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1
Content available Applying additive manufacturing technologies to a supply chain: A Petri net-based decision model
Patalas-Maliszewska Justyna, Wiśniewski Remigiusz, Zhou MengChu, Topczak Marcin, Wojnakowski Marcin
International Journal of Applied Mathematics and Computer Science
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2024
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Vol. 34, no. 3
513--525
EN
Nowadays, applying additive manufacturing (AM) technologies into a supply chain (SC) permits realization of the so-called “demand chains” and transformation of conventional production to mass customization. However, integration of AM technologies within an SC indicates the need to support managers’ decision about such an investment. Therefore, this work develops a Petri net-based decision support model that determines the changes in an SC by adopting AM and improving customer-perceived value (CPV), based on a case study regarding a real-life metal production process. The basis for building such a model is the supply chain operation reference model (SCOR), focusing on CPV, due to the need for redesigning the SC starting from the customer instead of the company. To achieve the research objective, this work introduces a novel verification methodology for a Petri net-based decision model. The research results show that applying the developed model, which is based on the selected characteristics of the production process and parameters describing the potential integration of AM within the SC, allows managers to perceive a scenario in the form of graphical models about positive or negative impacts of introducing AM into the SC. The managers find the Petri net-based decision support model presented in this paper a beneficial tool to support the implementation of changes in an SC and show the potential increase in customer satisfaction thanks to the integration of AM within an SC.
2
Content available The Role of Additive Manufacturing in Supply Chain Management
Patalas-Maliszewska Justyna, Kowalczewska Katarzyna, Rehm Matthias
Management and Production Engineering Review
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2023
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Vol. 14, No. 1
25--33
EN
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.
3
Content available Towards mass customisation: automatic processing of orders for residential ship’s containers - A case study example
Dudek Adam, Patalas-Maliszewska Justyna, Frączak Jacek
Bulletin of the Polish Academy of Sciences. Technical Sciences
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2023
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Vol. 71, nr 3
art. no. e145562
EN
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.
4
Content available Modelling information for the burnishing process in a cyber-physical production system
Patalas-Maliszewska Justyna, Posdzich Marco, Skrzypek Katarzyna
International Journal of Applied Mathematics and Computer Science
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2022
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Vol. 32, no. 3
345--354
EN
Currently, the manufacturing management board applies technologies in line with the concept of Industry 4.0. Cyber-physical production systems (CPSs) mean integrating computational processes with the corresponding physical ones, i.e., allowing work at the operational level and at the strategic level to run side by side. This paper proposes a framework to collect data and information from a production process, namely, the burnishing one, in order to monitor real-time deviations from the correct course of the process and thus reduce the number of defective products within the manufacturing process. The proposed new solutions consist of (i) the data and information of the production process, acquired from sensors, (ii) a predictive model, based on the Hellwig method for errors in the production process, relying on indications of a machine status, and (iii) an information layer system, integrating the process data acquired in real time with the model for predicting errors within the production process in an enterprise resource planning (ERP) system, that is, the business intelligence module. The possibilities of using the results of research in managerial practice are demonstrated through the application of an actual burnishing process. This new framework can be treated as a solution which will help managers to monitor the production flow and respond, in real time, to interruptions.
5
Content available Analysis and prediction of leak detection in the low-pressure heat treatment of metal equipment
Kłos Sławomir, Patalas-Maliszewska Justyna, Bazel Michal
Eksploatacja i Niezawodność
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2022
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Vol. 24, no. 4
719--727
EN
The low-pressure heat treatment of metals enables the continuous improvement of the mechanical and plastic properties of products, such as hardness, abrasion resistance, etc. A significant problem related to the operation of vacuum furnaces for heat treatment is that they become unsealed during operation, resulting from the degradation of seals or the thermal expansion of the construction materials. Therefore, research was undertaken to develop a prediction model for detecting leaks in vacuum furnaces, the use of which will reduce the risk of degradation in the charge being processed. Unique experimental studies were carried out to detect leakages in a vacuum pit furnace, simulated using the ENV 116 reference slot. As a consequence, a prediction model for the detection of leaks in vacuum furnaces- which are used in the heat treatment of metals- was designed, using an artificial neural network. (93% for MLP 15-10-1) was developed. The model was implemented in a predictive maintenance system, in a real production company, as an element in the monitoring of the operation of vacuum furnaces.
6
Content available Sustainable Business Model integrated with the Enterprise Resource Planning system: SBM-ERP
Patalas-Maliszewska Justyna, Kłos Sławomir, Dostatni Ewa
Bulletin of the Polish Academy of Sciences. Technical Sciences
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2022
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Vol. 70, nr 6
art. no. e143829
EN
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.
7
Content available Design optimization of the Petri net-based production process supported by additive manufacturing technologies
Patalas-Maliszewska Justyna, Wiśniewski Remigiusz, Topczak Marcin, Wojnakowski Marcin
Bulletin of the Polish Academy of Sciences. Technical Sciences
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2022
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Vol. 70, nr 2
art. no. e140693
EN
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 investments 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 production 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.
8
Content available Model oceny poziomu ryzyka w przedsiębiorstwie produkcyjnym
Topczak Marcin, Patalas-Maliszewska Justyna
Zarządzanie Przedsiębiorstwem
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2019
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Vol. 22, nr 4
14--21
EN
Risk is an inherent element of business operations. In the case of production enterprises, the risk can be considered in the area of material, machine, man and process organization. PN ISO 31000:2018 recommends integrating the risk management system with other management systems within a company, pointing to the significant role of understanding the external and internal context of the organization and identifying the needs of stakeholders. The risk management process consists of two steps: identification and analysis affecting the risk factors and quantitative measurement of its level. The article proposes a risk management model for a production enterprise, making a theoretical analysis. The proposed model and procedure are based on the guidelines of PN ISO 31000:2018. The proposed procedure for risk management includes the stage of hazard identification in a selected area, setting criteria and levels of admissibility of individual threats, risk analysis and evaluation of risk levels in relation to the adopted criteria.
9
Content available Survey questionnaire for the study on occupational safety culture in a production plant. Construction and statistical verification of data correctness
Krupa Patryk, Gabryelewicz Izabela, Edl Milan, Pantya Peter, Patalas-Maliszewska Justyna
Management and Production Engineering Review
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2019
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Vol. 10, No. 2
101–-108
EN
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.
10
Content available A Model for Assessing The Level of Automation of a Maintenance Department Using Artificial Neural Network
Halikowski Daniel, Patalas-Maliszewska Justyna, Skrzeszewska Małgorzata
Applied Computer Science
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2018
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Vol. 14, no 4
70--80
EN
With regard to adapting enterprise to the Industry 4.0 concept, the first element should be the implementation and use of an information system within a manufacturing company. This article proposes a model, the use of which will allow the level of automation of a maintenance department to be forecast, depending on the effectivity of the use of the Manufacturing Executions System (MES) within a company. The model was built on the basis of the actual times of business processes completed which were supported by MES in the maintenance departments of two manufacturing enterprises using artificial neural network. As a result of research experiments, it was confirmed that the longer the time taken to complete business processes supported by MES, the higher is the degree of automation in a maintenance department.
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