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Industrial big data refers to a large amount of diversified time series generated at a high speed by industrial equipment, [1] known as the Internet of things. [2] The term emerged in 2012 along with the concept of "Industry 4.0”, and refers to big data”, popular in information technology marketing, in that data created by industrial equipment might hold more potential business value. [3]
This is a valuable data source for Industry 4.0 to continuously improve the product design in the “NDE for Industry 4.0” process. [ 10 ] [ 18 ] Third, immersive training experiences, remote operation, intelligence augmentation, and data automation can enhance the NDE value proposition in terms of inspector safety and human performance in ...
Critics of the concept dismiss Industry 4.0 as a marketing strategy. They suggest that although revolutionary changes are identifiable in distinct sectors, there is no systemic change so far. In addition, the pace of recognition of Industry 4.0 and policy transition varies across countries; the definition of Industry 4.0 is not harmonised.
Work 4.0 (German: Arbeit 4.0) is the conceptual umbrella under which the future of work is discussed in Germany and, to some extent, within the European Union. [1] It describes how the world of work may change until 2030 [2] and beyond in response to the developments associated with Industry 4.0, including widespread digitalization. [3]
Smart manufacturing leverages big data analytics to optimize complex production processes and enhance supply chain management. [7] Big data analytics refers to a method for gathering and understanding large data sets in terms of what are known as the three V's, velocity, variety and volume.
The manufacturing readiness level (MRL) is a measure to assess the maturity of manufacturing readiness, similar to how technology readiness levels (TRL) are used for technology readiness. They can be used in general industry assessments, [ 1 ] or for more specific application in assessing capabilities of possible suppliers.
The Level 3 processes reach a level of detail that cannot exceed the boundaries determined by the industry-agnostic and industry-standard nature of the SCOR model. Therefore, all the set of activities and processes that build, for instance the M2.03 "Produce & Test" process, will be company-specific and therefore fall outside the model's scope.
The three (or four) levels of analysis cannot describe every effect and there is unlimited number of levels between the three primary ones, levels of analysis will help understand how one force in political power affects another. Generally, power is the concept that collects all the analysis together.
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