Digital twin software has evolved rapidly in recent years, thanks to innovative technology vendors like Siemens. The next generation of digital twins in manufacturing—comprehensive digital twins—provides benefits far beyond legacy solutions. They support technological capabilities like Agentic Artificial Intelligence (AI), closed-loop feedback, and full-stack software integration. The contextual awareness a comprehensive digital twin provides is unprecedented. It can be used by manufacturers to build more realistic simulation environments, enhance collaboration, and solve specific business challenges across multiple domains.

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.

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.

Industrial machinery suppliers are faced with the tall task of pushing the innovation envelope, while keeping production costs at bay. Customers require machines that unlock new use cases, but at the same time don’t necessitate significant training time for employees. To strike this balance, suppliers should strongly consider the adoption of immersive engineering solutions powered by Extended Reality (XR) hardware. As ABI Research already pointed in a recent blog post, cloud-based XR platforms foster greater collaboration between engineering and design teams, reduce error rates, and accelerate the design review process.

Immersive engineering is a game-changer for industrial machinery suppliers as they aim to enhance quality and productivity, while reducing costs and their carbon footprint. Their customers, who cite machine performance improvements as a top priority, have increasingly required new tools and products as they tackle recent headwinds such as COVID-19, worker strikes, and safety recalls.

Nearly 45,000 people and 900 exhibitors signed up for Automate, the largest robotics and automation trade show in North America, held in Chicago May 6-9, 2024. As a show, Automate is more focused than Hannover Messe or IMTS and, consequently, there were many automation and tooling suppliers, including ABB, Atlas Copco, and FANUC that chose this event over its larger peers.

Generative Artificial Intelligence (AI) is one of the booming technology topics of our time. ABI Research has multiple research products and articles covering how generative AI will transform the modern enterprise. The manufacturing sector is one industry that gains tremendous benefits from using generative AI. Whether it’s product design or inventory management, there are many use cases for generative AI in manufacturing. In this article, I’ll identify five examples of how generative AI can be applied to the manufacturing industry.

Despite volatile economic conditions, the manufacturing industry is continuing its push for the “smart” digital factory. Some of the pressures spurring growth in digitalization include labor shortages, sustainability goals, and increased demand for products. In our manufacturing industry outlook, we highlight 6 significant trends that manufacturers and technology suppliers should think about as it relates to a 2023 game plan.

In chemical manufacturing, potential failure can happen in many operational areas. This is because of the complex nature of chemical production and the large number of assets deployed in a chemical plant. To reduce the frequency of manufacturing inefficiencies, the chemical industry should invest in digital transformation. Beyond operational efficiency, digital transformation in the chemical industry helps companies meet other agendas, such as workplace safety, sustainability goals, and tackling labor shortages.