Research paper on An analytical overview of the techniques in quantitative risk management.

Running Head: CURRENT TECHNIQUES IMPLEMENTED IN CONSTRUCTION INDUSTRY TO ELIMINATE SECURITY RISKS 2

CURRENT TECHNIQUES IMPLEMENTED IN CONSTRUCTION INDUSTRY TO ELIMINATE SECURITY RISKS 2

Current Techniques Implemented in the Construction Industry to Eliminate Security Risks

Group 4

Balaram Chekuri

Laxmi Sravani Vallurpallis

Mohan Kadali

Shivasai Pabba

Vaagdevi Jali

University of the Cumberlands

ITS835-41 Enterprise Risk Management

Residency Assignment Research Paper

Professor Dr. James C. Hyatt

10/20/2019

Statement of the Problem and it is Setting

Risk management has been one of the breakthroughs for the modern world, and at the same one of the intellectual achievements is the identification, transformation, or risk and going from a world that described risk as fate to a world that looks at risk as an area of study. Risk management is the utilization of risk analysis to come up with management strategies used to reduce risk. Whereby generally in project management there are the two techniques categories qualitative; that involves impact and probability assessment, expected value calculations and influence diagrams and quantitative; that typically focuses on the overall risk which is managed more with numerical approach, and has techniques such as decision trees, Monte Carlo analysis and sensitivity analysis (McNeil et al. 2015). In various fields, there is enormous risk faced, and at the same time, there is a corresponding quantitative technique that can be used to address the risk. The focus is on the quantitative risk management techniques; they are based on scientific, mathematical and statistical background, that promise to give thorough and detailed management and quantification of risk that is imperative for designing the response (Teixeira et al. 2015).

This paper, based on the construction industry, provides an overview of the analytic overview of different quantitative risk analysis techniques. There is a focus on building on the existing quantitative techniques that are best for the construction industry around the world when it comes to utilizing relevant techniques after a qualitative risk analysis. In addition to this, there is looking further into the techniques and their details.

Guiding Questions

· Does Your Risk Management Process Address Root Cause of Failure?

· Are there gaps in this field?

· What Does Your Business Performance Tell You About Risk?

· What Do Controls Tell You About Your Risks?

· The main focus is looking at the practitioners and researchers looking: at the reason why they should simplify the existing techniques?

· Which, at the same looks at the research gaps in this field and propose areas of further research for project risk management in construction, which will improve the existing techniques.

Assumptions

Project Risk management will foresee and reduce the impact of risk on management.

Schedule, Time, and budget risk are the point of focus in construction.

Project Risk Management has benefitted the construction industry through the use of quantitative risk management techniques for the analysis of risk (Baker et al.1999).

Construction projects have a complex working environment, thus having the risks.

Delimitations and Limitations

Some apparent limitations and delimitations are presented in this research based on construction and quantitative risk management techniques that are, according to Bowles & Chauhan 2004.

Delimitations

· It provides quantification of construction safety aspects, through the utilization of systematic process based on a risk metric.

· Points toward the direction of better understanding, identification, and communication of safety issues in construction.

· Assures that critical issues have addressed after consideration.

· Estimated uncertainties have been considered.

· A more in-depth supporting analysis is included.

Fig1: The basis of formulating the interview questions

Limitations

· Estimation of probabilities is encountered with uncertainty.

· Cost

· Terminology that is new and complex.

· Decision-makers are encountering communication uncertainties.

· Experienced practitioners and standardized procedures are missing.

Fig2:Risk Management approach at site level

Importance of the Study

The significance of this study is to look at the present qualitative management techniques and at the same time building on existing quantitative methods used in construction globally. Looking at the research gaps in quantitative risk management techniques and propose further research. This will involve practitioners and researchers to stick to the construction of quantitative risk management and work harder to improve on the present methods and build new quantitative risk management techniques.

Fig3: Decision making tree analysis

Review of Literature

According to Thaheem et al. 2012, through a survey, the following revelations were made. First of all, the study was aimed at getting to know the degree of penetration, acceptance, and usage of the project management techniques, processes, and software tools in the field of construction. Among other findings, some revelations were unearthed based on quantitative analysis techniques whereby 64% of the respondents preferred expert judgment, the rest preferred interviewing as a technique as a quantitative analysis technique, at the same tim2% of the respondents did not use any methods of quantitative analysis. Which shows the industry trends in terms of project risk management. This indicates that there is a problem in the techniques of risk management, that is why the following literature will look at the various techniques and why they might not be considered by professionals and what can be improved. The following literature was carried out in a comprehensive manner that looked at 12 techniques of qualitative risk analysis, which are described in the following sections.

Bayesian Method

Reverend Thomas Bayes developed this method in 1764, whereby it was based on the Bayesian theory that offers “the possibility to use personal and objective probability estimates changing as new data appear as elements of uncertainty are numerous, subjective and may be revised, following the acquisition of information." There has been the development of procedures using the Bayesian models, which are utilized to change the initial values of an aspect-based ion the results of an experiment. According to (Anderson et al. 1999), the probability of an event to occur/happen is conditional to the unknown/uncertain event.

Belief Functions Method

This is based on the Dempster-Shafer theory, which is a generalized Bayesian theory that focuses on subjective probability. This method gives the green light of combining the evidence sources and results in a degree of belief that takes notes of all evidence available even in cases whereby sufficient information is inaccessible. Belief Function in a hypothesis is by adding all the masses of the sets enclosed by the hypothesis. Denoted as ‘Bel,' it measures the evidence strength in favor of various propositions and ranges from 0 to 1, which is no evidence to certainty, respectively (Shafer, 1976).

Decision-Making Matrix

This is also known as a risk matrix; it is a graphical tool which is used quantifying risk as a product of both occurrence and consequences of an event. The information on both the implications and occurrence of an event is combined with the risk matrix, and that is if the vent occurs. The main aim of the risk matrix is to prioritize after evaluation of the list of options by creating a weighted criteria list for the evaluation of every choice. It is principal based and simple, at the same time based on the expert judgment in addition to calculations used to obtain the results of the matrix. There is a lot of subjectivity in this model, which requires prepared and experienced participants (Barringer, 2008).

Decision Tree Analysis

It is a technique that utilizes graphics and contains the consideration of different situations and what implications each scenario holds, which compares these after the best option in each case is chosen. It involves the probability of occurrence, cost of each choice, which helps to assign an outcome and value. Decision trees are highly efficient in terms of the structure that is used to lay all the options and the conceivable outcomes each option might be investigated. This technique helps in creating a picture that is balanced of the regards and risks that are associated with each possible action taken. The decision tree analysis is flexible, but at the same time, can be applied to different project situations in risk management. Whereby there are types of risk that the decision tree can handle, and at the same time, there are types of risks that cannot be handled by the decision tree analysis (Olivas 2007).

Failure Mode and Effect Analysis (FEMA)

The Failure Mode and Effect analysis is a tool that evaluates risk priorities, evaluates potential product failures, and helps in the assessment and determination of the impact of the cause of the risks so that the identified problems might be avoided. After the risk is identified, the members of the project team assess the extent of the current control mechanism that can detect the cause of failure before its occurrence, which creates time for corrective action. Therefore, this technique is utilized for two reasons; prevent operational deficiencies, error reduction throughout project development, and to avoid design modification extra cost through discovering the problem in the initial design (Snee & Rodebaugh, 2008).

Fault Tree Analysis (FTA)

This technique was developed in 1962 by Bell Telephone Laboratories for the US Airforce to be utilized for the Minuteman system. It is structured a graphic model that has pathways in the system, which leads to undesirable loss and foreseeable events. The paths in this system interconnect conditions and events that are contributory by using the standards logic symbols. Therefore, this system represents a sequential and parallel combination of interrelated logic of events and possible failures that leads to the undesired event (Clemens 2003). It is imperative to establish that a fault tree is not suitable for all probable causes of a system failure or a model for all possible system failures (Ortmeier & Schellhorn, 2006).

Fig4: Fault Tree Analysis (FTA)

Interviewing

This technique is used to assess the impact and possibilities of achieving specific aims based on the input of relevant subject matter experts and stakeholders. The interview is held in a way that there is an excellent way to get pessimistic, optimistic, and most possible scenarios for the given objective. Risk practitioners have some risk that has been used to come up with an estimate which the project manager can use to identify almost 80% of risk through an approach of a structured interview (Hillson, 2004).

Monte Carlo Method

This method got its code name from the work of Ulam and Von Neuman during world war II for the atom bomb, where this method was used for integrating mathematical functions. This means that this technique was approximately developed since the 1940s, with a modern example of the roulette game, which was a perfect example for random number generation, which was well known in the Monte Carlo Casinos in Monaco. Whereby until technological advancement and the rise of computers, this technique was not used as much as before (Pengelly, 2002). This method mainly is based on the application of the laws of statistics and probability to physical and natural sciences, and the secret is the multiple distributions of random numbers that show a particular process inside a chain process. Therefore, this method is perfect for investigating the effect on strategy the initial risks as non-linear and simultaneous interaction that may have an impact on the theoretical results.

Probability Distributions

This technique describes how probabilities are spread out during an event. Probability, according to Simon et al. 1997, can be described as “a measure of the relative frequency or likelihood of occurrence of an event, whose values lie between zero (impossibility) and one (certainty)." The graphical illustration is the way probability is used to illustrate risk probability that represents the density functions of likelihood. For each distribution of probability, the horizontal axis shows the impact of the risk event, which may include time or cost, and the vertical axis shows the relative likelihood and risk event (Evans et al., 2000).

Program Evaluation and Review Technique (PERT)

This tool is handy for organizing, scheduling, and the coordination of various tasks in a project. This technique was built to simplify the scheduling and planning of large and complex projects, which are most likely to be construction projects. The main objective of this technique is to determine the probability, statistically estimate, and establish a range of values for the tasks of the uncertain project and the time they will take. (ADEAK, 2011). This technique is most useful when there is uncertainty in the project activities duration.

Scenario Analysis

This is a process of analyzing and estimating future events and looking at possible alternatives. This technique is used to develop robust strategic plans and manage risks during the time a project is under uncertainty. The scenario analysis is mostly used for the financial or economic evaluation of a project. In other worst, it can be described as "the estimation of the expected value of a portfolio after a given period, assuming specific changes in the values," according to McBurney & Parsons, 2003. This technique assists in risk management, augmenting understanding about the unforeseen future, building consensus used for change; and scanning the environmental changes and monitoring progress (Maack, 2001).

Break-Even Analysis

This is the most common tool in project management; it is used to study the relationship between variable costs, fixed costs, and returns. Also known as cost-volume-profit analysis, it is widely used by management accountants and production management. It is used throughout the organization by people as it is a continuous process of thinking because it involves various decisions. It is mathematical as thee is the breakeven point, which is determined by the number of units sold (Cafferty & Wentworth, 2011).

Outline of the Proposed Study

All the 12 techniques analyzed are instrumental when it comes to decision making, points at the consideration of all the alternatives' risks. In some methods, the risk is better quantified that other techniques, but all the methods require experience that ranges from medium to high level, detailed data, and rime resources. Another aspect is that the quantitative methods are more complex and uses more resources than qualitative techniques; the best part is that in terms of risks, the results are well analyzed and detailed.

From the techniques discussed above, probability distribution, EMV, and interviewing as transitional, as they are needed to implement the other techniques. There is also a possible lack of knowledge/complexity of these techniques among the professionals in construction management, and that is the reason they opt for easily performable or qualitative techniques. This causes a lack of efficiency in the project risk management processes, even if risk management is done frequently.

Therefore, such revelations point towards increasing the applicability, improving the existing techniques, or the creation of new techniques. This means that there can be the implementation of some methods that work well for other real problems; there is a need to improve the state of knowledge and to develop advanced technologies in quantitative methods, at the same time considering usability and understandability.

References

ADEAK, 2011. What is the PERT Method? http://www.adeak.com

Anderson, R., Sweeney, J., and Williams, A., 1999. Statistics for Business and Economics. Cincinnati, OH: South-Western College Publishing.

Baker, S., Ponniah, D., & Smith, S. (1999). Survey of risk management in major UK companies. Journal of Professional Issues in Engineering Education and Practice, 125(3), 94-102.

Barringer, P., 2008. Risk Matrix. Know when to accept the risk. Know when to reject the risk. Barringer & Associates.

Cafferty, M., and Wentworth, J., 2011. Break-Even Analysis - The Definitive Guide to Cost-Volume-Profit. (K. A. Merchant, Ed.) Managerial Accounting Collection.

Clemens, P.L., 2003. Fault Tree Analysis, Tutorial, www.sverdrup.com, 4th Edition.

Chauhan, S. S., & Bowles, D. S. (2004). Dam safety risk assessment with uncertainty analysis. Ancold Bulletin, 73-88.

Evans, M., Hastings, N., and Peacock, B., 2000. Statistical Distributions. John Wiley and Sons.

Fig3 Retrieved from https://www.toolshero.com/decision-making/decision-tree-analysis/

Fig4 Retrieved from https://www.palisade.com/risk/monte_carlo_simulation.asp

Hillson, D., 2004. Effective Opportunity Management for Projects: Exploiting Positive Risk. Marcel Dekker, Inc.

Kajsa Simu (2006). [Fig1, Fig2] Retrieved from

https://pdfs.semanticscholar.org/504b/67ad668b80f8db0a7c170772c44e9b1754c0.pdf

Maack, J., 2001. Scenario analysis: a tool for task managers. In: Social Development Paper no. 36. Social Analysis: Selected Tools and Techniques. World Bank, Washington, D.C.

McNeil, A. J., Frey, R., & Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques and Tools-revised edition. Princeton university press.

Olivas, R., 2007. Decision Trees. A Primer for Decision-making Professionals.

Ortmeier, F., and Schellhorn, G., 2006. Formal Fault Tree Analysis: Practical Experiences, Proceedings of AVoCS 2006.

Pengelly, J., 2002. Monte Carlo methods. http://www.cs.otago.ac.nz/cosc453/student_tutorials/monte_carlo.pdf.

PMBOK, 2008. Project Management Institute.

Simon, P., Hillson, D., and Newland, K., 1997. Project Risk Analysis and Management Guide. The Association for Project Management.

Snee, R., and Rodebaugh, W., 2008. Failure Modes and Effects Analysis. Encyclopedia of Statistics in Quality and Reliability.

Teixeira, A., Sou, K. C., Sandberg, H., & Johansson, K. H. (2015). Secure control systems: A quantitative risk management approach. IEEE Control Systems Magazine, 35(1), 24-45.

Thaheem, M. J., De Marco, A., & Barlish, K. (2012, June). A review of quantitative analysis techniques for construction project risk management. In Proceedings of the Creative Construct Conference (pp. 656-667).