From tariff impacts on the supply chain to advanced satellite innovations, ABI Research’s latest whitepapers shed light on the transformative forces shaping the global technology landscape in 2025. This blog highlights 10 must-read publications this year, each offering unique insights and strategic guidance across manufacturing, connectivity, smart infrastructure, and more.
Measuring Overall Equipment Effectiveness (OEE) is now essential for manufacturers striving to enhance productivity and streamline operations. As digital transformation continues to reshape the manufacturing landscape, demand for advanced data analytics solutions that provide real-time insights into machine performance and production bottlenecks has grown exponentially. ABI Research has observed manufacturers increasingly investing in purpose-built OEE software solutions, rather than relying on metrics embedded within Manufacturing Execution Systems (MESs) or Quality Management Systems (QMSs). This trend reflects the industry's pivot toward descriptive, predictive, and prescriptive analytics capabilities—areas where traditional MES or QMS platforms fall short. Up-and-comers like Litmus Automation, MachineMetrics, and Augury have stepped up to fill this market gap for OEE tools.
Robots are a cornerstone of a smart factory, automating a wide range of manufacturing tasks that are monotonous, physically straining, or even hazardous. However, real-world robotics deployments have not lived up to the revolutionary potential the industrial sector originally envisioned. Robot implementations are typically confined to specific applications, carry high costs, and are time-consuming. A recent ABI Research Industrial and Manufacturing Survey of 458 decision makers indicates that skill shortages and time constraints hinder roughly 9 in 10 manufacturers’ ability to implement and scale new technologies. These challenges serve as the impetus for industrial organizations to seek digital tools that simplify the creation of automation workflows.
New import tariffs introduced by the United States are shaking the pillars of a longstanding status quo in international trade. All countries will have to pay a 10% tariff on goods shipped to the United States, with rates going higher for countries with larger deficits. Not only is China in this latter group, but nearby Southeast Asian countries—critical to offshoring from China—are also impacted. A notable example is Vietnam’s 46% tariff rate, which will potentially disrupt Apple’s diversification strategy. Needless to say, the global supply chains that manufacturers have been accustomed to for decades are being uprooted, at least for the short term.
Will Artificial Intelligence (AI) redefine manufacturing, or is it just another tech buzzword? At Hannover Messe 2025, ABI Research analysts saw AI and Generative Artificial Intelligence (Gen AI) on full display. Practical applications are demonstrating efficiency gains, robust autonomous capabilities, and operational resilience. Their new cousin, Agentic AI, has also emerged as a key facilitator for digitally transforming the factory floor. Agentic AI is the brains behind fine-tuned copilots that enhance anything from inventory management to industry-specific Product Lifecycle Management (PLM). However, manufacturers remain skeptical about Agentic AI, and vital infrastructure considerations are still in discussion.
Digitalization in manufacturing is redefining operational efficiency and resilience, yet it’s not always so clear how digitally transformed manufacturers are. To get a better understanding of how technologically advanced manufacturers are, to date, ABI Research conducted the second edition of its Manufacturers’ Technology Adoption & Attitudes Survey, published in March 2025. It draws insights from 458 decision-makers across the United States, Germany, and Malaysia. The six revelations shared in this blog highlight key trends and challenges shaping manufacturing digitalization, offering strategic guidance for industry leaders and technology vendors.
Robotics has become a staple of the manufacturing sector, essential for maximizing productivity, ensuring quality, and reducing labor costs. As valuable as robotics are, many solutions fall short of the time-to-impact metric that manufacturers seek. Enter Artificial Intelligence (AI)-based, closed-loop automation.
Artificial Intelligence (AI) is being used in a variety of ways across oil & gas operations, from asset tracking and predictive analytics to data management. Oil & gas operations generate massive amounts of data—up to 2 terabytes daily in some cases. Human analysis can only uncover so much, leaving numerous opportunities in the shadows. By deploying AI software tools, this disparate data can be used to bring new insights to light. These tools identify drilling opportunities, streamline processes, and optimize performance.
What Is Overall Equipment Effectiveness?
Overall Equipment Effectiveness (OEE) is a critical metric manufacturers use to measure and optimize production efficiency. It evaluates three key factors: availability, performance, and quality, helping to pinpoint bottlenecks, downtime, and inefficiencies in the production process.