Error management model

Targeted observation instrument CORS observers use specially designed observation materials based upon the Threat and Error Management Model, and target external threats, crew errors, how these are detected and managed, and actrral outcomes. Observers record and code events according to threat and error code fists specifically developed for this project by driver representatives and Httman Factors experts. 

Jarvis, N., Brown, C.D., and Granitza, E. (2000) Sources of error in model predictions of pesticide leaching a case study using the MACRO model. Agricultural Water Management 44 247-262. 

In summary, the accidents main reason is closely related to human behavior. From macro-viewpoint, if the behaviors of corporate decision-making layer, management layer, operate layer are correct if equipments, machines and tools can achieve inherent safety through the scientific and technical personnel research and safety technical personnel operate standard if environment conforms to safety standards, the accident would not have happened. So, coal mine accidents human error mechanism model is established, human error is the primary reason for coal mine incidents. 

There is some uncertainty in all data, and model building must take this error into account. The first step in error management is error detection, error reduction, and error quantification. There are three types of error systematic error, random error, and blunders. Improved experimental protocol can reduce all these, but designing progressively better experiments eventually leads to diminishing returns so that at some point it is necessary to use some kind of error analysis to manage the uncertainty in the variable being quantified. 

Within the context of error mitigation and amehoration, perhaps the most popular of the recent models is that of Threat and Error Management (TEM). Developed predominantly from Professor Robert Helmreich s laboratory at the University of Texas, the TEM model emphasizes the positive functions involved in the recovery from inevitable error (Helmreich, 2000 Helitneich, Merritt, and Wilhelm, 1999). The TEM model offers a refreshingly honest appraisal of system safety, and sees error as a natural part of everyday expert performance. The model emphasizes the recovery processes inherent in expert performance and the ways in which error is detected and managed. 

Mgiire 13.1 The Swiss cheese model of error and accident causation Error management and Swiss cheese... 

The findings of this study reinforce a model of error management that emphasizes the process of mismatch emergence as the driver of error detection, problem identification and error resolution (see Figitre 15.1). 

This model of error management, as first proposed by Rizzo, Ferrante and Bagnara (1995) and subsequently expended in this study, has several unique features that suggest it offers considerable explanatory power in relation to the detection of safety breaches in dynamic real-world contexts. 

Secondly, the model is consistent with naturahstic explanations of expertbehaviour whereby action schema are frequently activated in response to environmental stimrrh with little or no conscious processing on behalf of the operator. Indeed, the model allows for situations whereby safety breaches are contained through immediate response to the emergence of a mismatch in system state. Accordingly, error management can itself be seen as dynamic expert behavioirr rather than a serial and rational process. 

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. 

For the air quality manager to place model estimates in the proper perspective to aid in making decisions, it is becoming increasingly important to place error bounds about model estimates. In order to do this effectively, a history of model performance under circumstances similar to those of common model use must be established for the various models. It is anticipated that performance standards will eventually be set for models. 

The model of human error held by management and the plant culture constitutes the environment in which the data collection system operates. Within this environment, all data collection systems need to address the topics listed in Figure 6.1. These topics, from the types of data collected, to the feedback systems that need to be in place, will be addressed in subsequent sections of this chapter. 

The type of data collected on human error and the ways in which these data are used for accident prevention will vary depending upon the model of error and accident causation held by the management of an organization. This model will also influence the culture in the plant and the willingness of personnel to participate in data collection activities. In Chapters 1 and 2 a number of alternative viewpoints or models of human error were described. These models will now be briefly reviewed and their implications for the treatment of human error in the process industry will be discussed. 

P cycling, pre chain emissions, animal welfare, economics, biodiversity, product quality, soil quality, and landscape aesthetics [60]. Whole farm model (WFM) uses pasture growth and cow metabolism for predicting CH4 emissions in dairy farms. Also included in the WFM is climate and management information. However, recent reports also suggests that WFMs may incorrectly estimate CH4 emission levels as they do not take into account the DMI and diet composition while predicting the enteric CH4 emission. This low prediction efficiency of WFMs may lead to substantial error in GHG inventories [10,11],... 

---