Statistical, technical and sociological dimensions of design of experiments

Applying design of experiments to optimize the performance level of a curling sport team

Purpose

This article presents a novel case study that analyzes the applicability of DoE in the curling sport in order to improve their own performance and the performance of its athletes. Specifically, this study analyzes the most important factors to increase accuracy and precision in the draw game with curlers' opinions. It was decided to use the “Last Stone Draw (LSD)’ as an appropriate play situation.

Design/methodology/approach

Specifically, this study analyzes most important factors to increase accuracy and precision in the draw game with curlers opinions from the German Curling association. Three research techniques were used in this study: case study, interviews and a well-designed experiment. The analysis through the use of DoE includes a measurement system analysis, an initial variance test between two players, a screening and a characterization experiment.

Findings

The results obtained from DoE suggest that the factors routine, stress, release, balance, and the previous play situation have a substantial impact on the score of the player's draw game. However, no factor has a statistically significant impact on the average distance to the center of the target. Moreover, the DoE analysis also concludes that the accuracy and precision of the player's performance is not affected equally by all analyzed factors, but they turn into highly significant when examining their relationship to the other factors.

Practical implications

The findings of this study can be beneficial to other sports events in improving the performance. Moreover, DoE has proved to be an invaluable tool for many people in the German Curling Association in understanding the factors which influence the curlers performance and also factors which do not affect the curlers performance.

Originality/value

This research attempts to contribute to the existing sports management literature by identifying a way in which DoE can be an effective tool in non-manufacturing settings for identification of most important factors which influence the curling performance.

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.