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This paper proposes a model for detecting unexpected examination scores based on past scores, current daily efforts and trend in the current score of individual students. The detection is performed soon after the current examination is completed, which helps take immediate action to improve the ability of students before the commencement of daily assessments during the next semester.

The scores of past examinations and current daily assessments are analyzed using a combination of an ANOVA, a principal component analysis and a multiple regression analysis. A case study is conducted using the assessment scores of secondary-level students of an international school in Japan.

The score for the current examination is predicted based on past scores, current daily efforts and trend in the current score. A lower control limit for detecting unexpected scores is derived based on the predicted score. The actual score, which is below the lower control limit, is recognized as an unexpected score. This case study verifies the effectiveness of the combinatorial usage of data in detecting unexpected scores.

Unlike previous studies that utilize attribute and background data to predict student scores, this study utilizes a combination of past examination scores, current daily efforts for related subjects and trend in the current score.

The availability of daily assessment data in a centralized monitoring system at school provides the opportunity to detect unusual scores soon after the assessment is carried out. This paper introduces a model for the detection of unusual scores of individual students to immediately improve performances that deviate from a normal state.

A student's ability, a subject's difficulty level, a student's specific ability in a subject, and the difficulty level of an assessment in a subject are selected as factor effects of a linear ANOVA model. Through analysis of variance, a case study is conducted based on 330 data points of assessment scores of primary grade students retrieved from an international school in Japan.

The actual score is below the lower control limit, which is recognized as an unusual score, and the score can be detected immediately after sitting for an assessment and is beneficial for students to take immediate remedies based on daily assessment. This is demonstrated through a case study.

The detection of unusual scores based on a linear model of individual students soon after each assessment benefits from immediate remedy aligns with a daily management concept. The daily assessment data in a school system enable detection based on individual students, subject-wise and assessment-wise to improve student performances in the same academic year.

This paper aims to develop a total quality management (TQM) model based on the Deming prize criteria extracted from the Deming Prize. The proposed model is aligned with the school education context and deployed with the goal of achieving school excellence.

The model is developed based on a literature review in which key concepts for formulating the criteria and sub-criteria were identified. A qualitative research approach is applied to validate the model’s effectiveness. Interviews and feedback from an international school in Japan which adopted the model are used to determine its impact.

The model emphasises the achievement of school objectives (A) by implementing TQM activities (B) and assessing their impact (C). Its implementation offers enhanced organisational ability and the achievement of society-oriented school objectives. The findings show that the model gives clarity to TQM application and offers benefits for schools’ future sustainability.

This research proposes the adoption of a TQM model for educational institutions based on Deming criteria. Based on a comparison with prescriptive models such as Malcolm Baldrige National Quality Award and ISO 9001, this study shows how the developed model ensures organisational ability in current practice and future growth towards school excellence. Through the formulation of society-oriented school objectives and their linkage with results, it enables schools to create a balanced recognition of important stakeholders and gain consensus in realising the school’s objectives.

Global warming has become one of the most important issues in today's world, and business behavior to prevent global warming is receiving more attention from stakeholders. Mandatory domestic emission trading schemes have not been implemented in Japan as of June 2011, resulting in more discretion being given to companies to set their carbon management strategies, especially in selecting optimal targets and measures to achieve them. The purpose of this paper is to demonstrate that quality function deployment for environment (QFDE), developed by incorporating environmental aspects into quality function deployment (QFD) in order to simultaneously handle environmental and traditional product quality requirements, can provide a framework for selecting targets and measures.

The authors apply a three‐step approach: first, a framework for selecting measures is derived through a review of good‐practice activities in two leading companies; second, a framework based on QFDE is generalized by collecting key elements through a literature review of environmental and corporate social responsibility (CSR) reports published by 35 companies; and third, the usefulness of the framework is verified through case studies.

The main outcomes of this study are the derivation of a hierarchical framework with QFDE as its base and verification, through the use of case studies, of the applicability of the framework as a template, as well as an effective communication tool to be used for setting targets and selecting measures.

The framework will be useful for companies which voluntarily set targets and select measures that could form an essential part of their carbon reduction strategies, based on QFDE concepts.

The uncertainty as to whether investments in riskier and less efficient markets allow managers to ‘beat the market’ remains a question to which answers are required. Accordingly, the purpose of this chapter is to offer new insights on portfolios of the US, European and Emerging Market (‘EM’) domiciled equity mutual funds whose objectives are the investment in emerging economies, and specifically analyses two main issues: alpha generation and the influence of the funds’ characteristics on their risk-adjusted performance.

The dataset is made up a survivorship-bias controlled sample of 137 equity funds over the period January 2004 to December 2014, which are then grouped into equally weighted portfolios according to the scheme’s origin. The Jensen’s (1968) Single-Factor model along with the Fama and French’s (1993) and Carhart’s (1997) multifactor models are employed to authenticate results and answer both research questions.

Research analysis reveals that EM exposed fund managers fail to collectively outperform the market. It thereby offers ground to believe that the emerging world is very close to being efficient, proving that the Efficient Market Hypothesis (‘EMH’) ideal exists in this scenario where market inefficiency might only be a perception of market participants as any apparent opportunity to achieve above-average returns is speedily snapped up by very active managers. Overall these managers take a conservative approach to portfolio construction, whereby they are more unperturbed investing in large cap equity funds so as to lessen somewhat the exposure towards risks associated with liquidity, stability and volatility.

Furthermore, the findings show that large-sized equity portfolios have the lead over the medium and small-sized competitors, whilst the high cost and mature collective investment vehicles enjoy an alpha which although is negative is superior to their peers. The riskiest funds generated the lowest alpha, and thereby produced doubts as to whether investors should accept a higher risk for the hope of earning higher returns, at least when aiming to gain an exposure into the emerging world.

Mutual fund performance is not an innovative topic so to speak. Nonetheless, researchers and academia have centred their efforts on appraising the behaviour of fund managers domiciled primarily in developed and more efficient economics, leaving the emerging region highly uncovered in this respect. This study, therefore aims at crafting meaningful contributions to the literature as well as to the practical perspective.

QS‐9000 was established by Chrysler, Ford, General Motors and some Truck Manufacturers to solve problems of efforts to prepare original standards for each company. When we compare the requirements in QS‐9000 with those in ISO 9000, we may observe that those of QS‐9000 are more specific, since the user of the standard is limited to the automobile industry. In particular, the requirements on stastistical technique are described in more detail. In this paper, we present the requirements on statistical techniques in QS‐9000. Specifically, we review the contents of QS‐9000 and explore the philosophy and the minimum requirement on statistical techniques behind the description.

The purpose of this report is to present the critical factors necessary for process improvement in pharmaceutical research.

Cross‐organizational process improvement projects, which can increase productivity as well as shorten cycle time in drug discovery, are used to determine the critical factors for success. A morphological analysis is conducted on these critical factors followed by KeyGraph visualization in a post‐process improvement survey.

Four scenarios, which are beneficial for process improvement, are explored through application of the KeyGraph. The critical factors identified are project scheduling, automation, resource management, and process improvement methodology. The project leaders also addressed critical factors, such as top management support, shared goals, and a data‐driven approach. The step for improvement is in alignment with the typical quality improvement process in most process improvement initiatives.

The identified critical factors for success provide a guide to process improvement during pharmaceutical research. In addition, the KeyGraph framework provides a practical approach to discovering the factors that contribute to success.

A reducing variation of quality characteristics is a typical example of quality improvement. In such a case, we treat the quality characteristic, as a response variable and need to find active factors affecting the response from many candidate factors since reducing the variation of the response will be achieved by reducing variation of the active factors. In this paper, we first derive a method of selecting an active factor by linear regression. It is well known that correlation between factors deteriorates the precision of estimators. We, therefore, examine robustness of the selecting method against the correlation in the data set and derive an evaluation method of the deterioration brought by the correlation. Furthermore, some examples of selecting and evaluation methods are shown to demonstrate practical usage of the methods.

It is widely recognized that ISO 9000 certification bring some profits, such as enhancement of quality system and some concerns. Since one of the major concerns is cost for certification, this paper considers the cost for ISO 9000 certification in Japanese companies through review some winning reports and case studies. Specifically, a model that describes the certification activities and their cost is derived through reviewing some winning reports in order to give a framework of cost estimation. Based on the model, we estimate the certification cost in an electric division of a company through an interview survey. Furthermore, we focused on the cost for promotion and administration as a cost for the sake only certification, because these activities will not be needed if a company does not implement ISO certification. It is concluded that this cost may vary approximately 300,000‐700,000 (US$) per certification by the interview and telephone surveys to Japanese stell companies.

Quality management tools such as failure mode and effects analysis (FMEA) have been implemented in various industries to improve quality. This report aims to demonstrate that FMEA can be applied as a performance improvement tool, based on case analysis of process improvement conducted for a drug discovery project.

The main points of the proposed FMEA process include: inclusion of an interface that makes it easy to visualize complicated processes in pharmaceutical research; identification of undesirable effects to indicate process defects; and a quantitative estimate of the undesirable effects related to quality and efficiency.

The effectiveness of the proposed FMEA process was evaluated based on in vivo screening/profiling during early drug discovery. The process targeted for improvement was visualized using a flow diagram. The undesirable effects identified included waiting, false operations, and errors in the decision‐making and reporting processes. The most serious flaws, determined by risk priority numbers for each category, were waiting and false operations.

The effectiveness of the proposed FMEA was demonstrated by applying the analysis to another in vivo profiling process. Quantitative evaluation of the undesirable effects determined that they were reasonable. This provides a benefit for scientists seeking to improve the drug discovery process.