quality management

This study explores experience-based error management on the basis of 23 participating companies. This study aims to identify essential criteria for effective error management in production. For this purpose, a comprehensive questionnaire was created, featuring 77 questions across eight key topics, including error culture, documentation, root cause analysis and software-supported knowledge management. The following analysis highlights both positive and negative measures, providing specific recommendations to optimize experience-based error management.

Manufacturing companies must work economically while delivering quality - in some industries with a zero-defect tolerance. Quality control often is carried out manually and with a time delay, thus errors can only be corrected at a late stage. Using artificial intelligence (AI), visual quality control can be automated, carried out in real time and integrated into the production process - making it more accurate, efficient and cost-effective. A case example shows the advantages of tackling AI issues in interdisciplinary teams with partners.

Manufacturing Analytics is the evaluation and use of data in the production context. This article shows which potentials can be realised by Manufacturing Analytics in the context of reactive quality management. First, a general definition of the term Manufacturing Analytics is given and then its classification in the context of quality management is carried out. On the basis of a literature analysis and the evaluation of existing use cases, findings regarding the potentials for reactive quality processes are derived. This shows that Manufacturing Analytics is particularly promising and can be used in root cause analysis, defect detection and avoidance. Subsequently, an application example is presented.

The view on quality as a critical competitive factor has been established not only in Western countries but is on the rise in emerging markets, too. In the complex of products and rising customer expectations, many companies try to reduce costs and increase the market opportunities for their products by applying broad-based quality initiatives. Normatively distinct quality management offers only few instructions for these approaches, which is constraining the success of the initiatives. Within the framework of an international study on quality management in manufacturing companies, critical success factors and best practices have been identified on the way to the sustainable establishment of effective quality management. On basis of a developed maturity assessment tool, companies are given the ability to identify the status of their own quality management in comparison to best practice.

The growing importance of knowledge in product development processes is followed by the urgent need to manage the quality of implemented knowledge management strategies in the process. All sorts of companies have been implementing knowledge management activities in their daily routines, either or not they are aware of it. This paper introduces a model to measure the maturity level of knowledge intensive business processes, which is adjusted to the needs of small and medium enterprises (SME). A set of success factors and their indicators were developed to help identifying the as-is process situation and plan improvement measures as well as a web-based self-assessment tool.

In aviation anonymous incident reporting systems (IRS) have been set up for decades to reduce or to control operational incidents and risks systematically. The aim of such instruments is to fall back on existing knowledge of the employees in order to optimize operational performance. The system can only be successful in the long run, if anonymity and non-punitivity are guaranteed. Moreover, the company requires a constructive error culture.

Quality in the production ramp-up significantly affects the success of new product introductions. At the same time the number of restarts in producing companies grows with more divers and specific customer requirements and less vertical range of onsite manufacturing. The resulting complexity is difficult to manage and often leads to quality issues. This article tackles the problem by focusing on quality aspects of the ramp-up management. Based on a holistic understanding of quality the authors present „PAM“, a model for production ramp-up. PAM structures the highly complex topic of ramp-up management and at the same time represents all relevant interdependent elements. The model provides a basis to systematically improve quality in production ramp-up.

Over the last two decades quality management has been shaped by a plethora of concepts for performance-focused quality design, ranging from six sigma, TQM, EFQM, Sustainable Quality Management to ISO 9000- certification and the Kano model. None of these mainstream models fully meets the requirements of navigation capacity, viability and balance. The balanced resilience concept provides a realistic approach for an integrated management of quality risks and opportunities. Establishing barriers to failure and dismantling barriers to success serve as key elements of the concept.

The quality of a product can be described with the fulfillment of requirements. Requirements of a product are derived from customer requirements, laws, technical, economic, normative and organizational requirements. To achieve profit for a product all of the requirements have to be specified exactly to calculate the effort and with that the correct prize of the product. This goal can only be achieved if all necessary information is implemented in the product and process specification before the documents are signed. Advanced product quality planning will help to provide the information completely, in time and up to date at the right place. This complex task presumes the implementation of a computer-aided quality information system. The experience of the implementation of a computer-aided information system for a medium sized enterprise will summarize the paper.

Due to the global harmonization of product quality it is essential for a firm’s success to pursue a quality strategy that balances the trade-off between customer satisfaction and quality costs. For selecting an appropriate quality strategy it would be necessary to include risk considerations and customer satisfaction into strategy evaluation approaches. However, current methods do not integrate these customer related effects. This article presents an approach for calculating the value added by quality strategies integrating risk considerations and customer satisfaction as a major success factor.