A fuzzy risk assessment model for software promotion risks

Abstract

Yuri P. Ekhlakov - Professor, Head of Department of Data Processing Automation, Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics
Address: 74, Vershinina street, Tomsk, 634034, Russian Federation.
E-mail: upe@tusur.ru

Natalya V. Permyakova - Post-graduate student, Department of Data Processing Automation, Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics
Address: 74, Vershinina street, Tomsk, 634034, Russian Federation.
E-mail: pnv@muma.tusur.ru

      This paper discusses the issue of risk assessment and analysis in development and implementation of a software promotion scheme, and demonstrates feasibility of fuzzy analysis as a mathematical tool for this purpose. It formulates the marketing goal of the scheme as being “to achieve the target sales within the specified period of time under a limited budget”. Given the clear logical connection between the goals of the scheme and the associated risks, the paper identifies three types of risks: failure to meet scheme implementation schedule, failure to meet target sales, failure to stay within the budget. Based on analysis of available publications, risk-contributing factors have been identified, a classification of such factors and their qualitative and quantitative characteristics have been offered. A real case study of building of a fuzzy risk assessment and analysis model for market launch of a Web-oriented geo-information technology for an enterprise master plan has been considered. The analysis has identified eleven input linguistic variables (risk-contributing factors) having impact on scheme risks and three output variables (degree of factors’ impact on the total risk of the project, budget overrun rate and target sales achievement rate). Two databases of rules have been built: the rules of the first database are used to determine the degree of factors’ impact on the total risk. The rules of the second database are applied to determine the risk exposure of the main goals of the scheme. The authors have used Mamdani’s fuzzy inference algorithm to calculate values of each of the risks and to offer their risk response scenarios. In practical terms the results are useful for heads of small IT companies and marketing experts in promotion of new products in industrial markets.