مکانیسم بهینه سازی شبیه سازی برای استراتژی توسعه با استفاده از تئوری گزینه واقعی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|9779||2009||9 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications,, Volume 36, Issue 1, January 2009, Pages 829-837
A right expansion strategy can bring a company more market shares and profits, and hence increase shareholders’ equities. However, limited financial resources and various uncertainties require business practitioners to achieve their goals while controlling the risks incurred at an acceptable level. Therefore, justification of expansion investments is an important and complex topic in industry. The traditional investment analysis tools such as net present value (NPV) often tend to undervalue investment decisions. We formulate the expansion investments using real options, and develop a financial model to assess the option value. Monte Carlo simulation is considered a good way to estimate the value of the option. This valuation gives decision makers a way to choose the appropriate expansion strategy based on an integrated view of the market dynamics, but optimization is still a difficult problem to resolve. This paper presents a model of optimization under uncertainty combining system simulation with GA-based optimization to resolve the expansion problem. An industry case is used to demonstrate the application of real options to value expansion investment by using simulation–optimization. This approach also provides some new insights for the real options theory.
Capital budgeting often involves large expenditures with long-term implications such as expansion decisions, equipment selection or replacement decisions, and lease/buy decisions, etc. An organization’s long-term health can be affected significantly by its capital-budgeting decisions. Hence, managers must carefully select those projects that promise the greatest future return. How well managers make these capital budgeting decisions is a critical factor for the long-term profitability of a company (Garrison and Noreen, 2003 and Hilton, 2004). The valuation of long-term investments is challenging because it is characterized by long payback periods, uncertainty, and changing business conditions. A few approaches are used for making capital budgeting decisions, such as the net present value method, the internal rate of return method, payback method, and simple rate of return. The net present value and internal rate of return methods, both using discounted cash flows (DCF), have gained widespread acceptance as decision-making tools. DCF estimates inflow and outflow cashes from an investment, and cash flows are discounted to their present value at a discount rate commensurate with the project risk. This approach has assumed that an investment cannot be postponed and that, once started, nothing can be done to alter the course of the project. In reality, investments can often be postponed. Therefore, this approach does not properly account for the flexibility inherent in most long-term expansion investment decisions. Real options analysis presents an attractive alternative because it explicitly accounts for the value of future flexibility in management decision-making (Amram and Kulatilaka, 1999, Smith and McCardle, 1998 and Trigeorgis, 1996). More recently, managerial operating flexibility has been likened to financial options. The goal of our research is to view the flexibility surrounding corporations’ operations in expansion strategy using financial options. In this paper, we specifically consider the manufacturing decision to increase flexibility through expansion strategy. We use the options approach to find the value of expansion decision during a specified length of time, considering future uncertain variables. The uncertainty is included through a Monte Carlo simulation. Then, genetic algorithms (GAs) are used to optimize a Monte Carlo simulation of real options expansion problems and to find the best investment timing and portfolio. In Nembhard et al., 2002 and Nembhard et al., 2003, they have investigated the use of Monte Carlo simulation to value the real options associated with applying statistical process control (SPC) charts to monitor quality and product outsourcing. The use of computational intelligence techniques in real options applications is very rare. In the research, a case example from the TFT manufacturing industry is used to illustrate the application of simulation–optimization method. The case example includes numerical results and a sensitivity analysis for key parameters to supplement our results.
نتیجه گیری انگلیسی
Traditional NPV evaluation methods such as net present value often tend to undervalue investment decisions (Kaplan, 1986). The inability of NPV methods to address dynamics in the market condition led to an undervaluing of the expansion strategy as illustrated in our case example. However, one of other major limitations of these techniques is that they do not capture management’s ability to alter the pace of investment, or to stop investment at some point if conditions are unfavorable. The real options approach acknowledges the importance of managerial flexibility and strategic adaptability. Its superiority over NPV analysis has been widely recognized in analyzing the strategic investment decisions under uncertainties (Amram and Kulatilaka, 1999 and Luehrman, 1998). In this paper, we have illustrated how the value of long-term capital investment can be determined by applying the real options analysis. The expansion problem was formulated as a series of European options. Monte Carlo simulation offers a flexible simulation tool which allows in coping with many of realistic aspects. This technique can reflect various kinds of solutions or projects under uncertainty. Its solutions, however, are not necessary the optimal ones. Combining with optimization techniques is then becoming worth developing. In this study, we propose an effective approach, simulation–optimization, based upon the hybrid of the Monte Carlo simulation with a genetic algorithm fast searching procedure to find the best solutions for expansion investment. By using real options analysis and Monte Carlo simulation as an input of optimization, the results obtained enabled the decision markers to choose the best investment strategy at the best timing to reach the maximized profit.