House of security: a structured system design &amp; analysis approach

Dror, Shuki; Bashkansky, Emil; Ravid, R.

doi:10.2495/safe-v2-n4-317-329

Cited by 11 publications

(21 citation statements)

References 13 publications

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“…It equals 1 if there is possible interaction between the two factors and equals 0 if there is no interaction between the two factors. This part of Equation is a little different from the equation presented by Dror et al , Bashkansky and Dror …”

Section: Example: Constructing the Hoss For Customer Order Handling Pmentioning

confidence: 70%

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Identify Important Factors for Service Simulation Experiments Using QFD

Dror

2016

Quality & Reliability Eng

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Service design is a form of conceptual design, which involves the activity of planning and organizing people, infrastructure, communication and material components of a service in order to improve its performances. A simulation experiment models various scenarios of a service system. One of the aims of the designer is selecting appropriate factors for the determination of the simulation scenarios. The complete set of possible scenarios is huge, and it is often useful to obtain subjective input to help screen out some vital factors. Taguchi advocates a three‐stage design procedure for off‐line quality control: (i) system design; (ii) parameter design; and (iii) tolerance design. In the parameter design stage, which is the key stage in Taguchi method, factors affecting the performance of Y are categorized as controllable factors and noise factors. In this paper, a Quality Function Deployment matrix highlights controllable factors and noise factors to be considered when running a simulation experiment for a service system. It is assumed that interactions between the factors might be changed for each performance measure, that is, several roofs, corresponding to the number of rows in the Quality Function Deployment matrix. The mean square error criterion is utilized here for selecting the vital service factors to be examined in the simulation experiments. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Example: Constructing the Hoss For Customer Order Handling Pmentioning

confidence: 70%

“…Dror et al , Bashkansky and Dror, utilized the roof of QFD in a different format. The authors presented applications where the presence of positive/negative synergy must be analyzed for each row of the QFD matrix separately.…”

Section: Example: Constructing the Hoss For Customer Order Handling Pmentioning

confidence: 99%

Identify Important Factors for Service Simulation Experiments Using QFD

Dror

2016

Quality & Reliability Eng

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“…This approach, developed originally in the field of security and safety, combined the house-of-quality matrix transformation of customer requirements into quality design by generic quality function deployment, and some ideas of failure mode and effect analysis [34].…”

Section: Quantification Of Human Errorsmentioning

confidence: 99%

“…For example, the geometrical scale with three values (1,3,9) was used originally in the house-of-security approach [34] in order to emphasize the dramatic character of expert judgments for the security system of an organization. However, this scale may not be informative enough.…”

Section: Elicitation Scalementioning

confidence: 99%

IUPAC/CITAC Guide: Classification, modeling and quantification of human errors in a chemical analytical laboratory (IUPAC Technical Report)

Kuselman¹,

Pennecchi

2016

Pure and Applied Chemistry

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Abstract:The classification, modeling, and quantification of human errors in routine chemical analysis are described. Classifications include commission errors (mistakes and violations) and omission errors (lapses and slips) in different scenarios at different steps of the chemical analysis. A Swiss cheese model is used to characterize error interaction with a laboratory quality system. The quantification of human errors in chemical analysis, based on expert judgments, i.e. on the expert(s) knowledge and experience, is applied. A Monte Carlo simulation of the expert judgments was used to determine the distributions of the error quantification scores (scores of likelihood and severity, and scores of effectiveness of a laboratory quality system against the errors). Residual risk of human error after the error reduction by the laboratory quality system and consequences of this risk for quality and measurement uncertainty of chemical analytical results are discussed. Examples are provided using expert judgments on human errors in pH measurement of groundwater, multiresidue analysis of pesticides in fruits and vegetables, and elemental analysis of geological samples by inductively coupled plasma mass spectrometry.

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“…As solutions to these and similar problems, Kuselman et al . proposed an approach similar to that suggested in Dror et al ., requiring the building of a HOS, with the following substitutions: HE instead of terrorist attack. QAS instead of organizational security system. …”

Section: The Proposed Approach To Quality Assurance System Deploymentmentioning

confidence: 99%

Matrix Approach to Analysis of Human Errors and their Prevention by Quality Engineering and Managerial Tools

Bashkansky

Dror

2015

Qual. Reliab. Engng. Int.

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We apply well‐known quality engineering matrix techniques such as quality function deployment; Teoriya Resheniya Izobretatelskikh Zadatch (TRIZ); and failure mode, effects, and criticality analysis for characterizing, mapping, and preventing human error (or, at least, reducing damage caused by errors). Human errors (‘WHATs’, in the language of quality function deployment) are classified according to 10 characteristics, while 20 typical types (or protective layers)—‘HOWs’—in quality assurance systems are proposed for preventing/stopping/minimizing to some extent damage caused by the error. During the analysis of a specific system, any error is estimated according to its likelihood and severity, and every protective layer receives a score according to its effectiveness in preventing errors. Synergy or antagonism between protective layers may also be taken into account when calculating the effectiveness. The approach facilitates evaluation and comparison of the effectiveness of different quality assurance systems dealing with human errors. The authors emphasize the need to create a ‘recipe book’ based on a historical database, which will enable, after characterizing the potential human errors according to the 10 criteria mentioned earlier, application of the optimal prevention efforts. The proposed approach is illustrated by an example of product delivery errors analysis. Copyright © 2015 John Wiley & Sons, Ltd.

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House of security: a structured system design & analysis approach

Cited by 11 publications

References 13 publications

Identify Important Factors for Service Simulation Experiments Using QFD

Identify Important Factors for Service Simulation Experiments Using QFD

IUPAC/CITAC Guide: Classification, modeling and quantification of human errors in a chemical analytical laboratory (IUPAC Technical Report)

Matrix Approach to Analysis of Human Errors and their Prevention by Quality Engineering and Managerial Tools

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