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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.

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.

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 aim of this article is to show how Taguchi methods can be applied to health care.

The quadratic loss function is at the heart of Taguchi methods. It is a powerful motivator for a quality strategy and can be used to adequately model the loss to society in health care. It also establishes a relationship between cost and variability. Therefore, it can be integrated with the performance and parameters of the design of medical applications. Signal‐to‐noise ratios give a sense of how close is the performance to the ideal. By maximizing the signal‐to‐noise ratio, quality‐engineering activities can be aimed at identifying near‐optimum levels of factors and making quality equal to zero.

This article shows that, when the patients' requirements are consistently met, lower losses can provide an impetus to improve patient satisfaction.

The article outlines areas in health care where Taguchi methods can easily be applied.

There can be activities that cannot reduce times by conventional single minute exchange of die (SMED) tools. In this case more advanced tools are needed. The purpose of this paper is to integrate the fuzzy Taguchi method into the SMED method in order to improve the setup time. The reason for using fuzzy logic is the subjective evaluation of factor’s levels assessment by experts. Subjective assessment contains a certain degree of uncertainty and is vagueness. The fuzzy Taguchi method provides to determining optimal setup time parameters in an activity of SMED. So it is possible to reduce time more than the conventional SMED method.

In this study, the SMED method and the fuzzy Taguchi method are used.

In this study, it has been shown that the setup time is reduced (from 196 to 75 min) and the optimum value can be given at the intermediate value by the fuzzy Taguchi method.

In this limited literature research, the authors have not found a study using the fuzzy Taguchi method in the SMED method.

Key performance indicators (KPIs) of maintenance systems serve as benchmarks to workers and organizations to compare their goals for decision-making purposes. Unfortunately, the effects of one KPI on the other are least known, restraining decisions on prioritization of KPIs. This article examines and prioritizes the KPIs of the maintenance system in a food processing industry using the novel Taguchi (T) scheme-decision-making trial and evaluation laboratory (DEMATEL) method, Taguchi–Pareto (TP) scheme–DEMATEL method and the DEMATEL method.

The causal association of maintenance process parameters (frequency of failure, downtime, MTTR, MTBF, availability and MTTF) was studied. Besides, the optimized maintenance parameters were infused into the DEMATEL method that translates the optimized values into cause and effect responses and keeping in view the result of analysis. Data collection was done from a food processing plant in Nigeria.

The results indicated that downtime and availability have the most causal effects on other criteria when DEMATEL and T-DEMATEL methods were respectively applied to the problem. Furthermore, the frequency of failure is mostly affected by other criteria in the key performance indication selection using the two methods. The combined Taguchi scheme and DEMATEL method is appropriate to optimize and establish the causal relationships of factors.

Hardly any studies have reported the joint optimization and causal relationship of maintenance system parameters. However, the current study achieves this goal using the T-DEMATEL, TP-DEMATEL and DEMATEL methods for the first time. The applied methods effectively ease decisions on prioritization of KPIs for enhancement.

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.

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 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.

The purpose of this paper is to propose a simple methodology in solving multi‐response optimisation problems by employing Taguchi methods and a non‐parametric statistical technique.

There is a continuous interest in developing effective and statistically sound multi‐response optimisation methods such that they will provide a firm framework in global product and process improvement. A non‐parametric approach is proposed for the first time in a five‐step methodology that exploits Taguchi's fractional factorial designs and the concept of signal‐to‐noise ratio in data consolidation. The distinct feature of this method is the transformation of each response variable to a single rank variable. The subsequent incorporation of the squared ranks for each of the investigated responses issues a single master‐rank response suitably referred to conveniently as a “Super Rank” (SR) response, thus collapsing all dependent product characteristic information into a single non‐dimensional variable. This SR variable is handled by standard non‐parametric methods such as Wilcoxon's two‐sample, rank sum test or Mann‐Whitney's test eliminating at the same time multi‐distribution effects and small‐sample complications expected for this type of experimentation.

The proposed methodology is tested on already published data pertaining a design problem in the electronic assembly technology field. The case study requires six‐factor simultaneous optimisation of three response variables. A second example is analyzed by the proposed method focusing on the optimisation of a submerged arc‐welding process problem due to a group of five factors. The Mann‐Whitney's test contrasts the effects of factor settings one‐to‐one on the SR response in order to assign statistical significance to the optimal factor settings.

The application of this methodology is tested at the same time in a real three‐response optimisation case study where each response belongs to different optimisation category.

The methodology outlined in this work eliminates the need for sophisticated multi‐response data handling. In addition, small‐sample considerations and multi‐distribution effects that may be inherent do not restrict the applicability of the method presented herein by this type of experimentation.

This investigation provides a new angle to the published methods of multi‐response optimisation by supporting Taguchi's design of experiments methods through a multi‐ranking scheme that leads to non‐parametric factor resolution.