A Real Options Perspective On R&D Portfolio Diversification

This paper shows that the conditionality of investment decisions in R&D has a critical impact on portfolio risk, and implies that traditional diversification strategies should be reevaluated when a portfolio is constructed. Real option theory argues that research projects have conditional or option-like risk and return properties, and are different from unconditional projects. Although the risk of a portfolio always depends on the correlation between projects, a portfolio of conditional R&D projects with real option characteristics has a fundamentally different risk than a portfolio of unconditional projects. When conditional R&D projects are negatively correlated, diversification only slightly reduces portfolio risk. When projects are positively correlated, however, diversification proves more effective than conventional tools predict.real options;portfolio analysis;research & development

Dynamic order acceptance and capacity planning within a multi-project environment.

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for regular as well as nonregular capacity.We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as an optimal capacity allocation for accepted projects, both with an eye on maximizing the expected revenues within the problem horizon.We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selectionand capacity usage.Capacity planning; multi-project; Order acceptance; Stochastic dynamic programming;

Application of Gini coefficient and semivariance as estimators of risk in project selection

La presente investigación tiene sus orígenes en la creciente necesidad de conocer y aplicar metodologías de selección y ordenamiento de proyectos, enmarcadas dentro de la utilización eficiente y efectiva de los diferentes recursos productivos de las organizaciones, que por ser escasos, deben utilizarse de la mejor manera posible. Lo anterior llevó al diseño y desarrollo de una herramienta de optimización que permite planear y ordenar un conjunto de proyectos dentro de un plan de inversión, de tal forma que se maximice el beneficio total generado por su ejecución. Por lo cual, se muestra de forma detallada la formulación de dos modelos matemáticos teóricos (Media-Gini y Media-Semivarianza) utilizados para hallar la solución de la problemática, es decir la obtención de un portafolio óptimo. El primer modelo involucra en su función objetivo tres índices de valoración: económico, financiero y social, y utiliza el coeficiente de Gini como estimador del riesgo del portafolio, el segundo modelo tiene una función bi-objetivo, que consiste en maximizar el valor presente neto (VPN) del portafolio y la minimización del riesgo de éste, los cuales normalmente se encuentran en conflicto, ya que la optimización de uno usualmente va en detrimento del otro, el estimador del riesgo en este modelo es la semivarianza. Con el objetivo de validar los modelos y su explicación metodológica, estos fueron aplicados a un caso particular adaptado de una empresa de servicios públicos de la región, teniendo como base la información suministrada por ésta, en cuanto a disponibilidad de capital, mano de obra y maquinaria, así mismo sus expectativas en torno a el riesgo máximo permitido y la rentabilidad mínima esperada

Methodology for project portfolios selection using multicriteria of the capm, semi variation, and the gini risk coefficient

Consta no arquivo em PD

Methods to Support the Project Selection Problem With Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies

The United States Air Force relies upon information production activities to gain insight regarding uncertainties affecting important system configuration and in-mission task execution decisions. Constrained resources that prevent the fulfillment of every information production request, multiple information requestors holding different temporal-sensitive objectives, non-constant marginal value preferences, and information-product aging factors that affect the value-of-information complicate the management of these activities. This dissertation reviews project selection research related to these issues and presents novel methods to address these complications. Quantitative experimentation results demonstrate these methods’ significance

Twenty years of linear programming based portfolio optimization

a b s t r a c t Markowitz formulated the portfolio optimization problem through two criteria: the expected return and the risk, as a measure of the variability of the return. The classical Markowitz model uses the variance as the risk measure and is a quadratic programming problem. Many attempts have been made to linearize the portfolio optimization problem. Several different risk measures have been proposed which are computationally attractive as (for discrete random variables) they give rise to linear programming (LP) problems. About twenty years ago, the mean absolute deviation (MAD) model drew a lot of attention resulting in much research and speeding up development of other LP models. Further, the LP models based on the conditional value at risk (CVaR) have a great impact on new developments in portfolio optimization during the first decade of the 21st century. The LP solvability may become relevant for real-life decisions when portfolios have to meet side constraints and take into account transaction costs or when large size instances have to be solved. In this paper we review the variety of LP solvable portfolio optimization models presented in the literature, the real features that have been modeled and the solution approaches to the resulting models, in most of the cases mixed integer linear programming (MILP) models. We also discuss the impact of the inclusion of the real features

Criteria Used in Research & Development Project Selection

Devido à corrida em busca de inovação, organizações do Brasil e do mundo todo enfrentam desafios constantes para se manterem relevantes no mercado, buscando as melhores formas de gerenciar seus projetos e utilizando dos recursos existentes para maximizar os benefícios e, em alguns casos, minimizar o risco ou custos de seus projetos. De acordo com uma revisão sistemática de 61 artigos, escritos entre 1970 a 2018, que utilizam métodos multi-critérios para tomada de decisões (MCDM) para selecionar projetos de Pesquisa & Desenvolvimento (P&D), apenas 19 deles dão uma explicação adequada dos critérios utilizados. Assim, a fim de contribuir com o processo de seleção de projetos, o objetivo principal deste trabalho é mostrar quais os tipos de critérios que apresentam maior relevância sobre os demais. Todo o processo é feito através de uma revisão sistemática da literatura: desde a seleção dos artigos, o agrupamento dos critérios e sua avaliação por dois especialistas utilizando o método Analytic Hierarchy Process (AHP). Ao final, percebe-se o quão importante é o benefício financeiro para os especialistas, e que a inovação não é considerada tão relevante para eles e para a maioria dos autores dos artigos analisados

Co-selection in R&D project portfolio management

In the study I analyze the conflicting aspects of project portfolio evolution in a firm. The evolutionary principles of variation, selection and retention are applied to the management of new product development projects. Managers select projects for prioritization. A selection rule is the prioritization rule. In biology, living creatures develop specific features for adaptation as a result of selection rules. However, the selection of specific adaptive features carries along the retention of other, even unforeseen non-adaptive features. Drawing on the evolutionary principles forwarded by Darwin I examine how they manifest in the project portfolio. I define this non-adaptive mechanism as co-selection. By analogy, in portfolio management, if the selection rule for project priority is high revenue and feasibility to global access, other features also survive when the selection rule relating to the prioritization of projects is applied. The evolution of the new product development project portfolio in the case firm displays conflicting trends in the emerging project portfolio over time. Managers pursue prioritization to decrease product development times. But, alas, in the project portfolio the prioritized projects age to a greater degree than non-prioritized projects. Managers prioritize the projects held by the focal business unit more often than those of other business units. However, ultimately the focal business unit has less than a due share of prioritized projects in the portfolio. The results of this study question the applicability of optimizing models in R&D portfolio management in the presence of co-selection. The project portfolio management literature does not provide a mechanism to account for this type of portfolio development. Co-selection provides a mechanism that explains the observed evolution. The study contributes to the conceptualization of the notion of co-selection. The study also provides empirical evidence on co-selection, a non-adaptive evolutionary mechanism to modify R&D project portfolio outcome. The findings give a better understanding of portfolio management of R&D driven new product development projects