A practical R&amp;D project-selection scoring tool

Henriksen, Anne De Piante; Traynor, Anne

doi:10.1109/17.759144

Cited by 277 publications

(161 citation statements)

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“…Extensive reviews on these methods are given by Gear et al (1971), Augood (1973), Baker (1974), Gear (1974), Baker 26/11/2003 16:06 3 and Freeland (1975), Jackson (1983, Booker and Bryson (1985), Souder and Mandakovic (1986), Weber et al (1990), Martino (1995), and Henriksen and Traynor (1999.…”

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confidence: 99%

Contingent Portfolio Programming for the Management of Risky Projects

Gustafsson

Salo

2005

Operations Research

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Methods for selecting a research and development (R&D) project portfolio have attracted considerable interest among practitioners and academics. This notwithstanding, the industrial uptake of these methods has remained limited, partly due to the difficulties of capturing all the relevant concerns in R&D portfolio management. Motivated by these difficulties, we develop Contingent Portfolio Programming (CPP) which extends earlier approaches in that it (i) uses states of nature to capture exogenous uncertainties, (ii) models resources through dynamic state variables, and (iii) provides guidance for the selection of an optimal project portfolio that is compatible with the decision maker's risk attitude. Although CPP is presented here in the context of R&D project portfolios, it is applicable to a variety of investment problems where the dynamics and interactions of investment opportunities must be accounted for. The selection of research and development (R&D) projects has attracted considerable interest in the literatures on technology management and operations research (OR). These projects involve many characteristics -such as uncertainties and interdependent resource constraints -that are potentially amenable to analysis by OR techniques. Indeed, there exists a variety of related methods, ranging from scoring methods such as value trees (e.g., Keeney and Raiffa, 1976) and the Analytic Hierarchy Process (AHP; Saaty 1980) to optimization models (see, e.g., Luenberger 1998, p. 106, Gear et al. 1971, Baker 1974, Baker and Freeland 1975, Jackson 1983, Fox et al. 1984, Schmidt and Freeland 1992, Ghasemzadeh et al. 1999 and dynamic programming methods such as decision trees and real options (Hespos and Strassmann 1965, Dixit and Pindyck 1994, Trigeorgis 1996. Yet, despite this plethora of methodological approaches, the industrial uptake of these methods has remained limited (see, e.g., Liberatore and Titus 1983), possibly due to the difficulties of capturing the full range of phenomena that are relevant to the problem of selecting and managing R&D projects.Building on the literatures on decision analysis, technology management, and portfolio optimization, we develop Contingent Portfolio Programming (CPP) as a modeling framework which accommodates most of the characteristics that are relevant to R&D project selection. In CPP, R&D projects are regarded as risky investment opportunities that consume and produce several resources over multiple time periods. The staged nature of R&D projects is captured through project-specific decision trees (cf. Cooper 1993;Gear and Lockett 1973) which support managerial flexibility by allowing the decision maker (DM) to take stepwise decisions on each project in view of most recent information (Trigeorgis 1996). Uncertainties, on the other hand, are modeled through a state tree in the spirit of stochastic programming (see, e.g., Birge and Louveaux 1997).The DM's risk attitude is captured through an objective function that is a combination of a mean-risk model (Markowitz 1959(Ma...

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confidence: 99%

Contingent Portfolio Programming for the Management of Risky Projects

Gustafsson

Salo

2005

Operations Research

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“…Hundreds of studies have been published on project selection, dating back for more than 50 years and illustrating many different approaches. According to Henirksen and Traynor (1999) This review looks at what Henriksen and Traynor (1999) say about project section using a practical project-selection scoring tool.…”

Section: Project Selectionmentioning

confidence: 99%

Optimizing Project Selection at the Department of Public Works, Presidio of Monterey

Whitfield¹

2012

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to ABSTRACT (maximum 200 words)Large organizations suffer from the problem of insufficient resources to complete all project requests submitted throughout the year. With the high volume of project requests received and the limited resources available, picking those projects to fund that return the highest value to the organization can be a daunting task. The purpose of this research is to help management make an optimal decision, and determine whether the introduction of an Excel-based optimization model would benefit an organization in its selection process. This research focuses on the project selection process for the Department of Public Works for the Presidio of Monterey Army instillations in the Monterey area. The results from the current fiscal year's selection process are compared with the results from the optimization model. This demonstrates how analytical tools, specifically an optimization model, can add value to an organization by increasing the number of projects selected. One of the conclusions of this thesis is that for the model to properly reflect the values of the organization, a different weighting system would be needed. Therefore, this research recommends that the optimization model be used, but only as a non-biased opinion on which projects should be selected. SUBJECT TERMS Knapsack, Excel, Optimization Model, DPW, POM, NUMBER OF PAGES 67 PRICE CODE SECURITY CLASSIFICATION OF REPORT Unclassified SECURITY CLASSIFICATION OF THIS PAGE Unclassified SECURITY CLASSIFICATION OF ABSTRACT Unclassified LIMITATION OF ABSTRACT UU

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“…Для того чтобы повысить качество принимаемых решений, в частности при подготовке программ ИиР, разработаны универсальные многокритериальные методы определения приоритетов [Henriksen, Traynor, 1999]. Основное преимущество подобных инструмен-тов -создание системы регулярного анализа будущих возможностей и ее использование в оценке потенци-альных исследовательских проектов, что обеспечива-ет открытость и последовательность формирования и реализации стратегии [Salo et al, 2003].…”

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Identification of Prospective Collaboration Networks in International R&D Programmes

Brummer¹,

Liesiö²,

Nissinen³

et al. 2011

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A practical R&D project-selection scoring tool

Cited by 277 publications

References 53 publications

Contingent Portfolio Programming for the Management of Risky Projects

Contingent Portfolio Programming for the Management of Risky Projects

Optimizing Project Selection at the Department of Public Works, Presidio of Monterey

Identification of Prospective Collaboration Networks in International R&D Programmes

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