This study addresses the scheduling problem in a single-machine system with variable processing speed. The performance of the Earliest Due Date (EDD) rule, operating at a fixed maximum speed, is compared with a Reinforcement Learning (RL)-based approach capable of dynamically adapting both processing speed and job selection. The analysis considers both temporal performance (tardiness) and sustainability (energy consumption). Simulation results, supported by statistical analysis, show that the RL approach achieves a significant reduction in total energy consumption compared to the EDD rule. Regarding temporal performance, analysis of variance reveals that the chosen policy significantly affects the number of tardy jobs, with an effect that depends on the tightness of job deadlines, whereas the overall average tardiness across all jobs does not differ significantly between the two policies. These findings highlight the potential of adaptive RL-based policies for more sustainable resource management, demonstrating the ability to achieve substantial energy savings while maintaining competitive temporal performance and dynamically managing the trade-off according to deadline pressure.

Information asymmetry and the misreporting of operational data severely challenge contemporary supply chains (SCs), leading to inefficiencies, disruptions and performance deterioration. Traditional anomaly detection techniques typically focus on detecting downstream consequences rather than proactively identifying misreporting behaviors. To address this gap, this study introduces an interpretable AI-driven framework, employing the Isolation Forest algorithm, for the early detection of misreported data in multi-tier SCs. Central to this approach is the introduction of Engineered Cross-Stage Features (ECSFs), designed to capture relational discrepancies across different SC echelons. The framework was evaluated using a synthetic three-tier SC dataset, comprising both standard operational conditions and artificially perturbed data points representing misreporting behaviors and different scenarios of information asymmetry among SC actors. In a comparative assessment against Hotelling’s T2 and an Autoencoder, our ECSF-based Isolation Forest framework demonstrated superior efficacy, highlighting its potential as a practical solution for proactively identifying and mitigating misreporting in complex SC environments.

In today’s environment, characterized by high complexity and volatility of demand, responsiveness, quality, and timeliness in the transmission of information between all parties involved in Supply Chain operations, are critical aspects to manage. In this context, the most successful companies have developed an integrated view of the Supply Chain to improve its efficiency. The realization of these objectives is achieved through adopting Supply Chain management methods and tools appropriate to their operations, with a view to continuous improvement through data analysis and forecasting. The difficulty lies in intercepting and organising data from disparate sources, multiple data sets provide incomplete information that inaccurately represents the performance and service levels received by suppliers and offered to customers, caused by the competitive nature of different companies in wanting to keep their information confidential. For this reason, this work proposes an ontological Supply Chain model with a governance element that enables the information exchange, preventing misreporting behaviour by different companies and optimising the parameters of the entire Supply Chain. In addition to the definition of all major incoming and outgoing information flows that characterise the relationships and performance of the Supply Chain actors as individual elements and as a whole.

To stay competitive, modern market scenarios are forcing a radical shift in the manufacturing concept, focusing companies’ attention on customer satisfaction through increased product customization and quick response strategies. Significant progress has been made in the field of Industry 4.0 technologies, but there is still an open gap in the literature regarding methodologies for efficiently managing a manufacturing system’s available productive resources. Spearman et al. proposed the CONtrolled Work-In-Progress (CONWIP) production logic, which allows controlling Work-In-Progress (WIP) in a production system while monitoring throughput. However, in order to face with the increased variability that enters into the production system, an affordable performances estimation tool is still required. Taking advantages of the recent innovation in the field of machine learning, this paper contributes to the development of a tool for estimating the performance of a production line using a deep learning neural network. The results demonstrated that the proposed estimation tool outperforms the current best-known mathematical model when estimating the throughput of a CONWIP Flow-Shop production line with a given processing time distribution and WIP value.

Considering a Flow Shop production line in an Industry 4.0 setting where the Cyber-Physical System (CPS) and Internet of Things (IoTs) can be deployed, a newly Performance-based Decentralised Dispatching Rule (PDDR) is proposed. It combines known dispatching rules with the knowledge of the monitored production system state. The goal is to provide a novel dispatching rule based on production line performance oversight. The governance system considers the machine condition in terms of machine utilisation. Regarding the assessment scenario, the proposed rule has been tested and compared with the well-known Short Processing Time (SPT) and the First-In-First-Out (FIFO) rule in a higher generality way by taking into account unforeseen events that may occur in production (such as breakdowns, potential rework, micro-stops, and unplanned machine setups). The simulation results showed interesting results where the flexibility of this rule, as well as its practical use with real hypotheses are its main advantages.

The paper presents advanced issues in modelling and simulation of complex packaging line. In particular, we developed a theoretical model of a line with two machines and a buffer, which is a simplified version of a real packaging line from Tetra Pak company. The paper reports also about simulation results that confirm theoretical supposals.