Management and Production Engineering Review (MPER) is a peer-refereed, international, multidisciplinary journal covering a broad spectrum of topics in production engineering and management. Production engineering is a currently developing stream of science encompassing planning, design, implementation and management of production and logistic systems. Orientation towards human resources factor differentiates production engineering from other technical disciplines. The journal aims to advance the theoretical and applied knowledge of this rapidly evolving field, with a special focus on production management, organisation of production processes, management of production knowledge, computer integrated management of production flow, enterprise effectiveness, maintainability and sustainable manufacturing, productivity and organisation, forecasting, modelling and simulation, decision making systems, project management, innovation management and technology transfer, quality engineering and safety at work, supply chain optimization and logistics. Management and Production Engineering Review is published under the auspices of the Polish Academy of Sciences Committee on Production Engineering and Polish Association for Production Management. The main purpose of Management and Production Engineering Review is to publish the results of cutting-edge research advancing the concepts, theories and implementation of novel solutions in modern manufacturing. Papers presenting original research results related to production engineering and management education are also welcomed. We welcome original papers written in English. The Journal also publishes technical briefs, discussions of previously published papers, book reviews, and editorials. Letters to the Editor-in-Chief are highly encouraged.

Management and Production Engineering Review (MPER) is a peer-refereed, international, multidisciplinary journal covering a broad spectrum of topics in production engineering and management. Production engineering is a currently developing stream of science encompassing planning, design, implementation and management of production and logistic systems. Orientation towards human resources factor differentiates production engineering from other technical disciplines. The journal aims to advance the theoretical and applied knowledge of this rapidly evolving field, with a special focus on production management, organisation of production processes, management of production knowledge, computer integrated management of production flow, enterprise effectiveness, maintainability and sustainable manufacturing, productivity and organisation, forecasting, modelling and simulation, decision making systems, project management, innovation management and technology transfer, quality engineering and safety at work, supply chain optimization and logistics. Management and Production Engineering Review is published under the auspices of the Polish Academy of Sciences Committee on Production Engineering and Polish Association for Production Management. The main purpose of Management and Production Engineering Review is to publish the results of cutting-edge research advancing the concepts, theories and implementation of novel solutions in modern manufacturing. Papers presenting original research results related to production engineering and management education are also welcomed. We welcome original papers written in English. The Journal also publishes technical briefs, discussions of previously published papers, book reviews, and editorials. Letters to the Editor-in-Chief are highly encouraged.

Along with changes in customer expectations, the process of ordering a house, especially one built with the most modern technology from prefabricated HQ 40-foot shipping containers, should take place in an atmosphere of free-flowing, customer-friendly conversation. Therefore, it is important that the company producing such a solution has a tool supporting such offers and orders when producing personalized solutions. This article provides an original approach to the automatic processing of orders based on an example of orders for residential shipping containers, natural language processing and so-called premises developed. Our solution overcomes the usage of records of the conversations between the customer and the retailer, in order to precisely predict the variant required for the house ordered, also when providing optimal house recommendations and when supporting manufacturers throughout product design and production. The newly proposed approach examines such recorded conversations in the sale of residential shipping containers and the rationale developed, and then offers the automatic placement of an order. Moreover, the practical significance of the solution, thus proposed, was emphasized thanks to verification by a real residential ship container manufacturing company in Poland.

Increasing the role of sustainable production benefits in transforming manufacturing towards the sustainable organisation. The proposed model integrates two dimensions, namely, the Sustainable Business Model (SBM) and the Enterprise Resource Planning (ERP) system, and defines it as the SBM-ERP. This paper focuses attention on determining SBM-ERP based on the literature research, Fuzzy Analytical Hierarchy Process (F-AHP) method and the results of the analysis on the experiences with the implementation of the ERP system in manufacturing. It was determined that the proprietary approach allows the company’s sustainable manufacturing activities to be organised and monitored, based on real-time data and information, as updated and included in the ERP system. We also emphasized the practicality of the proposed approach for managers of manufacturing companies with an implemented ERP system.

Nowadays, it is necessary to develop a conceptual framework for analysing the relationship between the implementation of Additive Manufacturing (AM) and Supply Chain Management (SCM). In this context, a gap in the research has been observed in the new approach to designing the importance of AM in SCM. The main contribution of this paper, therefore, is a new framework to formulate the role in adopting AM in SCM. The research methodology is based on detailed literature studies of AM in relation to the SCM process within a manufacturing company, as well on a case study, namely the COWAN GmbH manufacturing company who specialise in producing homewares for motorhome enthusiasts. As highlighted in the state-of-the-art analysis, no work, currently available, supports all the features presented.

The article presents the issue related with a proper preparation of a data sheet for the

analysis, the way of verifying the correctness and reliability of input information, and proper

data encoding. Improper input or coding of data can significantly influence the correctness

of performed analyses or extend their time. This stage of an analysis is presented by an

authorship questionnaire for the study on occupational safety culture in a manufacturing

plant, using the Statistica software for analyses. There were used real data, obtained during

the research on the issue of occupational safety and factors having the greatest influence on

the state of occupational safety.

In the era of smart manufacturing and Industry 4.0, the rapid development of modelling in production processes results in the implementation of new techniques, such as additive manufacturing (AM) technologies. However, large invest-ments in the devices in the field of AM technologies require prior analysis to identify the possibilities of improving the production process flow. This paper proposes a new approach to determine and optimize the production process flow with improvements made by the AM technologies through the application of the Petri net theory. The existing produc-tion process is specified by a Petri net model and optimized by AM technology. The modified version of the system is verified and validated by the set of analytic methods safeguarding against the formal errors, deadlocks, or unreachable states. The proposed idea is illustrated by an example of a real-life production process.