Utilizing Value Functions for Evaluating the Performance of Project Alternatives

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This article gives an overview of what a value function is and a guide on constructing various value functions. There will also be a discussion on how and when to use value functions and their application in program, project, and portfolio management. Finally, this article will discuss some of the limitations of value functions and suggest alternative tools for when value functions are not applicable or practical.
 
This article gives an overview of what a value function is and a guide on constructing various value functions. There will also be a discussion on how and when to use value functions and their application in program, project, and portfolio management. Finally, this article will discuss some of the limitations of value functions and suggest alternative tools for when value functions are not applicable or practical.
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== Value Functions==
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== Application ==
 
== Application ==

Revision as of 01:45, 5 April 2023

Value functions are a value measurement approach that is often used in multi-criteria decision analysis (MCDA), which strives to improve decision-making. MCDA is a decision support tool used to assess solution alternatives based on a wide range of criteria, which can be both monetized and non-monetized. To assess the various criteria, value functions can be utilized to assign value to the various alternatives fairly. Value functions are thus a mathematical model that translates stakeholder preferences into a measurable scale.

Value functions are especially useful when comparing different choices (this can be solutions, projects, or other decision-related alternatives) as it converts the different choices, or evaluation criteria, into a common scale for a fair and less biased comparison as value functions assign a specific value to each solution alternative, creating a preferential structure. Consequently, value functions can assess various aspects of a solution and give a concrete value to each solution alternative to support a less biased decision basis.

As the tool can support various decision-makers in their choices, it can be used in all sorts of decisions, depending on how the function is defined. For use in portfolio management, the value function should be defined based on how well a given project meets the organization’s strategic goals. The project which performs best on the value function(s) will thus display the project that is most beneficial to the portfolio. Likewise, program managers can use value functions to identify projects which best fulfill the program’s objectives, to find the project that best balances competing demands and possible trade-offs, and to find the optimal project pool. Finally, it can be used in projects to find the best alternative solution or support the resources allocated to project activities.

This article gives an overview of what a value function is and a guide on constructing various value functions. There will also be a discussion on how and when to use value functions and their application in program, project, and portfolio management. Finally, this article will discuss some of the limitations of value functions and suggest alternative tools for when value functions are not applicable or practical.

Contents

Value Functions

Application

• Different creation methods (eliciting scores and weight for value functions)

• How to read and understand the value functions

• How to consult stakeholders and present the results (handling stakeholder input, handling stakeholder presentation) (often in decision making, the basis cannot be to complicated)

Limitation

• Stakeholder sensitivity

• Criteria selection

• Application limitations


Bibliography

von Winterfeldt, D., and Edwards, W. Decision Analysis and Behavioral Research. Cambridge University Press

Belton, V., and Stewart, J.T. Multiple criteria decision analysis: an integrated approach, Kluwer Academic Publishers, London.

Stewart, T. J. Use of Piecewise Linear Value Functions in Interactive Multicriteria Decision Support: A Monte Carlo Study. Management Science 39, pp. 1369-1381, Informs.

Stewart, T. J. Robustness of Additive Value Function Methods in MCDM. Journal of Multi-Criteria Decision Analysis 5, pp. 301-309, Wiley.

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