manufacturing enterprise
5 factory technologies to look out for in 2022
Smart factories are no longer the future. In 2018, when the World Economic Forum (WEF) first began its global lighthouse network, there were just 16 flagship smart factories around the world. Today, four years later, the number of flagship industrial factories is 103. What's even more striking than numbers, however, is the growing catalogue of intelligence-driven, efficiency-boosting and robot-friendly technologies that are spreading further across the value chain. These technologies are getting traction in sectors as far and wide as consumer packaged goods, process industries, pharmaceutical products, and advanced industries including electronics, industrial machinery and automotive.
- North America > United States > Texas > Denton County > Lewisville (0.06)
- Europe > Italy (0.05)
AI for Automotive Strategy
A lot has been written, said and discussed in the domain of Artificial Intelligence. From the Turing test conducted by Alan Turing in 1950 which offered an opportunity to understand whether machines can exhibit intelligent behavior to AutoML (Auto machine learning) by google which claims to reduce the dependency on humans to build AI models, the technology has come a long way. However, the question that still intrigues many is whether this new wave of digital intelligence is intelligent enough to create value. This is one of the biggest challenges C-level executives in the manufacturing industry face when they propagate the idea of investing in this technology. Preparing a business case and binding the investment to the RoI, in an asset-heavy industry, becomes a daunting task and many at times hinder the buy-in or progress of such programs across the manufacturing enterprise.
- North America > United States (0.05)
- Asia > India > West Bengal > Kolkata (0.05)
- Automobiles & Trucks (0.54)
- Information Technology (0.35)
Knowledge Integration of Collaborative Product Design Using Cloud Computing Infrastructure
Bohlouli, Mahdi, Holland, Alexander, Fathi, Madjid
-- T he pivotal key for the success of manufacturing enterprises is sustainable and innovative product design and development. In collaborative design, stakehol ders are heterogeneously distributed chain - like . Due to the growing volume of data and knowledge, an effective management of the knowledge acquired in the product design and development is one of the key challenges facing most manufacturing enterprises. Opportunities for improving efficiency and performance of IT - based product design applications through centralization of resources such as knowledge and computation have increased in the last few years with maturation of technologies such as SOA, virtualization, grid computing, and /or cloud computing. The main focus of this paper is the concept of ongoing research in providing the knowledge integration service for collaborative product design and development using cloud computing infra structure . P otential s of the cloud computing to support the Knowledge integration functionalities as a Service by providing functionalities such as knowledge mapping, merging, searching, and transferring in product design procedure are described in this paper . Proposed knowledge integration services support users by giving real - time access to knowledge resources. The framework has the advantage of availability, efficiency, cost reduction, less time to result, and scalability . Changes made during the early design stage do not cause the significant increase in costs, while during the production stage, sharp increase in costs will occur since many blueprints, design documents or components would require re - work and re - design [ 5 ] . Today's research is focused on optimising the development methodologies to enable shorter time, lower costs and higher quality of the systems [ 2 ] . The pivotal key for the success of manufacturing enterprises is sustainable and innovative product design and development . In order to achieve this goal, it is required to have a real and deep knowledge of former and current procedures in the manufacturing enterprise [4] and future needs as well as customer feedback s and various stages of production cha in activities. Realization of an efficient knowledge transfer between different stakeholders of product development process such as linking customers and suppliers proactively throughout the entire value chain, and collaborating across boundaries in distri buted enterprise s is reinforcing this trend.
- Europe > Germany > Baden-Württemberg > Karlsruhe Region > Heidelberg (0.04)
- North America > United States > New Jersey > Mercer County > Princeton (0.04)
- Europe > Hungary (0.04)
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How can India influence adoption of AI/Machine Globally - Agile Intelligence
India is a country in South Asia. It is the seventh-largest country by area, the second-most populous country (with over 1.2 billion people), and the most populous democracy in the world. It is bounded by the Indian Ocean on the south, the Arabian Sea on the southwest, and the Bay of Bengal on the southeast. It shares land borders with Pakistan to the west; China, Nepal, and Bhutan to the northeast; and Bangladesh and Myanmar to the east. In the Indian Ocean, India is in the vicinity of Sri Lanka and the Maldives. According to the International Monetary Fund (IMF), the Indian economy in 2017 was nominally worth US$2.611
- Indian Ocean > Bay of Bengal (0.25)
- Indian Ocean > Arabian Sea (0.25)
- Asia > Sri Lanka (0.25)
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- Banking & Finance > Economy (1.00)
- Government > Regional Government > Asia Government > India Government (0.50)