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The aim of this paper is to model and interpret the results obtained from the assessment of the Level of Excellence of Slovak service organizations using the criteria of the European Foundation for Quality Management (EFQM) excellence model. The Gompertz logistic function is effectively employed to fit the incremental improvement and predict the values of future Levels of Excellence. The EFQM model is usually used to improve organizational development and performance. The study focuses on the problem of the slow growth or even stagnation of Slovak service organizations towards Excellence. The questionnaire method was used to assess the Level of Excellence of the selected organizations, and the approach of measuring efficiency as a ratio of results and enablers was used to evaluate the organization's ability to transform inputs into outputs. Data were collected from 30 service organizations over a period of 20 years. The first finding of the study is the demonstration of the applicability of the Gompertz function to model the evolution of the Level of Excellence. The accuracy of the model is very high, and this predisposes this function to be used to forecast the scores of organizations over time. Examining efficiency yielded a second finding, that organizations were failing to capitalize on the effort put into translating it into results. After the first few years of growth, efficiency stagnates and then even declines. This suggests that the application of the original EFQM excellence model has reached the end of its ability to improve the effectiveness of organizations as a whole. Individual firms may have been growing or declining, but the average service score across the country had no longer the capacity to improve anymore. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]