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.
