Schematic Models for Production Engineering [electronic resource] / by Ricardo Seidl da Fonseca
- Seidl da Fonseca, Ricardo
- Cham : Springer International Publishing : Imprint: Springer, 2023.
- 1st ed. 2023.
- Physical Description:
- XXI, 185 p. 107 illus., 17 illus. in color. online resource
- Additional Creators:
- SpringerLink (Online service)
- Introduction -- Concept of Work Study -- Conceptualization of Methods Engineering -- Analysis of Schematic Models.
- The book is a comprehensive guide to schematic models of methods engineering, offering a detailed analysis of these models and their applications in a variety of engineering fields. By bringing together the most significant schematic models in a single text and analyzing them according to a common structure, the book enables readers to visualize possible interventions and improvements in work situations. Focused on the conceptualization and analysis of schematic models, the text covers an area of knowledge that is central to production and industrial engineering, but also widely used in other engineering disciplines. The book presents an updated version of a representative set of schematic models, making it an invaluable resource for engineers in the field. With the growing automation of production and the introduction of robotics and the "internet of machines", the use of schematic models is more important than ever in achieving quality and safety in production projects, whether in manufacturing, industrial processes, or services. The book demonstrates how schematic models of methods are essential tools for the study and analysis of current business or production processes, as well as for the implementation of new systems and their maintenance. Overall, this book is a must-read for engineers seeking to improve their knowledge and practical application of schematic models, providing valuable insights and guidance for professionals in a range of engineering fields.
- Digital File Characteristics:
- text file PDF
- Part Of:
- Springer Nature eBook
View MARC record | catkey: 41605527