The objective of this study was to develop an integrated Quality Control 4.0 model for ready meals and canned meat production. The research was conducted as an industrial case study based on a 2024 supplier quality dataset, an inventory of control devices used in the production system, and a structured literature review on Food Quality 4.0 and ready-meal safety. The dataset included an aggregated total of 3,939 deliveries, 209 non-conforming raw materials and 209 complaints. Visible supplier-level records included 58 coded suppliers, 3,572 deliveries and 188 non-conforming raw materials. Descriptive statistics, Pareto analysis, supplier non-conformity rates and technology-function mapping were used. The results showed that the overall non-conformity rate in the aggregated dataset was 5.31%. The dominant defect descriptors were appearance non-conformity (110 cases), fat within the +2% tolerance zone (41 cases), exceeded fat content (38 cases), and odour/colour non-conformity (31 cases). Supplier risk was concentrated: the five suppliers with the highest number of non-conforming materials accounted for 59.0% of visible supplier-level non-conformities. The proposed model integrates raw material inspection, composition standardisation, thermal-process validation, hygiene control, packaging inspection and KPI-based decision support. The model provides a practical framework for moving from reactive quality control towards data-driven quality assurance and improvement in meat-based convenience food manufacturing.
This paper is conceptual and exploratory; it does not provide direct empirical measurement of firm-level B2B purchasing behaviour. It develops a sector-specific framework explaining the paradoxical resilience of construction activity in the dry construction distribution channel across selected Central and Eastern European (CEE) countries – Slovakia, the Czech Republic, Poland and Hungary – during the inflationary period 2021-2024. Drawing on dynamic capabilities theory, the resource-based view and the supply chain disruption literature, the framework theorises that when an inflationary shock and supply chain disruption occur simultaneously, the primary B2B supplier-selection criterion in the dry construction distribution channel temporarily shifts from price to product availability, granting distributors with strong inventory positions a temporary bargaining and competitive advantage. The empirical component is restricted to a descriptive analysis of Eurostat macroeconomic indicators (HICP, construction production index, construction PPI) for 16 country-year observations. These aggregated national-level indicators serve only as a macroeconomic backdrop and do not measure dry construction distribution behaviour directly; no inferential statistical model is reported. Four propositions are derived and presented as premises for future empirical research using surveys, case studies and longitudinal designs. The framework offers conceptual guidance for distributors formulating inventory strategies under shortage conditions and for manufacturers designing distribution-partnership mechanisms.
Production systems operating in Industry 4.0 environments are increasingly exposed to disturbances that affect process stability, performance, and managerial decision-making. The growing complexity and digital integration of production processes require structured approaches that go beyond reactive disturbance handling and support process-oriented management. In this context, artificial intelligence (AI) is increasingly used as a decision-support mechanism to enhance data interpretation and managerial responses. This article aims to examine how AI-supported process management tools can be used to handle disturbances in Industry 4.0 production systems. A structured qualitative coding methodology is proposed and applied to systematically identify disturbances, corresponding management actions, and disturbance-related process signals. Disturbances are classified using D-codes, management responses using A-codes, and observable indicators using signal codes, forming an integrated analytical framework for process-level analysis. Empirical data collected from real production systems were analyzed using the proposed coding approach. AI-supported data processing was used to assist pattern recognition, signal interpretation, and the linking of disturbances with management actions, without replacing human judgment in decision-making. The results provide an empirically grounded classification of disturbances and process management tools relevant to digitally integrated production environments. The study contributes to production engineering and management literature by offering a transferable methodological framework for analyzing disturbance handling in Industry 4.0 production systems. From a practical perspective, the findings demonstrate how basic process management tools, supported by AI-based decision-support, can enhance managers’ ability to identify, interpret, and respond to disturbances in complex production processes.
This work proposes an improved methodology for evaluating the abrasivity of particles in accelerated tribological tests, extending a previously developed approach to materials testing under friction against loose abrasive. Instead of a single scalar sharpness measure, a micromechanically interpretable triplet of geometric descriptors {hmax, βmax, ρmin} is proposed–the protrusion height, the tangent rotation angle across the peak, and the minimum radius of curvature along the contour, computed from the analytical curvature of the elliptic Fourier reconstruction. This triplet is combined into a single dimensionless index κ* (effective cutting curvature index) that preserves the micromechanical interpretation of its components. On a sample of n = 200 silicon carbide 64C particles (50 particles at each of four test stages: 0, 250, 500, and 825 min in the steel 40/SiC 64C system), hmax is shown to be the dominant component of the triplet with respect to the test stage (Spearman ρ = −0.51, p < 10-13); the combined index κ* yields ρ = −0.46 (p < 10-10); the canonical SPLm parameter, implemented via elliptic Fourier reconstruction with N = 50 harmonics and piecewise-linear approximation of the peaks, yields ρ = −0.48 (p < 10-12) on the same sample. The advantage of the triplet and the combined κ* over SPLm lies in the transparent micromechanical interpretation of their components. A population balance model is constructed for the evolution of the log-normal moments of the shape parameters and coupled with the kinetics of wear intensity I(τ) as an a posteriori description of the two-scale structure of the kinetics. A direct fit of the kinetic data (33 intervals of 25 min duration) yielded a characteristic relaxation time τc = 24 min and, accordingly, an optimal duration of accelerated tests τ* = τc ln20 ≈ 72 min under the 5% criterion of |dI/dτ|0. The conclusions are directly substantiated for the investigated steel 40/SiC 64C system; verification on other counterbody materials and abrasives is the subject of further work. The obtained result increases the productivity of laboratory screening of materials and coatings and has direct value for production engineering systems.
The article deals with the issue of modeling and motion control of a mobile wheeled robot at low speeds. Attention is focused on the basic methods of mobile robot control, namely synchronous steering, Ackermann steering, and differential drive control. The main emphasis is placed on differential drive control, which is suitable for small mobile robotic platforms due to its design, good maneuverability, and ability to rotate in place. The article describes the kinematic and dynamic model of a mobile robot, taking into account quantities such as position and orientation. The proposed solution also includes the design of a control loop that enables the control of the angular velocities of the wheels and the tracking of a desired trajectory. The proposed simulation model was created in the MATLAB/Simulink environment and divided into subsystems describing kinematics, dynamics, control, and velocity transformation. The practical part of the article is devoted to the structural design and implementation of an experimental prototype of a mobile robot. The result is a functional prototype suitable for verifying the principles of mobile robotics, regulation, and motion control under real conditions.
Green procurement has become increasingly important in the post-COVID-19 era as governments and organisations strive to strengthen supply chain resilience while advancing sustainability goals. The purpose of the review was to examine post-COVID procurement trends, describe their drivers and obstacles, analyse their theoretical applicability, and identify sector-specific insights. This study conducted a PRISMA 2020 systematic literature review to examine the post-pandemic adoption of green procurement in Malaysia, benchmarked against India, Indonesia, Brazil, and South Africa, covering the period from 2019 to August 2025. Following rigorous inclusion, screening, and quality assessment procedures, 20 peer-reviewed empirical studies were retained for analysis. The findings show that 55% of the reviewed studies (11/20) directly addressed post-COVID procurement issues, and only 40% (8/20) used theoretical concepts such as Institutional Theory, the Resource-Based View, the Technology Acceptance Model, or ISO 20400. The literature was dominated by sectoral public procurement, education, and manufacturing, and healthcare and logistics have not been well explored. Malaysia is performing moderately, driven by the private sector’s dominance in digitalisation and the use of ISO-based practices; however, it is worse off than India and Brazil in applying these in the public sector, in policy consistency, and in institutional cohesion. The thematic analysis identifies six reorganised themes: institutional preparedness and governance capacity, policy and regulatory drivers, technological and digital facilitation, sectoral execution practices, adoption barriers, and post-COVID adaptive competence. This review addresses the main gaps in providing a comparative, post-COVID, theory-informed synthesis of green procurement in the Global South. It also identifies gaps in theoretical synthesis, sectoral balance, and resilience-focused analysis. The research offers practical implications for policy-makers and further develops the academic literature by making the case for more robust behavioural, institutional, and systems-based frameworks in future procurement studies.
The study investigated the role and effectiveness of improvement projects (IPs) which have been implemented within the Continuous Improvement (CI) framework in Polish manufacturing companies. Although CI practices are widely recognized worldwide, little is known about how such projects are organized and managed in Poland. A survey has been conducted using computer-aided telephone interviewing (CATI) among 70 managers and quality specialists from manufacturing firms to address this gap. The research examined the types and frequency of IPs, project management methodologies (PMMs), the presence of project portfolios, and the impact of critical failure factors (CFFs) on project outcomes. The results show that projects focused on quality, efficiency, and waste reduction are the most common, whereas Six Sigma and 5S initiatives are less frequently applied. Organizations with higher CI maturity tend to engage more actively in improvement projects and use PMMs more consistently. In practice, tool-based approaches, such as project schedules or charters, are preferred over more formalized methodologies, like PMI or PRINCE2. The findings also reveal that operational barriers – especially insufficient engagement of project leaders – undermine the success of CI initiatives. This research offers the first empirical evidence on CI-related projects in Polish manufacturing, highlighting both enablers and barriers, and provides practical recommendations for managers seeking to strengthen the strategic impact of their improvement efforts.
The article is based on a systematic literature review on operational decision support in production and technological information processing. The content analysis aimed to examine how modern information processing systems are integrated into decision-making models in the context of production operations. The literature review follows the PRISMA method, designed to ensure transparent reporting of systematic reviews and meta-analyses. The article covers the following stages: literature search, thematic selection, and quantitative and qualitative analysis of the collected literature. Based on the bibliometric analysis, the literature can be divided into three interrelated thematic areas. The first thematic area concerns operational decision support in production planning, scheduling, capacity allocation, and production control. The second thematic area refers to technological information processing, including the representation of process knowledge through CAD model integration and the interpretation of technical documentation. The third thematic area focuses on tools that have gained increasing importance in recent years in the broadly understood industrial sector: machine learning, digital twins, and Industry 4.0 infrastructure. The literature analysis indicates that a major research gap lies not in the absence of individual decision-support methods, but in the limited integration of operational and technological information within coherent models supporting decision-making under real manufacturing conditions.
This study proposes a risk management framework for an IoT-based Social Manufacturing system using the House of Risk (HOR) method. In a social manufacturing system, all distributed and collaborative production activities are highly dependent on real-time internet connectivity. In this study, 27 risk events and 22 risk agents were identified through observation and interviews, as well as SCOR-based process mapping. The method used is HOR Phase 1 and HOR Phase 2 analysis. In HOR Phase 1, which calculates the Aggregate Risk Potential (ARP), it was found that 12 risk agents predominantly contributed 78.52% of the total system risk, with the highest ARP values related to production planning errors (1872), production delays (1813), and inaccurate customer order identification (1148). In HOR Phase 2, 21 preventive actions were evaluated using the Effectiveness-to-Difficulty Ratio (ETD), yielding a priority-based mitigation sequence. The most effective actions include more careful production planning, improved coordination with SMRs, early identification of production bottlenecks, comprehensive recording of customer requests, and selective SMR qualification. This research contributes to the risk management literature by applying HOR to Industry 4.0 and provides practical guidance for risk mitigation in IoT-based social manufacturing systems.
Fuel consumption efficiency is one of the key areas for evaluating the operational performance of vehicle fleets in transport enterprises. Rising operating costs and the need for rational resource management are making the analysis of data recorded during real-world vehicle use increasingly important. This article aims to comprehensively assess the operational efficiency of a vehicle fleet based on a case study of a selected transport enterprise. The analysis also includes the identification of vehicles characterised by unfavourable fuel consumption parameters. As part of the study, the consistency of input data on fuel consumption obtained from the CAN bus, invoices and fuel level sensor readings was assessed against reference data from the telematics system. On this basis, the data source demonstrating the highest level of consistency was identified and used in further analysis. Efficiency was assessed based on total and unit fuel consumption, CO2 emissions and the costs of excess fuel consumption. The applied criteria made it possible to identify vehicles with elevated fuel demand and suboptimal operational parameters. In addition, a scenario analysis was conducted to estimate potential savings and CO2 emission reductions achievable by improving the efficiency of the vehicle identified as requiring further inspection. This approach supports fleet management by facilitating the assessment of potential reductions in operating costs and CO2 emissions. The results indicate that targeted efficiency improvements may reduce fuel consumption and environmental impact without the need for immediate vehicle replacement.
This study investigates the influence of the innovation ecological network (EN) on inbound open innovation (IOI), outbound open innovation (OOI), and sustainable business model innovation (SBMI). Drawing on absorptive capacity theory, the study further examines the moderating role of absorptive capacity (AC) in the relationships between open innovation (IOI, OOI) and SBMI. Data were collected through a Google Forms survey, producing 506 valid responses from middle‑ and senior‑level managers in manufacturing enterprises in Vietnam. Partial Least Squares Structural Equation Modeling (PLS‑SEM) was employed to assess both the measurement and structural models. The findings show that EN positively influences IOI, OOI, and SBMI. Both IOI and OOI exert significant positive effects on SBMI. Moreover, AC significantly moderates the relationship between OOI and SBMI, whereas its moderating effect on the IOI–SBMI relationship is not supported. These results extend the theoretical foundation of SBMI by demonstrating that SBMI is shaped by the innovation ecological network, inbound and outbound open innovation, and firms’ absorptive capacity. The study concludes with recommendations and highlights limitations that provide avenues for future research.
In the absence of a reservoir pressure maintenance (RPM) system at Field X, a critical increase in water cut to 92.6% has been observed, with current production at 72.4% of the initial recoverable oil reserves. Such a high water cut at a stage that does not correspond to full production suggests the presence of atypical mechanisms. This study aimed to comprehensively analyze the causes of the abnormal water cut using modern diagnostic methods, including geophysical well logging (GWL), reservoir pressure monitoring, hydrogeochemical analysis of associated waters and detailed laboratory studies of core samples. The analysis revealed that the primary cause is a man-made breach in the production column of Well #4, resulting in the overflow of highly mineralized water from the overlying Albian-Cenomanian aquifer into the productive J-XI formation. Petrophysical studies revealed high reservoir heterogeneity. However, the oil displacement coefficient by water was 0.53 for highly permeable formations, ruling out low oil recovery as the main cause of water flooding. Key evidence of the human-made nature of the problem includes the abnormal increase in reservoir pressure, from 23.9 MPa in 2022 to 25.5 MPa in 2023, as well as the presence of sulphate ions in the production water, which is characteristic of the Albian-Cenomanian complex. A set of practical recommendations has been developed, including the urgent repair and insulation of Well #4, the introduction of a casing integrity monitoring system and the adaptation of the development strategy. The results emphasize the importance of monitoring the technical condition of the well fleet to ensure the effective late-stage development of similar fields. The study demonstrates that integrating this diagnostic methodology into a production management system enables proactive risk mitigation, supports decision-making for well intervention, and contributes to optimizing the overall production strategy in mature fields.
Production systems are increasingly required to maintain high quality standards while simultaneously reducing operational costs and ensuring production efficiency. This study presents a simulation-based approach to production process improvement considering the quality–cost trade-off. The main objective of the research was to develop a decision-support framework enabling the analysis of how processing time and repair policies influence production system performance under varying operational conditions. A discrete-event simulation model was developed in the FlexSim environment to represent selected stages of a manufacturing process, including processing operations, quality inspection, and rework activities for nonconforming products. The proposed approach incorporates the relationship between processing time and product quality, enabling the evaluation of multiple production scenarios characterized by different operational strategies and repair limits. The study applies a multi-criteria analysis focused on balancing quality-related outcomes and operational profitability. Simulation results were evaluated using Pareto-based analysis to identify efficient production configurations characterized by different levels of product quality, production cost, and economic performance. The obtained results indicate that extending processing time may improve product conformity; however, it can simultaneously reduce operational efficiency and increase production costs. Moreover, excessive rework intensity does not necessarily generate proportional economic benefits. The proposed approach may support managerial decision-making in manufacturing systems by enabling the identification of production strategies adapted to different quality and cost priorities.
This paper applies a temporal Hawkes process to the analysis of short-term clustering in crime event data. The study uses publicly available records from the Crime Map of the Police of the Czech Republic and focuses on events of general crime in the Ostrava region during the period 2016-2025. A homogeneous Poisson process is used as a baseline model, while the Hawkes process with an exponential kernel is used to capture possible self-exciting behavior in the event sequence. The parameters are estimated by maximum likelihood and the models are compared using log-likelihood, AIC, BIC and residual diagnostics based on the time-rescaling principle. The results show that the temporal Hawkes model provides a substantially better description of the data than the homogeneous Poisson model. The estimated branching ratio is approximately 0.143, which indicates the presence of a measurable but not dominant self-exciting component. The estimated excitation half-life is about 1.11 hours, suggesting that the detected temporal dependence is mainly short-term. The results demonstrate that even a simple one-dimensional Hawkes model can provide an interpretable description of temporal clustering in crime data. At the same time, the analysis highlights the limitations of a stationary temporal model and motivates future extensions including non-stationary background intensity and spatial information.
This paper deals with the structural design of a belt conveyor equipped with a handling mechanism. The research was carried out within the framework of the KEGA 004TU Z-4/2024 and VEGA1/0302/26 projects and is focused on the design and analysis of the supporting structure in terms of its strength and operational reliability. Particular attention is paid to the methodological procedure for determining the distribution of individual loads arising at critical points of the structure during the operation of the conveyor and the handling mechanism. The paper also identifies the decisive loading conditions and their influence on the dimensioning of the supporting elements of the device. The obtained results contribute to the optimization of the structural design and to increasing the operational safety of the proposed system. The structure was found to be significantly overdesigned with a safety factor of 41.8. A subsequent optimization reduced the weight, while maintaining a safe stress level.
Among the several machining techniques used in mold and die manufacture, electro-discharge machining (EDM) is a unique type of machining. One of the most recent developments in thermo-electric processes is nano powder-mixed electric discharge machining (NPMEDM), which involves suspending metallic nano powder in a dielectric. The purpose of this work is to investigate the effects on surface topography (outputs) of Al 2 O 3 -SiO 2 nano powders mixture, which were selected from several mixtures or individual nano powders, after reviewing previous research, as nano powder concentration and nano powder mixing ratio. At concentrations of (1, 2, 3, 4, 5 g/l) mixed into the dielectric fluid of EDM on stainless steel 304L, with a mixing rate that varies for each mixture from 30% to 70%, achieving 100% for the mixture as a whole with an interval of 10% at each level. Other electric parameters (inputs) include pulse on time (50, 100, 150, 200, 250 µs), current (20, 25, 30, 35, 40 A), and pulse off time (25, 50, 75, 100, 125 µs). Response Surface Methodology (RSM) has been utilized to determine the mathematical relationship between the input and output and the NPMEDM process. White Layer Thickness (WLT), Heat Affected Zone (HAZ), and Surface Crack Density (SCD) are used to gauge the effectiveness of the machining process. The experiments were conducted using 50 experiments using the Design Expert 11 program and Central Composite Design (CCD) methodology. The results obtained were analyzed to determine the extent of the influence of the inputs on the response of the stainless steel 304L surface; we received an improvement in surface response, decreasing WLT, HAZ, and SCD by 5.8%, 17.7%, and 2.6%, respectively.
Preventive (proactive) quality management requires monitoring process behavior over time and early detection of changes that may lead to future quality issues. Utilizing both classic and advanced Statistical Process Control (SPC) tools provides a more comprehensive picture of process behavior, identifying both large and small, incremental changes, and enabling proactive remedial action, which is a key component of quality prevention. The aim of this study was to demonstrate how combining different SPC tools can support preventive quality management by assessing current process stability and capability, identifying early warning signals, and predicting future process behavior. The study was conducted using data from a PCB copper plating process regarding copper layer thickness. The dataset of thickness measurements was analyzed using descriptive statistics, outlier detection, and normality tests. Classical control charts, including Individual and Moving Range charts, XmR Target USL/LSL, and XmR Trend charts, were applied together with advanced, special control charts such as MA, EWMA, and CUSUM. In addition, a multivariate Hotelling T & sup2; control chart was used to assess the combined effect of key process parameters, and process capability indices (Cp, Cpk) were calculated to evaluate compliance with specification requirements. The results confirmed that the analyzed process was statistically stable, predictable, and quality capable. At the same time, the advanced charts revealed temporary drifts and a slight downward tendency that were less visible in classical control charts. The study showed that integrating classical and advanced SPC tools improves the detection of both evident and subtle process changes, strengthens predictive insight into future process behavior, and provides effective support for preventive quality management.
Electric Discharge Machining (EDM) is known for its ability to machine hard and brittle conductive materials, as it can melt any electrically conductive substance irrespective of its hardness and geometric intricacy. Machining technologies have progressively advanced from a basic tool and die manufacturing process. The purpose of this paper is to investigate the performance in achieving the maximum material removal rate (MRR) while minimizing surface roughness (Ra) and tool wear rate (TWR) together and to optimize the parameters' operation utilizing ANOVA, RSM, and the hybrid BPNN-PSO. The AISI 304 L stainless steel is machined under different parameters, such as peak current (IP), pulse-on time (Ton), and pulse-off time (Toff). The ANOVA was used to identify significant components in single-objective optimization, whereas the Response Surface Methodology (RSM) was used for multiple-objective optimization of the parameters. The hybrid BPNN-PSO algorithm was used in single and multiple-objective modes to improve the parameters that were worked on in two scenarios. The results showed that the electrical current had a 75% impact on the output. From the analysis, the hybrid BPNN-PSO result achieved significant improvement over both the ANOVA and the RSM algorithms. More specifically, IP was optimized by 1.25 times on the quantity of MRS technique, whereas Ton was 58.18 mu s in the second scenario. Based on these data, the second scenario is capable of achieving even better results in terms of optimization.
The availability of public transport is one of the most important strategic goals of transport companies in terms of mobility, quality, safety, and environmental sustainability. Satisfying customer requirements for mobility and sustainability, while also achieving an acceptable level of environmental protection and quality of life, is the main goal of public transport. The breakdown of public transport vehicles represents a fundamental barrier to meeting the goals of public transport sustainability. The main goal of the article is to optimize the maintenance process in a transport company in Slovakia with an orientation to cost reduction from the perspective of the ineffective maintenance process. An important role in the elimination of faults is played by the maintenance process of public transport vehicles and the setting of a maintenance strategy in terms of minimizing costs and the number of vehicle faults. The results of economic analyses pointed to increasing costs for the maintenance process, increasing vehicle kilometers, increasing maintenance costs per vehicle kilometer, and increasing spare parts costs for public transport vehicles. Improving the economic and technical parameters of public transport vehicles makes it possible to increase the overall performance of the transport company in Slovakia. The proactive maintenance enriched with autonomous and predictive elements creates the basis for maintenance strategy in public transport with environmental, social, and economic sustainability.
Fire fighting with water is the most common method of fire suppression. In reference [1], the author states that 1 ml of water can extinguish a fire with a volume of 50 dm3. Although many fire detection and protection techniques are available for buildings, fires in residential buildings are still considered to be a significant risk to the occupants of apartment buildings. This paper presents a proposal for an approach to fire fighting in residential buildings, with particular emphasis on the need for water, a strategy for fire fighting by firefighting units using a specific example, and with a proposal for the possible application of modern technologies to support rescue and firefighting work. It addresses the questions of how the type of residential building influences the tactical approach in a fire, which techniques and intervention strategies are most effective for rapid fire location and occupant evacuation, how data, simulations and smart technologies can be used to support decision-making during intervention, what are the main failure points (critical factors) in real residential building interventions. Providing fire protection in residential buildings is a complex issue that requires cooperation between owners, building managers and professional companies. It is important to ensure regular inspection and maintenance of the systems and compliance with all applicable regulations in the field of fire protection, e.g. Act No.314/2001, Coll. 121/2002 Coll. on fire prevention, Decree of the Ministry of the Interior of the Slovak Republic No. 94/2004 Coll., STN 730802 Fire safety of buildings. The article concludes with the application of the HRA method, which is considered a systematic tool for identifying, assessing, and classifying risks arising from human error during critical intervention activities. In the case of firefighter intervention, it helps improve safety, efficiency, and decision-making under stressful and time-pressured conditions when fighting fires in residential buildings.
