Murray Cantor
Abstract
The investment value of innovation follows from a technology's uncertain net present value and derived ROI calculations.
- Ambler, S. Agile Modeling: Effective Practices for eXtreme Programming and the Unified Process. Wiley, New York, 2002. Google Scholar
Digital Library
- Baldwin, C. and Clark, K. Design Rules Volume 1 The Power of Modularity, MIT Press, Cambridge, MA, 2000. Google ScholarDigital Library
- Berg, C. Value-Driven IT. Cliff Berg Imprints, Reston, VA, 2008. Google ScholarDigital Library
- Biffl, S. Value-Based Software Engineering. Springer-Verlag, Berlin, 2005. Google ScholarDigital Library
- Cooper, R., Edgett, S., and Kleinschmidt E. Portfolio Management for New Products, Second Edition. Basic Books, Cambridge, MA, 2001.Google Scholar
- Damodaran, A. Strategic Risk Taking. Wharton School Publishing, Philadelphia, 2008.Google Scholar
- Fleischman, J. New York City Health and Human Services Connect; private communication.Google Scholar
- Hubbard, D. How to Measure Anything. Wiley, New York, 2007.Google Scholar
- Jones, C. Applied Software Measurement, Third Edition. McGraw Hill, New York, 2009.Google Scholar
- Kauffman, R. and Sougstad, R. Risk management of IT services portfolios: The profit-at-risk approach. Journal of Management Information Systems 25, 1 (Summer 2008), 17--48. Google ScholarDigital Library
- Kauffman, R. and Sougstad, R. Value-at-risk in services-oriented systems and technology investments: A framework for managing project portfolio uncertainties. International Journal of Services Science 1, 3--4 (2008), 225--246.Google ScholarCross Ref
- Kodukula, P. and Papudesu, C. Project Valuation Using Real Options. J. Ross Publishing, Fort Lauderdale, FL, 2006.Google Scholar
- Kroll, P. and Kruchten, P. The Rational Unified Process Made Easy: A Practitioner's Guide to the RUP. Addison-Wesley, Reading, MA, 2003. Google ScholarDigital Library
- Louvier, J. Analyzing Decision Making: Metric Conjoint Analysis (Quantitative Applications in the Social Sciences). Sage Publications, Thousand Oaks, CA, 1988.Google Scholar
- Lutchen, M. Managing IT As A Business. Wiley, New York, 2004.Google Scholar
- Martin, R. Agile Software Development, Principles, Patterns, and Practices. Prentice Hall, Upper Saddle River, NJ, 2002. Google ScholarDigital Library
- Mathews, S. Valuing risky projects with real options. Research Technology Management Journal 52, 5 (Sept. 2009), 32--42.Google Scholar
- McConnell, S. Software Estimation. Microsoft Press, Redmond, WA, 2006. Google ScholarDigital Library
- Michaud, R. Efficient Asset Management. Harvard Business School Press, Cambridge, MA, 1998.Google Scholar
- Mun, J. Applied Risk Analysis. Wiley, New York, 2004.Google Scholar
- Mun, J. Real Options Analysis: Tools and Techniques for Valuing Strategic Investment and Decisions, Second Edition. Wiley Finance, New York, 2005.Google Scholar
- Prigent, J. Portfolio Optimization and Performance Analysis. Chapman & Hall/CRC, Boca Raton, FL, 2007.Google ScholarCross Ref
- Sullivan, K. J., Griswold, W. G., Cai, Y., and Hallen, B. The structure and value of modularity in software design. In Proceedings of the Eighth European Software Engineering Conference, held jointly with the Ninth ACM SIGSOFT International Symposium on Foundations of Software Engineering (Vienna, Austria, Sept. 10--14). ACM Press, New York, 2001, 99--108. Google ScholarDigital Library
- Tockey, S. Return on Software. Addison-Wesley, Reading, MA, 2005.Google Scholar
- U.S. Department of Transportation. Treatment of the Economic Value of a Statistical Life in Departmental Analyses - 2009 Annual Revision; http://ostpxweb.ost.dot.gov/policy/reports/VSL%20Guidance%20031809%20a.pdfGoogle Scholar
- Wiederhold, G. What is your software worth? Commun. ACM 49, 9 (Sept. 2006), 65--75. Google ScholarDigital Library
Index Terms
- Calculating and improving ROI in software and system programs
Recommendations
Approaches of Confirming ROI and Calculating Similarity for ROIBIR System
ICICTA '08: Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 02Approaches of ROI(Regions of Interest) confirmed and similarity calculated are key techniques for ROIBIR (Regions of Interest-Based Image Retrieval) System. In this paper, two approaches of confirming ROI and a Region-based similarity measure approach ...
A Follow-Up Reflection on Software Process Improvement ROI
The software engineering discipline's continuing growth urges a shift toward value-based software engineering, to prove its contributions to the bottom line. However, the number of studies containing return-on-investment (ROI) data on software process ...
A new ROI based image retrieval system using an auxiliary Gaussian weighting scheme
In state-of-the-art region of interest (ROI) based image retrieval systems, the user defined ROI query is considered more effectively reflecting the user's intention than an ROI query automatically selected by the system. Compared with existing image ...
Reviews
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
Access critical reviews of Computing literature here
Become a reviewer for Computing Reviews.
Comments