Funding risky Research and Development (R&D) and New Product Development (NPD) projects is crucial for the long term health of any large company. Such funding decisions are multiobjective and are undertaken with sparse data. Therefore prescriptive or algorithmic solutions are not adopted by practitioners. Descriptive solutions that provide managerial insights and illuminate the underlying structure of the decision making framework are more likely to appeal to decision makers. While academics, perhaps motivated by more tactical problems, have contributed to the former approach, the latter approach is less explored. This has resulted in practitioners resorting to ad hoc and often suboptimal approaches. The motivation of this dissertation is to bridge this gap. Towards this end, in Chapter 2 we consider the Project Portfolio Management Problem (PPMP) in which a limited resource must be allocated among a set of candidate projects (e.g., Research and Development (R&D) initiatives) over time so as to maximize expected net present value. We formulate this problem as a dynamic program but conclude that this approach is too computationally complex to be useful in real situations. So, we investigate the structural properties of the optimal solution and demonstrate that it reduces to a simple policy under certain environmental conditions. Through numerical tests we demonstrate that this simple heuristic performs robustly well on the general PPMP. Hence, we conclude that this policy is a practical way to incorporate economic and timing issues into a multi-dimensional scoring model for addressing real-world project portfolio management situations. In Chapter 3, we take a step back and look at the R&D budget as a whole. We consider a company that generates research concepts and then converts them into product ventures via R&D activities. The resulting products generate revenue in a volatile market over their product lives, which in turns funds further R&D efforts. We seek an R&D funding policy that maximizes expected NPV under these conditions. We formulate the problem using a stochastic control framework and characterize the optimal solution via extensive sensitivity analysis. Then we contrast the optimal policy with some commonly used R&D policies and show that they may be inefficient. Finally, we suggest a class of simple but effective R&D funding policies. Chapter 4 explores the aggregate effect of macroeconomic business cycles on large technology replacement decisions. We show that replacement policy is procyclic and that cyclical variation of the market imposes a premium on the price of technology. Further, we show that this premium is nonlinear in the frequency of phase reversal. The paper also proposes a new theoretical model for describing business cycles that captures some of the most important characteristics while remaining analytically tractable

Last modified

• 09/13/2018

Creator

• Souvik Banerjee

DOI

• https://doi.org/10.21985/N26X5K

Subject

• Industrial Engineering and Management Sciences

Keyword

• Industrial

Date created

• 2008-05-22

Resource type

• Dissertation

Rights statement

• In Copyright