What is a Unified Namespace and How Does it Work in Manufacturing?
A unified namespace (UNS) provides a single point of access for all manufacturing data within an organization. It aggregates data from various sources like orders, historical records, telemetry, and execution information. Instead of functioning as a single version of the truth, a UNS
Making Renewable Energy More Efficient with Time Series Data | SPONSORED
Renewable energy is more important than ever, so integrating advanced technologies is pivotal for enhancing efficiency and reliability. Among these technologies, time series databases, particularly InfluxDB, are proving instrumental. This article delves into how the renewable energy sector uses time
Building Trust and Security in the Evolving IIoT Landscape
In the evolving landscape of the Industrial Internet of Things (IIoT), cybersecurity has become a critical concern for manufacturers. The increased connectivity of industrial environments exposes Operational Technology (OT) to significant threats, particularly from supply chain attacks. Ensuring the security
From Data to Decision: The Role of AI and ML in Modern Manufacturing
Manufacturing is on the cusp of a technological revolution, driven by advancements in artificial intelligence (AI) and machine learning (ML). These technologies promise to transform how manufacturers operate, making processes more efficient, productive, and innovative. Let's explore the main ideas
Three Essential Uses of Generative AI in Manufacturing
Generative AI is transforming the manufacturing industry by offering innovative solutions to enhance efficiency, improve product design, and optimize processes. Let's explore three specific ways generative AI can be applied to jobs in manufacturing, providing concrete examples for each. 1. Enhancing
Artificial Intelligent Seasons
Birth of AI. Winter The terms "artificial intelligence" and "machine learning" began shaping the technological landscape in the mid-20th century, with the official birth of AI at the Dartmouth Conference in 1956. John McCarthy and his colleagues coined "artificial intelligence" there.
Anomaly Detection for IoT: A Basic Primer
In the world of IoT, ensuring the reliability, efficiency, and security of connected devices is critical. As IoT devices generate massive amounts of data, detecting anomalies becomes increasingly important. Anomaly detection helps identify potential issues before they escalate, providing businesses
[White paper] Accelerating Innovation in Manufacturing: A Pathway to Seamless Integration and Efficiency | SPONSORED
In today's fast-paced industrial landscape, manufacturers are constantly seeking ways to stay ahead by optimizing processes and embracing new technologies. For a successful IIoT transformation, it's essential to bridge the gap between IT (business data) and OT (factory equipment data). This
Empowering IIoT Transformation through Leadership Support
Transformation can be slow, particularly for industries seeking to integrate IIoT into their operations. This integration often occurs at a more constrained pace, requiring careful planning and implementation of transformative plans to ensure its success. Addressing the need for change
July 2024 Industrial IoT & ICS Cybersecurity Events
July 2024 promises to be a landmark month for Industrial IoT and ICS cybersecurity events, showcasing groundbreaking developments and strategic insights essential for securing industrial infrastructures in today's digital age. From the NOG Energy Week in Abuja to the Gartner
Private Cellular Conquers Challenging Wireless Environments
For years, network connectivity for industrial environments ranked lower among business imperatives. Providing baseline coverage was considered the norm, and companies felt comfortable deploying their tried-and-true Wi-Fi networks to provide connectivity in factories, warehouses, food processing plants, and other facilities. In
Data Modeling Best Practices and Pitfalls
At first glance, data modeling projects in an industrial ecosystem can seem daunting. There are many factors that manufacturers must consider to ensure their data modeling project is successful. The following guidelines address commonly encountered challenges and provide best practices