بودجه بندی برای تحقیق و توسعه: یک رویکرد شبیه سازی مالی پویا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|12814||2003||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Socio-Economic Planning Sciences, Volume 37, Issue 1, March 2003, Pages 15–27
The research and development (R&D) budgeting decision is crucial for at least two reasons: if too much is spent, short-term financial stability is at risk, while, if the budget is too small, long-term competitiveness is threatened. Nevertheless, many enterprises simply extrapolate the past without further reflection. This paper presents a computer-based dynamic stochastic simulation model that allows one to assess the impact of alternative R&D budgeting policies on corporate development. The core decisions to be evaluated concern timing and funding of investments in R&D. Our approach substantially expands earlier work by Brockhoff (R&D Manage. 19 (1989) 265). In particular, it distinguishes between product and process innovation, considers market dynamics related to technical progress via a modifiable S-curve, integrates marketing, and takes into account essential financial aspects. As a result, our model is closer to reality than previous ones. A sample application with real company data illustrates its potential usage.
In 1999, the member countries of the Organization for Economic Co-operation and Development (OECD) spent 2.21% of their gross domestic product on research and development (R&D). Most of these resources are provided directly by companies; for the United States, these investments totaled $ 158 billion in 1999 . Even at the corporate level, the numbers are quite impressive: In the same year, IBM, for example, invested $ 4.6 billion in activities covering basic and applied scientific research in the development of new and improved products . Clearly, then, within the field of R&D management, large amounts of resources are at stake. The task of R&D budgeting, i.e. striving for the best timing as well as optimum levels of investments, is thus key for many organizations. In this regard, it is critical to avoid financial waste while seeking to achieve long-term goals. Spending too little could mean reducing future profits, while spending too much could overtax company resources. For a recent review of R&D budgeting refer to . In this paper, we present a computer-based, dynamic, stochastic simulation model that allows one to test the effects of R&D budgeting rules on a firm's further development. It is an improvement over its predecessor  mainly because it is more realistic, treats product and process R&D separately, explicitly integrates marketing expenses, considers an S-curve interaction between cumulative R&D budgets and market dynamics related to technological progress, and embeds the funding decisions into a financial framework. Furthermore, its decision support system (DSS) provides assistance for both risk and scenario analyses. Our focus is on the simulation model that can be used as an evaluation tool for various budgeting policies. The policies themselves, however, are not at the core of our reasoning here, and will thus be explored in future research. The remainder of the paper is organized as follows: First, we present the model and discuss salient new features of our approach. This is followed by a sample application to a real company, demonstrating the potential usage of our DSS. Finally, key features are summarized and suggestions for further research are outlined.
نتیجه گیری انگلیسی
Budgeting is one of the most important and complex tasks in R&D management. Hence, we presented a dynamic stochastic simulation model of a corporation's R&D investment activities that we believe to be more realistic than previous approaches (e.g.  and ). Among its salient new features are the separate treatment of product and process R&D, the explicit integration of marketing, the consideration of an S-curve interaction between technological-progress-related market dynamics and cumulative R&D budgets, and the embedding of R&D budgeting, all in a sufficiently detailed financial framework. Obviously, the DSS can only be applied if all necessary data are available. This may be a limitation since empirical studies (e.g. ) have revealed that some companies are simply not maintaining these kinds of records. Moreover, the system's configuration for a given firm requires experts who must adequately adjust the parameters to given circumstances. This paper offers a basis for further research in at least four directions. The first addresses issues related to technology progress. For example, the jump from one technology to another (with a different S-curve) could be considered. The second area concerns budgeting strategies. Future systems should, for example, allow for a switch between funding policies depending on the prevailing situation. Third, our model might be utilized as the evaluation module in an interactive algorithm for a near-optimal budget trajectory over an entire planning horizon. Thus, selected shortcomings of the budgeting heuristics we tested could be further explored. And, finally, the financial consequences of a holding company could be of interest.