Search results

Discusses the keys to successful total quality management and how total quality management is accomplished. Explains the results of Kepner‐Tregoe′s research into the conditions for successful quality improvement. Describes the total quality management process at the Baxter Healthcare plant in Mississippi, USA. Asserts that the quality leadership process at the plant exemplifies total quality management. Contends that quality problem solving plays a key role in TQM. Concludes that the organization needs a commitment to quality, problem solving skills to achieve it, and the opportunity to use them.

The way of how we introduce a problem is often decisive for a successful solution. This, actually the basic insight of systems engineering was used with the representation of the problem of quality in the problem solving domain. The implementation of systems engineering as a relatively new profession, originating from the system's view, for quality problem solving is conditioned by its relatively methodological perfection, independence of technologies and a well defined problem as the starting point for the use of system methods. In spite of that it was necessary to a certain extent to adapt the methods of systems engineering to the features of quality problems. This approach results in a new dimension of quality understanding, understanding of problems generating non‐quality, and a new dimension of solving these problems. The discussion is limited to the problem and assurance of the information product quality, the quality of the process which provides the information product as its output, and the information quality. Additionally, various problem situations, considerably affecting quality, are identified, and their solution requires an interdisciplinary and system approach. To treat them only within the frame of quality assurance, does not provide quality solutions.

The main purpose of this study is to revisit Dr. Ishikawa's statement: “95% of problems in processes can be accomplished using the seven quality control (QC) tools” from his book “What is Quality Control?”. The authors are interested in critically investigating if this statement is still valid nowadays. It involves the analysis of the usage of the seven QC tools in the manufacturing and service sectors and the benefits, challenges and critical success factors (CSFs) for the application of the seven QC tools.

In order to evaluate Kaoru Ishikawa's statement and how valid his statement is for manufacturing and service industries nowadays, an online survey instrument was developed, and data collection was performed utilising a stratified random sampling strategy. The main strata/clusters were formed by senior quality professionals working in operational excellence, quality consultants, quality directors, quality engineers, quality managers and quality supervisors working in both manufacturing and service sectors from South American companies. A total of 97 participants from different countries in South America responded to the survey.

The main finding of this study is that only about 20% of respondents felt that the original seven basic tools of QC can solve above 80% of quality related problems in their businesses. This is quite different from the findings reported by Dr Ishikawa in his work in between 1970 and 1980s. Another relevant finding presented in this paper is that Pareto analysis, histograms and cause and effect analysis are the most used tools in both manufacturing and service sectors. This paper also revealed that the seven QC tools proposed by Dr. Ishikawa were least used by human resources (HR), information technology (IT) and finance functions. This work presents a list of critical success factors required for the proper application of the seven QC tools.

All data collected in the pilot survey came from professionals working for South American companies. So, this paper does present limitations in terms of generalisation of the results. Also, data were collected at an individual level, so parameters such as the inter reliability of judgements on a particular survey item could not be evaluated. It is important to highlight that n = 97 is a low sample size, enough for a preliminary survey but reinforcing the limitation in terms of generalisation of the results.

Authors understand that this is the very first research focused on challenging Dr. Ishikawa's statement: “95% of problems in processes can be accomplished using the seven quality control (QC) tools” from his book “What is Quality Control?”. The results of this study represent an important first step towards a full understanding of the applicability of these tools in manufacturing and service industries in a global scale.

This viewpoint seeks to provide evidence that up until now, neither a systematic analysis of quality problems in terms of resource, process and product quality has been undertaken, nor has the respective impact on productivity been sufficiently described and understood.

A structured literature review is used to define productivity and quality problems and shows their interdependencies as well as problem solving approaches in a model.

In a “Four Level Model of Quality” the impact of quality problems on productivity is apparent. A flow chart has been developed which not only identifies quality problems but also provides for approaches to resolve such problems through techniques of quality management. A practical example within the context of asset management further illustrates the overarching problems.

Utilizing the developed flow chart in order to identify quality problems and use techniques of quality management as a solution to overcome such problems, will contribute to an increase in productivity of a company.

This paper provides the development of methodical basics in reference to the definition and identification of quality problems including systematic attribution of quality management techniques for problem solving. It presents a “Four Level Model of Quality” to show how to secure the best impact on productiveness. This viewpoint shall lay foundations for target‐oriented continuation of research in this particular field and provide a scientific platform for companies of positive influences of quality on productivity.

Introduction Consider a hi‐fi loudspeaker manufacturing company acquired on the brink of insolvency by an American multinational. The new owners discover with growing concern that the product range is obsolete, that manufacturing facilities are totally inadequate and that there is a complete absence of any real management substance or structure. They decide on the need to relocate urgently so as to provide continuity of supply at the very high — a market about to shrink at a rate unprecedented in its history.

Quality Circle is a very effective program and low‐cost tool for implementing total quality management (TQM) in industry. A good number of manufacturing industries have so far adopted the Quality Circle program successfully, however the Quality Circle concept has failed in many other cases. To make successful use of the Quality Circle tool in manufacturing industries, knowledge and awareness of the probable reasons behind the failures of the Quality Circle is essential.

In this general review paper, after thoroughly reviewing literatures, the authors consolidate and highlight the probable reasons behind the failures of the Quality Circle, especially in manufacturing industries.

This study reveals that organizational Issues at the background stage; circle‐formation Issues at the implementation stage; and operational Issues at the operating/running stage of Quality Circle implementation, are the three main key areas of the problems which generally limit the success of the Quality Circle.

If the management is aware of and takes care of such anticipated problems, it is more likely that the Quality Circle programme will definitely be a success, leading to the effective implementation of TQM in the organization.

Manufacturing companies can competitively be recognized among the most advanced and influential companies in the world by successfully implementing Quality 4.0. However, its successful implementation poses one of the most relevant challenges to the Industry 4.0. According to recent surveys, 80%–87% of data science projects never make it to production. Regardless of the low deployment success rate, more than 75% of investors are maintaining or increasing their investments in artificial intelligence (AI). To help quality decision-makers improve the current situation, this paper aims to review Process Monitoring for Quality (PMQ), a Quality 4.0 initiative, along with its practical and managerial implications. Furthermore, a real case study is presented to demonstrate its application.

The proposed Quality 4.0 initiative improves conventional quality control methods by monitoring a process and detecting defective items in real time. Defect detection is formulated as a binary classification problem. Using the same path of Six Sigma define, measure, analyze, improve, control, Quality 4.0-based innovation is guided by Identify, Acsensorize, Discover, Learn, Predict, Redesign and Relearn (IADLPR²) – an ad hoc seven-step problem-solving approach.

The IADLPR² approach has the ability to identify and solve engineering intractable problems using AI. This is especially intriguing because numerous quality-driven manufacturing decision-makers consistently cite difficulties in developing a business vision for this technology.

From the proposed method, quality-driven decision-makers will learn how to launch a Quality 4.0 initiative, while quality-driven engineers will learn how to systematically solve intractable problems through AI.

An anthology of the own projects enables the presentation of a comprehensive Quality 4.0 initiative and reports the approach’s first case study IADLPR². Each of the steps is used to solve a real General Motors’ case study.

The purpose of this study is to establish empirical relationships between patient flow problems, healthcare service quality and patient satisfaction with emergency department (ED) service factors from the patient perspective.

In the overall study, of which the current investigation is a part, a mixed-method research approach was to achieve the research objectives. The results reported in this paper are based on a comprehensive questionnaire survey where a well-designed and reliable questionnaire was used to survey ED patients. This study conducted partial least squares structural equation modelling (PLS-SEM) by using Smart PLS software.

Results show that the respondents mostly agreed with the proposed concept of quality in ED and patients were less satisfied with ED services in general and with the internal and external environments in ED in particular. It was found that relationships between nine identified scales of patient flow problems, healthcare service quality, and patient satisfaction are significant. The findings reveal that the relationship between patient flow problems and patient satisfaction is positively mediated through healthcare service quality, which shows the predictive capability of the model, indicating high predictive relevance.

This research involves a relatively small sample from a single case study. The positive relationship between patient flow problems and patient satisfaction indicate practical significance of the model for guiding to improve overall patient satisfaction.

This research, through the involvement of both hospital staff and the patient, brings out a holistic approach in terms of operational excellence in a critical unit such as the ED. The empirically established relationships form the benchmarking and guide for developing guidelines for designing policies for service improvements of ED practices.

As a problem-solving tool, the kaizen event (KE) is underutilised in practice. Assuming this is due to a lack of group process quality during those events, the authors aimed to grasp what is needed during high-quality KE meetings. Guided by the phased approach for structured problem-solving, the authors built and explored a measure for enriching future KE research.

Six phases were used to code all verbal contributions (N = 5,442) in 21 diverse, videotaped KE meetings. Resembling state space grids, the authors visualised the course of each meeting with line graphs which were shown to ten individual kaizen experts as well as to the filmed kaizen groups.

From their reactions to the graphs the authors extracted high-quality KE process characteristics. At the end of each phase, that should be enacted sequentially, explicit group consensus appeared to be crucial. Some of the groups spent too little time on a group-shared understanding of the problem and its root causes. Surprisingly, the mixed-methods data suggested that small and infrequent deviations (“jumps”) to another phase might be necessary for a high-quality process. According to the newly developed quantitative process measure, when groups often jump from one phase to a distant, previous or next phase, this relates to low KE process quality.

A refined conceptual model and research agenda are offered for generating better solutions during KEs, and the authors urge examinations of the effects of well-crafted KE training.