Improved Data Visibility Drives Manufacturing Performance

whitePaper | August 9, 2023

The industrial sector contributed approximately one-third of the 35 billion metric tons of carbon dioxide emitted in 2020. Industrial market engagement is going to be crucial to meet the climate change targets being set by governments around the world. However, challenges extend beyond just the industry’s carbon footprint, with concerns around raw material depletion, harmful emissions, pollution, and water consumption.

whitePaper | December 22, 2022

Minimizing downtime and increasing production efficiency is one of the main goals of an effective maintenance strategy. Knowing the working hours, operating parameters, and consumption of one’s assets is the starting point for identifying operating conditions, studying their behavior, finding anomalies, and understanding the reasons for individual downtime. In this way, using different algorithms, it is possible to achieve the ideal strategy

whitePaper | August 3, 2020

Edge computing is one of today's leading disruptive digital technologies. Until recently, it was overshadowed by the Cloud with its power, scalability, and as-a-service platform capabilities. However, there is an increasing realization that both have their place in the Industrial Internet of Things (IIoT) environment and that Edge offers a number of capabilities and advantages that make it a key player in the new Operational Technology (OT) Ecosystem, as well as in evolving Operational Architectures.

whitePaper | November 11, 2022

The manufacturing and hi-tech industries are accustomed to economic downturns, legislative impacts, tax and tariff complexities, and supply chain concerns. During the Great Recession, the United States’ manufacturing downturn resulted in a loss of 10% of its GDP, compared with only 4% for the overall economy. With these already poignant experiences, it is not hard to believe the pace of change has picked up in the last decade.

whitePaper | October 23, 2021

Artificial Intelligence (AI) will increase the level of intelligence in the manufacturing industry by promoting, inter alia, the matching of production and demand, improving quality inspection, increasing product yield, reducing product failure rates, and improving production efficiency.1 While the last decade of Industry 4.0 was determined by technology-driven innovation, the coming years will focus on data- and intelligence-driven innovation. In this perspective, AI is an enabler for the transition from smart factories towards intelligent factories with self-optimising and self-healing characteristics. While smart factories are capable of applying previously acquired knowledge, intelligent factories will be able to autonomously acquire new knowledge and apply it for self-optimisation purposes.

whitePaper | November 10, 2022

Evolving Design Tools To Meet Increasingly Complex Demands Packaging machine designs evolve at a rapid pace. The emergence of smart manufacturing and the need for smarter machines is creating a challenge in electromechanical design, information management, and robotic adoption. New vision and sensor technologies provide increased capabilities at the component level, providing faster and more accurate insight into production quality and improving throughputs.