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According to many authors, differences in firm performances are increasingly attributed to tacit knowledge that cannot easily be transmitted or imitated. On the other hand, current quality management models knowledge typically relates only to people. Situations, in which knowledge that is related to people is not available, sufficient, reliable or lucrative for application, are not considered. This paper aims to investigate how to overcome this gap.

Based on the adopted classification, types of knowledge typically present in an organisation are identified, and are discussed. Techniques for acquiring and formalising tacit knowledge are explored, and related criteria are defined. Particular attention is shown to knowledge management and artificial intelligence techniques.

A new approach to quality management called deep quality concept (DQC) is conceptualised, and mechanisms and concepts needed to acquire and integrate formalised knowledge into quality systems are identified. Other concepts that need to be incorporated are also identified. Finally, a new quality management model based on the DQC is developed.

In further research the main points of the presented theoretical framework need to be validated through real examples from practice, and the resulting quality standard, i.e. award criteria, as well as the related handbooks completed and formalised.

Knowledge‐related and other relevant concepts need to be incorporated into contemporary quality management systems, as systematically and carefully as conventional quality management concepts. Knowledge of methods and tools suitable for that also needs to be assimilated.

In the paper a novel knowledge‐focused approach to quality management is presented. For this reason the paper is of great value for quality management theory and practice.

Examines the thirteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Defines an expert system (ES) as a decision‐making tool that captures perishable expertise from an expert and stores this knowledge in the computer, and also as a computer system that can solve problems using human expertise and knowledge of the system environment. Notes that expert systems have found many applications in the area of production and operations management (POM). Explains the key role of the ES as a management tool to enhance productivity, improve quality, increase profits and capture expertise in many business settings. Highlights that the field of POM has a heavy orientation towards problem solving and decision making, hence it is a very fertile area for application for ES. Evaluates the current use of ES in POM and discusses future prospects.

Capital restrictions normally exist in the creation of a startup, requiring investors to analyze funding alternatives in a highly competitive climate. Although different types of incentives to support startups exist, these incentives are only available to those companies that fulfill the requirements of the funding program to which they have applied. Due to social and economic changes introduced by the digital economy, however, existing mechanisms for assessing the potential growth of startups are scarce, outdated or simply incomplete, distorting the results of such evaluations.

Evaluating business opportunities and how to exploit them are critical activities for an entrepreneur. This study sought to address this issue through the combined use of cognitive mapping and the Decision EXpert (DEX) technique. Assuming a constructivist stance, the study brought together a panel of experienced entrepreneurs and business investors to identify and articulate the criteria to be considered in the evaluation and classification of startups.

The evaluation system created in this study was tested, and the results were validated by the expert panel on a collective basis, demonstrating that the dual methodology used can increase our understanding of the decision problem at hand and lead to more informed and potentially better evaluations of the potential growth of startups.

The authors know of no prior work reporting the integrated use of cognitive mapping and DEX in this study context.