Applications of Risk assessments

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Flow chart. 2

Root cause. 4

Event tree. 6

Decision tree. 8

References. 10

Flow chart, root cause, event tree, and decision tree are all risk assessment tools. Each of them has its advantages and disadvantages. The main similarity in all of them is that they are crucial tools for simplifying the process of risk assessment through analyses of processes with the help of diagrams and symbols.

Flow chart

Flow charts are simple diagrams that show how different steps in a process fit in together. Flow charts are very effective tools for explaining how a process works as well as documenting the channels that should be followed in order for a certain task to be done. The main advantage of a flow chart is that when processes are being mapped out, one’s understanding of these processes is clarified. During the clarification processes, some improvements can be made in the flow charts whenever they are deemed necessary.


             A flow chart can be a very effective tool for defining and analyzing the processes that are required for a certain function in a large organization to be performed. In the local government, the different stages involved in the process can best be exemplified through the use of flow diagrams. A properly drawn flow chart should be used to derive analyses, communication, and discussion. In this way, areas that need improvement can be defined and singled out for standardization purposes.

Researchers are often comfortable while working out with complex structures with the help of flow charts. Without these risk assessment tools, a researcher may be overwhelmed by the complexity of the bigger picture.  The success of this research tool is hedged on the understanding of each of the meanings attached to the symbols used. Elongated circles represent the beginning or end of processes; rectangles represent actions or instructions; while diamonds represent decisions that have to be made by the person who is implementing the process.

One of the most obvious limitations of a flow chart is that unlike an event tree, it gives the illusory impression that everything “flows smoothly”. One may say that it oversimplifies a problem and makes all hurdles in any process seem to be of an equal level of complexity. In other words, there are no symbols to show where subtle complexities lie within the process. In an event tree, all the potentially hazardous consequences that can result if a certain action is taken during a certain process are highlighted.

In the case of local government decisions, sometimes there are many approvals that need to be made with a process. a flow chart will show that such a decision needs to be made but will not show the possible hazardous consequences of delays in the approval process. Attempts to indicate the factors that determine approval durations may make the flow chart seem complicated and very obscure. For this reason, only a part of the audience may understand the whole process clearly.

Duplication of work is very common in flow charts, even after the chart has been challenged by different people. One’s level of understanding of a flow chart is determined by how acquainted one is with the topic or process under discussion. Local government employees can understand the bureaucratic procedures that are explained using flow charts but the public may not. This contrasts sharply with decision trees, which have room for examples to guide the user in matters of interpretation.

However, Quinlan (1986, p. 2) observes that both flow charts and decision trees present users with the challenge of seeking the help of a “knowledge engineer”  or in other words, someone who is conversant with all the processes that are described using these risk assessment tools. It is only the knowledge engineer who can derive and refine a clearly outlined set of rules to guide the user throughout the process. The knowledge engineer can understand the decisions that need to be made in a flow chart better than a layperson.

Root cause

Root cause analysis is a method of using structured steps in order to find out the reality of a problem with a core aim of finding out the best way of solving it. Root cause analysis is an attempt to understand the problem so as to prevent it from recurring by achieving a permanent solution.

Root cause identification is the most intricate part of root cause analysis. The biggest advantage of root cause analysis as a risk assessment tool according to Ammerman (1998, p. 2), is that the same tools can be used effectively to evaluate “significant events”. For Ammerman, “significant events” include potential problems, near misses or actual events. This approach, notes Ammerman (1998, p. 3), was first used in the Human Performance Enhancement System (HPES), which was used in all nuclear power stations in the United States.

In Ammerman’s book entitled The Root Cause Analysis Handbook: A Simplified Approach to Identifying, Correcting and Reporting Workplace Errors, all the nine chapters are arranged in a format that corresponds to the process of carrying out a root cause analysis. These include a definition of a problem, task analysis, control barrier analysis, event, and causal factor chart, interviews, root causes, corrective actions, and finally, report. In each of the stages, he presents the most widely accepted approach, meaning that there is flexibility in terms of choice of methodologies, depending on the nature of the organization.

In nuclear power stations, for instance, control barrier analysis would entail technical knowledge of the potential negative effects of nuclear power and how these effects can be averted. Interviews, though very popularly used, are not the only tools for evaluating an event. In order for potential problems that exist in different departments within the local government institution, it may seem necessary to make observations and carry out investigations before embarking on interview tasks.

The concept of ‘root cause’, argues Ammerman (1998, p. 4) may seem like a misnomer; the umbrella term to use would be “causal factors”, under which root causes, possible causes and contributing causes would fall. Causal factor worksheets are rather complicated risk assessment tools for non-experts to use and understand. One may find it difficult to get the bigger picture, compared to the simplicity that is associated with other tools such as flow charts (Andersen&Fagerhaug 2006, p. 134).

Perhaps the greatest drawback of using root cause analysis is that some problems may not have any possible solutions or it may not be a feasible idea to embark on corrective measures. In this case, the analysts have to settle for mitigating and adaptive measures. The nature of measures to be taken has to be figured out through a convoluted process that entails the use of methods such as countermeasures matrix.

Andersen&Fagerhaug 2006, p. 118 observe that finding out the root cause of the problem using root cause analysis entails the use of several iterations. They add that the impression created by the chapter-based subdivisions, that Root Cause Analysis is a simple process, is far from accurate. The main difficulty lies in the use of many tools and techniques before the root cause of a problem can be finally identified.

Event tree

            The event tree makes use of graphical representations in a logical manner in order to identify and quantify all the possible outcomes initiated by a certain event. Like root-cause analysis, the event tree makes it possible for the magnitude of damage that can arise after the occurrence of a potentially hazardous event. Both event trees and root cause analyses were used for the first time to assess risks facing the nuclear industry. Today, both processes are used to assess risks in other industries such as gas production, chemical processing, and transportation.

In an event tree, the concept of forwarding logic is used to study possible risks, often expressed in terms of fatalities and their frequency of occurrence, such that an F-N curve is derived in order to be used to assess the acceptability of the manner in which an organization responds to hazards. For makers of event trees, the main challenge is singling out an initiator, or an event that can potentially cause fatalities. In some industries such as chemical processing and transport, almost any event can be an initiator. Therefore, one is often forced to rank initiators in a hierarchy, whereby the one that is likely to cause the highest fatalities is considered for further analysis. The accuracy of such a procedure may be heavily contested.

Every branching adds a new dimension to success, partial failure or complete failure of a system. The relationships between different causative factors of success or failure can easily be discerned by a non-professional or someone who does not work in the institution under analysis. This makes this risk assessment tool very much suited for use in assessing risks that may befall different local government departments, the main point of reference being the various hazards that exist in this institution and the relationships between them.

            Martía (2008, p. 545) mentions three main stages of an event tree that he used to identify long-term volcanic hazards: the first stage requires the risk assessment analyst to determine the spatial probability of vent opening in order for basaltic eruptions to take place. During this stage, geological and geophysical data has to be relied on. The second stage requires that different types of eruptions that have occurred in a specific area under study be analyzed the third stage requires an event tree to be generated from the information that is generated in the first and second stages.

            In a business situation, an event tree can be used to determine the possibility of certain hazards occurring. The tree becomes a good reference point in complex business processes whereby the entrepreneur cannot manage to keep track of all the events occurring as well as internalize the specific hazards posed by each of the processes. The ease of analysis is there for every tree event user to see, considering that a deductive, bottom-up approach is always adopted in the measurement of risks in businesses (Henley & Kumamoto 1991, p. 301)

            In today’s technological world, many business managers can find event trees very attractive owing to their ability to analyze all physical system whether they are human-operated or not. According to Henley & Kumamoto (1991, p. 304), they are relatively accurate when used to complement other risk assessment tools such as fault tree analysis, flow charts, and decision trees.


            However, Henley & Kumamoto (1991, p. 324) point out that when risk analysis methods are heavily dependent on traditional fault tree analysis, results are both inefficient and inaccurate. In this case, binary decision diagrams have to be resorted to in order for these deficiencies to be overcome.

Decision tree

            A decision tree, like an event tree, helps business managers to make decisions as well as ascertain their possible consequences through the use of tree-like graphs. Additionally, decision trees can serve to give accurate indicators relating to resource costs, event outcomes, and utility. These aspects make these trees suitable in institutions that suffer from bureaucratic bottlenecks such as the local government.

The decision tree by virtue of being a data mining technique with its origin in linear regression is a fundamental pillar of a modern global society that is technology-dependent, especially in areas relating to scientific inquiry and business. The results produced by decision trees communicate information very accurately are easy to understand, and can be used to build incrementally complex rules that are both simple and powerful risk analysis tools.

Decision trees indicate different levels of measurements in both qualitative and quantitative manner (De Ville 2006, p.24). It also measures interval levels of measurement, for example, weight ranges and levels of income. Decision trees are can be compared more with statistical approaches of risk assessment than with graphical approaches such as event trees, flow charts, and root cause analysis. The fact that decision trees can adapt well to data inconsistencies and nonlinearities makes them better tools compared to other numerical and statistical approaches to risk assessment (De Ville 2006, p. 39).

Although decision trees have been in use for about 50 years ago when, according to Hesposand Strassmann (1965 p. 256), they were first developed, new forms keep evolving, each of which promises exciting possibilities in machine learning, data mining, and knowledge discovery. However, these exciting possibilities are yet to be fully realized. Hespos and Strassmann, (1965, p. 252) report that immediately after its discovery, the decision tree was referred to as a stochastic decision tree. It was used only for making investment decisions. Today, this tool has become a versatile, more accurate tool that is of help both to business operators and scientists.


Ammerman, M, 1998, The Root Cause Analysis Handbook: A Simplified Approach To Identifying, Correcting And Reporting Workplace Errors, Productivity Press, New York.

Andersen, B,&Fagerhaug, T, 2006, Root Cause Analysis: Simplified Tools and Techniques, American Society for Quality, New York.

Andrews, J, & Dunnett, S, 1989, Event Tree Analysis Using Binary Decision Diagrams, Loughborough University, Loughborough.

De Ville, B, 2006, Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner, SAS Publishing, Cary.

Henley, E, & Kumamoto, H, 1991, Reliability Engineering and Risk Assessment, Institute of Electrical & Electronics Engineering, New York.

Hespos, R, and Strassmann, P, 1965,‘Stochastic Decision Trees for the Analysis of Investment Decisions’, Management Science, vol. 11, no. 10, pp. 244-259

Martía, J, 2008, ‘A long-term volcanic hazard event tree for Teide-Pico Viejo stratovolcanoes (Tenerife, Canary Islands)’ Journal of Volcanology and Geothermal Research, vol.178, no.3, pp. 543-552

Quinlan, J, 1986, Simplifying decision trees, Massachusetts institute of technology artificial intelligence laboratory, Massachusetts.

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