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This paper deals with total quality management (TQM) with an emphasis on developing suitable strategies for improving quality and productivity in manufacturing systems. In recent years, TQM has been seen as an important strategy for achieving success in business both in terms of quality and productivity. However, there seem to be no clear strategic framework and guide‐lines for implementing TQM in manufacturing in the light of available advanced production concepts and technologies. A review of previous implementation approaches of TQM in practice has been presented in order to gain further insights into the implementation aspects of TQM. The main objective of this paper is to present a general framework for the development of TQM in manufacturing organizations considering the recent developments in production concepts and technologies and competitiveness among firms to utilize quality as a competitive weapon.

Given the increasing emphasis on performance measurement and improvement, the purpose of this paper is to examine the current views of manufacturing executives on key aspects of performance measures. Specifically, this research focuses on current practices related to extent of use, predictive value and availability of information for 63 performance measures.

This study was survey based. The research instrument used to collect the data was constructed based on the literature. Several statistical analysis procedures including regression analysis, cluster analysis and gap analysis are utilized to accomplish the objective of this study.

The results are compared to findings obtained from a previous study, conducted five years ago, to assess any potential changes with regard to the issues studied. The comparison of the findings from both studies revealed some similarities and significant differences attributed to the changing environment.

The focus of this study was Portuguese manufacturing organizations. It is quite possible that the findings may or may not hold in a global context. Since this study was survey based, it had all the limitations of survey‐based research.

Despite some limitations, this study provides practicing managers with useful information regarding performance measures and measurement practices. It has significant and direct implications to organizational performance information systems.

This paper represents an important step toward refining the theory and practice of performance measurement in manufacturing organizational operational context.

The purpose of this paper is to offer small to medium‐sized organizations (SMOs) with global business aspirations an innovative approach to performance measurement and management.

The first phase of this research is based on a literature review. The second phase capitalizes on the literature review to offer a conceptual framework aimed at improving the performance measurement approach utilized by SMOs. The advocated approach stresses performance measurement, benchmarking, and effective implementation.

The conceptual approach offered in this study represents the main outcome of this applied research. The advocated approach integrates several frameworks in an effort to address practical concerns related to performance measurement, management, and improvement.

The research offered in this study has practical and theoretical implications. The proposed approach offered by this study should be refined and validated through future research.

The approach presented in this study offers practicing managers a systematic and practical approach to performance measurement, management, and improvement.

The approach offered in this study capitalizes on several methodologies and tools to offer managers a benchmarking‐based performance management approach suitable for SMOs with global operational aspirations.

The purpose of this commissioned paper is to offer some personal reflections on sustainability within the hospitality industry.

The paper opens by identifying sustainability as a teasing paradox for the hospitality industry and a short discussion of the characteristics of sustainability. It then explores the growing interest in corporate sustainability and offers a review of the range of academic research into sustainability within the hospitality industry literature. More generally, the authors suggest three fundamental sets of issues that currently face the industry, namely, defining sustainability within the industry, materiality and independent external assurance and sustainable consumption and the industry’s commitment to continuing economic growth.

In addressing these three sets of issues, the authors make a number of suggestions. First that definitions of sustainability within the hospitality industry can be interpreted as being constructed around business imperatives rather than an ongoing commitment to sustainability. Second that materiality and external assurance are not treated comprehensively within the industry, which undermines the credibility of the sustainability reporting process. Third that the concept of sustainable consumption and any critique of the industry’s commitment to economic growth are conspicuous by their absence in the both the research literature on sustainability and in sustainability reporting within the industry.

The paper suggests that the hospitality industry may need to examine how it defines sustainability, to extend its sustainability reporting to embrace materiality and external assurance and to address the issues of sustainable consumption and continuing economic growth if it is to demonstrate a worthwhile and enduring commitment to sustainability.

The paper provides some accessible personal reflections on sustainability within the hospitality industry and, as such, it will be of interest to academics, students and practitioners interested in the hospitality industry and more widely within the business and management community.

The purpose of this paper is to advance supply chain network theory by applying theoretical and empirical developments in complex network literature to the context of supply chains as complex adaptive systems. The authors synthesize these advancements to gain an understanding of the network properties underlying efficient supply chains. To develop a suitable theory of supply chain networks, the authors look to mirror the properties of complex network models with real‐world supply chains.

The authors review complex network literature drawn from multiple disciplines in top scientific journals. From this interdisciplinary review a series of propositions are developed around supply chain complexity and adaptive phenomena.

This paper proposes that the structure of efficient supply chains follows a “scale‐free” network. This proposal emerges from arguments that the key properties of efficient supply chains are a short characteristic path length, a high clustering coefficient and a power law connectivity distribution.

The authors' discussion centres on applying advances found in recent complex network literature. Hence, the need is noted to empirically validate the series of propositions developed in this paper in a supply chain context.

If efficient supply chains resemble a scale‐free network, then managers can derive a number of implications. For example, supply chain resilience is derived by the presence of hub firms. To reduce the vulnerability of supply chains to cascading failures, it is recognized that managers could build in redundancy, undertake a multi‐sourcing strategy or intermediation between hub firms.

This paper advances supply chain network theory. It offers a novel understanding of supply chains as complex adaptive systems and, in particular, that efficient and resilient supply chain systems resemble a scale‐free network. In addition, it provides a series of propositions that allow modelling and empirical research to proceed.

Learning machine scheduling strategies are addressed while concentrating on the dynamic nature of real systems. A framework is proposed consisting of two modules: intelligent simulation (IS) and incremental learning. A simulation technique is basically exploited to mirror the manufacturing system. The knowledge base incorporated within the simulation environment enables the IS to behave intelligently as well as to evaluate the knowledge base (KB). A genetic algorithm drives the learning module. Its ingredients are tailored to tackle such a problem with a huge search space. A set of decision rules is identified as a chromosome. The rule set’s fitness is related to the scheduling performance measure and is scaled. A crossover and three kinds of mutations together with a steady‐state replacement technique are designed to discover the (near) best rule set. The whole framework is designed to work in an automated way. A series of test results on a basic model show that the proposed system learns, adapts itself to the dominating dynamic patterns, and converges to the optimum solution.