Proposed expansion of quality information framework (QIF) standard schema with potential failure mode and effects analysis (FMEA) information model

PurposeThis paper aims to propose a framework on complaint management system for quality management by applying the text mining method and potential failure identification that can support organization learning (OL). Customer complaints in the form of email text is the input of the framework, while the most frequent complaints are visualized using a Pareto diagram. The company can learn from this Pareto diagram and take action to improve their process.Design/methodology/approachThe first main part of the framework is creating a defect database from potential failure identification, which is the initial part of the failure mode and effect analysis technique. The second main part is the text mining of customer email complaints. The last part of the framework is matching the result of text mining with the defect database and presenting in the form of a Pareto diagram. After the framework is proposed, a case study is conducted to illustrate the applicability of the proposed method.FindingsBy using the defect database, the framework can interpret the customer email complaints into the list of most defect complained by customer using a Pareto diagram. The results of the Pareto diagram, based on the results of text mining of consumer complaints via email, can be used by a company to learn from complaint and to analyze the potential failure mode. This analysis helps company to take anticipatory action for avoiding potential failure mode happening in the future.Originality/valueThe framework on complaint management system for quality management by applying the text mining method and potential failure identification is proposed for the first time in this paper.

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Anticipatory Failure Determination (AFD) for Product Reliability Analysis: A Comparison Between AFD and Failure Mode and Effects Analysis (FMEA) for Identifying Potential Failure Modes

Advances in Systematic Creativity ◽

10.1007/978-3-319-78075-7_12 ◽

2018 ◽

pp. 181-200 ◽

Cited By ~ 1

Author(s):

Renan Favarão da Silva ◽

Marco Aurélio de Carvalho

Keyword(s):

Reliability Analysis ◽

Failure Mode ◽

Failure Modes ◽

Product Reliability ◽

Potential Failure

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Quality information model

Digital Quality Management in Construction ◽

10.1201/9780429423062-6 ◽

2019 ◽

pp. 67-77

Author(s):

Paul Marsden

Keyword(s):

Information Model ◽

Quality Information

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Addressing Health Insurance Deductions through an Interventional Study: the Case of a Large Central Hospital

Asia Pacific Journal of Health Management ◽

10.24083/apjhm.v11i2.187 ◽

2016 ◽

Vol 11 (2) ◽

pp. 39-48

Author(s):

Erfan Kharazmi ◽

Asiyeh Salehi ◽

Neda Hashemi ◽

Shekufe Ghaderi ◽

Nahid Hatam

Keyword(s):

Healthcare Provider ◽

Failure Mode ◽

Armed Forces ◽

Healthcare Services ◽

Intervention Programs ◽

Medical Services ◽

Mode Effects ◽

The Social ◽

Potential Failure ◽

Efficiency And Productivity

Objective: A large proportion of hospitals’ private income is provided by insurance organisations. Hospitals in Iran face various problems in terms of insurance deductions from insurance organisations resulting from inefficient performance by both the hospitals and the insurers. These problems necessitate more specific cost control in this area. This research assesses the causes of insurance deductions by using the Failure Mode Effects Analysis (FMEA) technique, and addresses the issues resulting in deductions by providing some interventions through the Pareto technique. Design: The 10-step pattern of FMEA was implemented for assessing the main causes of insurance deduction in this study. Setting: Data was collected from deduced amounts by three main/largest contracting party insurance organisations (e.g. the Social Security Insurance Organisation, Medical Services Insurance Organisation and Armed Forces Medical Services Insurance Organisation of Namazi Hospital, a large healthcare provider in the South of Iran, in 2014. Findings: Sixty-five potential failure causes were identified, of which 26 were related to the anaesthesia unit, 23 were related to the surgery room unit and 16 were related to the hospitalisation unit. Deductions in the anaesthesia and hospitalisation units and the surgery room were reduced after intervention programs by 14.42%, 57.76%, and 51.52%, respectively. Conclusions: Using the FMEA technique in a large healthcare provider in Iran resulted in identifying the main causes of insurance deductions and provided intervention programs in order to increase the efficiency and productivity of healthcare services. Abbreviations: FMEA – Failure Mode Effects Analysis; RPN – Risk Priority Number.

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