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Many organizations in the UK are still learning about the implementation and use of total quality management (TQM) techniques such as statistical process control (SPC), even though in other parts of the world, especially in Japan, they have been used widely and successfully for many years. Research carried out by the authors shows that almost the only thing taught to engineers within many UK academic institutions in relation to SPC is control charting. Although the understanding of control charts is a very important aspect of SPC, it is by no means the only one for the successful introduction and development of SPC. It is argued that the emphasis should not be on the control charts themselves, but on the essential ingredients for getting SPC started in organisations and the various issues associated with the implementation of SPC. This paper discusses the ingredients that are needed for the successful implementation of SPC.

Statistical process control (SPC) is a powerful technique for improving process quality by systematically eliminating special or assignable causes of variation. SPC is not a simple and automatic task. The successful application of SPC requires many skills such as engineering, management, statistical, teamwork and planning. Although the use of control charts is a very important aspect of SPC, it is by no means the only one for the implementation of SPC in organisations. Many engineers graduating today from the academic institutions are exposed to only control charts (i.e. different types) and not the other important ingredients for the successful implementation of SPC. This paper will briefly discuss the ten key ingredients that are needed for the effective application of SPC in any organisation.

Firms are increasingly depending on supplier portfolios in the quest for firm innovation. However, whether concentrated supplier portfolios are beneficial to innovation remains highly disputed. This study aims to investigate the effect of supplier portfolio concentration on firm innovation and the contingencies that shape this effect.

The authors build on the knowledge search view to theorize a U-shaped effect of supplier portfolio concentration on firm innovation and further propose that the U-shaped effect is contingent on financial slack and growth opportunities. The authors collected panel data from 1,320 manufacturing firms in China. The negative binomial regression analyses were performed to test the hypotheses.

Supplier portfolio concentration has a U-shaped effect on firm innovation. This U-shaped effect is weakened and flipped by financial slack but strengthened by growth opportunities.

The findings extend current understandings of the influence of supplier portfolio on firm innovation by clarifying the U-shaped effect of supplier portfolio concentration on innovation and the circumstances under which supplier portfolio concentration is more effective for firm innovation.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

Statistical process control (SPC) is widely applied for control and improve processes in manufacturing, but very few studies have reported on the successful application of SPC in the food industry, in particular. The purpose of this paper is to critically assess the status of SPC in the UK food manufacturing industry and to suggest future research avenues.

A research project was carried out in the UK food manufacturing sector through questionnaires. The results of the study were analysed using descriptive statistics and statistical tests to be applied in the hypothesis testing.

Findings revealed that 45 per cent of the respondents are SPC users and x ¯ -R and x ¯ -S charts are the most commonly applied SPC charts in this industry. It was determined that top management commitment is the most critical factor, while lack of SPC training is the most alarming challenge, and lack of awareness of SPC and its benefits are the main reasons for the food companies not implementing SPC.

The study considered only the food manufacturing companies. Future research could be addressed toward the food service and food supply chain.

The paper provides information to food companies in the UK on most common practiced and important quality tools, SPC charts and critical success factors in the food industry. Moreover, the most challenging factors of SPC implementation in the food industry are presented.

This study depicted the current state of SPC practices in the food industry and the process performance in SPC and non-SPC companies is compared.

Statistical process control (SPC) is a powerful technique for managing, monitoring, analyzing and improving the performance of a process through the use of statistical methods. The purpose of this paper is to present results of a survey on SPC in the software industry. The focus is on understanding the critical success factors (CSFs) for successful implementation of SPC in the software industry.

In total, 12 critical success factors (CSFs) with 36 variables were identified from the literature and discussions with software quality professionals. An e‐mail questionnaire was used to gather the data.

The results reveal that management commitment and involvement are the most critical success factors, followed by selection of control charts. The use of SPC facilitators was found to be the least important factor in successful deployment of SPC in the software industry.

This research project was conducted with a limited number of participants, and was limited to software firms in India; cultural differences in other nations may yield different results.

These results provide an increased understanding of how to better implement SPC in the software industry, and provide managers with improved guidelines for identifying the most important factors that will lead to success.

Indian software companies are leading exporters to Europe and the USA. Considering the growth of the Indian software industry and increased inclination towards acquiring quality certifications and implementing quality management techniques, a better understanding of the implementation of SPC can provide companies with a stronger competitive advantage.

States that total quality management (TQM) is an accompanying philosophy for companies who desire to be competitive in the global market, and the importance of statistical process control (SPC) to TQM is well recognized. Points out that, however, in practice, the implementation of this portion of TQM has not been easy for small companies as many practitioners have experienced. Reviews problems that a small organization encounters when installing or attempting to install an SPC system. Highlights many factors that are important to SPC implementation such as planning for customer satisfaction, identifying tasks to be performed, and empowering workers with responsibility. Suggests a six‐step procedure and several useful devices for implementing an SPC pilot study. The result of the case study demonstrates to managers and workers that SPC has validity in a small company even with batch processes. This successful SPC pilot study should encourage small companies to incorporate SPC in a similar manner within their organizations.

The principal application domain for statistical process control (SPC) charts has been for process control and improvement in manufacturing businesses. However, the number of applications reported in domains outside of conventional production systems has been increasing in recent years. Implementing SPC chart approaches in non‐standard applications gives rise to many potential complications and poses a number of challenges. This paper reviews non‐standard applications of SPC charts reported in the literature from the period 1989 to 2000, inclusive. Non‐standard applications are analysed with respect to application domain, data sources used and control chart techniques employed. Applications are classified into five groups according to the types of problem to which control chart techniques have been applied. For each group the nature of the applications is described and analysed. The review does not show a paradigm shift in the types of SPC control chart applications but does show clearly that the application boundaries extend considerably beyond manufacturing and that the range of problems to which SPC control chart techniques can be applied is much wider than commonly assumed. The paper highlights the critical fundamental and technical issues that need to be addressed when applying SPC chart techniques in a range of non‐standard applications. Wider managerial issues of importance for successful implementations in non‐standard applications of SPC control charts are also discussed.

Presents a synthesis of the early findings from an ongoing project researching the issues surrounding the use of SPC in a food packing environment. A cognitive mapping approach has been utilised to make sense of the complex and varied data resulting from the survey, case studies and interviews carried out to date. This methodological approach is described, and its application illustrated in relation to the research topic. Argues that SPC is one weapon in an arsenal of quality management techniques that food companies can use to consolidate or improve their position in an increasingly competitive marketplace. Once successfully adopted SPC can offer proven operational and financial benefits, but the ability of the organisation to successfully achieve implementation will depend upon a number of organisational factors. Finally, presents the agenda for further research which outlines how this ongoing project is intended to be taken forward from this point.

Most organisations experience difficulties when implementing and developing SPC. The mistakes can be avoided. This paper provides the framework for the successful introduction of Statistical Process Control.