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Application of the Fuzzy Weighted Average in Strategic Portfolio Management*

Decision Sciences, Aug 2005 by Lin, Chinho, Tan, Bertram, Hsieh, Ping-Jung

ABSTRACT

We propose a systematic approach that incorporates fuzzy set theory in conjunction with portfolio matrices to assist managers in reaching a better understanding of the overall competitiveness of their business portfolios. Integer linear programming is also accommodated in the proposed integrated approach to help select strategic plans by using the results derived from the previous portfolio analysis and other financial data. The proposed integrated approach is designed from a strategy-oriented perspective for portfolio management at the corporate level. It has the advantage of dealing with the uncertainty problem of decision makers in doing evaluation, providing a technique that presents the diversity of confidence and optimism levels of decision makers. Furthermore, integer linear programming is used because it offers an effective quantitative method for managers to allocate constrained resources optimally among proposed strategies. An illustration from a real-world situation demonstrates the integrated approach. Although a particular portfolio matrix model has been adopted in our research, the procedure proposed here can be modified to incorporate other portfolio matrices.

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Subject Areas: Fuzzy Weighted Average, Integer Linear Programming, Portfolio Matrix, and Strategic Portfolio Management.

INTRODUCTION

Strategy formulation is the process of determining appropriate courses of action or a plan for achieving organizational objectives, and thus accomplishing the organizational purpose (Hatten & Hatten, 1987). The goal of formulating a strategic plan is to identify feasible strategic alternatives and then to select the best one. In multibusiness organizations, the evaluation and selection of appropriate strategic plans that the firm will pursue involve the business strength/industry attractiveness analysis of Strategic Business Units (SBUs) as well as the feasibility analysis of those strategic plans submitted by the SBUs. The whole process, from identifying the competitive position of SBUs to determining suitable strategic plans, is a very complicated task involving a structured evaluation procedure and requires experienced decision makers (Tan, Lin, & Hsieh, 2003). Generally, strategic planning approaches with structured procedures are employed to guide managers in establishing each level of a strategic plan so that a strategy can be completely formulated (Hax & Majluf, 1991; Archer & Ghasemzadeh, 1999; Ghasemzadeh & Archer, 2000; Tan & Platts, 2003). Most of these studies have focused on the fields of Research and Development (R&D) (Ringuest, Graves, & Case, 2004), information technology (Klapka & Pinos, 2002), marketing (Thieme & Song, 2000), customer relations management (Zhu, Sivakumar, & Parasuraman, 2004), and optimal portfolio of investments (Levary & Seitz, 1990). In these studies, the methods of capital budgeting (Levary & Seitz, 1990), Analytic Hierarchy Process (AHP) (Hahn, 2003), scoring model, and portfolio matrices used in conjunction with optimization models are popular among decision makers to consider a broad range of quantitative and qualitative characteristics, as well as multiple objectives. Of these methods, capital budgeting has been commonly applied to select capital investments, including physical assets like equipment and nonphysical investments like stocks (Levary & Seitz, 1990; Chan, 2004). However, Edvinsson and Malone (1997) stated that traditional financial data as presented in the annual report are no longer leading indicators of future financial performance. It has been gradually acknowledged that traditional financial measurement is inadequate in guiding strategic policy making (Waterhouse & Svendsen, 1998). Traditional financial methods, which are based on tangible assets and historical, transaction-based information, are inadequate for valuing intangible benefits of strategic plans. Hence, in evaluating and selecting strategic plans, capital budgeting should be supplemented by measurements covering intangible benefits of strategic plans.

In addition, in the aforementioned strategic planning approach, decision makers must confirm that all of the information available or needed is brought to bear on the problem or issue at hand. As previous cases indicate (Ansoff & McDonnell, 1990; Chien, Lin, Tan, & Lee, 1999), identifying all relevant information for a decision does not mean that the decision makers have complete information; in most instances, information is incomplete. Decisions must be made with limited information because decision makers do not have full knowledge of the problem they face and generally cannot even determine a reasonable probability for alternative outcomes; thus, they must make their decisions under conditions of uncertainty. In addition, many decisions in organizations, especially important decisions that have far-reaching effects on organizational activities and personnel, are made in groups. One problem with group decision making is that not every member in the decision group has the same knowledge of the problem as the others have. This means that decision makers will face a decision-making situation with various peers possessing different confidence levels regarding the problem to be handled. Thus, the domain of strategic management has already been recognized as a field appropriate for the application of a fuzzy set theory (Pap, Bosnjak, & Bosnjak, 2000). Some prior studies employed fuzzy set theory to do project evaluation and selection. However, existing studies generally concentrate on evaluating projects at the functional level, for example, R&D (Buyukozkan & Feyzioglu, 2004), information technology (Chiu, Shyu, & Tzeng, 2004), and operations management (Bozdag, Kahraman, & Ruan, 2003), and neglect the demands of making strategic evaluations at the corporate level. Therefore, a project selection method constructed with strategy-oriented evaluation and selection processes will meet many firms' practical needs. Furthermore, after identifying the competitive position of SBUs and the feasibility of strategic plans submitted by SBUs, a firm needs to select the most suitable strategic plans. The most common application of integer linear programming involves the general problem of finding the best way to allocate limited resources among competing activities (Walukiewicz, 1991; Robinson & Lawrence, 2004). Hence, a suitable technique, integer linear programming, will act as a tool to select the optimal strategic plans.

Application of the Fuzzy Weighted Average in Strategic Portfolio Management*

Decision Sciences, Aug 2005 by Lin, Chinho, Tan, Bertram, Hsieh, Ping-Jung

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