Digitalization is rushing throughout the world, namely, in industrial and societal infrastructures. Hence, digital transformation becomes a pillar of industrial policy, known in Europe as “Industry 4.0,” in China as “made in China 2025,” in Asia as “Smart Cities,” in Japan as “Society 5.0,” and in North America as “Industrial Internet.” These transformations will change the industrial landscape, toward Quality 4.0 and therefore, our lives. The presented research was conducted on quality management employees in Portugal and it aims to analyze if such employees have the perception of the impact of Industry 4.0 in the quality management profession and the required and needed skills. After analyzing the answered questionnaires, 90 results were considered valid. This is the sample of our study. Simultaneously, the goal of this research is also to review and analyze the main topics in progress related to quality management for the fourth industrial revolution and how quality emerges of this change. The professionals that work on quality management must have creative thinking, be leaders, know how to communicate and work as a team, as well as, to have knowledge and understanding of ICT (Information and Communications Technology), and main pillars of Industry 4.0. These are the main findings. Besides that, they must know how to motivate their work teams, be open to change, know how to use Big Data to make decisions and above all, they must know how to manage conflicts. In addition, quality professionals must promote the design and production of first class products, be the defenders of their customers within the organization, and finally, they must create value for the stakeholders.

One of the common problems of organizations with turn-key projects is the high scrap rate. There exist such traditional methods as Lean Six Sigma (LSS) and DMAIC tools that analyze causes and suggest solutions. New emerging intelligent technologies should influence these methods and tools as they affect many areas of our life. The purpose of this paper is to present the innovative Small Mixed Batches (SMB). The standard set of LSS tools is extended by intelligent technologies such as artificial neural networks (ANN) and machine learning. The proposed method uses the data-driven quality strategy to improve the turning process at the bakery machine manufacturer. The case study shows the step-by-step DMAIC procedure of critical to quality (CTQ) characteristics improvement. Findings from the data analysis lead to a change of measurement instrument, training of operators, and lathe machine set-up correction. However, the scrap rate did not decrease significantly. Therefore the advanced mathematical model based on ANN was built. This model predicts the CTQ characteristics from the inspection certificate of the input material. The prediction model is a part of a newly designed process control scheme using machine learning algorithms to reduce the variability even for input material with different properties from new suppliers. Further research will be focused on the validation of the proposed control scheme, and acquired experiences will be used to support business sustainability.

The motivation for research on 3D printing of protective face shields was the urgent societal demand for healthcare in the fight against the spread of COVID19 pandemic. Research is based on a literature review that shows that objects produced by additive technologies do not always have consistent quality suitable for the given purpose of use. Besides, they have different effects on the environment and leave different footprints. The overall goal of the research was to find out the most suitable thermoplastic material for printing shield frames in terms of mechanical properties, geometric accuracy, weight, printing time, filament price, and environmental sustainability. Fused deposition modeling (FDM) technology was used for 3D printing, and three different filaments were investigated: polylactic acid (PLA), polyethylene terephthalate (PETG), and polyhydroxyalkanoate (PHA). The weighted sum method for multi-objective optimization was used. Finally, PHA material was chosen, mainly due to its environmental sustainability, as it has the most negligible impact on the environment.

This paper aims to present the results of comparative research on the culture of quality in the largest foreign and domestic organisations operating in the automotive industry in Slovakia. The empirical research examines quality strategy, quality vision, leadership, quality values, and customer involvement practices. The comparative research is based on in-depth personal interviews with professionals responsible for quality and innovation management in the organisations. The empirical research is combined with secondary resources (e.g. annual reports). The culture in Slovakia as described according to the Hofstede model has a ‘high power distance’ and is strongly ‘masculine’. Empirical research on the large foreign organisations operating in the automotive industry in Slovakia recognises that the culture of quality varies significantly according to the country of origin. Inherent differences exist within French, German, Korean, Mexican, Norwegian, and US companies. Proximity in culture can be found between France and the United States as well as Mexico and Germany. The main differences in the culture of quality are primarily in practices and less in vision, values and leadership approaches. Despite the variability within the group of foreign companies, as a group, the set shows a common culture that is fundamentally different from the culture of the sample of Slovak companies. The research focused only on the group of the largest organisations operating in the automotive industry in Slovakia, both foreign and domestic. The paper provides original findings on the culture of quality in foreign organisations operating in Slovakia. Previous studies have not examined what culture of quality arises when a foreign-owned company is faced with the culture of Slovak employees.

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel and thus support decision making about supplier selection or acceptance of a material supply. This research presents a step-by-step decision-making procedure, which enables the trained staff to make quick decisions based on commonly available information in the Mill Test Certificate (MTC). A new multivariate second-order polynomial model and feedforward backpropagation artificial neural network (ANN) models have been developed using input variables from the MTC: Tensile Strength, Yield Strength, Elongation, and Hardness. Models were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions.

The purpose of this paper is to contribute to learning, knowledge creation and knowledge transfer for building organization innovability by integrating the management systems in the SIMPRO-IMS web based environment. The paper content consists of the interpretation of role-play simulation, role-play simulation process description, methodology, and the employment of role-play simulation outcomes, as well as the discussion of the knowledge thus obtained. Primary the model of the SIMPRO-Q education environment has been developed and tested during a period of 15 years in several industrial organizations as well as service organizations such as Higher Education Institution (HEI) and Healthcare Organization (HCO). The newest version SIMPRO-IMS has recently been developed to support a need of integration of management systems and information archiving. With the last development, SIMPRO-IMS web based environment, processes of five ISO systems are integrated for parallel development, implementation, auditing, maintaining and leading. SIMPRO-IMS provides management with the apparatus necessary to realize a systematic and verifiable approach to the creation and control of IMS documentation. At the same time contributes to the preservation of organization memory in response to the growing challenges of globalization and digitalization. The research is limited by the complexity of a real system and possible empiric results verification. The results achieved are verified when people really overcome the resistance to change. This can be assessed thoughtfully only after some period of time. Another limitation is presented by measurability of real enhancement achieved in quality, safety and environmentality of production, and business continuity and social responsibility of an organization. Development and progress in the methodology of SIMPRO-IMS web based environment is encoded in upgrading the SIMPRO database by processes of the environmental management, occupational health & safety management, information security management, energy management, service management, business continuity management and social responsibility management into one cohesive system with a holistic set of documentation, policies, procedures and processes, and a preserving knowledge for learning, new knowledge creation and knowledge transfer for building organization innovability.