rss_2.0Sciendo RSS Feed for Management Systems in Production Engineeringhttps://sciendo.com/journal/MSPEhttps://www.sciendo.comhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/66786580dd1c3d1f8713e1ea/cover-image.jpghttps://sciendo.com/journal/MSPE140216https://sciendo.com/article/10.2478/mspe-2024-0037<abstract> <title style='display:none'>Abstract</title> <p>Making a Master Production Schedule (MPS) is a very important activity for a manufacturing industry. This is due to the fact that MPS serves as an input for material and production planning. Between the years 2020 and 2022, there were significant fluctuations observed in container freight rates. As response, a lot of manufacturing industry focus on optimizing their container delivery schedule. Hence, there is a need for aligning the master production schedule with the delivery schedule. This paper presents the development of a novel heuristic approach to address problems with the creation of MPS. Specifically, the focus is on the situation where container delivery schedules are prearranged and serve as a main input for creating the MPS. There are two objective functions that are going to be reached: 1) minimize the total number of product variations or Stock Keeping Units (SKU) per month; and 2) minimize the number of SKU per container. The proposed heuristic approach uses the similarity concept to group objects in a clustering technique. It is then implemented in a real-world case of a furniture manufacturing company. Further results were obtained and then compared to the heuristic technology that had previously been used by business entities. The results show that the number of product variations (SKU) that must be performed per month is 10% lower than that of the existing heuristic. In addition, the ratio of SKU variations per container is 9% lower than that of the existing heuristic. The time required to complete the task of creating MPS is less than one minute, as opposed to the one working day required by the company’s existing heuristic.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00372024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0035<abstract> <title style='display:none'>Abstract</title> <p>Additive manufacturing, or 3D printing, has become very common in professional applications in many industries. The 3D printing technology is especially suitable for making prototypes, demonstrators and small-batch production. The stiffness and strength of 3D prints depend on many factors, including among others infills, which are specific to this technology, as well as the orientation of the object during 3D printing. Where the stiffness or strength of an element is crucial, the only way is to empirically assess its properties. The advantage of 3D printing, i.e. incomplete infill of the interior of an object with the use of different types of infills (patterns) and different amounts of material, means that its mechanical properties differ from those of a solid element. The application of numerical tests, i.e. the finite element method (FEM), requires the creation of a 3D model while taking this infill into account. The modelling of elements for performing numerical strength calculations is time-consuming and labour-intensive. The article presents a proprietary original analytical method for generating various types of infills with varying infill density. The method was developed for typical infills (Grid, Triangular, Honeycomb). It was next implemented in the CAD environment using the iLogic tool of Autodesk Inventor. As a result, a tool for creating 3D models of objects consistent with those obtained from 3D printing was obtained. The method and tool were verified. Next, the influence of selected parameters of the 3D print on its mechanical properties was presented on three real objects. The results of numerical analyses revealed measurable benefits of such tests. The research conclusions also constitute recommendations for selecting the type and infill density of an object and its orientation in the printer with regard to the strength and stiffness obtained.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00352024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0041<abstract> <title style='display:none'>Abstract</title> <p>Electrical Discharge Machining (EDM) process is considered as one of the ultimate famously process used in the components production like tools of surgical, dies, punch and aerospace. The EDM technique perform on the control of loss metal principle according to power as thermal-electric through the electrode and blank piece. Alloy steel (X210) was selected as a workpiece, whereas copper electrodes selected by in different angled electrodes (0°, 22.5°, 45°, 67.5°, and 90°). The technique of composite design (CCD) was applied in this work and accomplish (ANOVA) was concluded to consider the highly important variables. The experimental results show that the material removal rate is accelerate at highest value using Angle of Electrode (E A 67.5°) and (I 30A). Electrode Wear Rate (EWR) can be attained using (E A 22.5°) and (I 20A).Thickness of White Layer (WLT) is increased at (E A 22.5°) with enlarge current at maximum magnitude (30 A), minimize WLT is obviously at (E A 45°) and current is (15A). An optimization technique was utilize to conclude of optimal parameters at maximize MRR and consequently reduce EWR. The optimum values for the responses such as MRR and EWR with optimum value are gained with the following parameters: (E A 22.5°), (I 27), (P-on299) and (P-off 50).</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00412024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0039<abstract> <title style='display:none'>Abstract</title> <p>Trolley rack is a material handling that significantly affects production productivity in Indonesian Tea factories. The design of this tool needs to consider the anthropometric approach. This study raises the problem of the dimensions of the trolley racks used in the industry without thinking about the anthropometric approach. Of the twenty-two trolley rack operators, 92.5% of workers experienced back pain when operating the equipment. This problem has an impact on musculoskeletal disorders (MSDs), which causes a decrease in effectiveness and workers. The REBA and Nordic Body Map Questionnaire methods are used to obtain optimal operator posture analysis related to the redesign of the trolley rack. Five anthropometric dimensions of a comfortable trolley rack were obtained, including shoulder height (SH), standing shoulder width (STW), leg length (FL), foot width (FW), and hand grip width (HGW). The final anthropometric dimensions of this tool are SH = 126 cm, STW = 33 cm, FL = 11 cm, FW = 13 cm, and HGW = 5 cm. Using new trolley racks in this study increased productivity by 80%.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00392024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0036<abstract> <title style='display:none'>Abstract</title> <p>The transformation of supply chains is an important factor for European Automotive OEMs to compete internationally. But it turns out that these are not as resilient as required. A resilient supply chain requires appropriate risk management measures that minimize internal and external influences. However, in some areas of value creation and supply chains, weaknesses are evident that are determined by a high dependence on international supplier markets. This approach to research in this area investigates whether Automotive OEM internal departments perceive certain risks differently. This was done by means of a survey of European OEMs to reveal different perceptions on important aspects of risk-based factors. Internal resilience would be necessary as a first step towards strengthening the supply chains in order to subsequently position themselves against stronger emerging competition from the Asian and US economies. This requires a strong European centering of supplier and partner networks, for example in the area of physical goods such as semiconductors and batteries. In order to support these necessary developments, networks are being established and promoted by the EU. GAIA-X and especially Catena-X are bases for the Automotive industry to adapt and optimize resilience and compliance regulations to governmental and intranational guidelines. As result of this research, it remains to be said that the realignment of supply chains must succeed in improving cooperation and delivery conditions across the entire OEM spectrum while at the same time reducing costs. In this way, the European Automotive market can be positioned as a resilient, strong market player for the future. The basic prerequisite for this is a uniform and coordinated view of risk-based factors in the individual companies and departments.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00362024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0029<abstract> <title style='display:none'>Abstract</title> <p>The furniture industry is one of the industrial sectors that has a potential market in Indonesia. This industry requires a lot of wood raw materials but is faced with a wood legality verification system that limits raw materials. Industrial players still need to start using waste as raw materials, which will reduce the use of primary raw materials. The circular economy concept can be applied to waste treatment. This study aims to design a pump-gallon product made from waste, considering the relatively high level of gallon container consumption. With this design, it is hoped to utilize waste into economically valuable products while reducing the environmental impact it causes. The product design process uses an integrated QFD-TRIZ method combined with circular economy principles. QFD functions to determine consumer desires and make technical responses, while TRIZ resolves contradictions in technical responses. The circular economy attribute is used as a reference in making gallon pump products from wood waste. After the design process is complete, it is followed by an economic feasibility analysis using the cost-benefit ratio. The result of this research is the design of multifunctional and modular products for gallon pumps. The gallon storage is designed to store not only gallons but also a small table for placing dirty glass and a drawer at the bottom that can be used to keep the glass or other items. There is a detachable system between the upper and lower components, making it easier to repair and use. At the bottom, they mounted castor wheels to facilitate product movement. Designing products made from waste will increase the income of furniture SMEs. In addition, it is expected that this will overcome waste management problems and shortages of raw materials experienced by furniture SMEs. Future research can utilize powder and smaller pieces of wood.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00292024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0032<abstract> <title style='display:none'>Abstract</title> <p>This article examines the utilization of computer simulation techniques for optimizing warehouse locations, an essential component of efficient supply chain management. The study employs a detailed simulation model built using FlexSim software to analyze various decision-making scenarios and identify the optimal warehouse locations while considering market demand for different products. The model integrates a finite set of decision variables and constraints specific to the logistics problem, offering a structured approach to evaluate alternative strategies. Key stages in the development of the simulation model are outlined, including the definition of input parameters, the execution of simulations, and the interpretation of results. The findings demonstrate that virtual simulation modeling significantly enhances decision-making processes by providing precise insights into the interactions within the distribution network. Additionally, the use of simulation results in considerable time and cost savings by reducing the need for costly physical trials. This research underscores the effectiveness of computer simulation in optimizing warehouse locations, contributing to improved supply chain performance and operational efficiency.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00322024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0040<abstract> <title style='display:none'>Abstract</title> <p>Fused deposition modeling (FDM) is a commonly used additive manufacturing (AM) technique in both domestic and industrial end-product fabrications. It produces prototypes and parts with complex geometric designs, which has the major benefits of eliminating the need for expensive tooling and flexibility. However, the produced parts often face poor part strength due to anisotropic fabrication strategies. The printing procedure, the kind of material utilized, and the printing parameters all have a significant impact on the mechanical characteristics of the printed item. In order to predict the mechanical properties related to printed components made with the use of FDM and Polylactic Acid (PLA) material, this study concentrates on developing a prediction model utilizing Artificial Neural Networks (ANNs). This study used the Taguchi design of experiments technique, utilizing (L25) orthogonal array as well as a Neural Network (NN) method with two layers and 15 neurons. The effect of FDM parameters (layer thickness (mm), percentage of infill density, number of top/bottom layers, shell thickness (mm), and infill overlap percentage) on ultimate tensile and compressive strength (UTS and UCS) was examined through analysis of variance (ANOVA). With an ANOVA result of 67.183% and 40.198%, respectively, infill density percentage was found to be the most significant factor influencing UCS and UTS dependent on other parameters. The predicted results demonstrated valuable agreement with experimental values, with mean squared errors of (0.098) and (0.326) for UTS and UCS, respectively. The predictive model produces flexibility in selecting the optimal setting based on applications.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00402024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0031<abstract> <title style='display:none'>Abstract</title> <p>The present research discusses the application of risk management tools and Real Option Analysis (ROA) to assess and quantify managerial flexibility in machine replacement decisions under uncertain conditions. Different management configurations are used for the real options approach: options to execute, options to delay, and options to cancel. This reflects the uncertainty inherent to each stage of planning. Uncertainties such as future demand and life-cycle costs are implemented in the model as probability distributions. Monte Carlo simulation is employed to deal with such uncertainties and to facilitate experimental trials. The net present value is used as a decision criterion to determine the best replacement option under different replacement and real option scenarios. Herein, a case study to evaluate different replacement alternatives was conducted for the garment industry. Results of the stochastic net present value, mean-standard-deviation scatter plot, and stochastic dominance showed that the best option was to rent and then buy a new machine of reduced size but greater technological advancement. Finally, tornado diagrams and perfect control methods were used to analyze uncertain factors in order to improve the model and further minimize uncertainty effects.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00312024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0042<abstract> <title style='display:none'>Abstract</title> <p>Overall equipment effectiveness (OEE) is a key indicator to measure the effectiveness of production systems. This paper aims to evaluate and improve a plastic production line based on OEE evaluation. An integrated framework is proposed to enhance the production system efficiency. This paper presents the data for a Plastic production line in Jordan under real working conditions. The data covers three months. A framework process to improve the OEE of the Plastic production system was proposed. Six major stoppage losses were inspected with the help of Pareto analysis. Furthermore, the actual availability, efficiency, and quality rate measures, together with the whole OEE for each working day, week, and month of the production line were shown. The methodology is based on determining the OEE of a Plastic production line after determining the causes of failures. The fishbone diagram tool is used to determine the root causes of failures. To improve the OEE measure, several losses are identified. The results reveal that the company should improve its policy to improve the production line’s performance and reduce losses. Top management should also pay attention to reducing the speed losses, which consist of 58.1%, and eliminate the planned and unscheduled disruptions covering 12.73% of all losses. This can be achieved by establishing a proper operation management procedure and strategy. This, in turn, optimized the equipment’s effectiveness. The quality procedure should include the changeover program that may be executed every day. Similarly, all preventive maintenance procedures for the six machines should be properly executed in predetermined intervals. There are several limitations in the research. Firstly, the research case study is only the plastic production system. Secondly, the research is related to the downtime or stoppage by analyzing it using fishbone diagram. Further, supported by other techniques such as the Pareto chart, six big losses analyses and CED. This research conducted on a Plastic industry. However, similar studies can be carried out in future in other manufacturing industries like electronic, pharmaceutical, textile industries, etc., and service industry. However, as future research work the contributions of this paper with other lean manufacturing concept like six sigma, quality function deployment, TQM, and just-in-time manu-facturing, can also be conducting to assess the overall production line efficiency. On the other hand, several statistical tests can be implemented based on data collected of TPM performance indicators. The proposed method supports policymakers in their decision-making process on the operations management line. Further-more, it improves the production systems’ productivity quality, and performance, reducing unplanned stop-pages and breakdowns, and reducing maintenance costs.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00422024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0034<abstract> <title style='display:none'>Abstract</title> <p>The main objective of the work was to assess the possibility of using and implementing the FMEA method as an effective support for the HACCP system in a selected food industry enterprise. The research entity was a food enterprise located in central Poland and the subject of the research was canned meat with gravy in glass jars and their production line. In the study, programs such as draw.io, Excel, and Statistica were used. The study was conducted based on interviews with company employees, value stream analysis and nonconformance reports. During the site visit, an assessment of the company’s infrastructure was also carried out to evaluate the possibility of implementing the FMEA method. Data analysis showed that in the examined company there are non-compliances with varying degrees of impact on the final quality of the product or on the production process of this product. The analysis of the company’s infrastructure, in turn, confirms that it is possible to integrate the HACCP system with the FMEA method. The results indicate that the synergy of HACCP and FMEA will bring benefits to the company in the form of improved risk management, quality control and safety in food production. The results of this study suggest that implementing such a connection may bring many, various benefits to food companies not only in Poland but also in another countries.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00342024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0030<abstract> <title style='display:none'>Abstract</title> <p>The introduction of the obligation to prepare ESG reports taking into account EU Taxonomy is a challenge for enterprises, but at the same time opens up the possibility of using disclosures in this area to assess entities in the context of environmentally sustainable activities. Legal changes in the field of the Green Deal have been introduced in the last three years, and in the area of EU taxonomy this process is still ongoing, resulting in a deficit of research on the effects of implementing the new legal regulations. The main goal of our study is to assess the importance of the newly applicable ESG reporting and environmental disclosure requirements under EU Taxonomy in improving the quality and comparability of sustainability reporting and the creation of ESG ratings. A qualitative research method was applied based on multiple case studies using content analysis on the basis of ESG reports for 2021-2022 for entities listed on the Warsaw Stock Exchange. The research results indicate a very low level of activities classified as environmentally sustainable and taxonomy-aligned. Additionally, the results may also indicate problems with implementing the new solutions in reporting practice. At the same time, a positive impact is noted of the implementation of taxonomic reports on improving the comparability and detail of disclosures.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00302024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0038<abstract> <title style='display:none'>Abstract</title> <p>The article presents the author’s approach to the classification of types of attractiveness that may be characteristic of certain European countries. The migration attractiveness of countries is highlighted and a list of factors that can affect it is given. Factors were ranked according to the level of their impact on population migration, among which the following were highlighted: the economic development of countries, production processes, the use of more modern technologies, ensuring one’s financial well-being, and the culture of the production environment. The dynamics of the number of immigrants who moved to the studied European countries since 2020 are given. The main programs under which European countries accept migrants for permanent residence are considered the issues of providing housing, employment in the industrial infrastructure, and increasing the level of professional competence in various fields. To conduct a comparative assessment of the attractiveness of European countries for potential migrants, a set of available statistical indicators was selected, which fully reflects the main parameters of the countries’ attractiveness. Individual results obtained in the process of assessing the migratory attractiveness of the countries of the European Continent are presented, and proposals for its improvement are presented. It is proved that increasing migration attractiveness is possible by improving the social and economic attractiveness of countries. To do this, countries should create conditions for the development of various industries, create additional jobs, provide high wages, social services, protection, etc. to attract as many highly skilled labor resources from other countries as possible. As a result, the growth in the number of professional labor migrants in countries will contribute to the development of those industries that are common in the respective countries.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00382024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0033<abstract> <title style='display:none'>Abstract</title> <p>Supply chain (SC) efficacy and efficiency can be severely hampered by supplier delays in orders, especially in the fast-paced business environment of today. Effective risk reduction necessitates the identification of suppliers who are prone to delays and the precise prediction of future interruption. Accurately predicting availability dates is therefore a key factor in successfully executing logistics operations. By leveraging machine learning (ML) techniques, organizations can proactively identify high-risk suppliers, anticipate delays, and implement proactive measures to minimize their impact on manufacturing processes and overall SC performance. This study explores and utilizes various regression and classification ML algorithms to predict future delayed delivery, determine the status of order deliveries, and classify suppliers according to their delivery performance. The employed models include K-Nearest Neighbors (KNN) Random Forest (RF) Classifier and Regression, Gradient Boosting (GB) Regression and Classifier, Linear Regression (LR), Decision Trees(DT) Classifier and Regression, Logistic Regression and Support Vector Machine (SVM) Based on real data, our experiments and evaluation metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) demonstrate that the ensemble based regression algorithms (RF Regression and GB Regression) provide the best generalization error and outperforms all other regression models tested. Similarly, Logistic regression and GB Classifier outperforms other classification algorithms according to precision, recall, and F1-score metrics. The knowledge obtained from this study could aid in the proactive identification of high-risk suppliers and the application of proactive actions to increase resilience in the face of unanticipated disruptions, in addition to increasing SC efficiency and decreasing manufacturing disturbances.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00332024-09-05T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0019<abstract> <title style='display:none'>Abstract</title> <p>The European Green Deal (EU Green Deal) has set the direction for the EU’s energy transition towards climate neutrality by 2050. In Poland, this means moving away from the extraction and use of coal. The Social Contract for the Mining Industry signed in 2021 states the necessity of last mine closure by 2049. Mine closure is a complex, lengthy and costly process. A complex scientific solution may concern the use of rational operations and minimization of mine closure costs. This article presents a system for the elementary assessment of the potential time and cost of coal mine liquidation. Estimating coal mine closure costs in the early design phase is an key aspect of supporting the company dealing with mine closure. The aim of the research was to improve the tool for assessing mine liquidation price. The extended assessment solution proposed in the article is formed on base of statistics of past mining institution liquidation processes. This method can, with minor modifications, be used for each restructuring and revitalizing task for mining industries in the process of liquidation. At the core of the developed method is a preliminary data analysis, which should be confirmed by a complex and multi-criteria estimation of the costs of the planned mine liquidation.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00192024-06-23T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0027<abstract> <title style='display:none'>Abstract</title> <p>Small and Medium Enterprises (SMEs), thus it is pursues to improve their performance to stay in the global competitive markets through adopting an efficient manufacturing systems, one of them is lean production (LP). LP is a continuous improvement philosophy that based on using various lean activities to improve enterprise performance by eliminating various type of waste. In this paper, a Lean Level Assessment Methodology is proposed which integrated Exploratory Factor Analysis (EFA), Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) to identify level of importance of lean activities and assess the level of SMEs leanness concerning five dimensions of lean activities. SMEs lean activities have surveyed through a comprehensive literature review, where twenty six lean activities have identified as the most common SMEs activities that classifying into five lean dimensions. A questionnaire was developed to collect data related to the levels of adoption of these lean activities by SMEs using a triple Likert scale. EFA was used to extract the most influencing lean activities on SMEs leanness based on questionnaire data. FAHP was used to determine the weights and the level of importance of these lean activities, while FTOPSIS was employed to investigate and assess the level of SMEs’ leanness related to the five lean dimensions activities. The proposed methodology has applied in four Iraqi SMEs (A1, A2, A3, and A4) for producing healthy water and soft drink in Baghdad. The results have explained that only 19 lean activities are the most influencing on SMEs leanness. Efficient manager is an important lean activity that has 58.90% level of importance. Although the four Iraqi SMEs have approximately acceptable level of leanness related the five lean dimensions, there is variation in adoption these lean dimensions activities by SMEs. SMEs management should develop a continuous improvement strategy based on utilizing SMEs’ efforts and resources to improve activities of the weaker dimension for improving their competitiveness and ensuring sustainability in the rapidly changing business environment. One limitation of this paper is the difficulty in obtaining data related to lean activities and their performance through SMEs’ processes and activities.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00272024-06-23T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0023<abstract> <title style='display:none'>Abstract</title> <p>The development of the post-industrial society requires the acceleration of the integration of the national economy into the globalized economic space. This stage is characterized by active informatization of all spheres of life in society, which requires information security and cyber protection for high-quality information provision of the country’s population, intellectualization of economic processes and prevention of destructive informational influence on the social status of the individual. It is also necessary to consider that for Ukraine the specified stage is complicated by the hybrid war with Russia, which requires strengthening the protection of information from cyber-attacks and the formation of new approaches in preserving the quality of information on social networks. Therefore, the purpose of this article is the development of scientific and methodological approaches in information security management, strengthening its social significance. This requires solving a certain range of tasks of identifying and preventing socially dangerous information based on the use of economic and mathematical methods and models. The article highlights the main directions of socialization of modern technological development and theoretically substantiates its significant impact on human consciousness and behavior, puts a person in front of serious challenges and, under conditions of hybrid warfare, requires strengthening of information security in social networks. In the work, the components of information security are supplemented, its impact on social aspects of society’s life is highlighted. As a result of the research, the authors proposed a scientific and methodical approach to the construction of a system for countering the spread of socially dangerous information in social networks. Besides, its functional elements are highlighted. A method of combating the spread of harmful information in social networks has been developed, which solves the problem of information support of the decision-making process and includes providing the chosen and alternative options to the person making the decision, with the justification of the choice. This creates prerequisites for information support for making a well-founded management decision.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00232024-06-23T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0026<abstract> <title style='display:none'>Abstract</title> <p>The application of the lean principle can play an important role in enhancing the competitiveness and performance of small and medium-sized enterprises (SMEs). The lean principle can reduce the cost of production, maximize resource optimization, and enhance the firm’s ability to provide superior value to the customers. The empirical research studying the effect of the implementation of lean principles in the SMEs has been widely studied. The researchers have used varying amounts of contextual variables as antecedents and consequences of the lean principle implementation in SMEs. The purpose of the present research study is to address this gap by reviewing the literature on Lean implementation in SMEs with a focus on finance-related variables as antecedents and consequences. To achieve the purpose of our study, the current research has employed a systematic literature review as a methodology to collect the studies from academic databases of ABI/INFORM world, Taylor &amp; Francis, Emerald, Sage, Inderscience, Premier, ScienceDirect, and Scopus Business Supplies. The results of the study have yielded insight and knowledge into four different themes. Finally, an area for future research has also been developed.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00262024-06-23T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0017<abstract> <title style='display:none'>Abstract</title> <p>With the concept of Industry 4.0 production processes are moving towards autonomy and intelligence. Technologies equipped with artificial intelligence (AI) are involved into processes that are more and more digitized. Collaborative technologies are a feature of discrete processes. The automotive industry has achieved many successes in the process innovation towards smart factories. Other plants, such as smelters or coal mining are also striving to develop smart manufacturing with integrated computer systems to support processes. A continuous production is different from a discrete or batch production. Industry 4.0 concept is focused on discrete production (with high level of automation and robotization of manufacturing) meanwhile there is a gap in implementation of these approach in the continuous production. The objective of the publication is to prepare and design the integrated computer management system based on processes realized in coal and steel manufacturing. Coal and steel production are key elements in a chain of any industrial manufacturing e.g. automotive or machinery engineering. These processes are crucial in building of smart value chain. In our paper we present the structure of processes for the continuous production. Based the processes model we proposed the next steps to build the smart manufacturing for continuous production.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00172024-06-23T00:00:00.000+00:00https://sciendo.com/article/10.2478/mspe-2024-0025<abstract> <title style='display:none'>Abstract</title> <p>Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/mspe-2024-00252024-06-23T00:00:00.000+00:00en-us-1