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.

Developing a framework for make-or-buy decisions

The make-or-buy question represents a fundamental dilemma faced by many companies. Companies have finite resources and cannot always afford to have all manufacturing technologies in-house. This has resulted in an increasing awareness of the importance of make-or-buy decisions. This paper reports on the development of a make-or-buy framework to address the make-or-buy decision for either a specific individual part or family of parts. Firstly, a literature review of the principal make-or-buy approaches is discussed. Secondly, the development of a make-or-buy framework is described and the framework is explained and illustrated using case studies. Thirdly, the operationalisation of the framework is outlined. The paper concludes with a discussion of its contribution to both the academic understanding of the subject, and the improvement of industrial practice.

Value roadmapping: A structured approach for early stage technology investment decisions

Deciding to invest in early stage technologies is one of the most important tasks of technology management and arguably also the most uncertain. It assumes a particular significance in the rise of technology companies in emerging economies, which have to make appropriate investment decisions. Technology managers already have a wide range of methods and tools at their disposal, but these are mostly focussed on quantitative measures such as discounted cash flow and real options techniques. However, in the early stages of technology development there seems to be a lot of dissatisfaction with these techniques as there appears to be a lack of accuracy with respect to the underlying assumptions that these models require. In order to complement these models this paper will discuss an alternative approach that we call value road-mapping. By adapting roadmapping techniques the potential value streams of early stages technologies can be plotted and hence a clearer consensus based picture of the future potential of new technologies emerges. Roadmapping is a workshop-based process bringing together multifunctional perspectives, and supporting communication in particular between technical and commercial groups. The study is work in progress and is based on a growing number of cases. (c) 2006 PICMET.