The logistics management involves the supply chain management that is charged with the responsibility of planning, implementing and controlling the efficient forwarding and the reverse flow of goods and the storage of the goods. It involves all the processes that take place from the time a good or service is produced to the time it will be consumed by the consumers. These must all be done in order to meet the use requirements (Lichtenstein, 1996). There are several risks that are involved during the time a given commodity is being transported from the point of production to the time it will find its way to the consumers. The method of handling the risks involved must be reliable and the safety of the goods and services must always be ensured in order to remove the likelihood of the occurrence of any kind of disaster.
Overview of the risk analysis in logistics management
Incase of oil and gas transportation, there are several risks that can be exposed to the people involved in the logistics management. Some of the risks include fatal accidents and the failure of the transportation system. The transportation of oil and gas are some of the most dangerous activities in the field of logistics management. This is because a failure in the system can easily result into a fatal accident that can result into massive destructions and even loss of lives. Therefore during the transportation of such dangerous commodities, safety should be ensured and a reliable channel needs to be used that will ensure that there is no accident that occurs during the transportation process.
Risk acceptance criteria
A cost effective risk acceptance criteria can easily help in he reduction of oil tanker spillage. The transportation of harmful substances such as the crude oil should be done with a lot of care so as to ensure that the process is done safely. A risk acceptance criterion can be used in such a situation so as to determine whether oil spill prevention can be achieved through the use of various means. This has been successful in the recent past when risk acceptance criteria have been used so as to prevent oil spillage thus ensuring the safety during oil transportation (Aven and Vinnem, 2005).
Methods of risk analysis
There are several methods of risk analysis that can be used in order to ensure the safety of the oil when it is being transported and also ensure that people involved in the transportation process are always safe when they are discharging their activities. The methods of risk analysis that can be used here include but not limited to the following:
Tree based techniques
There are several tree based techniques that can be used in the analysis of risks and they include: i. Fault tree
A fault tree is shows the relationship between the system failure and the failures of the components of the system being analyzed. The technique is based on deductive logic (Sankar and Prabhu, 2002). The following diagram summarizes the fault tree analysis.
Source: Sankar and Praghu, 2002
I. Event tree analysis
This method is used to illustrate the sequence of outcomes which may occur after the occurrence of some initial event. The technique uses inductive logic in its operation. In this system, the left side will connect with the initiator while the right side connects with the plant damage state. The top side of the system defines the system. If the system for instance goes up at the node then we can say that the system has failed. However, if the path goes down then the system failed. This method has been extensively used in the nuclear industries for operability analysis (Aven and Vinnem, 2002).
II. Management Oversight Risk Tree
This is a diagram that arranges safety program elements in an orderly and logical manner. The analysis of this method is carried out by a means of a fault tree. The tree here gives an overview of the causes of the top event from the management oversights and omissions or from some assumed risks (Aven and Vinnem, 2002).
III. safety Management Organization Review Technique
This is a simplified version of the Management Oversight Risk Tree. The technique is structured by some means of analysis levels with the associated checklists. The SMORT analysis includes data collection methods which are based on the check lists and their associated questions. Information to be used in this technique can be collected from interviews or the studies of documents and investigations that may be conducted during the study process. The SMORT technique can also be used to carry out detailed investigations of accidents and near misses. The method also serves very well as a method for safety audits and planning of safety measures (Aven and Vinnem, 2002).
Statistical data analysis
This is a concept that is widely used in reliability engineering and can describe a particular form of a given hazard function (Greenfield, Kuhn and Wojtys, 1998). It comprises of three parts namely:
Decreasing failure rate which is also known as early failures
When performing reliability engineering, the cumulative distribution function which corresponds to a bath tub curve may be analyzed using a Weibull chart (Greenfield, Kuhn and Wojtys, 1998).
The diagram below shows a typical representation of a bath tub.
When conducting a risk analysis procedure, the following concepts need to be taken into consideration.
The statistical reliability and validity of the system has to be ensured at all times so as to have an effective risk management criteria
Construct validity - this is a concept that confirms whether a given scale measures or correlates with the theorized psychological and scientific values. In most cases, the two may not be the same but they should be as close as possible.
Criterion validity – this concept ensures that a given measure only measures what it says it will measure and not a different concept. A test can be said to have criterion validity if it has demonstrated its effectiveness in predicting the criterion or indicators of a given construct.
Content validity – this is a concept which is based on the extent to which a given measurement reflects the specific intended domain of content. It is also referred to as the logical validity or rational validity. It actually represents how much a given measure represents every single element of a construct.
External validity - this is the validity of generalized inferences in scientific studies. It is usually based on some experiments as experimental validity.
RISK ANALYSIS TECHNIQUES
Conditions prompting possible misuse of statistical data. There are several conditions that can prompt the misuse of statistical data whenever some analysis or research is being conducted. The misuse of statistical data can occur when some statistical argument affirms mendacity. The misuse can be accidental in some cases while it could also be purposeful for the gain of the initiator. When there is some misuse of statistical data, then the condition can be referred to as statistical fallacy. This can result into false information being portrayed thus the concept under study may not be fully and accurately exhausted. Some of the conditions that cause statistical fallacy include, the researcher lacking proper knowledge of probability theory or lack of standardization when carrying out the tests (Xie, Tan, Goh and Huang, 2000).
Some types of misuse of the statistical data include:
Discarding unfavorable data – this involves the researcher disregarding and ignoring some data that may prove hard to work with or that which may seem in appropriate to the research. In such cases, the researcher can leave out some important information that could have been very useful in the process of carrying out the research.
Loaded questions – there are some questions that can easily misguide the researcher and lead to portrayal of false information during the research process.
Overgeneralization - this concept can lead to some crucial data being left out as a result of overgeneralization or inaccurate estimations of the concepts under study.
Biased samples – during a research process, there is a high possible that biased samples can be collected. The collection of biased samples can be intentional or accidental. Whichever way, biased samples are inappropriate for any research process as it leads o a wrong presentation of the concepts thus leading to inaccurate information being reported.
Misunderstanding the concepts under study – some of the researchers may not be well acquainted with the concepts that they are studying. This can then lead to the researchers making some assumptions which may not be very productive in the process of research design.
Coarse risk analysis
This is a concept also referred to as preliminary risk analysis. This is a very common method for establishing some crude risk picture with a relatively modest effort. This type of analysis covers selected parts or the entire bow-tie. The team members involved in this type of analysis usually consists of between 3 to 10 team members. The process of risk analysis here is performed by dividing the subject of analysis into some given sub-elements and then carrying out the risk analysis for each of the sub-elements. In this method, checklists can then be used to identify and analyze the hazards and threats for each of the sub-elements to be analyzed.
The form that is used to document risks in this situation is usually standardized and the risk is described using categories. When stating probability in this section, terms such as often and seldom should be avoided as much as possible since they can lead to different interpretations. The best alternative to such problem is to state directly the given situation. A coarse risk analysis can be often combined with other methods of analysis so as to give a better picture of the given situation. The coarse risk analysis method actually identifies the most important risks and then gives the casual picture or the consequence picture that can be assessed in a more detailed manner (MacStravic, 1999).
Failure mode and effect analysis
The failure modes actually represent any errors or defects that can be found in a process or design equipment. The effect analysis on the other hand refers to studying the consequences of the failures in a system or in the design process. The Failure mode and effect analysis is a procedure that is used in product development and operations management for the purposes of analyzing a potential failure mode within a system for classification. A successful Failure mode and effect analysis helps a team in developing identifying some potential failure modes based on the past experiences using similar processes or products. This helps the team to develop and design the failures with a minimal usage of resources. This process is widely used in manufacturing industries and its use is also becoming more and more useful in the service industry (MacStravic, 1999).
The diagram below show the Failure mode and effect analysis cycle and how it can affect a system or a process
Source: MacStravic, 1999
The Failure mode and effect analysis can be used to provide an analytical approach when dealing with some potential failure modes and the associated causes. The Failure mode and effect analysis provides a tool that can be used to determine the degree of several risks and whether which of the risks has the greatest concern. An action can then be prompted depending on the extent or the nature of the risk in question. Failure mode and effect analysis will help in ensuring that a given process meets the defined requirements and customer needs (Brown, 1996).
The use and construction of an event tree
The event tree can be used to asses the risk reduction which is obtained from rockfall protection devices. The event tree analysis also provides an inductive approach that can be used for assessment. The event tree module can be used to handle the primary and secondary event trees, multiple branches and multiple consequence categories (Brown, 1996).
The event tree construction
The event tree has several features that need to be taken into consideration whenever an event tree is to be constructed.
The procedure for constructing an event tree includes:
Put together the primary and secondary event trees, multiple branches and consequences. Once all these have been put together, a provision need to be made for pruning of event trees. Some descriptive texts and bitmap labels can then be placed anywhere on the event tree page. An appropriate font selection can then be done for the names and the labels. Once all these have been done, undo all the automatic backup facilities that were used during the construction of the event tree.
TECHNIQUES OF RISK ANALYSIS
The fault tree analysis techniques were first developed in the early 1960s and since that time, its concepts have been readily developed and adopted by various disciplines as it has proved to be a good method for performing reliability and safety analysis. The fault trees represent the interaction of failures with other events in the system the basic events at the bottom of the fault tree are connected through logic symbols which are known as gates. The top events on the tree represent the hazard or the system failure modes. In most cases the basic events at the bottom of the tree represents components and the effects of human failure in the system.
A fault tree analysis is a failure analysis method that uses a top down approach. In this method of analysis, an undesired state of a system is analyzed using some Boolean logics. The Boolean logics combine a series of lower level events so as to adequately perform the analysis of the concept. It is mainly used in reliability engineering and in safety industries to determine the likelihood of the probability of a safety accident or some given form of system level failure. A fault tree analysis can be used to understand the logic leading to the top event or an undesired state. It also helps in showing the fulfillment with system safety and reliability requirements. It can also be used in monitoring and controlling the safety performance of the complex system. The fault tree analysis can also be used as a diagnostic tool for identifying and correcting the causes of the top event.
The fault tree analysis involves the following five steps:
I. Define the undesired event that needs to be studied
II. Obtain an in-depth understanding of the system
III. Construct the fault tree once you have understood all the events that needs to be taken into consideration.
IV. Evaluate the fault tree which has been constructed
V. Control all the hazards that have been identified.
Construction and analysis of fault tree
When constructing a fault tree, the following steps that can be used:
Determine the level which the examination or the research study should be constructed. Once the level has been determined, begin with the system level fault. When constructing the system level fault, ensure that you describe all the events that may cause this event fully. Continue describing its immediate causes until a component level failure or human error can be attributed to the fault. This should be done with each lower-level fault.
Overview of the cause consequence analysis
The cause consequence analysis is a combination of the fault tree and event tree analysis. The main purpose of the cause consequence analysis is to identify the chains of events than can lead to some undesirable consequences (Andrews and Ridley, 2002). The probabilities of various consequences can then be calculated using the probabilities of various events in the consequence cause analysis. This can then make it possible to establish the risk level of the system. The technique has proved to be very effective in estimating the safety of a protective system. In this method, both the deductive and inductive analysis is used (Andrews and Ridley, 2002). The main reason for using cause consequence analysis is to identify the chains of events that lead to unwanted effects. The diagram below shows a typical representation of the Cause consequence Analysis.
1. Andrews, J.D and Ridley, L.M (2002) “Application of the cause consequence diagram method to static system”, reliability Engineering & System Safety, 75 (1): 47-58
2. Aven, T. & Vinnem, J.E (2005) “On the Use of Risk Acceptance Criteria in the Offshore Oil and Gas industry”, Reliability Engineering and System Safety, 90: 15-24
3. Brown, J. (1996). “Risk Data: What is often Doesn’t Tell Us”, Professional Safety, January: 26-29
4. Elting, L.S., Martin, C.G., Cantor, S.B and Rubenstein, E.B. (1999) “Influence of data display formats of physician investigators’ decisions to stop clinical trials: Prospective repeated measures”, BMU, 318: 1527-1531
5. Greenfield, M.L, Kuhn, J.E. and Wojtys, E.M. (1998) “A Statistical Primer: Validity and Reliability”, The American Journal of Sports Medicine, 26(3): 483-485.
6. Lichtenstein, S. (1996) “Factors in the Selection of a risk assessment method”, Information Mangement and Computer Security, 4(4): 20-25.
7. MacStravic, S (1999) “Quality indicators and Specious inferences”, Health Care Strategic Management, June: 15-18
8. Puente, J., Pino, R., Priore, P. and Fuente, D. de la (2002) “A decision support system for applying failure mode and effects analysis”, International Journal of Quality & Reliability Management, 19 (2): 137-150
9. Sankar, N.R., Prabhu, B.S. (2001) “Modified approach for prioritization of failures in a system failure mode and effect analysis”, International Journal of Quality & Reliability Management, 18 (3): 324-335
10. Xie, M, Tan, K.C, Goh, K.H. and Huang, X.R. (2000) “Optimum Prioritization and resource allocation based on fault tree analysis” , International Journal of Quality and Reliability management, 17 (2): 189 -199.