<p>The financial portfolio model often referred to as the Black-Litterman model is analyzed using two approaches; a mathematical and a behavioral finance approach. After a detailed description of its framework, the Black-Litterman model is derived mathematically using a sampling theoretical approach. This approach generates a new interpretation of the model and gives an interpretable formula for the mystical parameter<b> τ</b>, the weight-on-views. Secondly, implications are drawn from research results within behavioral finance. One of the most interesting features of the Black-Litterman model is that the benchmark portfolio, against which the performance of the portfolio manager is evaluated, functions as the point of reference. According to behavioral finance, the actual utility function of the investor is reference-based and investors estimate losses and gains in relation to this benchmark. Implications drawn from research results within behavioral finance indicate and explain why the portfolio output given by the Black-Litterman model appears more intuitive to fund managers than portfolios generated by the Markowitz model. Another feature of the Black-Litterman model is that the user assigns levels of confidence to each asset view in the form of confidence intervals. Research results within behavioral finance have, however, shown that people tend to be badly calibrated when estimating their levels of confidence. Research has shown that people are overconfident in financial decision-making, particularly when stating confidence intervals. This is problematic. For a deeper understanding of the use of the Black-Litterman model it seems that we should turn to those financial fields in which social and organizational context and issues are taken into consideration, to generate better knowledge of the use of the Black-Litterman model.</p>

Background – Increase in global competition, technological changes and demanding customers have resulted in more knowledge-intensive, unstable, complicated and an uncertain environment. In order to overcome these demand uncertainties and tough circumstances, manufacturers are required to investigate methods to increase flexibility. To achieve the flexibility improvements, each component of supply chain such as suppliers, manufacturing plants, warehouses, and distributors must possess the potential to be flexible. Theory implies that the main link between company’s upstream supply chain namely suppliers and its own business unit are the sourcing strategy. Usually sourcing practitioners distinguish between sourcing strategies using portfolio models. They normally categorize purchased items based on the strategic importance of the item and characteristics of its supply market. It is a critical issue to explore how different sourcing strategies, for different categories of procured items, can influence sourcing flexibility. Purpose– The purpose has been diagnosed as to study how the prerequisites of Bombardier’s procurement procedures along with its associated strategies, can affect the flexibility that can be provided through sourcing namely sourcing flexibility. This study desires to investigate the concept of sourcing flexibility with considerations on category level. It can be beneficial to investigate how different strategies, related to different categories of procured component, can influence the level of sourcing flexibility specified to that category. This would lead us to two key questions: How can sourcing flexibility be defined in Bombardier and why is it required? How can sourcing strategies influence sourcing flexibility considering different categories of components? Methodology– To provide appropriate definitions for sourcing flexibility and strategic procurement, this study went through a comprehensive review on the relevant literatures. By a deep analysis, accompanied with several unstructured interviews on one of the undergoing projects in the company, the drivers for the sourcing flexibility have been diagnosed. The procured components have been categorized into four categories of strategic, bottleneck, leverage, and noncritical through 4 different structured quantitative questionnaires. 33 diverse individuals with purchasing and/or engineering background answered those questionnaires. The categorization criteria have been extracted out from two models suggested by Kraljic (1983) and Olsen &amp; Ellram (1997). Four independent components, one from each category, were selected for further observations. Finally, the links between sourcing strategies and sourcing flexibilities were expansively analyzed through 9 semi structured interviews with company’s strategic purchasers and suppliers’ representatives. Conclusion– Sourcing flexibility can be defined from two perspectives. First one refers to the capability of the focal firm to change the structure of its upstream supply chain. Second aspect refers to the ability of company’s suppliers to provide it with flexibility in three dimensions of delivery, volume and product. Both two aspects along with related dimensions can be measured in three different conditions of required, actual and potential by using range, mobility and uniformity as measuring elements. The results showed that the first perspective has a direct relationship to the sourcing strategies that focal firm may apply for different categories of procured component. Furthermore, the availability of second perspective is highly dependent on the relationship between the focal company and its suppliers, where strategic procurement plays an indispensable role. Based on the results the required level of sourcing flexibility, related to each category, differs significantly with other categories. The findings also suggested that the levels of delivery, volume and product flexibility have a close connection to the diverse strategies and attributes of the four different categories. Additionally they are well dependent on the internal operational capabilities of the suppliers along with the established relationship between buyer and supplier. Originality/Value– Main portions of previous studies have explored the concepts of sourcing flexibility and strategic procurement separately. Although, there exists some narrow numbers that have analyzed the relationship between sourcing strategies and sourcing flexibility to some limited extent. This study tries to contribute to the existing literature by empirically exploring the principal reasons for companies necessitating to increase sourcing flexibility. It investigates how sourcing flexibility can be improved through strategic procurement. The main contribution is to consider sourcing flexibility from the category perspective. Latter is a subject that has been neglected in the previous literatures. It is extremely hard to find literature which has analyzed sourcing flexibility at the category level. This report analyses the level of sourcing flexibility specified to different categories of strategic, bottleneck, leverage, and noncritical components. It suggests some factors that may influence the selection of a specific sourcing flexibility strategy regarding different component categories. Finally, it may introduce some extra elements that can be influential on the level of sourcing flexibility dimensions. Some examples of those influential elements are bargaining power and establishment of a close relationship.

The financial portfolio model often referred to as the Black-Litterman model is analyzed using two approaches; a mathematical and a behavioral finance approach. After a detailed description of its framework, the Black-Litterman model is derived mathematically using a sampling theoretical approach. This approach generates a new interpretation of the model and gives an interpretable formula for the mystical parameter τ, the weight-on-views. Secondly, implications are drawn from research results within behavioral finance. One of the most interesting features of the Black-Litterman model is that the benchmark portfolio, against which the performance of the portfolio manager is evaluated, functions as the point of reference. According to behavioral finance, the actual utility function of the investor is reference-based and investors estimate losses and gains in relation to this benchmark. Implications drawn from research results within behavioral finance indicate and explain why the portfolio output given by the Black-Litterman model appears more intuitive to fund managers than portfolios generated by the Markowitz model. Another feature of the Black-Litterman model is that the user assigns levels of confidence to each asset view in the form of confidence intervals. Research results within behavioral finance have, however, shown that people tend to be badly calibrated when estimating their levels of confidence. Research has shown that people are overconfident in financial decision-making, particularly when stating confidence intervals. This is problematic. For a deeper understanding of the use of the Black-Litterman model it seems that we should turn to those financial fields in which social and organizational context and issues are taken into consideration, to generate better knowledge of the use of the Black-Litterman model. / QC 20101119