Estimating and Managing Enterprise Project Risk Using Certainty

Estimating and Managing Enterprise Project Risk Using Certainty

Scheljert Denas (Tulane University, New Orleans, LA, USA)
Copyright: © 2017 |Pages: 13
DOI: 10.4018/IJRCM.2017040104
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Abstract

The literature reports that 73% of enterprise projects fail due to insufficient risk estimation and management. This study focuses on developing a model to estimate the risk in enterprise software development projects. The traditional practice of measuring the risk in enterprise projects uses risk exposure which is unable to quantify the risk beyond the expected value of the socio-economic cost. To address this, the current study examines how enterprise project managers may quantify the uncertainty underlying risk by calculating certain probability and then using a probability distribution to determine the unknown probability values. Thus, this paper is unique in that it presents a model to measure the risk based on certain probability rather than unknown probability. A case-study of 19 enterprise software projects is used to apply and validate the model.
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1. Introduction

Nowadays businesses recognize the benefits of using project management to accomplish corporate objectives but at the same time the importance of risk management has also become important. According to the literature, only about 27% of the enterprise projects have been successful with the remaining 73% being challenged or a complete failure (Galorath, 2008; Haughey, 2009). Therefore, measuring and managing enterprise project risk is recognized as a critical success factor for effective corporate performance (Ruefli, Collins, & Lacugna, 1999; Bakker, Boonstra & Wortmann, 2010; Dey, Tabucanon & Ogunlana, 1994). According to Smith and Fischbacker (2009), over 85% of enterprise projects fail and the underlying reason is the lack of risk management. Therefore, if so many enterprise projects are failing due to insufficient risk management, then we need more research to explain how to estimate risk in enterprise projects.

Numerous algorithms have been proposed in the literature to estimate the risk in enterprise projects which include factors of strategic performance, investment returns and socio-economic impact (Baucus et al., 1993; Chakravarthy, 1986; Miller & Reuer, 1996; Wit, 1988; Sherer, 1994).. Boehm (1991) showed that the measured risk of enterprise projects help to select a project management option among various management alternatives. Researchers have adopted different expressions of risk in their specific fields of interest. In essence, risk is referred to as the possibility of loss, which is the literal meaning of uncertainty (Goodwin & Strang, 2012) and when the socio-economic cost is added, this becomes the expected value or cost of risk. The central notion of risk is that it is an event that may or may not take place (Aven & Renn, 2009); and it poses a threat to the successful completion of a project (Bannerman, 2008). The risk of an enterprise project is defined as events that bring adverse cost impacts to the enterprise project and their occurrence can be defined with a probability. Therefore, risk events add additional cost to the estimated cost; hence, risk is the cost due to unexpected events.

The cost of an enterprise project is the amount of man-month effort that is needed to complete the enterprise project (Pfleeger & Atlee, 2006). There are various cost estimation models available for the development of enterprise projects (Pfleeger & Atlee, 2006). Traditional cost estimation models produce single point estimates of cost of enterprise project (Lum et al., 2003; Dillibabu & Krishnaiah, 2005; Karen et al., 2003). However, single point estimates do not capture the cost due to risk events. More recently, probabilistic models are utilized to model the cost of enterprise projects. Probabilistic models represent random values where each random value has a probability; hence, the impact of risk events can be mapped to random cost impacts. Kitchenham et al (1997, 2003) proposed gamma distribution to represent the cost of enterprise projects where is the gamma distribution and and are the parameters of the gamma distribution [Appendix B]. Whereas, Fairley (1995) generated a discrete non-parametric probability distribution model of the cost of enterprise projects using Monte Carlo simulation.

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