Academic journal article International Journal of Business and Information

Quantitative Metrics to Assess and Manage Business Contracting Risk Using Risk-O-Meter Software

Academic journal article International Journal of Business and Information

Quantitative Metrics to Assess and Manage Business Contracting Risk Using Risk-O-Meter Software

Article excerpt

1. INTRODUCTION

The sources of business operation vulnerabilities and threats can range from the quality of personnel to macro-economic factors. The consequence to those corporations and organizations that fail to identify and manage vulnerabilities and risks is diminished financial performance, if not business failure. Indeed, the U.S. Census Bureau puts the survival rate of new firms founded in 2005 through 2010 at only 43% [1].

To minimize and avoid such threats and potential business failures, a rational, scientific approach that identifies, assesses, and manages business risk is required. Many methods of predicting business risks and failure have been developed by academia and researchers over the last three decades.

Altman's 1968 model [2] uses discriminant analysis to develop a discriminant function with five financial ratios for predicting business failure and at-risk. Altman's is the first model to use discriminant analysis and is, by far, the most popular and cited research for business at-risk and failure prediction. Other discriminant models - such as those using multivariate discriminant statistical analysis for business at-risk and failure prediction - were also developed by Altman [3], Beaver [4], Courtis [5], and Dimitras et al. [6].

Since Altman's 1968 model was developed, other methods have been developed to improve prediction accuracy. These methods are logit analysis by Ohlson [7], probit analysis by Zmijewski [8], mathematical programming by Gupta et al. [9], expert systems by Messier and Hansen [10], and neural networks by Altman et al. [11]. A complete review of these methods for the prediction of business at-risk and failure can be read in Dimitras et al. [6].

The identification and management of risk are key aspects of successful business operations. The Business Risk-O-Meter tool proposed here provides a special and objective methodology that is critically needed. This pioneering work represents a paradigm shift in risk assessment. The Business Risk-O-Meter provides a quantitative risk assessment, unlike the subjective high-medium-low or red-yellow-green scales commonly seen in other assessment methodologies.

There are other approaches to identifying and managing risk as detailed in the Institute of Management Accountants' Enterprise Risk Management: Tools and Techniques for Effective Implementation [12], but none provide a means of allocating risk mitigation expenditures. In contrast, the Business Risk-O-Meter provides objective and scientific guidance in allocating monetary resources for managing risk in accordance with budgetary constraints. Additionally, the Business Risk-O-Meter provides a means to shift from often subjective and crude risk evaluation mechanisms to a verifiable, quantitative approach to risk management, resulting in an optimized expenditure of risk remediation dollars.

The current research adopts a model of business risk that quantifies the respondent's experience with 10 crucial aspects of business risk. Those responses were subsequently used to calculate the business risk index through a designed algorithm by the principal author. To accomplish this task, the authors collected numerical and/or cognitive data from 40 respondents to supply the input parameters to calculate the quantitative business risk index. This paper not only presents a quantitative model, but also provides a remedial cost-optimized gametheoretic analysis about how to bring an undesirable risk down to a userdetermined "tolerable level." The proposed framework is adaptable and can be customized and configured by the analyst with no custom coding (XML inputs).

2. METHODOLOGY

This applied research implements a methodology on how to reduce business risk using modern probability analysis and game theoretic risk computing. A software-centered holistic approach is proposed to aid management and decision makers in identifying, assessing, and managing business risk. …

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