social challenges, but also take advantage of the commercial opportunities and competitive advantages that sustainability offers.
For several years, sustainability issues were approached as something to be dealt with cautiously in reaction to increasingly stringent regulations. However, massive commercial opportunities have emerged beyond just regulatory compliance. Many companies are discovering that ramping up digital transformation initiatives can help reach their sustainability goals. Sustainability can provide business value across several dimensions, including increased energy efficiency, new processes for carbon capture and storage, and hydrogen production and transportation.
Spending on sustainability increases
For instance, Bank of America (BoA) reports that, prior to COVID-19, the U.S. spent about 0.4% of GDP on sustainability-related industrial policy, while Germany spent 0.5% or more, and the EU’s other member nations spent more traditionally. Today, the U.S. spends 0.8% of GDP on sustainability-related industrial policy thanks to passing its $1.8 trillion stimulus package. The most recent stimulus is structured quite differently from what the U.S. did before. It doesn’t peak until 2026, and will run until 2031. The stimulus was designed so that many projects are in “red states,” and $2.4 trillion is needed to meet net-zero emission targets laid out for the U.S. over the next five years, according to BoA’s research.
Most companies are enhancing energy efficiency and switching to clean-energy sources. Carbon capture, utilization and storage (CCUS) facilities have also been growing significantly. Under the Inflation Reduction Act, Section 45 Q tax credits for capturing carbon have increased to $85 per ton, and most companies can do this at a much lower cost. This makes CCUS extremely profitable, which is why we see so much activity in it, including ExxonMobil’s acquisition of Denbury, Occidental’s acquisition of Carbon Engineering and others.
Edge computing push intensifies
Industrial, edge-computing solutions can overcome limitations such as latency, reliability and security by extending the cloud into OT environments, while also reducing cloud-input data volumes and egress costs. Data at the edge of networks can be aggregated and contextualized to deliver summary information and insights directly to the cloud, ultimately reducing cloud-based, data-storage costs. Edge functionality has likewise expanded digital transformation’s reach into remote, hazardous and other distributed environments, simultaneously addressing potential lacks of reliable network connections and skilled IT personnel in the field.
Customers and suppliers alike are aligning their edge strategies to take advantage of the cloud’s centralized management, security and scaling capabilities, while preserving operational integrity by marrying these with onsite, edge capabilities. The descent of cloud-native, containerized architectures to the edge is simplifying deployment and management of edge applications, a key edge value driver, by providing a lightweight and portable way to package software and its dependencies. Industrial data can be pre-processed at the edge, sent to the cloud for analytics or model training, and then redeployed at the edge in lightweight software containers.
Industrial automation and cloud-native architectures are coming together, while industrial automation itself is increasingly software-defined. The data sought by edge and cloud-based solutions resides in the logic, process and motion controllers sold by traditional automation suppliers, which are typically large players with deep domain knowledge that closely integrate their edge offerings with their higher-level, software-based solutions.
These products typically focus on domain-specific use cases that relate to their core automation competencies. Early offerings look to apply analytics to monitoring and maintenance applications focused on reduced downtime and asset utilization objectives. Automation suppliers are also taking advantage of existing edge technology and ecosystems, and partnering with and/or investing in complementary infrastructures, open-source and multiple cloud providers.
