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.
Transportation logistics plays a critical role in ensuring equitable access to medical supplies in low-infrastructure and insecure environments. This paper proposes a three-stage hybrid framework to solve a Vehicle Routing Problem with Safety and Time Windows (VRP-ST) in high-risk contexts. The methodology combines a Clarke–Wright savings heuristic for rapid initialization, an Ant Colony Optimization (ACO) metaheuristic to minimize a risk-adjusted distance objective, and Google OR-Tools to strictly enforce hard constraints, including vehicle capacities and delivery time windows. The approach is evaluated on a nationwide case study involving 44 healthcare centers in Burkina Faso. The results show a 19% reduction in total risk-adjusted distance (from 10,425 to 8,280 units) relative to the initial heuristic solution, together with improved fleet utilization. Beyond this case study, the framework provides a robust and fully feasible decision-support tool for medical logistics planning in crisis-affected regions. A sensitivity analysis under ±10% demand and risk-penalty perturbations confirms the stability of the solutions.
This special issue of "Decision Making in Manufacturing and Services" is devoted to the field of Business Process Management (BPM) and contains ten selected papers presented at the 5th Polish BPM Symposium. This event is part of a series of meetings bringing together the Polish BPM community, including researchers, business professionals, and educators involved in BPM. The field of BPM continues to develop actively and attracts significant interest, as demonstrated by the popularity of events such as the International Conference on Business Process Management, the 22nd edition of which was held at AGH University of Krakow in September 2024. This special issue of DMMS, as a direct outcome of the symposium, presents works by Polish authors and showcases the implementation of the process approach in Polish companies. It is a diverse collection of studies and case analyses that contribute to the ongoing discourse on Business Process Management and its applications across various industries. These papers offer valuable insights into both theoretical advancements and practical implementations, addressing key technological, organizational, and educational aspects of BPM. By publishing these contributions in English with open access, this volume helps to disseminate the achievements of the Polish BPM Symposia and contributes to the global BPM research and practice. Together, these contributions provide a comprehensive overview of contemporary BPM challenges and solutions, bridging the gap between research and practice. The studies included in this volume not only expand the theoretical foundation of BPM but also offer actionable insights for organizations aiming to optimize their processes and drive innovation in an increasingly complex business environment. As a Poland-based initiative, this issue is a direct outcome of the 5th Polish BPM Symposium, marking another step in making BPM more popular as a subject of research and more suitable for practical implementation. Furthermore, it contributes to the consolidation and strengthening of the Polish BPM community by bringing together researchers and practitioners dedicated to advancing BPM methodologies and their real-world applications.
Six Sigma is a widely adopted method across many companies from different industries based in various locations in the world. As the proper implementation of Six Sigma is critical for the further success of the method in companies, researchers and practitioners are analyzing which factors play an important role in early stages of adaptation. This paper refers to Critical Success Factors (CSF) analyzed in various types of companies, industries and locations. Additionally, the paper brings the perspective of the problem from a company in the automotive sector of Poland. Survey studies on Critical Success Factors (CSF) for Six Sigma implementation were performed across engineering teams working on product development and process optimization. The summarized results show the perspective of Six Sigma practitioners on the most important factors, specific to the given sector and location. The findings offer valuable insights for managers who are defining Six Sigma implementation strategies and teams which are planning individual implementation steps. The results are important for companies in Poland but also can be generalized for any Six Sigma implementation practices.
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.
