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The purpose of this paper is to propose a way of implementing data mining (DM) techniques and algorithms to apply quality improvement (QI) approaches in order to resolve quality issues (Rokach and Maimon, 2006; Köksal et al., 2011; Kahraman and Yanik, 2016). The effectiveness of the proposed methodologies is demonstrated through their application results. The goal of this paper is to develop a DM system based on the seven new QI tools in order to discover useful knowledge, in the form of rules, that are hidden in a vast amount of data and to propose solutions and actions that will lead an organization to improve its quality through the evaluation of the results.

Four popular data-mining approaches (rough sets, association rules, classification rules and Bayesian networks) are applied on a set of 12,477 case records concerning vehicle damages. The set of rules and patterns that is produced by each algorithm is used as an input in order to dynamically form each of the seven new quality tools (QTs).

The proposed approach enables the creation of the QTs starting from the raw data and passing through the DM process.

The present paper proposes an innovative work concerning the formation of the seven new QTs of quality management using DM popular algorithms. The resulted seven DM QTs were used to identify patterns and understand, so they can lead even non-experts to draw useful conclusions and make decisions.

e-Business (e-B) organization is an integrated synergy of internet and supply chain. It is an organizational environment which consists of associations among suppliers, customers, work force and information technology. An important issue for an e-B organization is the comprehension of the critical factors that affect its success, in order to realize its full advantages and how it results in efficiency improvements of the organizations. The purpose of this paper is twofold: first to identify the critical success factors (CSFs) that determine the successful implementation of e-B and second to develop an integrated e-B model based on the CSFs.

By following a quantitative approach, primary data were collected through a structured questionnaire from 174 Greek e-B firms. An initial exploratory factor analysis was conducted, followed by confirmatory factor analysis (CFA) and structural equation modeling (SEM).

Findings have shown that there was a direct association of the Resource Based View (RBV) on the supply chain view (SCV) of a firm, which in turn influenced the performance/results of an e-B organization. It is concluded that e-B performance results and value creation are linked to customers’ and partners’ orientation (SCV) and human resources and information technology (RBV).

The proposed framework contributes to the understanding of the successful implementation of e-B, by proposing a fifthfold framework. The proposed framework portrayed a very fundamental result, that the resourced based view and the SCV of the organization are the critical priorities and have a direct and positive affect on the performance and efficiency of the successful implementation of e-B.

In this study, the deployment of an integrated Quality Function Deployment (QFD) decision framework is presented to help cities design targeted strategies to become “smart”. Arguably smart cities leverage advanced technologies to enhance their smartness to improve everyday urban life. To this end, a QFD – Analytic Hierarchy Process – Analytic Network Process (QFD-AHP-ANP) framework is proposed to deliver guidance for selecting the appropriate mix of smart technologies based on the specific smart needs of each city.

The AHP and ANP methods are incorporated into QFD to enhance its methodological robustness in formulating the decision problem. AHP accurately captures and translates the “Voice of the Experts” into prioritized “Smart City” dimensions, while establishing inter-relationships between these dimensions and “Smart City Technologies”. Meanwhile, ANP explores tradeoffs among the technologies, enabling well-informed decisions. The framework’s effectiveness is evaluated through an illustrative application in the city of Thessaloniki.

Applying the framework to this real-world context confirms its practicality and utility, demonstrating its ability to particularize local, social, political, environmental and economic trends through the resulting mix of technologies in smart urban development strategies.

The importance of this study lies in several aspects. Firstly, it introduces a novel QFD decision framework tailored for smart city strategic planning. Secondly, it contributes to the operationalization of the smart city concept by providing guidance for cities to effectively adopt smart technologies. Finally, this study represents a new field of application for QFD, expanding its scope beyond its traditional domains.

The purpose of this paper is to adopt a multiple case-study approach, of three companies, in order to identify the factors affecting Lean Six Sigma (L6σ) implementation in service industry.

Secondary data were collected through companies’ documents, written procedures and quality assurance policies. Primary data were collected through a number of in-depth interviews with managers and quality experts.

The analysis of qualitative data gathered through in-depth interviews with managers in all three cases resulted in the emergence of variety of critical success factors (CSFs) regarding L6σ implementation in service industry. As it can be seen the great majority of the factors have been identified in all three cases. Moreover the analysis shows that there are two categories of factors emerged.

This study has four major contributions. First, it provides an intergraded multi-factor framework regarding the implementation of L6σ in service industry. In particular, this study contributes with three more particular factors that influence the implementation of L6σ in services, namely, top-management active involvement, HR support activities, and practices and systems. Second, it focusses on the responses of managers, who play the critical role in the adoption of L6σ. Third, supports and expands current literature on the key success factors of L6σ application. Finally, it provides future ideas to explore and develop more the suggested L6σ framework.

The purpose of this paper is to argue that the quality function deployment (QFD) matrix that has been successfully used for developing products from customer needs can also be applied to improve the service quality of telecommunication companies.

The paper presents a case study of the two big telecom companies (Ufone and Mobilink) of Pakistan. Data have been collected by a cross‐sectional field study of users, frontline employees and management of telecom companies. The QFD matrix has been applied to identify customer needs, to do comparative analysis of the prepaid services of these companies and to recommend the suitable measure for incorporating these requirements into their services.

It has been found that through emphasizing two technical descriptors (Antennas and Base Station Controller System), a company will not only be in a competitive position to meet the customer's demand but will also gain an advantage over its competitor.

Research findings are limited to telecom companies. Further research can be carried out in other service providing companies.

Research outcomes also imply that if managers want to listen to the voice of their customers and update their products and services accordingly, the proposed methodology of QFD is much more effective than the traditional approaches, in which decisions regarding the product or services are prioritized based on the manager's point of view.

Most of the previous research has applied QFD for developing products from customer needs. This is one of the few studies which have successfully applied the QFD Matrix in service providing companies.

The purpose of this paper is to exploit student preference and propose, discuss and experimentally validate a strategy that aims to reduce time necessary to introduce tutoring of mind mapping to engineering students.

A survey instrument was designed and used to collect student preferences about mind mapping. Preferences were linked to alternative scenarios of mind mapping deployment. Survey responses from a 100 second‐year students from the Department of Production Engineering and Management were analyzed using conjoint analysis.

Results indicate that an effective strategy to present mind mapping to engineering students is to explain in detail all the possible applications of mind mapping; present mind maps with different colours using both words and drawings and encourage students to use mind maps in team assignments.

The findings of this paper provide a well documented framework in addressing mind mapping technique to engineering students. The proposed framework tries to make the best use of the available time for creativity development programs, in engineering departments. This framework may also be applied to other creativity enhancing techniques, providing solutions for incorporating creativity in engineering curricula.

To demonstrate the applicability of machine‐learning tools in quality management.

Two popular machine‐learning approaches, decision tree induction and association rules mining, were applied on a set of 960 production case records. The accuracy of results was investigated using randomized experimentation and comprehensibility of rules was assessed by experts in the field.

Both machine‐learning approaches exhibited very good accuracy of results (average error was about 9 percent); however, association rules mining outperformed decision tree induction in comprehensibility and correctness of learned rules.

The proposed methodology is limited with respect to case representation. Production cases are described via attribute‐value sets and the relation between attribute values cannot be determined by the selected machine‐learning methods.

Results demonstrate that machine‐learning techniques may be effectively used to enhance quality management procedures and modeling of cause‐effect relationships, associated with faulty products.

The article proposes a general methodology on how to use machine‐learning techniques to support quality management. The application of the technique in ISDN modem manufacturing demonstrates the effectiveness of the proposed general methodology.

Supply chain (SC) is a homogeneous and interconnected network of firms which manages supplies, storages and handles material, information, personnel, equipment and final products throughout its length. The SC can be the means by which businesses add value to customers and therefore competitive advantage in the international market. Competitiveness no longer exists among individual members of a SC but between SCs. International literature pointed out, that strategy and competitiveness associated with specific strategic priorities. In this paper will be documented that the priorities as objectives of strategic importance comprise the key areas for the performance of the SC network. To support this, a systematic framework of strategic priorities, will be developed, which will visualize the major SC assessment areas that the SC network should turn its improvement efforts. The paper aims to discuss these issues.

In total, 200 high level managers from 71 Greek manufacturing companies covering the whole country using their own SC networks took part in the study. Data analysis were conducted to create a model that describes the strategic priorities which are important, for the competitive advantage of their SC network.

The results revealed seven critical strategic priorities: internal flexibility, production quality, waste reduction, customer focus, sustainability, reduction of production cost and efficiency. This paper analytically explores the strategic practices that seem to influence SC network performance and generates a systematic framework of the critical strategic priorities of the SC network performance.

The proposed framework has six major advantages. First the research outcome enables managers to design their SC strategy. Second, proved the great importance of the strategic priorities institutionalization. Third, makes clear to stakeholders which are the SC network performance issues to consider. Fourth, the proposed framework could serve as a suitable formula for assessing the effectiveness and readiness of SC to face the competition. Fifth, it is an effective way of prioritizing the strategic practices as it provides the most important areas on which the firm can base tasks like evaluation, benchmarking and comparison of its SC both as a network and as individual firms. Finally, the proposed framework is an effective way to evaluate the consistency of strategic objectives and actions adopted for the SC.

Literature review revealed a very important conclusion. It has been developed a broad discussion concerning issues on strategic practices which are responsible for the competitive advantage of the SC network. However, there has not been a significant effort on integrating all the practices under the spectrum of the SC in order to distinguish the most important of these practices that can lead to competitive advantages.

The purpose of this paper is to evaluate and improve two popular business process modelling languages (BPMLs) the Integration definition for function modelling (IDEF0) and event‐driven process chain (eEPC).

The paper aims to select, compare and evaluate against a proposed criteria framework two popular BPMLs. In order to meet end‐user requirements, it suggests concrete improvements for either language. Evaluation findings and improvement attempts are documented over a case study within the context of a large European aerospace industry. The languages are applied through the use of appropriate software such as BPWin and Arena Simulation Software for IDEF0 and Architecture of Integrated Information Systems (ARIS) Toolset for eEPC.

Improved languages seem to overcome several deficiencies and increase their modelling performance. Improvements in IDEF0 include: classification of component information and insertion of logical operators. In this approach the language describes not only the information flow within the process, but also the time sequences via component discrimination. Improvements in eEPC include event chain diagram in ARIS house of business engineering methodology, which represents the interrelations between functions of different diagrams and the linkages among components and resources to include resources planning.

Results showed that language deficiencies were clarified. Thus, the improvements made, constitute better modelling performance and the development of more reliable models.

The paper outlines a systematic evaluation framework with concrete criteria. Furthermore, the improvements made, are a further step towards increased modelling performance, lean and comprehend diagrams.