A Fuzzy Risk Assessment Model (FRAM) for Risk Management (RM)


BY Ashwani Kharola

Govt. Of India, Ministry of Defence

Institute of Technology Management (ITM), Defence R&D Organisation

Uttarakhand, India


This study deals with the application of fuzzy logic reasoning to develop a Fuzzy Risk Assessment model i.e. FRAM to enhance the Risk Assessment (RA) process while considering uncertainties in each phase of RA. The main advantage of using fuzzy reasoning approach is limitations of subjectivity in RA.

In this study a Matlab Simulink model has been build that can effectively estimate the amount of Risk involved under a particular set of situations. FRAM provides a flexible framework built on the top of experience of experts which can prove to be an effective control system for regular RA in an organization.

  1. Introduction 

One of the most important and complicated task of Project Manager is to make effective decisions that could ultimately improves efficiency of Organization. The factors that adversely affect the decisions of Project Manager are Risks associated with the Project. Risk can be defined as, “the chance that an undesirable event will occur and the consequences of all its possible outcomes”. Risk therefore can be conceptually defined as the function of Probability/Likelihood of occurrence of the event and Severity of the event occurring i.e. R=f (Probability, Severity) [1].

Estimating risk involves identifying the events that present hazards and produce risk, communicating the magnitude of the consequences associated with these events and estimating the likelihood of a given risk[2].Since probability of likelihood and consequence of severity are not directly measurable, therefore risks are difficult to measure in crisp terms. Fuzzy logic approach provides a new methodology to deal with these attributes that could only be estimated since exact values are impossible to determine.

This paper is described in six parts viz Part 1 gives Introduction. Part 2 explains Risk Management and Risk Assessment. Part 3 defines the concept of Fuzzy Risk Management. Part 4 shows Modelling and Simulations. Part 5 and Part 6 includes Conclusion and References respectively.


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About the Authors

flag-indiapmwj18-jan2014-kharola-singh-PHOTO1 KHAROLAAshwani Kharola

Ministry of Defence

Government of India

Mr Ashwani Kharola, Government of India, Ministry of Defence, ITM (DRDO), Mussoorie, India, is presently serving as Research Scholar (JRF) in ITM. He is Mechanical Engineer and has completed M. Tech (honours) in CAD/CAM & Robotics and B. Tech (honours) in Mechanical Engineering. He can be contacted at [email protected].