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Editorial This special issue of Industrial Management & Data Systems is a huge departure from our usual journal/ monograph style. This is an additional issue to the year's volume — a bonus in fact.

This study aims to devise generalized unconstrained optimization models for ascertaining the optimal level of product quality and production capacity level by modeling both product price and production cost as a function of product quality. Further, interrelations among investment for quality, product quality and production volume are considered. This study contributes toward the extant research, in that nuances related to price, production volume, and product quality are fused together such that two broad operational strategies of product quality optimization and production capacity optimization can be contrasted.

To achieve the research objectives, the authors evolve unconstrained optimization models such that optimal product quality level and optimal production capacity level can be obtained employing the principles of differential calculus aimed at maximizing the manufacturer's profit. Specifically, nuances related to quality technology and efficiency, and quality loss cost has also been integrated in the integrated model. Thereafter, employing numerical analysis for a generalized product, the detailed workings of evolved models are demonstrated. The authors further carry out the sensitivity analysis to understand the impact of investment for quality onto the manufacturer's profit for both operational strategies.

The research demonstrates that the manufacturer would be better off adopting production capacity optimization strategy as an operational policy, as opposed to product quality optimization policy for the manufacturer's profit maximization. Further, considering the two operational strategies, the manufacturer does not obtain the highest possible theoretical profit when pertinent variables (product quality and production capacity) are set at highest possible theoretical level. This research discusses that in low-volume and high-margin products, it might be useful to adopt a product quality optimization strategy as a production capacity optimization strategy results in significantly high quality loss cost.

The findings of our study have a significant implication for industries such as steel-making, cement production, automotive industry wherein the conventional wisdom dictates that higher level of production capacity utilization always results in higher level of revenues. However, the authors deduce that beyond certain production capacity utilization, striving for higher utilization does not fetch additional profit. This work also adds to the extant research literature, in that it integrates the nuances of product quality, production volume and pricing in an integrative manner.

The authors analyze the principles and results of lean production and compare the lean production philosophy with the six sigma quality process and the principles of total quality management (TQM). At the end of the paper, it is discussed how to build the necessary company culture for having success with these principles/management philosophies.

Literature search and comparative analysis complemented with a Danish case on wastage in a core process.

It is shown that the lean production philosophy and the six sigma steps are essentially the same and both have developed from the same root – the Japanese TQM practices. The improvement process from six sigma, the DMAIC process, can be regarded as a short version of the Quality Story, which was developed in Japan in the 1960s as a standard for QC‐circle presentations. We conclude that the roadmaps of lean production and six sigma quality are examples of new alternative TQM roadmaps. We also conclude that especially with lean production and six sigma quality there seems to be too much focus on training people intools and techniques and at the same time too little focus on understanding the human factor, i.e. how to build the right company culture.

The detailed and historical analysis of six sigma quality, lean production and TQM combined with a focus on the human factor and the needed corporate culture.

Manufacturers use several quality assurance systems to assure quality. However, their effectiveness cannot be assessed because an instrument does not exist. This article is based on a study that was set up to identify performance measurement indicators of an instrument that measures effectiveness of food quality systems, called IMAQE‐Food.

The instrument has been developed by translating a conceptual model in quantifiable performance measurement indicators. Literature research, qualitative research, Delphi sessions, and quantitative research were used.

In total, 28 relevant and comprehensible indicators were obtained that measure performance of quality management, production quality and their influencing factors in the bakery sector.

This paper will make a contribution to the body of knowledge in the field of food quality management by developing an instrument to measure effectiveness instead of compliance with norms and requirements or merely performance. This will support food manufacturers in deciding which system is most suitable to achieve their objectives. The developed procedure can be used for providing insight in determining the desired level of quality management, and for extending the instrument for other applications.

THE statistical method of controlling product quality during manufacture—a technique commonly known by the shorter title, Quality Control—is a very recent development in the field of production engineering. In point of fact the first application of this technique to machine‐shop production in this country took place late in 1940 at the Croydon works of Messrs. Creed & Co. Ltd., a firm whose name is a household word in electrical communications, for the Creed teleprinter is known all over the world. At the same time it must not be thought that quality control as such, or the use of the so‐called quality control chart as an aid to manufacture, is a purely wartime development. Statistical methods have been employed in British industry, notably in electric lamp manufacture, and in the production of both cotton and woollen textiles, for more than fifteen years, whilst the technique of quality control itself was originated by Dr. W. A. Shewhart of the Bell Telephone Laboratories, New York, in 1922. But where engineering practice was concerned, the application of statistical methods did not present itself until quite recently as a necessary or even convenient solution of manufacturing difficulties.

The paper aims to evaluate how progressive stakeholders view the adoption of contemporary techniques such as virtual technology in driving sustainable quality in an emerging economy context.

The authors adopted a systematic literature review to develop the theoretical framework for virtual reality (VR) technology adoption in sustaining quality in agriculture production. The framework was refined after discussion with a panel of academic experts. The refined theoretical framework was further empirically validated using Partial Least Square Structure Equation Modelling.

The study focuses on the future perspective of the perception for progressive farming with the adoption of VR technology in an emerging economy. The data were collected from the stakeholders (farmers, collectives, cooperative, etc.), for their future perspectives for the adoption of VR technology and sustainable quality agriculture production. The study may help build up VR technology in emerging economies which may take years to be established.

The perception of the future perspective of VR technology study conducted has limitations. The findings are well established on technology adoption; however, the technology used will take many extra years to find its application in the agriculture sector. The study offers insightful theoretical, managerial and policy implications for sustainable quality in agriculture production through the adoption of virtual reality (VR) technology. The authors found very few works that focused on VR technology adoption.

The study discusses VR, which has an impact on sustaining the quality of agriculture production. The study has notable managerial and policy implications that suggest the future perspective for VR technology in agriculture production. The study is an unexplored area that needs research to capture future perspectives.

The ideas expressed in this work are based on those put into practice at the Okuma Corporation of Japan, one of the world′s leading machine tool manufacturers. In common with many other large organizations, Okuma Corporation has to meet the new challenges posed by globalization, keener domestic and international competition, shorter business cycles and an increasingly volatile environment. Intelligent corporate strategy (ICS), as practised at Okuma, is a unified theory of strategic corporate management based on five levels of win‐win relationships for profit/market share, namely: ,1. Loyalty from customers (value for money) – right focus., 2. Commitment from workers (meeting hierarchy of needs) – right attitude., 3. Co‐operation from suppliers (expanding and reliable business) – right connections., 4. Co‐operation from distributors (expanding and reliable business) – right channels., 5. Respect from competitors (setting standards for business excellence) – right strategies. The aim is to create values for all stakeholders. This holistic people‐oriented approach recognizes that, although the world is increasingly driven by high technology, it continues to be influenced and managed by people (customers, workers, suppliers, distributors, competitors). The philosophical core of ICS is action learning and teamwork based on principle‐centred relationships of sincerity, trust and integrity. In the real world, these are the roots of success in relationships and in the bottom‐line results of business. ICS is, in essence, relationship management for synergy. It is based on the premiss that domestic and international commerce is a positive sum game: in the long run everyone wins. Finally, ICS is a paradigm for manufacturing companies coping with change and uncertainty in their search for profit/market share. Time‐honoured values give definition to corporate character; circumstances change, values remain. Poor business operations generally result from human frailty. ICS is predicated on the belief that the quality of human relationships determines the bottom‐line results. ICS attempts to make manifest and explicit the intangible psychological factors for value‐added partnerships. ICS is a dynamic, living, and heuristic‐learning model. There is intelligence in the corporate strategy because it applies commonsense, wisdom, creative systems thinking and synergy to ensure longevity in its corporate life for sustainable competitive advantage.

The purpose of this paper is to propose an integrated model of decision support system (DSS), artificial neural network, information and communication technologies and statistical process control (SPC) to facilitate agreement by different stakeholders with special interests to commit to the decision that will be taken to stop the production line when something goes wrong somewhere in a supplier network environment.

A DSS is proposed to capture exceptional signals from source on deterioration of product quality to alert preventive actions needed before the problems are getting out of hand. The supervisors are given a set of guidelines to support making the decision. Real‐time SPC and rule‐based decision support procedures are used to trigger pre‐defined exceptional signals for forwarding to the most appropriate person (the knowledge holder in the problem domain) to make a decision to stop the line. All servers in all remote sites are internet‐connected and provide real‐time quality data to the regional supply chain manager. A case study is described to show how this is implemented in a lens manufacturing company.

A significant improvement in quality level can be achieved by holding the knowledge worker accountable for making the decision to stop the production line rather than made by default as is with most traditional operations.

To provide a concept to structure activities for decision support so that the persons responsible for making the decision to stop the production line is held accountable by all stakeholders.

Practitioners can replicate the approach used in this paper to their own situations involving decisions to be made to address un‐structured problems and unclear responsibilities.

The purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.

The proposed integrated policy is defined and modeled mathematically.

The paper focuses on finding simultaneously the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval and the control chart limits, such that the expected total cost per time unit is minimized.

The paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance and quality. The considered system consists of one machine subject to a degradation process that directly affects the quality of products. The process and product quality control is carried out using an “x-bar” control chart. In the proposed model, a preventive maintenance action is performed every α inspections of product quality in order to reduce the shift rate to the “out-of-control” state. A corrective maintenance action is undertaken once the control limits are exceeded. In order to palliate perturbations caused by the stopping of the machine to undergo maintenance actions, a buffer stock is built up to ensure the continuous supply of the subsequent machine. The main goal of this work is to develop a model that captures the underlying link between the preventive maintenance policy, the buffer stock size and the parameters of an “x-bar” control chart used to control the quality of the product. Numerical experiments and a study of the effects of the input parameters variation on the obtained results are performed.

The existing models that simultaneously consider maintenance, inventory and control charts consist of a condition-based maintenance (CBM) policy. Periodic preventive maintenance (PM) has not been considered in such models. The proposed integrated model is original, in that it links production through buffer stocks, quality through a control chart and maintenance through periodic preventive maintenance (different practical settings and modeling approach than when CBM is used). Hence, this paper addresses practical situations where, for economic or technical reasons, only systematic periodic preventive maintenance is possible.

Unlike the traditional approach to quality control that is administered by quality inspectors at the end of the production process, the Japanese approach to quality control focuses on building superior quality in the production process through the development of team‐oriented human resource management (HRM) practices. Develops a conceptual model on how HRM functions are integrated into the production system in Japanese firms. The theoretical discussion is supported by empirical reports of how Japanese plants in the USA use team‐oriented HRM policies to control product quality.