Management and Production Engineering Review

Zawartość

Management and Production Engineering Review | 2025 | Vol. 16 | No 4

Abstrakt

This study optimizes the Friction Stir Welding (FSW) process for aluminum alloys AA6061 and AA7075, crucial for the automotive and shipbuilding industries. The Taguchi method combined with Grey Relational Analysis (GRA) was employed to determine optimal process parameters: rotational speed, travel speed, and pin depth in the Z-axis. Experiments revealed that a rotational speed of 1000 RPM, travel speed of 20 mm/min, and pin depth of 0.16 mm achieved the highest tensile strength (166.68 MPa) and hardness (97.86 HV). Analysis of Variance (ANOVA) confirmed the significant impact of rotational speed on mechanical properties. The study demonstrates the efficacy of combining Taguchi and GRA methods for FSW optimization, providing a framework for improving material performance in lightweight, highstrength applications. Future research should explore broader material scopes, advanced control systems, and environmental impacts.
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Autorzy i Afiliacje

Panuwat THOSA
Kiattipong ONBUT
Jessada HONGNEE
Thanatep PHATUNGTHANE
Somchat SONASANG

Abstrakt

This study examines the problem of minimizing job lateness in the paper manufacturing industry, focusing on cut-size machine scheduling under fluctuating demand. Historical demand data (2018–2019) were forecast using Double Exponential Smoothing (DES) and Holt–Winters’ Triple Exponential Smoothing (TES), with accuracy assessed via Mean Absolute Percentage Error (MAPE). The forecasts informed scheduling models for single- and parallel-machine environments using dispatching rules, including Earliest Due Date (EDD), Shortest Processing Time (SPT), Critical Ratio (CR), Longest Processing Time (LPT), and Least Slack Time (LST). Results show Holt–Winters’ TES achieves the most accurate forecasts, while EDD consistently minimizes lateness, reducing delays by more than 70% compared with alternatives. These findings highlight the value of integrating forecasting and scheduling to enhance machine utilization and delivery performance. The framework offers practical guidance for demand planning and resource allocation in export-oriented manufacturing sectors facing high demand variability.
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Autorzy i Afiliacje

Filscha Nurprihatin
Yulistia YULISTIA
Ester Lisnati JAYADI
Ivana Tita Bella WIDIWATI
Yogi Tri PRASETYO
Hendy TANNADY

Abstrakt

Today, industries faces ongoing challenges related to the cost and quality of products, delivery, and increased global competition. These challenges have forced the industry to change its strategy, improve product quality, and reduce production costs in order to remain competitive in global markets. One of the most effective strategies for dealing with these challenges is Lean Manufacturing. The aim of this article is to establish a methodology for assessing and identifying the level of implementation of Lean Manufacturing practices in medium-sized industrial enterprises in the dairy industry. The proposed methodology integrates Lean Position Map, Percent Point Score method, and Impact Matrix of Lean Practice–Waste. This methodology classifies and categorizes companies based on the level of implementation of ten common Lean Manufacturing practices and examines the impact of implementing these practices on reducing or eliminating eight types of waste, facilitating the development of an action plan for improvement. The Lean Position Map was applied to evaluate twenty dairy companies located in Baghdad in order to examine and understand the level of implementation of Lean Manufacturing practices in the processes and activities of these companies. A matrix of the impact of Lean Manufacturing practices on waste elimination was developed to understand the impact of ten Lean Manufacturing practices on eight types of waste.
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Autorzy i Afiliacje

Zainab Al-BALDAWI

Abstrakt

This study evaluates the implementation of the Lean, Agile, Resilient, and Green (LARG) approach in the electric motorcycle industry in Indonesia using the Bayesian Best Worst Method (BWM). The main focus of the study is to identify and determine the weight of the most relevant LARG indicators to improve the competitiveness and sustainability of the industry. From the analysis results, the Resilience indicator has the highest weight, while the Lean indicator has the lowest weight. Important sub-indicators identified include the ability to take corrective action when disruptions occur, waste management according to regulations, flexibility in collaboration with industry partners, and component quality testing. Recommended priority strategies include developing a standards-based safety system, consistent technology transfer, and provision of adequate infrastructure. The results of this study provide data-based strategic guidance to improve efficiency, flexibility, durability, and environmental sustainability in the electric automotive industry in Indonesia.
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Autorzy i Afiliacje

Humiras Hardi PURBA
Siti AISYAH
Choesnu JAQIN
Erry RIMAWAN
Ades Yulia APRIANI

Abstrakt

This paper aims to demonstrate that the implementation of Lean Management (LM) can benefit from prior process modelling and redesign using Business Process Model and Notation (BPMN). Single case study research was conducted, which allowed the detailed documentation of the operations of a wind blade manufacturer and the evaluation of the applicability of combining Business Process Management (BPM) and LM, which are two different process management approaches. BPMN allowed the identification of handoffs in the production process and the reassignment of tasks to eliminate waste. It provides a visual representation of process flows, facilitating a common understanding of the processes and providing a solid basis for discussing process improvements. As a result of the interventions, the cycle time of the target processes was reduced by up to around 30%. This work enriches the still scarce literature that crosses both fields. It also responds to the claims for more case study research on LM and studies with business collaboration.
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Autorzy i Afiliacje

Mariana COSTA
Liliana ÁVILA

Abstrakt

This research deals with an operator assignment and cell loading problem in multi-cell production systems with inter-cell operator sharing. A two-phase methodology is proposed to minimize the total manpower requirement. In the first phase, manpower configurations for all products with all levels of manpower available will be generated taking inter-cell operator sharing into consideration. The second phase optimizes the cell loading and selects the manpower configuration for all cells when given a product mix. In order to further reduce manpower requirements, lot-splitting is considered. For both phases, the corresponding mathematical models, which can be optimized by commercial software, LINGO 17, are developed. A case study of a jewelry manufacturing company proposed taken from the literature is adopted to test the proposed methodology. The results show the strategy of inter-cell operator sharing can save 11.56% manpower at most. Moreover, if lot-splitting is considered, at most 14.66% of manpower can be saved.
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Autorzy i Afiliacje

Yiyo KUO
Shih-Chen CHEN

Abstrakt

The article presents an in-depth literature review on the performance of metaheuristics in operations scheduling problems and aims to evaluate the use of metaheuristics, concerning job-shop scheduling problems. In the first part, a literature review was conducted on the significance of operations scheduling and its different types, as well as metaheuristics and jobshop scheduling problems, providing historical context to the three topics. The methodology for the selection of the papers included in the bibliometric study is explained. Twenty articles from Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and Tabu Search, addressing job-shop problems were selected. Then, various statistical analyses were conducted, such as the analysis of the evolution of results throughout the years and the performance comparison analysis between metaheuristics. Finally, a discussion about the results obtained is held, presenting the conclusions. The statistical analyses revealed that the performance of metaheuristics depends on multiple factors and that their evaluation should not be carried out in isolation. In terms of practical results, the analysis showed that Genetic Algorithms achieved the highest average makespan reduction, followed by Simulated Annealing, Particle Swarm Optimization, and Tabu Search. For example, GA consistently reduced makespan by more than 15% compared to industrial cases, while Tabu Search showed the least consistent performance across studies.
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Autorzy i Afiliacje

Tomás SOUSA
André S. SANTOS
Leonilde R. VARELA
Justyna TROJANOWSKA

Abstrakt

In the industrial sector, Supervisory Control and Data Acquisition (SCADA) systems are essential for managing Industrial Internet of Things (IIoT) networks. However, these systems have become increasingly exposed to cyberattacks targeting the communication layers embedded in industrial processes. Such vulnerabilities can cause severe disruptions in manufacturing and production environments. The ongoing digitalization of Industrial Control Systems (ICS) has further amplified these risks, emphasizing the need for robust security mechanisms such as Intrusion Detection Systems (IDS). This research aims to develop a high-precision AI-based IDS capable of protecting SCADA systems from evolving cyber threats. To achieve this, three categories of machine learning algorithms were evaluated: Deep Learning models (CNN, RNN, LSTM), Boosting algorithms (XGBoost, GBoost, AdaBoost), and classical methods (RF, DT, KNN). Extensive experiments were conducted using two benchmark SCADA datasets, WUSTL-IIoT-2018 and WUSTL-IIoT-2021. The results demonstrated outstanding detection performance, with all models achieving accuracy rates above 99.91%. Specifically, RF, DT, KNN, and XGBoost reached perfect accuracy (100%) on the WUSTL-IIoT-2018 dataset, while XGBoost, LSTM, and CNN achieved 99.99% accuracy on WUSTL-IIoT-2021. Additional evaluation metrics, including precision, recall, and F1-score, confirmed the robustness of the models. The findings highlight the potential of AI-driven IDS solutions to enhance the security and resilience of industrial SCADA infrastructures.
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Autorzy i Afiliacje

Belayadi DJAHIDA
Djeghlouf ASMAA
Lebkara HAITHEM
Ouar NARIMANE

Abstrakt

The growing dependence on high quality data in industrial environments drives the adoption of artificial data generation techniques, especially in the development and implementation of Digital Twins (DTs). This article presents a critical review of the main approaches to creating synthetic data, with an emphasis on their application in Industry 4.0 intelligent cyber-physical systems. Initially, traditional techniques such as Random Oversampling (ROS), SMOTE and its variants are analyzed, as well as statistical models such as the Gaussian Mixture Model (GMM). Next, Deep Learning-based methods are explored, namely Autoencoders, Variational Autoencoders and Generative Adversarial Networks (GANs), highlighting their ability to produce realistic and diverse data. The study also includes the analysis of practical cases in which DTs have been developed using synthetic data, covering domains such as wind energy, aviation and urban infrastructure. In this way, the aim of this study is to critically explore the different techniques of artificial data generation with an integration with the technology of DTs. The results suggest that the appropriate use of synthetic data can not only overcome limitations related to privacy and the scarcity of real data, but also improve the robustness and effectiveness of Digital Twins models. The article concludes by discussing the current challenges and future opportunities in integrating these techniques into smart industrial environments.
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Autorzy i Afiliacje

Francisco ZENZA
Ana L. RAMOS
José V. FERREIRA
Luís P. FERREIRA
Ricardo RIBEIRO

Instrukcja dla autorów

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The average time during which the preliminary assessment of manuscripts is conducted - 14 days

The average time during which the reviews of manuscripts are conducted - 6 months

The average time in which the article is published - 12 months

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The ethics statements for the journal Management and Production Engineering Review are based on the guidelines of Committee on publication ethics (COPE) and the ELSEVIER publishing ethics resource kit.
For Authors: All articles, published in the journal Management and Production Engineering Review have to comprise a list of references which correspond with the journal’s Instructions to authors for paper preparation. The authors should ensure that they have written entirely original works, and if the authors have used the work and/or words of others that this has been appropriately cited or quoted. All articles are tested using antyplagiarism programme. An author should not in general publish manuscripts describing essentially the same research in more than one journal or primary publication. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behaviour and is unacceptable. Authorship should be limited to those who have made a significant contribution to the conception, design, execution, or interpretation of the reported study. The corresponding author should ensure that all co-authors have seen and approved the final version of the paper and have agreed to its submission for publication. All authors should disclose in their manuscript any financial or other substantive conflict of interest that might be construed to influence the results or interpretation of their manuscript. All sources of financial support for the project should be disclosed.
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Procedura recenzowania

Received manuscripts are first examined by the Management and Production Engineering Review Editors. Manuscripts clearly not suitable for publication, incomplete or not prepared in the required style will be sent back to the authors without scientific review, but may be resubmitted as soon as they have been corrected. The corresponding author will be notified by e-mail when the manuscript is registered at the Editorial Office (marta.grabowska@put.poznan.pl; mper@put.poznan.pl). The ultimate decision to accept, accept subject to correction, or reject a manuscript lies within the prerogative of the Editor-in-Chief and is not subject to appeal. The editors are not obligated to justify their decision. All manuscripts submitted to MPER editorial office (https://www.editorialsystem.com/mper/) will be sent to at least two and in some cases three reviewers for passing the double-blind review process. The responsible editor will make the decision either to send the manuscript to another reviewer to resolve the difference of opinion or return it to the authors for revision.

The average time during which the preliminary assessment of manuscripts is conducted - 14 days
The average time during which the reviews of manuscripts are conducted - 6 months
The average time in which the article is published - 8.4 months

Recenzenci

Name Surname Affiliation Hind Ali University of Technology, Iraq Katarzyna Antosz Rzeszow University of Technology, Poland Bagus Arthaya Mechatronics Engineering Universitas Parahyangan, Indonesia Sarini Azizan Australian National University, Australia Zbigniew Banaszak Management and Computer Science, Koszalin University of Technology, Poland Lucia Bednarova Technical University of Kosice, Slovak Republic Kamila Borsekova UNIVERZITA MATEJA BELA V BANSKEJ BYSTRICI, Slovak Republic RACHID Boutarfa Hassan First University, Morocco Anna Burduk Wrocław University of Science and Technology, Poland Virginia Casey Universidad Nacional de Rosario, Argentina Claudiu Cicea Bucharest University of Economic Studies Romania, Romania Ömer Cora Karadeniz Technical University, Turkey Wiesław Danielak Uniwersytet Zielonogórski, Poland" Jacek Diakun Poznan University of Technology, Poland Ewa Dostatni Poznan University of Technology, Poland Marek Dźwiarek Milan Edl University of West Bohemia, Czech Republic Joanna Ejdys Bialystok University of Technology, Poland Abdellah El barkany Sidi Mohamed Ben Abdellah University Faculty of Science and Technology of Fez, Morocco Francesco Facchini Università degli Studi di Bari, Italy Mária Magdolna Farkasné Fekete Szent István University, Hungary Çetin Fatih Başkent Üniversitesi, Turkey Mose Gallo Materials and Industrial Production Engineering, University of Napoli Federico, Italy Mit Gandhi Gujarat Gas Limited, India Józef Gawlik Cracow University of Technology, Institut of Production Engineering, Poland Andrzej Gessner Politechnika Poznańska, Poland Pedro Glass Universitatea Valahia din Targoviste, Romania Arkadiusz Gola Lublin University of Technology, Faculty of Mechanical Engineering, Poland Alireza Goli Department of industrial engineering, Yazd university, Yazd, Iran Iran, Iran Magdalena Graczyk-Kucharska Politechnika Poznańska, Poland Damian Grajewski Poznan University of Technology, Poland Łukasz Grudzień Production Engineering Department, Poznan University of Technology, Poland Patrik Grznár University of Žilina, Slovak Republic" Anouar Hallioui INTI International University, Malaysia Ali HAMIDOGLU Adam Hamrol Mechanical Engineering, Poznan University of Technology, Poland ni luh putu hariastuti itats, Indonesia Christian Harito Bina Nusantara University, Indonesia Muatazz Hazza "Mechanical and Industrial Engineering Department; School of Engineering. American University of Ras Al Khaimah. United Arab Emirates, United Arab Emirates" Ali Jaboob Dhofar University, Oman Małgorzata Jasiulewicz-Kaczmarek Poznan University of Technology, Poland Oláh Judit University of Debrecen, Hungary Jan Klimek Szkoła Główna Handlowa, Poland Nataliia Klymenko National University of Life and Environmental Sciences of Ukraine, Ukraine Peter Kostal Slovenská Technická Univerzita V Bratislave, Slovak Republic Martin Krajčovič University of Žilina, Slovak Republic Robert Kucęba Wydział Zarządzania, Politechnika Częstochowska, Poland Agnieszka Kujawińska Poznan University of Technology Edyta Kulej-Dudek Politechnika Częstochowska, Poland Sławomir Kłos Institute of Mechanical Engineering, University of Zielona Góra, Poland Christian Landschützer Graz University of Technology, Austria Anna Lewandowska-Ciszek Department of Logistics, Poznań University of Economics and Business, Poland Damjan Maletič University of Maribor, Faculty of Organizational Sciences, Slovenia Marcela Malindzakova Technical University, Slovak Republic Józef Matuszek Janusz MLECZKO Rami Mokao MIS - Management Information Systems, HIAST, Syria Maria Elena Nenni University of Naples, Italy Nor Hasrul Akhmal Ngadiman School of Mechanical Engineering, Universiti Teknologi Malaysia, Malaysia Dinh Son Nguyen The University of Danang, University of Science and Technology, Viet Nam Duc Duy Nguyen Department of Industrial Systems Engineering,
Ho Chi Minh Technology University (HCMUT), Viet Nam Filscha Nurprihatin Sampoerna University, Indonesia Filip Osiński Poznan University of Technology Ivan Pavlenko Department of General Mechanics and Machine Dynamics, Sumy State University, Ukraine Robert Perkin BorgWarner, United States Alin Pop University of Oradea, Romania Ravipudi Venkata Rao "Department of Mechanical Engineering S. V. National Institute of Technology, Surat, India" Marta Rinaldi University of Campania, Italy Michał Rogalewicz Division of Production Engineering, Institute of Materials Technology, Faculty of Mechanical Engineering, Poznan University of Technology, Poland David Romero Tecnológico de Monterrey, Mexico ELMADANI SAAD Hassan First university of Settat, Morocco Krzysztof Santarek Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, Poland shankar sehgal Panjab University Chandigarh, India Robert Sika Faculty of Mechanical Engineering and Management, Institute of Materials Technology, Poland Chansiri Singhtaun Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, Thailand Bożena Skołud Silesian University of Technology, Poland Lucjan Sobiesław Jagiellonian University, Poland Fabiana TORNESE University of Salento, Italy Stefan Trzcielinski Poznan University of Technology, Poland Amit Kumar Tyagi Centre for Advanced Data Science, India Cang Vo Binh Duong University, Viet Nam Jaroslav Vrchota University of South Bohemia České Budějovice, Faculty of Economics, Department of Management, Studentská, 370 05 České Budějovice, Czech Republic Radosław Wichniarek Poznan University of Technology, Poland Ewa Więcek-Janka Wydział Inżynierii Zarządzania, Politechnika Poznańska, Poland Josef Zajac Uniwersytet Techniczny w Koszycach, Slovak Republic Aurora Zen Universidade Federal do Rio Grande do Sul, Brazil

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