Quality prediction for reconfigurable manufacturing systems via human error modelling

ElMaraghy, Waguih; Nada, Omayma A.; ElMaraghy, Hoda A.

doi:10.1080/09511920701233464

Cited by 63 publications

(37 citation statements)

References 33 publications

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“…It was found that different modelling techniques have been proposed to represent different parts of a system (e.g. enterprise modelling [10][11][12], product models [13], cost models [14] and quality models [15][16][17]). Under these conditions, the consideration of a whole manufacturing system would be a combination of models loosely connected.…”

Section: An Integrated Modelling Approachmentioning

confidence: 99%

An integrated approach to Design for Quality (DfQ) in the high value added printed circuit assembly (PCA) manufacturing: A pilot tool

Huertas-Quintero

Conway

West

et al. 2010

3rd Electronics System Integration Technology Conference ESTC

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High value added electronics manufacturing is a challenging sector that requires compliance with demanding quality standards. Tools and methods to support Design for Quality (DfQ) are limited within the domain. In this paper a software toolkit to support (DfQ) is proposed based on the underlying concept of integrated modelling. Based on this principle a simulation module to predict quality in terms of manufacturing defects and a Root Cause Analysis (RCA) module to support their elimination have been developed. The focus of this paper is on the latter RCA module. After a description of the integrated modelling concept and the software toolkit, a case study is presented to demonstrate the value of the software. The results illustrate possible roles of the quality support toolkit in solving real quality problems in printed circuit assemblies (PCA's) manufacturing. The results include measures of product quality improvements and savings in terms of time and cost.

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Section: An Integrated Modelling Approachmentioning

confidence: 99%

An integrated approach to Design for Quality (DfQ) in the high value added printed circuit assembly (PCA) manufacturing: A pilot tool

Huertas-Quintero

Conway

West

et al. 2010

3rd Electronics System Integration Technology Conference ESTC

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“…Results obtained from the analysis of a real case study provide an empirical and a theoretical contribution referring to the framework used to detect human error, evaluating the workers' reliability under emergency conditions [23]. A model for assessing the probability of human errors in reconfigurable manufacturing systems based on tasks characteristics, work environment as well as workers capabilities has been developed using the multi-attribute utility analysis by Elmaraghy et al (2008) independently of time. The attributes considered allow identifying the HEP neglecting the effect due to 'time-dependent' phenomena like fatigue, forgetting, learning, etc [24].…”

Section: Introduction Issn 2683-345xmentioning

confidence: 99%

“…A model for assessing the probability of human errors in reconfigurable manufacturing systems based on tasks characteristics, work environment as well as workers capabilities has been developed using the multi-attribute utility analysis by Elmaraghy et al (2008) independently of time. The attributes considered allow identifying the HEP neglecting the effect due to 'time-dependent' phenomena like fatigue, forgetting, learning, etc [24]. Consistently with this lack, Givi et al (2015) introduced a Learning-Forgetting-Fatigue-Recovery Model (LFFRM) where the HEP depends on two 'time-dependent' utility functions [25]:…”

Section: Introduction Issn 2683-345xmentioning

confidence: 99%

“…The mathematical model developed on the basis of multi-attribute analysis proposed by [24] allows to identify the HEP at the beginning of the work shift (t=0). Starting from HEP value at t=0 and considering the work environment, the task complexity, and the worker skill level as well as the work break schedule, the model allows identifying the dynamic variability of the HEP over time.…”

Section: Introduction Issn 2683-345xmentioning

confidence: 99%

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Model of Human Error Probability based on dual-phase approach for learning process in cognitive-oriented tasks

Facchini¹,

Digiesi²,

Mummolo³

2020

Int J Ind Eng Manag

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New accidents or incidents due to human error do not "just happen" and in most of the cases the cause relies not only on human incompetence or negligence [1]. In scientific literature the Human Error Probability (HEP) is defined, in case of discrete tasks, as the number of errors divided by the number of opportunities for making errors. The HEP is a key element in system accidents and disasters, especially in high-risk fields, such as industrial plants, nuclear plants, and aerospace. According to Health and Safety Executive agency (HSE), the errors can be classified in two categories. The first category includes the so-called "slips or lapses" errors: they are considered "actions that were not as planned" or unintended actions. These kinds of errors occur during the completion of a familiar task and include slips (e.g. pressing the wrong button or reading the wrong gauge) and lapses (e.g. forgetting to carry out a step in a known procedure). Therefore, they cannot be avoided by means of training, but an improved workplace design can reduce their likelihood and provide a more error tolerant system. The second category of errors includes the errors of judgement or decision-making, so-called "mistakes". They occur when the worker accomplishes the wrong action believing it to be right. In most cases, a mistake occurs in situations where Human Error Probability (HEP) is a key element in the chain of events that could lead up to system accidents and despite the efforts of recently studies to evaluate the human reliability, many of the limitations and problems have not yet been solved. The model proposed tries to overcome these limitations by means of a method that combines the advantages of a dual-phase learning approach with that of a multi-attribute utility analysis, in accordance with Dar-El' s theory. Results of numerical simulations show the effectiveness of the model in quantifying, over time, the HEP and in evaluating the human task error proneness by varying the work breaks schedule.

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“…This approach considers interrelations between technological variables, such as workers’ physical attributes and ergonomics evaluations. ElMaraghy, Nada, and ElMaraghy () introduce a model to assess the probability of human errors in reconfigurable manufacturing systems, which is based on tasks characteristics, the work environment, and workers’ capabilities, using the multiattribute utility analysis. This approach can predict the probability of errors caused by human involvement.…”

Section: Introductionmentioning

confidence: 99%

Human factors’ complexity measurement of human‐based station of assembly line

Fan

Zhao

et al. 2018

Hum Ftrs & Erg Mfg Svc

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Cognitive heterogeneity causes operation time variability and high human error rates at humanbased stations of assembly lines. It will affect the performance of the production activities, the design process of which depends on reliable estimate of operation time. To quantify operator cognition, we measured the human factors' complexity of human-based stations using an information entropy approach in aspect of operation time. Initially, the influence of the operators' cognition on the operation time is analyzed. Based on the analysis, the operation time model that considers cognition is presented. Then, the operation time model is modified by applying a correction method using the behavior correction factor, and, finally, the human factors' complexity measurement model is built. A case study is used to illustrate the application of the proposed method in order to measure the influence of human cognition on the performance of the human-based station. K E Y W O R D Sassembly line, complexity measurement, human cognition, human factors, human-based station

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Quality prediction for reconfigurable manufacturing systems via human error modelling

Cited by 63 publications

References 33 publications

An integrated approach to Design for Quality (DfQ) in the high value added printed circuit assembly (PCA) manufacturing: A pilot tool

An integrated approach to Design for Quality (DfQ) in the high value added printed circuit assembly (PCA) manufacturing: A pilot tool

Model of Human Error Probability based on dual-phase approach for learning process in cognitive-oriented tasks

Human factors’ complexity measurement of human‐based station of assembly line

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