Guidelines to help practitioners of design of experiments

Design of experiments in the service industry: results from a global survey and directions for further research

Purpose

The purpose of this study is to critically evaluate the current status of Design of experiments (DoE) in the service industry or non-manufacturing processes in the manufacturing sector. This involves the identification of critical success factors and challenges in the use of DoE in the context of the service industry as well as the key skills needed for its successful applications.

Design/methodology/approach

In order to identify the critical success factors and challenges, an online survey instrument was developed and distributed across Six Sigma Black Belts (SSBB), Six Sigma Master Black Belts (SSMBB), process improvement champions, continuous improvement professionals, DoE experts and specialists as well as academics who are involved in DoE research and applications.

Findings

A total of 109 participants from over 20 countries responded to the study. One of the first findings of the study is that over 40% of the process improvement professionals in the service sector do not have any hands-on experience with the application of DoE. Further, this study highlights the following critical factors for successful DoE applications: dedicating adequate resources in terms of time, budget and people for the experiment; getting a clear understanding of the problem at hand; securing a reliable and validated measurement system for measuring the response or quality characteristics of interest; and identifying input independent variables, which influence the quality characteristics during the brainstorming stage. Some respondents also mention the importance of having training or education in DoE as a critical success factor. At the same time, the lack of adequate planning to experimentation, mathematical knowledge and statistical reasoning remains as challenge to be solved.

Research limitations/implications

As data were collected at an individual level, inter-reliability, which measures the consistency of judgements on a particular survey item, could not be gauged. Moreover, due to low sample size across some continents, an intercontinental comparative study on the extent of the application of DoE across various continents could not be performed.

Originality/value

To the best of the authors’ knowledge, this is the first global survey carried out to understand the status of DoE in the service industry. The results of the global study can be used to encourage quality, continuous improvement and process improvement professionals in the service sector to drive wider acceptance of one of the most powerful tools in the Six Sigma toolkit for problem-solving scenarios. Moreover, senior managers in the service industry can take away some of the key benefits, challenges and success factors in the implementation of DoE.

In Vitro Impact Testing to Simulate Implant-Supported Prosthesis Retrievability in Clinical Practice: Influence of Cement and Abutment Geometry

Cement-retained implant-supported prosthetics are gaining popularity compared to the alternative screw-retained type, a rise that serves to highlight the importance of retrievability. The aim of the present investigation is to determine the influence of luting agent, abutment height and taper angle on the retrievability of abutment–coping cementations. Abutments with different heights and tapers were screwed onto an implant and their cobalt-chrome copings were cemented on the abutments using three different luting agents. The removals were performed by means of Coronaflex^®. The number of impulses and the forces were recorded and analyzed with a Kruskal–Wallis test. Harvard cement needed the highest number of impulses for retrieval, followed by Telio CS and Temp Bond. However, abutment height and taper showed a greater influence on the cap’s retrievability (p < 0.05). Long and tapered abutments provided the highest percentage of good retrievability. The influence of the luting agent and the abutment geometry on the cap’s retrieval performed by Coronaflex^® reflects data from literature about the influence of the same factor on the maximum force reached during uniaxial tensile tests. The impulse force was slightly affected by the same factors.

Fuzzy assessment analysis and key improvements to a production system

Purpose

The framework of Six Sigma Define-Measure-Analyze-Improve-Control was applied in this study, and various tools and techniques were used at different stages to implement lean measures to ensure quality. The purpose of this paper is to develop a decision-making framework that assesses key quality performance to ensure that practitioners improve quality and control by modeling and optimizing production processes.

Design/methodology/approach

A model of a quality performance index system was established. The weights of factors and sub-factors, which were estimated using an FAHP, were used as a reference for the decision maker under fuzzy uncertainly to make a decision, and thus, results present the bottlenecks in processes. Furthermore, any other factors that may affect the key process bottlenecks must be considered. The critical to quality characteristics were determined, and factor levels were set. The interaction between the factors was analyzed, their significance was studied using the Design of experiments and the parameters were predicted. Finally, quality improvement decisions were made through failure mode and effects analysis.

Findings

The implementation results of this research prove that the proposed model could successfully determine the key processes and focus on the improvement of critical quality factors under limited resources.

Originality/value

This study establishes a set of performance appraisal methods for production systems, which can be used for improving productivity and quality.

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.