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Credit scoring is widely used to make credit decisions, to reduce the cost of credit analysis and enable faster decisions. However, traditional credit scoring models do not account for the influence of noises. This study proposes a robust credit scoring system based on Mahalanobis‐Taguchi System (MTS). The MTS, primary proposed by Taguchi, is a diagnostic and forecasting method using multivariate data. The proposed approach’s effectiveness is demonstrated by using real case data from a large Taiwanese bank. The results reveal that the robust credit scoring system can be successfully implemented using MTS technique.

The purpose of this paper is to develop a methodology to reduce the field failures of splined shafts. The paper also demonstrates the application of Mahalanobis-Taguchi system (MTS) for identifying the optimum hardness profile to avoid failures.

Through the usage profile analysis and comparison between the failed and good shafts, the major reason for shaft failure was identified as hardness variation. Then MTS approach was used to identify the optimum hardness profile for the shafts. An experiment was designed with power, feed and the gap between inductor and quench ring representing the heat transfer rate, heat removal rate and the time between heat transfer and removal of induction hardening process as factors. Based on experimental results, the optimum combination factors that would reduce the variation around the optimum hardness profile were identified.

The study showed that the shaft failures can be reduced by optimizing the hardness profile of the shafts rather than warning customers on overloading, changing the raw material or investing on machining operation to achieve better shaft finish. The study suggested heat transfer rate, heat removal rate and the time between heat transfer and removal had significant impact on the shaft's hardness profile. The study resulted in reducing the field failures from 0.32 to 0.029 percent.

This study provides valuable information on how to identify optimum hardness profile using MTS methodology to reduce shaft failures and how to minimize the variation around the optimum hardness profile using design of experiments.

To the best of author's knowledge, no study has been conducted to identify optimum hardness profile using MTS methodology. The study also provides an approach to minimize the variation around a non-linear hardness profile using design of experiments.

Strategic planning (SP) enables enterprises to plan management and operations activities efficiently in the medium and large term. During its implementation, many processes and methods are manually applied and may be time consuming. The purpose of this paper is to introduce an automatic method to define strategic plans by using text mining (TM) algorithms within a generic SP model especially suited for small- and medium-sized enterprises (SMEs).

Textual feedbacks were collected through a SWOT matrix during the implementation of a SP model in a company dedicated to the local distribution of food. A four-step TM process (performing acquisition, pre-processing, processing, and validation tasks) is applied via a framework developed under the cloud computer paradigm in order to determine the strategic plans.

The use of categorization and clustering algorithms show that unstructured textual information produced during the SP can be efficiently processed and capitalized. Collected evidence reveals the potential to enhance the strategic plans creation with less effort and time, improving the relevance, and producing new technological resources accessible to SMEs.

An innovative framework especially suited for the SMEs based on the synergy assumption of the coupling between TM and a generic SP model.

The purpose of this paper is to present and analyze the current literature related to developing and improving the Mahalanobis-Taguchi system (MTS) and to present the shortcomings related to this method for future research.

In this paper, articles in the literature are classified to give an overview on the MT strategy. For this purpose, 46 articles are considered for classification from 2000 to 2013 on the basis of: MTS contribution area, description of the issue, and results.

In this paper a review on the concepts and operations of the MTS was provided as a new method in the field of pattern recognition, multivariable diagnosis, and forecasting. A large number of studies were performed in recent years consisting of developing MTS and MTS case studies. The analysis of the articles showed the fields of MTS which had more potential for future studies and developing. The comparison of the MTS to other methods and the selection of the normal group for constructing the Mahalanobis space have received the most attention by researchers. In addition, several studies concentrated on the use of other methods instead of design of experiments, finding applications for multiclass MTS and finding an alternative for the SN ratio.

This paper contains the publications in the field of MTS chronologically and shows different areas for developing and case studies. It will be useful to researchers and professionals who are interested in pattern recognition, multivariate analysis, and forecasting.

The purpose of this paper is to show a bibliographical review of the applications of the MTS throughout the time and the different fields.

The Mahalanobis-Taguchi system (MTS) is an analytical method used for the diagnosis and/or pattern recognition of multivariate data for quantitative decision making.

Its scope is very broad, ranging from engineering, medicine, education, and manufacturing, among others. This work presents a classification of the literature in the following areas of the MTS: introduction of the method, cases of study/application, comparison with other methods, integration and development of the MTS with other methods, construction of Mahalanobis space, dimensional reduction and threshold establishment. It realized a wide search of the publications in magazines and congresses.

This paper is a summary of the main applications, contributions and changes to MTS.

The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize out-of-control points, identify the key influential variable for the out-of-control state, and determine necessary changes to achieve the state of statistical control.

The proposed approach integrates the control chart technique, the Mahalanobis-Taguchi System concept, the Andrews function plot, and nonlinear optimization for multivariate process control. Mahalanobis distance, Taguchi’s orthogonal array, and the main effect plot concept are used to identify the key influential variable responsible for the out-of-control situation. The Andrews function plot and nonlinear optimization help to identify direction and necessary correction to regain the state of statistical control. Finally, two different real life case studies illustrate the suitability of the approach.

The case studies illustrate the potential of the proposed integrated multivariate process control approach for easy implementation in varied manufacturing and process industries. In addition, the case studies also reveal that the multivariate out-of-control state is primarily contributed by a single influential variable.

The approach is limited to the situation in which a single influential variable contributes to out-of-control situation. The number and type of cases used are also limited and thus generalization may not be debated. Further research is necessary with varied case situations to refine the approach and prove its extensive applicability.

The proposed approach does not require multivariate normality assumption and thus provides greater flexibility for the industry practitioners. The approach is also easy to implement and requires minimal programming effort. A simple application Microsoft Excel is suitable for online implementation of this approach.

The key steps of the MSPC approach are identifying the out-of-control point, diagnosing the out-of-control point, identifying the “influential” variable responsible for the out-of-control state, and determining the necessary direction and the amount of adjustment required to achieve the state of control. Most of the approaches reported in open literature are focused only until identifying influencing variable, with many restrictive assumptions. This paper addresses all key steps in a single integrated distribution-free approach, which is easy to implement in real time.

The relationships among multi‐dimensional data (such as medical examination data) with ambibuity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back‐propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi‐dimensional feature selection. The first one proposed is a feature selection procedure from the trained back‐propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis‐Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi’s fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD; it can deal with a reduced model, which is the focus of this paper. In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

The purpose of this paper is to describe a leanness assessment methodology that takes into account the interaction between lean elements for computing the systemic leanness and for assisting continuous improvement of lean implementation.

Key elements determining the leanness level were identified by reviewing the relevant literature and were structured as a framework. Graph-theoretic approach (GTA) was used as the assessment methodology for its ability to evaluate the interaction between the elements in the developed framework.

Interactions between the lean elements were configured. Application of the proposed GTA for assessing systemic leanness was demonstrated. Scenario analysis was performed and a scale was developed to assist firms in comparing their systemic leanness index.

This paper is unique in developing an assessment approach for measuring the systemic leanness. In addition, this study explains how the implementation of lean thinking (LT) in a value stream can be continuously improved by proposing a systemic leanness index that can be benchmarked. The proposed approach to measure systemic leanness can be tested across different value streams in future for extending its generalizability.

Proposed framework and leanness assessment approach presents an innovative tool for practitioners to capture the systemic aspect of LT. Proposed assessment approach supports practitioners in achieving continuous improvement in lean implementation by revealing the lean elements that need to be focused in future.

Study introduces a new perspective for LT by studying the importance of interactions between the lean elements and by incorporating them to assess the systemic leanness.

Quality awards, commonly, have basic criteria and sub-criteria to evaluate applicants based on the quality management principles and philosophies. The purpose of this paper is to examine the method of selecting award winners and its consequences.

Award winners’ and non-award winners’ performances of the Ethiopian Quality Award are the study groups. The criteria and sub-criteria evaluation results of the award were collected and analysed by the Mahalanobis-Taguchi System.

The research assumed that award winners’ performances are exceptionally outstanding. However, the result does not justify the assumption. Hence, the drawback of aggregating multivariate performance measures in a quality award is proven. Mahalanobis distance is proposed as alternative approach to evaluate and select organizations.

The outcome of this research will help award givers, evaluators and participants to understand the real difficulty to select very few organizations among applicants. Furthermore, it helps to consider the possible error when aggregating individual performance.

Aggregating performances is a common practice in quality awards evaluation process but this paper proved its drawback.

The purpose of this paper is to develop an unsupervised classification algorithm including feature selection for industrial product classification with the basic philosophy of a supervised Mahalanobis‐Taguchi System (MTS).

Two novel unsupervised classification algorithms called Unsupervised Mahalanobis Distance Classifier (UNMDC) are developed based on Mahalanobis' distance for identifying “abnormals” as individuals (or, groups) including feature selection. The identification of “abnormals” is based on the concept of threshold value in MTS and the distribution property of Mahalanobis‐D².

The performance of this algorithm, in terms of its efficiency and effectiveness, has been studied thoroughly for three different types of steel product on the basis of its composition and processing parameters. Performance in future diagnosis on the basis of useful features by the new scheme is found quite satisfactory.

This new algorithm is able to identify the set of significant features, which appears to be always a larger class than that of MTS. In industrial environment, this algorithm can be implemented for continuous monitoring of “abnormal” situations along with the general concept of screening “abnormals” either as individuals or as groups during sampling.

The concept of determining threshold for diagnostic purpose is algorithm dependent and independent of the domain knowledge, hence much more flexible in large domain. Multi‐class separation and feature selection in case of detection of abnormals are the special merits of this algorithm.