José Blanco, John H. Lewko and David Gillingham
Systems in the natural resource industry vary in their tolerance of human errors. Such operations are open to fallible decisions resulting from the way in which the organization…
Abstract
Systems in the natural resource industry vary in their tolerance of human errors. Such operations are open to fallible decisions resulting from the way in which the organization deals with information. Organizations must therefore improve on their ability to learn from incidents in order to reduce the frequency and severity of errors. Presents information on fallible decisions from the management and cognitive sciences, as well as major disasters (for example Challenger; Herald of Free Enterprise). Describes a framework for increasing organizational learning through incident analysis and presents a five‐step method for systematically analysing incidents.
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M. Rabiul Ahasan, Donna Campbell, Alan Salmoni and John Lewko
Shift work can be seen as one of the many factors and conditions associated with the health, safety, and wellbeing of industrial workers. Social, cultural and emotional quality…
Abstract
Shift work can be seen as one of the many factors and conditions associated with the health, safety, and wellbeing of industrial workers. Social, cultural and emotional quality also deserves our attention on human aspects of shift work, because it concerns individuals’ physiology, psychology, genetic and family heritage, social and cultural traits, life style, and circadian rhythms. It is more likely to become apparent that intervening and local factors are related with human aspects of shift work that should be carefully considered in order to improve individuals’ performance, tolerance, familiarity with different shift schedule, family and social lives, as well as to control work‐related difficulties. To address this concern, this paper describes some intervening factors involved with human aspects of shift work in the context of a developing country, Bangladesh, with the aim of identifying local factors and situations in making shift work safe, healthier and productive.
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José A. Blanco, David W. Gillingham and John H. Lewko
The purpose of this paper is to propose a simple heuristic model that provides diagnostic capabilities and prevention insights.
Abstract
Purpose
The purpose of this paper is to propose a simple heuristic model that provides diagnostic capabilities and prevention insights.
Design/methodology/approach
The paper brings together findings from previous research including injury statistics from several industries to illustrate that the model's predicted results can be found in practice. This is a conceptual paper that applies a simple heuristic model to existing data. The model leads to an equation with four parameters: a rate of improvement reflecting prevention, a rate of deterioration reflecting obsolescence and lapsing of procedures and practices, an intrinsic limit reflecting technological capability, and a “viscosity” that adds the impact of management system malfunction to the technological limits and normal delays.
Findings
The model says that, on the average, injury rates decrease with time if the rate of rejection is greater than the rate of mortality. If “r”<“m” injury rates increase exponentially with time, and drastic results can follow. When “r”=”m” the model produces a constant rate of failure that will continue until something is done to increase “r” or decrease “m”. A constant rate of failure means that an apparent safety limit has been reached. Unless this corresponds to the technological limit, a constant rate means that some preventable failures are recurring with regularity: they risk being accepted as “hazards of the job”. Stable periods may be normal, but they can lead to complacency.
Practical implications
The heuristic power of the model is evident in that parameters and insights from applying it can help define prevention activities to reduce the rate of injury and, by implication, to lengthen operational periods between consecutive injuries.
Originality/value
The drum model can help managers understand the separate but related effects of technology and management on injury rates. The model can be used to seek prevention possibilities hidden in the aggregate data, and it can help the manager to use period data to identify areas or groups in need of help.
David W. Gillingham, José Blanco and John H. Lewko
Describes an integrated model of error management which includes: the external environment; the corporate environment; the manager and the managed; incident management; inquiries;…
Abstract
Describes an integrated model of error management which includes: the external environment; the corporate environment; the manager and the managed; incident management; inquiries; and, learning from errors. Includes classification of error types with examples. By understanding this model organizations can improve their ability to manage error.