Framework for accident analysis

Table 15.1 presents a proposed smallest efficient data set on accidents and near accidents. It is based on the framework for accident analysis presented in Chapter 6 and takes the author s experiences from evaluations of SHE information systems into consideration. In this proposal, no additional information is requested from the supervisor other than that collected on a traditional accident-investigation form. The reason why a SHE expert is needed to feed additional data is to secure the reliability of the data. In addition, the SHE expert will be responsible for checking the quality of the data from the supervisors. 

Figure 16.1 presents an overview of different SHE performance indicators. This overview is based on the framework for accident analysis in Chapter 6. Loss-based SHE performance indicators will be our starting point. Among these we find the most commonly used indicator, the lost-time injury frequency rate. We proceed by reviewing process-based indicators, similar to those developed in the fertiliser-plant case in Chapter 4. Finally, we will look into indicators relating to causal factors, i.e. indicators based on information about the organisation and SHE management system. 

The first three methods analyse the risk of occupational accidents. They have a joint theoretical basis in the framework for accident analysis described in Chapter 6. The methods serve different purposes. Often, Coarse and Job-safety analysis are used in combination by starting with the Coarse analysis. A Job-safety analysis will follow when severe hazards have been identified and there is a need to go into details on how they may result in harm. We will... 

Figure 6.2 Framework for the analysis of an accident sequence. The example shows an accident in a process plant, where an operator is carrying a heavy load (pipe).

As an alternative, the current paper presents an approach for analysis of aviation incidents that takes a multi-agent perspective, and is based on formal methods. The approach is an extension of the approach introduced in the work of Bosse and Mogles [4], which was in turn inspired by Blom, Bakker, Blanker, Daams, Everdij and Klompstra [1]. Whereas this approach mainly focuses on the analysis of existing accidents (also called accident analysis or retrospective analysis), the current paper also addresses analysis of potential future accidents (called risk analysis or prospective analysis). This is done by means of a multi-agent simulation framework that addresses both the behaviour of individual agents (operators, pilots) as well as their mutual communication, and interaction with technical systems. By manipulating various parameters in the model, different scenarios can be explored. Moreover, by means of automated checks of dynamic properties, these scenarios can be assessed with respect to their likelihood of the occurrence of accidents. The approach is illustrated by a case study on a runway incursion incident at a large European airport in 1995. 

An accident analysis technique should provide a framework or process to assist in understanding the entire accident process and identifying the most important systemic causal factors involved. This chapter describes an approach to accident analysis, based on STAMP, called CAST (Causal Analysis based on STAMP). CAST can be used to identify the questions that need to be answered to fully understand why the accident occurred. It provides the basis for maximizing learning from the events. 

The systems approach seeks to identify situations or factors likely to contribute to human error. James Reason s analysis of industrial accidents revealed that catastrophic safety failures almost never result from isolated errors conunitted by individuals. Most incidents result from smaller and multiple errors in components and environments with underlying system flaws. Reason s Swiss Cheese Model describes this phenomenon. Errors made by individuals can result in disastrous consequences due to flawed systans that are represented by the holes in the cheese. Reason believed human error would happen in complex systems. Striving for perfection or punishing individuals who make errors does not appreciably improve safety. A systems approach stresses efforts to catch or anticipate human errors before they occur. Reason used the terms active errors and latent errors to distinguish individual errors from system errors. Active errors almost always involve frontline personnel. They occur at the point of contact between a human and some element of a larger system. Latent errors occur due to failures of the organization or designs that allow inevitable active errors to cause harm. The terms sharp end and blunt end correspond to active error and latent error. The systems approach provides a framework for analysis of errors and efforts to improve safety. 

It is worth to notice that this work applies a retrospective method for data collection based on CREAM taxonomy, but certainly not following the procedures developed by Hollnagel to perform accident analysis. It means that the CREAM retrospective technique is not used whatsoever, considering that the accidents collected were submitted to extensive investigation and the causes and contributing factors are already exposed in the reports, being the current gap the need for interpretation and classification of the data under a common framework. 

ABSTRACT Shipping accidents can have severe consequences, causing concerns to various stakeholders. This has led to research into risk assessment methods for the maritime transportation system. This paper presents an analysis of risk concepts and perspectives adopted in 44 methods for risk assessment in maritime transportation. As a methodological framework for analysis, a historical classification of risk concepts and a novel categorization of risk perspectives along the realist-constructivist continuum are apphed. Analyses reveal that in the maritime transportation, risk is strongly tied to probability and that a wide range of perspectives is found, from realism over scientific proceduralism to more constructivist views. 

The three types of errors enumerated above match with those defined in the Human Factors Analysis and Classification System (HFACS), which is a general human error framework for classifying aviation accidents. It has been developed and used within the U.S. military, applied to commercial aviation accident records and proved to be a valuable tool in the civil aviation area. But it makes also sense to apply the classification scheme in other areas, like in COOPERS. 

This chapter presents a framework for the collection and analysis of data on accident risks, Figure 6.1. It borrows aspects from the different accident models presented in Chapter 5. We will apply this framework in a review of classification systems and variables used in the collection and analysis of accident risks. It will also be a common denominator in our reviews and analyses of different SHE management methods and tools in Parts HI to V. 

We will later apply the accident-analysis framework in a review of different types of methods used in the collection and analysis of data of accident risks. We will start at the output side of the model by reviewing the different types of classification systems used to document the consequences of accidents and different measures of loss. We will then continue by looking into the classification systems used to document incidents and deviations. Finally, we will review the different classification systems for contributing factors and root causes. Our aims will be twofold first, to be complete, i.e. by presenting all alternative means of measuring and classification, and second, to give specific advice on the preferred method. The reader will find recommended alternatives in shaded tables and checklists. 

The accident-analysis framework of Chapter 6 provides a basis for tying together information from these different activities, see Section 15.1. 

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