A decision model for interdependent information system project selection

Santhanam, Radhika; Kyparisis, George J.

doi:10.1016/0377-2217(94)00257-6

Cited by 170 publications

(78 citation statements)

References 46 publications

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“…There is consequently a need for dynamic modeling of resources; early attempts into this direction have been presented by Brandenburg and Stedry (see Gear et al 1971). The consideration of project interactions entails challenges, too (see, e.g., Santhanam and Kyparisis 1996, Schmidt 1993, Czajkowski and Jones 1986, Fox et al 1984, Aaker and Tyebjee 1978, Gear et al 1971. The approach of Santhanam and Kyparisis (1996), for example, requires the analyst to specify the synergetic impacts due to the simultaneous selection of two or more projects.…”

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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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“…The approach of Santhanam and Kyparisis (1996), for example, requires the analyst to specify the synergetic impacts due to the simultaneous selection of two or more projects. Such impacts may be difficult to assess, unless there is a clear understanding of how these synergetic impacts are generated and how they map onto financial outcomes.…”

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Contingent Portfolio Programming for the Management of Risky Projects

Gustafsson

Salo

2005

Operations Research

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“…Kumar et al (2002) applied basic statistics in a real ERP selection case. Mathematical optimization methods such as goal programming, 0-1 binary programming and non-linear programming are also widespread been presented (Santhanam and Kyparisis, 1995, 1996, Lee and Kim, 2000, Talluri, 2000. Owing to the essence of IT system selection problem is a multi criteria decision making (MCDM) process, several papers adopted analytic hierarchy process (AHP) to be the analytical tool (Schniderjans andWilson, 1991, Wei et al, 2005).…”

Section: Previous Research Erp Software Selection Methodsmentioning

confidence: 99%

Selecting the Optimal ERP Software by Combining the ISO 9126 Standard and Fuzzy AHP Approach

Liang

Lien

2006

CMR

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Enterprise resource planning (ERP) software is one of costly and crucial projects for business investment. Due to the selection criteria of ERP software are numerous and fuzziness, selecting the optimal ERP software is a critical process in the early phase of an ERP project. This paper presents a practical procedure which combines both the ISO 9126 standard and the fuzzy analytic hierarchy process (FAHP) approach to optimize the ERP selection problems.There are total 32 criteria sifted out which include 21 software quality criteria of ISO 9126 standard. Two practical cases which belong to different industries are applied to illustrate the practicality of the procedure, one is semiconductor manufacturing industry and another is chain store retailer service industry. We find that there are diverse weights for software quality criteria between these two industries. We also find that the cost issue and time issue are significantly important in both two cases.

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“…For example Taudes et al (2000) as well as Benaroch and Kauffman (1999) evaluate single IT infrastructure investments using real option approaches. Santhanam and Kyparisis (1996) and Verhoef (2002) present quantitative approaches how to aggregate single IT investments to evaluate a firm's IT portfolio. Thereby, Verhoef (2002) just like Boehm et al (2000) address the impact of different project sizes or investment levels on the project's business value.…”

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confidence: 99%

The optimal level of CRM IT investments

et al. 2012

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In light of the growing relevance of customeroriented business strategies IT investments in the field of Customer Relationship Management have increased considerably. However, firms often could not realize sufficient returns on these IT investments. One major reason for this failure seems to be the lack of appropriate approaches to determine the economic impact of such investments ex ante. Therefore, we develop an economic model to determine the optimal level of Customer Relationship Management IT investments. Using this approach, firms can evaluate, to what extent investments in Customer Relationship Management IT are reasonable. One major result is that in most cases the "all or nothing strategy" pursued by many firms does not lead to the optimal level of investments. To illustrate the practical utility and applicability of the approach, we provide a real world example of a German financial services provider.

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A decision model for interdependent information system project selection

Cited by 170 publications

References 46 publications

Contingent Portfolio Programming for the Management of Risky Projects

Contingent Portfolio Programming for the Management of Risky Projects

Selecting the Optimal ERP Software by Combining the ISO 9126 Standard and Fuzzy AHP Approach

The optimal level of CRM IT investments

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