3D Printing Buyer’s Guide: Unlock the Potential of Additive Manufacturing

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 | December 12, 2022

Artificial intelligence (AI) can enable a new era in the digital transformation journey, offering tremendous potential to transform industries for greater efficiency, sustainability and workforce engagement. Even though the impact of AI applications in manufacturing and value chains is known, the full opportunity from their deployment is still to be uncovered due to a number of organizational and technical roadblocks. Recognizing this need, the World Economic Forum’s Platform for Shaping the Future of Advanced Manufacturing and Value Chains and Platform for Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning, together with the Centre for the Fourth Industrial Revolution Türkiye, convened industry, technology and academic experts to shed light on these challenges and propose a step-by-step approach to overcome them.

whitePaper | November 1, 2022

Many businesses are experiencing an increase in supply chain disruptions reverberating down to the customer. A rapidly growing population adding to the swell of demand, the threat of a looming recession, and complex logistics systems create an incredibly delicate balance. We are facing a pivotal time for the future of supply chains.

whitePaper | November 1, 2022

Industry 4.0—also referred to as smart manufacturing, connected manufacturing, the Industrial Internet of Things (IIoT), and other monikers—has revolutionized the way companies manufacture, enhance, and distribute products through the use of new technologies. However, not all sectors and markets are evolving at the same pace. To help both manufacturing providers and industry leaders understand the landscape, ABI Research’s Industrial & Manufacturing Markets Research provides insights, perspectives, and data related to digital maturity within individual markets and across the industry landscape. Our research illuminates drivers and inhibitors that are shaping adoption of digital technologies across market maturity, practitioners’ investment priorities, and best practices for deploying the technologies. We keep abreast of key trends, including increasing demand for electric vehicles and semiconductor chips, the adoption of machine learning and automation, and a reliance on digital twins.

whitePaper | January 3, 2023

In order to achieve digital manufacturing through Industry 4.0, it is critical to aggregate and manage the huge amount of data generated in the factory by Industrial Internet of Things (IIoT) sensors and devices. Having connected machines, workers and systems is just the start of the digital transformation journey. To extract real value from this connectivity, a digital twin of machines and processes have to be modeled in a standardized manner which will help aggregate heterogeneous data and provide valuable insights to drive productivity and efficiency. The interoperability and flexibility of different assets is an important challenge due to the volume and variety of data that needs to be modeled. This paper will go over Industry 4.0 compliant data modeling principles using the MongoDB Smart Factory testbed as an example. We will model the data using MongoDB Atlas in a way that fulfills requirements and needs of Industry 4.0: interoperability, flexibility and scalability.

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.