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The purpose of this paper is to present some fundamental and critical differences between the two methods of experimental design (i.e. Taguchi and classical design of experiments (DOE)). It also aims to present an application of Taguchi method of experimental design for the development of an optical fiber sensor in a cost effective and timely manner.

The first part of the paper shows the differences between classical DOE and Taguchi methods from a practitioner's perspective. The second part of the paper illustrates a simple framework which provides guidance in the selection of a suitable DOE strategy. The last part is focused on a simple case study demonstrating the power of Taguchi methods of experimental design.

One of the key questions from many quality and production related personnel in organisations are “when to use Taguchi and Classical DOE?”. The purpose of this paper is to make an attempt to address the above question from a practitioner's perspective.

The case study is based on Taguchi method of experimental design. It would be great to see the results of the study if classical DOE is performed to this study.

The paper will be an excellent resource for both research and industrial fraternities who are involved in DOE projects.

Case study and frame work.

The paper describes the methods of manufacturing process optimization, using Taguchi experimental design methods with historical process data, collected during normal production.

The objectives are achieved with two separate techniques: the Retrospective Taguchi approach selects the designed experiment's data from a historical database, whilst in the Neural Network (NN) – Taguchi approach, this data is used to train a NN to estimate process response for the experimental settings. A case study illustrates both approaches, using real production data from an aerospace application.

Detailed results are presented. Both techniques identified the important factor settings to ensure the process was improved. The case study shows that these techniques can be used to gain process understanding and identify significant factors.

The most significant limitation of these techniques relates to process data availability and quality. Current databases were not designed for process improvement, resulting in potential difficulties for the Taguchi experimentation; where available data does not explain all the variability in process outcomes.

Manufacturers may use these techniques to optimise processes, without expensive and time‐consuming experimentation.

The paper describes novel approaches to data acquisition associated with Taguchi experimentation.

To solve the trade‐offs between marketing and R&D domains and to minimize information loss in new product development (NPD), this study proposes an integrated design process as a new solution to the interface system between the two domains.

House of Quality integrated with multivariate statistical analysis is used for determining important design features. These design features are used as parameters for conjoint analysis and Taguchi method, and then the results of analyses are compared. Sequential application of conjoint analysis and Taguchi method, depending on the differences in utilities and signal to noise ratios, is applied for the integrated design process. An automotive interior design is illustrated for the validation of the integrated design process.

The integrated design process determines a point of compromise between the optimums of conjoint analysis and Taguchi method. Sequential application of two methods ensures full utilization of both methods and no loss of information.

More illustrations on NPD are needed to verify the proposed process.

The design process suggested in this study can be used for process innovation in six sigma approach and be integrated with value chain intelligently. This study proposes the strategic guideline of the integrated design process for enterprises.

The integrated design process suggests the solution for the trade‐offs between marketing domain that pursues the utility of product and R&D domain that emphasizes robustness of product quality. This integrated design process will give enterprises competitive advantages in NPD.

The purpose of this paper is to present computer‐generated combined arrays as efficient alternatives to Taguchi's crossed arrays to solve robust parameter problems.

The alternative combined array designs were developed for the cases including six to twelve variables where CMR designs are not smaller than Taguchi's designs. The efficiency to estimate the effects of interest was calculated and compared to the efficiency of the corresponding CMR designs.

For all the cases investigated at least one computer generated combined array design was found with the same size as the CMR design and with higher efficiency.

Robust parameter design identifies appropriate levels of controllable variables in a process for the manufacturing of a product. The designed experiments involve the controllable variables along with the uncontrollable or noise variables to design a product or process that will be robust to changes in these noise variables. Response surface methodology estimates the actual relationship between the response and the input variables with an empirical model based on the designed experiment. Once the empirical model is fitted, the surface described by the model can be used to describe the behavior of the response over the experimental region. The higher efficiency of the computer generated combined array designs proposed in this research produces lower variances for the parameter estimates and lower variance of prediction for the model. As a result, the response will be described in a more realistic form.

The paper shows that using a computer‐generated design to solve a robust parameter problem will result in a better approximation to the true response of the process. Consequently, optimizing the fitted model will produce settings for the parameters closer to the real optimal settings.

There is growing evidence of a knowledge gap in the association of maintenance with production activities in bottling plants. Indeed, insights into how to jointly optimise these activities are not clear. In this paper, two optimisation models, Taguchi schemes and response surface methodology are proposed.

Borrowing from the “hard” total quality management elements in optimisation and prioritisation literature, two new models were developed based on factor, level and orthogonal array selection, signal-to-noise ratio, analysis of variance and optimal parametric settings as Taguchi–ABC and Taguchi–Pareto. An additional model of response surface methodology was created with analysis on regression, main effects, residual plots and surface plots.

The Taguchi S/N ratio table ranked planned maintenance as the highest. The Taguchi–Pareto shows the optimal parametric setting as A4B4C1 (28 h of production, 30.56 shifts and 37 h of planned maintenance). Taguchi ABC reveals that the planned maintenance and number of shifts will influence the outcome of production greatly. The surface regression table reveals that the production hours worked decrease at a value of planned maintenance with a decrease in the number of shifts.

This is the first time that joint optimisation for bottling plant will be approached using Taguchi–ABC and Taguchi–Pareto. It is also the first time that response surface will be applied to optimise a unique platform of the bottling process plant.

Innovative restaurant service designs impart food wellbeing to diners. This research comprehends customer aspirations and concerns in a restaurant-dining experience to develop a service design that enhances the dining experience using the design thinking approach and evaluates its efficiency using the Taguchi method of robust design.

The sequential incidence technique defines diners' needs, which, followed by brainstorming sessions, helped create multiple service designs with important attributes. Prototype narration, as a scenario, acted as the stimulus for evaluators to respond to the WHO-5 wellbeing index scale. Scenario-based Taguchi experiment with nine foodservice attributes in two levels and the wellbeing score as the response variable helped identify levels of critical factors that develop better FWB.

The study identified the best combination of factors and their preferred levels to maximize FWB in a restaurant. Food serving hygiene, followed by information about cuisine specification, and food movement in the restaurant, were important to FWB. The experiment revealed that hygiene perceptions are critical to FWB, and service designs have a significant role in it. Consumers prefer detailed information about the ingredients and recipe of the food they eat; being confident that there will be no unacceptable ingredients added to the food inspires their FWB.

Theoretically, this study contributes to the growing body of literature on design thinking and transformative service research, especially in the food industry.

This paper details a simple method to identify and evaluate important factors that optimize FWB in a restaurant. The proposed methodology will help service designers and technology experts devise settings that consider customer priorities and contribute to their experience.

This study helps to understand the application of design thinking and the Taguchi approach for creating robust service designs that optimize FWB.

The purpose of this paper is to determine the impact level of parameters affecting wing design at low speeds using Taguchi method.

Using brain storming approach airfoil shape, wing angle of attack and Reynolds number are determined as important wing design parameters. Most important parameters over these parameters are determined using Taguchi method. The lift-to-drag ratio (CL/CD ratio) is chosen as the performance criterion and L8 orthogonal index is chosen as experimental study scheme for this study.

Experimental results are examined using Taguchi method. After making experiments and also analyses, Reynolds number is found as the most important and identifier parameter for aircraft wing design.

Taguchi method makes the experimental design for experimental studies. This method reduces the number of experiments substantially using orthogonal indices while keeping effects of uncontrolled parameters to a minimum. Reduction in number of experiments helps save time and also cost.

In this study, with less number of experiments, the most important parameter for aircraft wing design is determined. Moreover, with less number of experiments, not only is time saved but the design stage is also made faster.

Dr Taguchi is a Japanese engineer and an international quality consultant who has made breakthrough improvements in product and process quality through the use of statistical design of experiments (SDOE). The Taguchi method became popular in the West in the 1980s as a means to design robust products and processes. Although many companies and industries have used the method with success, the real benefits of the approach were not realised and fully understood in many cases. This lack of success could be attributed to a number of factors, but mainly because the experiments were treated in isolation and not integrated into a continuous improvement strategy. This paper briefly presents the results of the application of the Taguchi methodology in the UK industry. The paper also illustrates the application of the Taguchi method for optimising the production process of retaining a metal ring in a plastic body in a braking system.

Dr Genichi Taguchi is a Japanese engineer and quality consultant who has promoted the use of statistical design of experiments for improving process/product quality at minimal costs. Taguchi has developed a practical and strategic approach for designing quality into products and processes at the product planning, design and development stages, which is often referred to as off‐line quality control. Although many companies in Europe and the USA have used the Taguchi approach to statistical design of experiments with success, very few applications of this method are realised in countries such as the Czech Republic. This paper reports the applications of experimental design advocated by Taguchi in two manufacturing companies in the Czech Republic. The results of the study are stimulating and will lead to wider applications of this methodology for tackling process and quality‐related problems in the Czech Republican industries in the near future.

The aim of the paper is the fine‐tuning of proportional integral derivative (PID) controllers under model parameter uncertainties (noise).

The fine‐tuning of PID controllers achieved using the Taguchi method following the steps given: selection of the control factors of the PID with their levels; identification of the noise factors that cause undesirable variation on the quality characteristic of PID; design of the matrix experiment and definition of the data analysis procedure; analysis of the data; decision regarding optimum settings of the control parameters and predictions of the performance at optimum levels of control factors; calculation of the expected cost savings under optimum condition; and confirmation of experimental results.

An example of the proposed method is presented and demonstrates that given certain performance criteria, the Taguchi method can indeed provide sub‐optimal values for fine PID tuning in the presence of model parameter uncertainties (noise). The contribution of each factor to the variation of the mean and the variability of error is also calculated. The expected cost savings for PID under optimum condition are calculated. The confirmation experiments are conducted on a real PID controller.

As a further research it is proposed the contiguous fine‐tuning of PID controllers under a number of a variant controllable models (noise).

The enhancement of PID controllers by Taguchi method is proposed with the form of a hardware mechanism. This mechanism will be incorporated in the PID controller and automatically regulate the PID parameters reducing the noise influence.

Application of Taguchi method in the scientific field of automation control.