The Six Sigma DMAIC can be considered a guide for problem solving and product or process improvement. The majority of companies start to implement Six Sigma using the DMAIC methodology. The paper deals with application of Six Sigma using the DMAIC methodology in the process of product quality control. The case study is oriented on the field of metallurgical operations. The goal of the Six Sigma project was to ensure the required metallurgic product quality and to avoid an increase in internal costs associated with poor product quality. In this case study, a variety of tools and techniques like flow chart, histogram, Pareto diagram, analysis of FMEA (Failure Mode and Effect Analysis) data, cause and effect diagram, logical analysis was used. The Sigma level has improved by approximately 13%. The achieved improvements have helped to reduce the quantity of defective products and the processing costs (technology for re-adjusting). Benefits resulting from the DMAIC implementation can be divided into three levels: the qualitative, economic and safety level.
The main task of safety and health at work is to protect the most important thing we have, the health of each of us. Employers are able to anticipate and prevent risks by properly implemented occupational safety and health management systems. The basic task of the article is to describe the history of safety management systems to identify the state of implementation of the ISO 45001 system in the world. Subsequently, the article describes the ISO 45001 standard from the perspective of the PDCA cycle and describes the benefits and importance of implementing the ISO 45001 standard. The conclusion of the article deals with the development trend of the occupational health and safety management system according to STN ISO 45001:2019 in the context of occupational accidents in the conditions of the Slovak Republic.
<p><strong>Purpose:</strong> The purpose of this paper is to present the results of an analysis and evaluation of data on employee turnover based on deep data mining using association rules and decision trees in a specific organisation.</p><p><strong>Methodology/Approach:</strong> For the analysis, we chose deep data mining methods, primarily a search for association rules using the Apriori algorithm in the R programming language. For the sake of supplementation and comparison of results, data were also analysed using the predictive decision trees method, applying the C5.0, rpart and ctree algorithms in the R program.</p><p><strong>Findings:</strong> The results of the analyses showed that observing the basic principles of correct communication from the beginning of an employment relationship, or during hiring, is justified. Communication and regular conversations between a superior and employees can help identify problems earlier, address them and reduce the number of people leaving the company. The results of the analysis helped the organisation to set measures to reduce the number of an employee leaving.</p><p><strong>Research Limitation/implication:</strong> A limiting factor in performing such analyses is the availability of quality data in the required quantity. Our most significant advantage when performing our analysis was that quality data were available. To create the final structure of the required data set, we used data from the organisation’s internal information systems.</p><p><strong>Originality/Value of paper:</strong> This contribution offers a new approach to analysing data on employee turnover, whose essence is that we need to find the most interesting and frequent correlations in a significant amount of data.</p>
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