This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0.

This research communication aims to present three mathematical methods for analyzing inventory management in dynamic supply chains, starting from the basic definition in differential equations for inventory levels, which relates with production and demand rates. Initially, the study adopts a systemic perspective to examine the role of energy within a production–inventory system. Subsequently, the analysis focuses on inventory dynamics under parameters expressed in complex variables, with the aim of quantifying fluctuations in a generic production system and demonstrating the influence of inventory variation rates on system behavior. Finally, the investigation addresses the impact of variable capacity on production system inventories, drawing on analogies with corresponding physical systems to support the analysis.

Agri-food supply chains have experienced notable changes in recent decades, with tomatoes (Solanum lycopersicum) maintaining their status as a key global crop in terms of both production and consumption. These supply chains comprise a complex network of stakeholders—including producers, processors, distributors, and retailers—who collectively ensure the delivery of tomatoes from farms to consumers. This study develops mathematical models of agri-food tomato supply chains (AFTSCs) and examines their behavior through stability analysis and dynamic simulations based on a compartmental approach. Furthermore, the environmental impact is evaluated using a sustainability index, to which the waste diversion rate is introduced. This metric is defined as the proportion of diverted waste (i.e., materials recycled, reused, or composted) relative to the total waste generated, thereby enabling the quantification of sustainability performance within the system. Finally, a sensitivity analysis is conducted on the proposed dynamical models to validate and reinforce the findings.

The optimal placement of robotic manipulators within industrial workstations is a critical problem that directly affects task feasibility, accessibility, and operational efficiency. Improper base positioning can lead to joint saturation, reduced manipulability, and limited workspace utilization. This work presents an optimization framework for determining the optimal base placement of robotic manipulators by maximizing a joint-centering performance index based on the κ-index, which quantifies the proximity of joint variables to their allowable limits. The proposed methodology integrates geometric accessibility constraints with a constrained optimization formulation to ensure feasible robot configurations within the workspace. Three optimization strategies—constrained nonlinear programming, gradient projection methods, and genetic algorithms—are evaluated and compared in terms of solution quality and computational performance. Numerical simulations are conducted using a planar 2-DOF manipulator to illustrate the proposed framework and to analyze the influence of workspace geometry on optimal base placement. Additionally, an industrial case study involving the ABB IRB 120 robotic manipulator is presented to assess the practical applicability of the proposed approach. The results demonstrate that the optimization framework improves joint distribution within the allowable limits and enhances robot accessibility across the task workspace. The proposed method provides a practical tool for intelligent workstation design and robotic cell layout optimization in modern industrial environments.

Speed of convergence and robustness against disturbances affecting a dynamical system are of great importance in real-life applications. To satisfy these requirements, another approach called predefined time control has been developed in the last decade, which ensures the convergence of the states of a class of dynamical systems to the desired reference value in a time set by the control designer at will. However, previously proposed predefined time control algorithms have drawbacks that need to be tackled. For this reason, the authors present another form of predefined time control algorithm that can be coupled with any observer (in the case where not all the states of the system can be measured) or any other robust controller to robustify the system against matched disturbances. The development shows that the necessary form of the control law can be obtained using basic integration and backstepping. The performance of the control law is demonstrated through a series of numerical simulations utilizing the mathematical model of a permanent magnet synchronous motor (PMSM). The results show that the proposed control algorithm is, indeed, robust against any matched disturbance, and the convergence of the system states is ensured independently of any initial condition in the time set by the control designer at will.

Recent global disruptions have exposed the fragility of tightly coordinated supply chains, particularly under transportation and information delays, motivating the need for analytical tools to assess their stability limits. This study analyzes a two-echelon supply chain system to determine how delays affect stability and performance, with an emphasis on the role of feedback coordination. A continuous-time delay-differential modeling framework was developed to examine both uncoupled and coupled configurations. Stability is analyzed through characteristic equations, and explicit closed-form expressions for the critical delay threshold are derived as functions of the coupling gain and shipment rate. The uncoupled system is shown to exhibit delay-independent marginal stability but lacks the ability to regulate downstream inventory. In contrast, the coupled system achieves inventory regulation but introduces delay-dependent stability with a critical delay, beyond which oscillations grow unbounded. A key result revealed an inverse relationship between coupling strength and delay tolerance, highlighting a trade-off between responsiveness and robustness. An optimal control formulation further demonstrates that the stability constraints limit the achievable performance. These findings provide a theoretical explanation for the vulnerability of just-in-time systems and offer practical guidelines for resilient logistics design, enabling supply chain practitioners to quantify stability margins and balance coordination efficiency with robustness to transportation delays.