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Autonomous Mobile Robots in Automotive Remanufacturing: A Case Study for Intra-Logistics Support

100%

Pizoń J. , Wójcik Ł. , Gola A. , Kański Ł. , Nielsen I. E.

Advances in Science and Technology. Research Journal

2024

tom Vol. 18, no 1

213--230

The article focuses on the role of modern logistics 4.0 technologies and lean management in optimizing ancillary processes in intralogistics. The literature review presents critical aspects of intralogistics, including using autonomous mobile robots (AMR) and the challenges associated with their successful implementation. The article also discusses the concepts of Industry 4.0 and Industry 5.0. highlighting the importance of synergies between workers and advanced technologies. In optimizing logistics processes, the authors emphasize the importance of lean management and tools such as 5S and Kaizen. The authors analyze the research gap related to the organization of auxiliary processes, intralogistics, and the introduction of modern technologies. The lack of good practices and strategies for implementing new technologies for ancillary processes makes this a critical issue for managers and production engineers. The article provides practical strategies that can be implemented in companies. It is a valuable resource for managers seeking to manage intralogistics and effectively improve support processes in manufacturing plants. In summary, the article provides a comprehensive look at modern approaches to optimizing support processes in internal logistics. It highlights the importance of integrating modern logistics technologies with lean management principles, which can increase companies' efficiency and competitiveness.

Data engineering in CRISP-DM process production data – case study

86%

Brzozowska J. , Pizoń J. , Baytikenova G. , Gola A. , Zakimova A. , Piotrowska K.

Applied Computer Science

2023

tom Vol. 19, no 3

83--95

The paper describes one of the methods of data acquisition in data mining models used to support decision-making. The study presents the possibilities of data collection using the phases of the CRISP-DM model for an organization and presents the possibility of adapting the model for analysis and management in the decision-making process. The first three phases of implementing the CRISP-DM model are described using data from an enterprise with small batch production as an example. The paper presents the CRISP-DM based model for data mining in the process of predicting assembly cycle time. The developed solution has been evaluated using real industrial data and will be a part of methodology that allows to estimate the assembly time of a finished product at the quotation stage, i.e., without the detailed technology of the product being known.