Designing industrial systems, such as building, improving, and automating distribution centers and manufacturing plants, involves critical decision-making with limited information in the early phases. The lack of information leads to less accurate designs of the systems, which are often difficult to resolve later. It is effective to use simulators to model the designed system and find out the issues early. However, the modeling time required by conventional simulators is too long to allow for rapid model creation to meet decision-making demands. In this paper, we propose a Rapid Modeling Architecture (RMA) for a lightweight industrial simulator that mitigates the modeling burden while maintaining the essential details in order to accelerate and improve decision-making. We have prototyped a simulator based on the RMA and applied it to the actual factory layout design problem. We also compared the modeling time of our simulator to that of an existing simulator, and as a result, our simulator achieved a 78.3% reduction in modeling time compared to conventional simulators.

AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows -- such as condition monitoring, maintenance planning, and intervention scheduling -- to reduce human workload and minimize system downtime. Traditional AI/ML approaches have primarily tackled these problems in isolation, solving narrow tasks within the broader operational pipeline. In contrast, the emergence of AI agents and large language models (LLMs) introduces a next-generation opportunity: enabling end-to-end automation across the entire asset lifecycle. This paper envisions a future where AI agents autonomously manage tasks that previously required distinct expertise and manual coordination. To this end, we introduce AssetOpsBench -- a unified framework and environment designed to guide the development, orchestration, and evaluation of domain-specific agents tailored for Industry 4.0 applications. We outline the key requirements for such holistic systems and provide actionable insights into building agents that integrate perception, reasoning, and control for real-world industrial operations. The software is available at https://github.com/IBM/AssetOpsBench.

Field service organizations have traditionally operated under the break-fix model--that is, responding to a device failure after the customer reports an issue. This operating model has grown antiquated due to rising costs and inefficiencies in labor and operations. It is also proving less than effective in satisfying the customer's growing expectations. The field service industry is evolving quickly in new and exciting directions with cutting-edge technology continuing to enter the arena. These innovations in technology and workflows are helping to transform field service by providing customers optimal device uptime, along with greater visibility, efficiency, and profitability.

For the past 13 years, ServiceMax has been enabling the digital transformation of field service organizations through our asset-centric field service management solution. Today, we are the only leading software application that is 100% dedicated to equipment and asset maintenance. Our focus on equipment and asset maintenance has brought the idea of asset-centricity to the forefront for many service businesses and is allowing them to move from reactive maintenance to proactive maintenance. This focus on the asset has also led to our ground-breaking joint solution with Salesforce Field Service--Asset 360 for Salesforce. As we look to the future of field service management technology, we see emerging technologies like artificial intelligence, augmented reality and the Internet of things playing a big role in advancing asset-centric field service management and supporting new outcome-based business models.

The integrity of sensors and actuators is critical to the safe and profitable operations of industrial processes. However, the lack of visibility into the heath of those sensors and actuators makes it challenging to ensure their integrity. The slightest sensor variation can have a rippling effect on production rate, scrap, and waste. Sensor integrity affects consumer-facing issues such as safety, customer satisfaction, and higher warranty costs. Nielsen conducted a survey for Advanced Technology Services and founded that the average cost of poor-quality calibration costs manufacturers $1,734,000 each year.

Honeywell to leverage Microsoft Azure cloud platform and connect Microsoft Dynamics 365 to Honeywell Forge, enabling predictive maintenance applications with closed-loop maintenance workflows in the buildings industry. Honeywell and Microsoft announced that Honeywell will bring to market its domain-specific applications built on the Microsoft cloud platform to drive new levels of productivity for industrial clients. With the integration of the AI-driven autonomous controls of the Honeywell Forge enterprise performance management software with Microsoft Dynamics 365 Field Service, customers will be able to access operating data that includes workflow management support to improve performance and energy efficiency within the enterprise environment. Workers in the field will benefit from real-time access to critical data that will help them prioritize, analyze and solve problems more quickly. The first area of focus will be in automating maintenance for building owners and operators.

As the United States wildfire season continues to lengthen, electric utilities could find new value from drones backed by advanced analytics to help prevent disasters. Also known as unmanned aerial vehicles (UAVs), drones can deliver literal birds-eye views of potential problems – encroaching vegetation, damaged equipment, nearby hazards – when there is still plenty of time to fix things. This year, during the COVID-19 pandemic, drones can also help keep people safe, going into the field to gather data while human experts stay safely inside and receive high-quality data for better business decisions. "Drones can quickly and efficiently gather information from power poles across vast expanses of the landscape," said Ron Gray, a senior solution engineer at SAP. "With analytical insights on where the biggest potential hazards are, electric companies can develop a prioritized schedule of inspections and maintenance plans, including outage management timeframes. This would also help utilities correct missing or inaccurate information on equipment with fact-based mapping data and prove compliance with regulatory reporting mandates."

This will be an important topic discussed at POWERGEN International only two weeks away in New Orleans. Click here to learn more! Today, utility engineers spend a significant portion of their time completing repetitive administration tasks. Some organizations estimate that upwards of 40 percent of the time of highly trained engineers is spent on these mundane tasks. The maturation of artificial intelligence (AI) techniques such as machine learning and natural language processing (NLP) has made them increasingly viable for use in automating more complex and higher impact tasks.

Like so many industries it came for over the past few years, the fourth industrial revolution is now heading straight for real estate, making huge waves that are only going to get bigger. Some of these advancements, particularly in artificial intelligence (AI), are going to have significant impact on the management of commercial real estate properties. One of the biggest benefits to advancements in AI and other technologies is having the ability to streamline so many of the property management processes, processes that are all critical to the management of a commercial building, especially ones that house a significant number of tenants. Managing these types of properties has never been an easy feat, but technology is reshaping what property management looks like, ultimately giving time back to property management companies to focus on revenue, continue to grow their business and provide more positive tenant experiences. AI in Commercial Property Management When it comes to commercial real estate, just like residential, there is constant change.

In the 21st century the world still faces many geographical challenges including climate change, disease outbreaks, natural disasters and a growing scarcity of vital resources such as water, food and land. Overcoming these problems is dependent on our ability to chart these issues and analyse them spatially. This comes at a time when we're increasingly able to produce millions of data points from connected devices – the internet of things (IoT) – such as mobiles, drones, satellites, vehicles and social media, combined with more affordable, powerful cloud computing and machine-learning. Technologists realise the potential for smart mapping has never been greater. "If you think about it, there isn't an area that isn't touched by location, from responses to hurricanes and typhoons, wars, international health scares or utility outages," explains Stuart Bonthrone, managing director of Esri UK, a world leader in mapping and spatial analytics software.