Lean, as a concept focused on eliminating waste, optimizing processes, and creating customer value, has enjoyed widespread applications across numerous industries. However, its effective implementation requires adaptation to the specific characteristics of a given sector and enterprise. This article presents an evaluation and analysis of the use of Lean Management and selected Lean tools in an enterprise operating in the food industry. Based on a structured literature review, the paper outlines the origins, fundamental assumptions, and key tools associated with Lean Manufacturing. Subsequently, an empirical study based on a questionnaire survey was conducted among employees of a food industry company that has been applying the Lean Management concept for several years and systematically uses a range of Lean tools to optimize and improve its processes. The findings provided the basis for the formulation of conclusions confirming the applicability and effectiveness of the Lean Management approach in the food industry, as well as for identifying and evaluating the extent of the use of individual Lean tools. The results may serve as a starting point for further research and support practical implementations of Lean in food industry enterprises.
(Wydawnictwa AGH, 2025) Bunyak, Yuri; Kvyetnyy, Roman; Sofina, Olga
The problem of data structure analysis through their multidimensional representation as a d-dimensional tensor is considered to assess dependencies on influencing factors in the decision-making process. The higher-order Singular Value Decomposition (SVD) is developed as a d-SVD schema to identify significant and trivial dependencies. The d-SVD includes the SVD of the tensor reshaped as a matrix and the SVDs of reduced size of the previous SVD vectors reshaped as matrices. The entropy of the distribution of the Singular Values (SVs) of the vectors’ decomposition is used for the separation of the significant and trivial vectors, in contrast to the commonly used approach based on the magnitude analysis of SVs. The singular projection in the significant vector space in selected dimensions gives the tensor’s low-rank approximation without loss of information in comparison with the truncated SVD. The tensor projection on a vector subspace of reduced dimension can be obtained by using a part of the SVs and the corresponding vectors as an alternative to the commonly used averaging. It was shown that data prediction in the subspace of the significant vectors allows stable assessments of the predicted values to be obtained.
