ILCI model

A problem with causal-sequence models such as the Domino and ILCI models is that no clear distinction is made between the observable facts about the accident sequence on the one hand and the more uncertain causal relationships at the personal, organisational and management levels on the other hand. The user is thus led into believing that information on, for example, personal factors such as mental stress, has the same objective status and is as unambiguous as information on observable facts about the sequence of events. There is thus a risk of misunderstanding and false... 

The TRIPOD model presents an accident model of causal sequences rather similar to the logic principles of the ILCI model, Figure 5.3 (Reason, 1991 Reason, 1997). It has had a large influence on current thinking, because it models how erroneous decisions at different management levels lead up to the circumstances of which the accident is a result. We will apply aspects from the TRIPOD model in Chapter 6. We will illustrate how accident occurrence is affected both by operational decisions at the work system level immediately before the accident, and by higher management decisions. 

This distinction between facts and interpretations is not always made in the causal sequence models of accidents. Such linear accident causation models as the ILCI model do not make a clear distinction between observable facts and conditions on the one hand and opinions about effects of personal and... 

Bird and Germain (1985) have further developed this taxonomy in the so-called ILCI model. They start from Heinrich by distinguishing between substandard acts and substandard conditions. Based on practical experience. 

Table 6.10 shows the causal hierarchy of different models of accidents. Here we recognise the two SHE management models of Section 5.7, MORT and SMORT. Also the ILCI model and TRIPOD include upper-management elements. TRIPOD is unique in the sense that it analyses the relations between human errors at different hierarchical levels. 

Level ILCI model (Bird SMORT (Kjellen MORT-based TRIPOD (Reason, 1991)... 

The theoretical basis of the different causal models becomes more obvious at the upper management level. MORT was the first comprehensive model to include organisational and individual factors at the top management level. At this level, it draws from quality assurance management principles. The SMORT and ILCI models have been influenced by this pioneering work and represent variations on the same theme. The concept of root causes originates from the MORT model. The checklist above shows the different items of a root-cause analysis. 

The SMORT and ILCI models represent further developments of the original MORT concept mainly for the purposes of improving user-friendliness. In the application of both models, the analysis proceeds from... 

In the ILCI model, three common reasons for the lack of control at the workplace level are identified (1) inadeqnate SHE programme, (2) inadequate programme standards and (3) inadequate compliance with the standards (Bird and Germain, 1985). These elements also coincide with the main elements of the quality-assurance management model. 

Example 2 In the same study, ten safety delegates were independently asked to code a written description of an accident by means of the ILCI model. No two such causal classifications were similar. 

Example 3 An evaluation of the accident-reporting system of an offshore installation showed that remedial actions were identified before and independently of the identification of causal factors based on the ILCI model. This classification of causal factors was done in order to satisfy formal requirements rather than as a tool in order to come up with better safety measures. 

The ILCI model has been applied in the design of accident investigation checklists according to the second principle. Such checklists are relatively easy to apply in accident investigations and in the subsequent analysis and coding of the results for statistical purposes. They suffer from a poor reliability (different investigators will come up with different results) and from an inadequate coverage of the detailed circumstances of the accidents and near accidents. 

All relevant data about the accidents and near accidents are stored in a coded format. This coding may take place during data collection, see Section 13.4. Nominal scales of measurement are applied in coding the descriptions of losses, the sequence of event and causal factors, etc. The ISA, ILCI and MAIM accident models presented in Chapters 5 and 13 have typically been developed for this purpose. Table 15.2 shows a coding schedule based on the ILCI model. 

In coding accidents or near accidents, we run the risk of introducing errors and losing information. As an example, we will here use the ILCI model for classification of accidents and near accidents. Table 15.2. In applications of this model, the analyst checks the alternative in the second column that suits the actual circumstances. The reliability of this classification and the biases introduced are concerns. The analyst does not document these uncertainties, and the resulting accident statistics will appear as objective facts to the end user. 

There is another problem connected to the classification of data. Such classification will result in a loss of information about the detailed circumstances of the accident. These details are often essential to our understanding of why the actual accident happened, especially when we study complex human behaviour. Fortunately, the problems with biases and with loss of information run in parallel. The losses and the incident at the right side of the ILCI model of Figure 15.4, for example, are usually adequately represented by coded data. The level of details in the precursory events and conditions and in the causal factors on the left side of the model is often too high to allow for a meaningful coding. We here face a situation where rich information has to be forced into too small a frame. The person responsible for coding will then make a more or less arbitrary selection. It follows that statistical summaries of accident causes too often represent... 

Structures of various dioxacarbenium and oxiranium intermediates were modeled using CAChe MOPAC molecular orbital software along with a Macintosh Ilci workstation. The conformations and energies of the structures were first computationally minimized and then the heats of formation were calculated. 

We also know that incentives and motivational factors play an important role in causal attributions and the selection of remedies. An illustration of this is when decision-makers attribute accidents to unstable causes rather than stable causes. In the ILCI causation model, we find examples of both types of causes. Mental stress is an example of an unstable factor as opposed to inadequate equipment, which is a stable characteristic. Unstable causes produce less certain predictions about the efficiency of remedial actions. By selecting such causes, the decision-maker may escape from the obligation to decide on binding and resource-demanding solutions to the safety problem (Kjellen, 1993). 

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