Reusing steel and aluminum components at end of product life.

Reusing steel and aluminum components would reduce the need for new production, possibly creating significant savings in carbon emissions. Currently, there is no clearly defined set of strategies or barriers to enable assessment of appropriate component reuse; neither is it possible to predict future levels of reuse. This work presents a global assessment of the potential for reusing steel and aluminum components. A combination of top-down and bottom-up analyses is used to allocate the final destinations of current global steel and aluminum production to product types. A substantial catalogue has been compiled for these products characterizing key features of steel and aluminum components including design specifications, requirements in use, and current reuse patterns. To estimate the fraction of end-of-life metal components that could be reused for each product, the catalogue formed the basis of a set of semistructured interviews with industrial experts. The results suggest that approximately 30% of steel and aluminum used in current products could be reused. Barriers against reuse are examined, prompting recommendations for redesign that would facilitate future reuse.

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.