Beyond certification: a proposed framework for ISO 9000 maintenance in service

Factors for improving performance in ISO 9001 certified small- and medium-sized service enterprises

Purpose

The purpose of this paper is to investigate the factors that are important for improving the performance of ISO 9001 certified small- and medium-sized service enterprises (SMSEs).

Design/methodology/approach

Preliminary research questions were set on the basis of a literature review and then submitted to a Delphi panel of experts. The results from the Delphi panel were transformed into hypotheses in the form of a survey. A sample comprising 115 Italian SMSEs answered the survey, which also included specific fields for qualitative notes. The hypothesis tests used 95% confidence intervals, and notes and suggestions from the respondents were also collected.

Findings

The findings relate to the role and responsibilities of the quality manager, as well the entrepreneur and partners, the quality of the enterprise culture, training, awareness concerning process key performance indicators, visual management and modern digitised documentation and communication. Furthermore, criticisms emerged regarding the ISO 9001 standard in terms of cost of quality measurement, day-to-day improvement and problem-solving.

Research limitations/implications

This research is limited to SMSEs. The limitations of this research open avenues for further research. In particular, many suggestions from the respondents seem generalisable to other industries and also to large manufacturing firms.

Practical implications

The findings of this research could be used as a guideline for implementing ISO 9001 in the SMSE sector. Moreover, some of the criticisms could be taken into account for the next revision of the ISO 9001 standard.

Originality/value

This is one of the first papers to discuss important factors for improving the performance of ISO 9001 in the SMSE sector.

Framework of decision in data modeling for quality improvement

Purpose

– The purpose of this paper is to propose a framework of decision making to aid practitioners in modeling and optimization experimental data for improvement quality of industrial processes, reinforcing idea that planning and conducting data modeling are as important as formal analysis.

Design/methodology/approach

– The paper presents an application was carried out about the modeling of experimental data at mining company, with support at Catholic University from partnership projects. The literature seems to be more focussed on the data analysis than on providing a sequence of operational steps or decision support which would lead to the best regression model given for the problem that researcher is confronted with. The authors use the concept of statistical regression technique called generalized linear models.

Findings

– The authors analyze the relevant case study in mining company, based on best statistical regression models. Starting from this analysis, the results of the industrial case study illustrates the strong relationship of the improvement process with the presented framework approach into practice. Moreover, the case study consolidating a fundamental advantage of regression models: modeling guided provides more knowledge about products, processes and technologies, even in unsuccessful case studies.

Research limitations/implications

– The study advances in regression model for data modeling are applicable in several types of industrial processes and phenomena random. It is possible to find unsuccessful data modeling due to lack of knowledge of statistical technique.

Originality/value

– An essential point is that the study is based on the feedback from practitioners and industrial managers, which makes the analyses and conclusions from practical points of view, without relevant theoretical knowledge of relationship among the process variables. Regression model has its own characteristics related to response variable and factors, and misspecification of the regression model or their components can yield inappropriate inferences and erroneous experimental results.