Understanding function and function-to-form evolution

This is a report on a workshop held at Cambridge University Engineering Design Centre, 17-10 June 1992. This workshop was held to discuss the issue of 'function' and 'function-to-form' evolution in mechanical design. The authors organised this workshop as they felt that their understanding of these topics was incomplete and that discussions between researchers might help to clarify some key issues.

The topic chosen for the workshop proved to be a stimulating one. The term 'function' is part of a designer's daily vocabulary, however there is poor agreement about its definition. In order to develop computer systems to support product evolution, a precise definition is required. Further the value of 'function' and 'function-to-form' evolution as a good choice of workshop topic is evident from the lack of firm conclusions that resulted from the sessions. This lack of consensus made for lively discussion and left participants questioning many of their preconceived ideas.

Attendance at the workshop was by invitation only. A list of the participants (not all those invited could attend due to time and financial constraints) is given in Appendix 1.

Value of information in product recovery decisions: A Bayesian approach

There is a widespread recognition of the need for better information sharing and provision to improve the viability of end-of-life (EOL) product recovery operations. The emergence of automated data capture and sharing technologies such as RFID, sensors and networked databases has enhanced the ability to make product information; available to recoverers, which will help them make better decisions regarding the choice of recovery option for EOL products. However, these technologies come with a cost attached to it, and hence the question 'what is its value?' is critical. This paper presents a probabilistic approach to model product recovery decisions and extends the concept of Bayes' factor for quantifying the impact of product information on the effectiveness of these decisions. Further, we provide a quantitative examination of the factors that influence the value of product information, this value depends on three factors: (i) penalties for Type I and Type II errors of judgement regarding product quality; (ii) prevalent uncertainty regarding product quality and (iii) the strength of the information to support/contradict the belief. Furthermore, we show that information is not valuable under all circumstances and derive conditions for achieving a positive value of information. © 2010 Taylor & Francis.