This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.
The global automotive industry is experiencing significant changes in competition due to globalized markets, advanced technologies, and increasingly demanding customers. As automotive companies evolve, the importance of well-functioning networks becomes evident for gaining a competitive edge. Key benchmarks for these companies include higher product quality, improved efficiency, and reduced time to market. This context serves as the foundation for Daniel's research on the product development process within the automobile sector, a topic that has not been thoroughly addressed.
Daniel aims to elucidate the value of engineering product development paradigms, particularly point-set-based concurrent engineering, from a holistic perspective. He identifies capabilities based on empirical studies of best practices, especially at Toyota Motor Corporation, highlighting why different product engineering systems can provide a competitive advantage. Furthermore, he applies a real option valuation model within a financial economics framework to quantify the value of concurrent engineering from a shareholder's perspective. This approach equips automotive firms with a robust tool to determine the optimal level of innovation in their product development process. Lastly, Daniel outlines five clear principles of product development, offering significant guidance for automotive executives in effectively managing the product development process
