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Cited By

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Sakthivel S(2023)A critical review of cost-benefit analysis for business software investmentsAmerican Journal of Business10.1108/AJB-09-2022-014538:4(229-247)Online publication date: 9-Oct-2023
https://doi.org/10.1108/AJB-09-2022-0145
Horcas JGalindo JHeradio RFernandez-Amoros DBenavides D(2023)A Monte Carlo tree search conceptual framework for feature model analysesJournal of Systems and Software10.1016/j.jss.2022.111551195:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111551
Oni OLetier E(2021)Analyzing Uncertainty in Release Planning: A Method and Experiment for Fixed-Date Release CyclesACM Transactions on Software Engineering and Methodology10.1145/349048731:2(1-39)Online publication date: 24-Dec-2021
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Index Terms

Calculating and improving ROI in software and system programs

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Reviews

Reviewer: Alexei Botchkarev

Return on investment (ROI) is one of the most popular metrics for assessing the value of software and systems during project selection and solution acquisition. The topic of ROI evaluation is highly important to chief information officers (CIOs), chief technology officers (CTOs), chief financial officers (CFOs), business, and information technology (IT) project teams. There are multiple ways to calculate the ROI. Murray Cantor, an IBM Distinguished Engineer, presents a novice approach. It is worth noting because it offers the potential of aligning ROI evaluations with program management decisions to maximize the enterprise strategic value. The foundational notion of this approach is that future costs and benefits, which constitute the main components of ROI calculations, are uncertain and need to be presented using random variables. An investment value (IV) metric is proposed, which is based on the net present value (NPV) of the program, and all future values are defined as random variables. Any future value of costs and benefits is specified through a triangle distribution with high, expected, and low values. The IV probability distribution function is calculated through Monte Carlo simulation. The value of the program is estimated by the mean of the IV. The likelihood of delivering the program value (or the investment risk) is calculated as the standard deviation divided by the absolute value of the mean. The last parameter does not seem to be intuitive for investors. The validity of the proposed method has been tested in a real-life healthcare program, in New York, using IBM Rational Focal Point. The utility (practical use) of the approach is complicated by the rather high requirements of the members of the implementation project team, who need to have a good understanding of statistics methods, modeling, and simulation. Readers who are interested in ROI can find additional information on the subject [1,2]. Online Computing Reviews Service

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Communications of the ACM Volume 54, Issue 9

September 2011

121 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1995376

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Sakthivel S(2023)A critical review of cost-benefit analysis for business software investmentsAmerican Journal of Business10.1108/AJB-09-2022-014538:4(229-247)Online publication date: 9-Oct-2023
https://doi.org/10.1108/AJB-09-2022-0145
Horcas JGalindo JHeradio RFernandez-Amoros DBenavides D(2023)A Monte Carlo tree search conceptual framework for feature model analysesJournal of Systems and Software10.1016/j.jss.2022.111551195:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111551
Oni OLetier E(2021)Analyzing Uncertainty in Release Planning: A Method and Experiment for Fixed-Date Release CyclesACM Transactions on Software Engineering and Methodology10.1145/349048731:2(1-39)Online publication date: 24-Dec-2021
https://dl.acm.org/doi/10.1145/3490487
Horcas JGalindo JHeradio RFernandez-Amoros DBenavides DMousavi MSchobbens P(2021)Monte Carlo tree search for feature model analysesProceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A10.1145/3461001.3471146(190-201)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1145/3461001.3471146
Holgeid KJørgensen MSjøberg DKrogstie J(2021)Benefits management in software development: A systematic review of empirical studiesIET Software10.1049/sfw2.1200715:1(1-24)Online publication date: 3-Feb-2021
https://doi.org/10.1049/sfw2.12007
Holgeid KJorgensen M(2020)Benefits management and agile practices in software projects: how perceived benefits are impacted2020 IEEE 22nd Conference on Business Informatics (CBI)10.1109/CBI49978.2020.10057(48-56)Online publication date: Jun-2020
https://doi.org/10.1109/CBI49978.2020.10057
Karimpour RRuhe G(2017)Evolutionary robust optimization for software product line scopingComputer Languages, Systems and Structures10.1016/j.cl.2016.07.00747:P2(189-210)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.cl.2016.07.007
Gatrell M(2016)The Value of a Single Solution for End-to-End ALM Tool SupportIEEE Software10.1109/MS.2016.10933:5(103-105)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1109/MS.2016.109
Cantor MMacIsaac BMannan R(2016)Steering Software Development Workflow: Lessons from the InternetIEEE Software10.1109/MS.2016.10533:5(96-102)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1109/MS.2016.105
Karimpour RRuhe GEsparcia-Alcázar A(2015)A Search Based Approach Towards Robust Optimization in Software Product Line ScopingProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2764650(1415-1416)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2764650
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Recommendations

Approaches of Confirming ROI and Calculating Similarity for ROIBIR System

A Follow-Up Reflection on Software Process Improvement ROI

A new ROI based image retrieval system using an auxiliary Gaussian weighting scheme

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