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

Bashkansky, Emil; Dror, Shuki

doi:10.1002/qre.1770

Cited by 8 publications

(5 citation statements)

References 24 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: 65%

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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: 65%

“…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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“…GT is also intended to group components into associated families with minimal variation within families. GT is limited to binary strings; hence, it will not fit nonbinary strings (eg, Bashkansky and Dror 8 ). The clustering of HEs represented by strings, based on the Hamming distance metric, can serve as a basis for developing an optimal system of protection against quality risks associated with HEs.…”

Section: Suggested Methods For Overcoming Problemsmentioning

confidence: 99%

“…Bashkansky and Dror 8 classified human errors (HEs) according to a 10‐dimensional HE characterization space. Every dimension is based on a categorical scale (binary, nominal, or ordinal) consisting of two (eg, intentional or unintentional) or more categories (eg, error origin), which can be designated in some way by corresponding symbols.…”

Section: Types Of Data and Their Descriptionmentioning

confidence: 99%

Processing new types of quality data

Marmor

Bashkansky

2020

Quality & Reliability Eng

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Quality engineers are increasingly faced with the need to deal with new types of data, which are significantly different from ordinary numerical data by virtue of their nature and the operations that can be performed with them. Basic concepts related to processing of such data, ie, data similarity, measurement system analysis, variation analysis, and data fusion, need to be thoroughly rethought. Reviewing recent publications in the field, we suggest a common approach to processing all data types on the basis of the idea of defining the distance metric for the appropriate data space. The article discusses six types of quality data (nominal, ordinal, preference chains, strings, tree structured, and product/process distribution) and four data processing aspects (calculating data similarity, error description, data fusion, and intradispersion and interdispersion studies). Necessary information and recommendations are given for each combination of data type and problem. They are also summarized in a table that refers the reader to various sections of the article. Any other data type for which the distance metric is definable can be included into the framework of the proposed unified approach.

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“…the number of scale values should be limited. Therefore, the mentioned geometrical scale (1,3,9) was extended by 0 only for an unfeasible scenario, and the known scale of four values (0,1,3,9) [38] was considered to be the optimal solution for a chemical analytical task.…”

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

Cited by 8 publications

References 24 publications

Identify Important Factors for Service Simulation Experiments Using QFD

Identify Important Factors for Service Simulation Experiments Using QFD

Processing new types of quality data

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

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