Digital Twins (DTs) enable real-time monitoring and adaptive decision-making, yet they are often developed through expert-driven approaches that only partially reflect real operations. Process Mining (PM) offers an alternative by extracting process knowledge from event logs, supporting DT creation, validation, and continuous updating. Despite growing interest, PM-DT research remains fragmented. This Systematic Literature Review (2020-2026) examines sectors adopting PM for DTs, the PM techniques used, and the modelling approaches combined with them. Results show the prevalence of generic frameworks, with manufacturing as the most mature field; Process Discovery is the dominating technique, while Conformance Checking is less explored. Discrete Event Simulation is the main modelling approach, complemented by Agent-Based Modelling, Petri Nets, and System Dynamics. The review also focuses on limited supply chain applications and frames PM-DT contributions in relation to the pillars of Supply Chain 5.0, highlighting directions for future research and practical development of data-driven PM-DT integrated systems.

Modern supply chain management requires balancing economic performance, operational efficiency, and environmental sustainability among dynamic demand uncertainties. Traditional analytical models often fail to capture the stochastic nature of these complex, multi-echelon networks, driving the need for advanced data-driven methodologies. This study investigates the application of Reinforcement Learning to address the gap in jointly optimizing procurement, production, and replenishment decisions while explicitly minimizing carbon emissions and maintaining high service levels. A simulation-based approach was developed to model a three-echelon juice supply chain, comprising two suppliers, one manufacturing factory, and five regional warehouses facing stochastic seasonal demand. Two algorithms, Proximal Policy Optimization and Advantage Actor-Critic, were trained and evaluated within this continuous, multi-objective environment. The computational results reveal a significant divergence in policy stability and performance. The Proximal Policy Optimization algorithm demonstrated superiority, converging to a low-variance strategy that generated approximately 35% more profit and reduced total accumulated operational costs by 33%. Furthermore, it successfully optimized the distribution of goods, achieving a superior customer fulfilment rate while simultaneously operating with lower inventory levels. On the other hand, the Advantage Actor-Critic agent achieved lower carbon emissions.

The fashion industry has a significant environmental impact. To minimize its effects, circular business models like fashion renting emerged. However, simulation models that combine supply chain operations with customer behaviour to assess environmental sustainability are still underexplored. This study develops a simulation model to evaluate how fashion renting supply chains perform when customer behaviours are integrated. Using a real-world use case, this research compares six scenarios with different rental periods, transport frequencies, and truck types. The results indicate that higher transport frequencies increase the share of renting among enjoyment-driven customers but can negatively impact sustainability. Notably, the findings demonstrate that fashion renting is not inherently more sustainable than traditional buying, presenting underperforming results in some scenarios. Only specific supply chain configurations successfully lowered aggregate emissions. This research highlights the importance of aligning supply chain design with behavioural factors to achieve true sustainability.

In the face of ever-increasing complexity and global uncertainty, today’s industries are under immense pressure to develop adaptive and resilient production models. The ability to effectively manage and respond to disruptions has become not only a competitive advantage but an operational necessity. Digital Twins enables the creation of a virtual, real-time model of physical assets and processes. Through continuous data collection, a Digital Twins accurately mirrors the state and behaviour of machines, workflows, and supply chains, allowing companies to monitor, analyse, and predict operational conditions as they evolve. This research centres on a case study within the fashion industry, exploring the practical application of Digital Twins to manage and mitigate disruptions in mixed-model production environments. This work contributes to the growing body of knowledge on Digital Twin technology, emphasizing its potential as a tool for immediate operational resilience.

The Fashion Industry is one of the most polluting economic sectors, contributing to a massive generation of greenhouse gas emissions. This characteristic makes the shift towards a sustainable production and consumption model in fashion challenging and urgent. In this context, Fashion Renting emerges as a solution that may extend the product lifecycle and reduce environmental impact. Nevertheless, although different studies focus on analyzing the correlation between variables in specific scenarios, different nomenclatures are given to the same factor, and no study has comprehensively aggregated these parameters to develop a broader model. This gap makes it difficult for researchers to develop simulations that truly represent the customers’ behavior. The present paper aims to fill this gap by the development of a Systematic Literature Review. The results present a guideline explaining and correlating the different factors influencing the behavior that may drive or hinder customer intention to engage in fashion collaborative consumption. The achieved results can be used both by researchers when developing simulations to compare different approaches and scenarios in shared economy for fashion and by practitioners, when planning strategies for the business.

This paper reports a literature review on the Artificial Intelligence (AI) techniques applied in the field of production planning and scheduling and explores the synergistic integration of AI with simulation for enhancing production scheduling. Leveraging Microsoft Project Bonsai and AnyLogic, we present a case study that demonstrates the effectiveness of AI-driven simulation models in optimizing scheduling tasks. Our research highlights the potential of combining AI with traditional simulation methods. The results offer insights into the practical applications and future potential of AI in industrial automation and production management.

Industry 4.0 technologies have led to the affirmation of the Smart Manufacturing. In this paradigm Digital Twins (DTw) are defined as simulation models that are both getting data from the field and triggering actions on the physical equipment. With this paper, our aim is to extend the existing analysis of DTw in manufacturing from a technical and architecture point of view, trying to define correlations among DTw features. This work proposes a new framework, illustrating three reference architectures for the DTw that could help to define a guideline for the design and implementation of this technology. The results found are proposed as a driver for future research, aspiring to identify an architecture enabling a complete integration between DTw in manufacturing systems.

Building a simulation model is a good practice for comparing and evaluating different design alternatives. In recent years, many companies in the fashion industry have shown a great interest in simulation topics. However, there is a lack of research papers that discuss the comparison of simulation software in the context of fashion. This paper proposes an analysis of the capabilities and features of two commercial simulation software packages, Anylogic and Simio. The article presents a benchmarking analysis based on a case study in the fashion industry.

The paper deals with the development of a data-driven simulation model for the process optimization of an automatic electroplating plant in the fashion industry. Starting from the process mapping of the production process using the Business Process Modelling and Notation (BPMN standard), an object-oriented simulation model has been defined using the commercial software AnyLogic®. Finally, the model has been validated and the plant has been optimized.

HVLV environments are characterized by high product variety and small lot production, pushing companies to recursively design and optimize their production systems in a very short time to reach high-level performance. To increase their competitiveness, companies belonging to these industries, often SMEs working as third parties, ask for decision-making tools to support them in a quick and reactive reconfiguration of their production lines. Traditional discrete event simulation models, widely studied in the literature to solve production-related issues, do not allow real-time support to business decisions in dynamic contexts, due to the time-consuming activities needed to re-align parameters to changing environments. Data-driven approach overcomes these limitations, giving the possibility to easily update input and quickly rebuild the model itself without any changes in the modeling code. The proposed data-driven simulation model has also been interfaced with a commonly-used BI tool to support companies in the iterative comparison of different scenarios to define the optimal resource allocation for the requested production plan. The simulation model has been implemented into a SME operating in the footwear industry, showing how this approach can be used by companies to increase their performance even without a specific knowledge in building and validating simulation models.

Fashion is one of the world’s most important industries, driving a significant part of the global economy representing, if it were a country, the seventh-largest GDP in the world in terms of market size. Focusing on the footwear industry, assembly line balancing and sequencing represents one of the more significant challenges fashion companies have to face. This paper presents the results of a simulation-optimization framework implementation in such industry, highlighting the benefits of the use of simulation together with a finite capacity scheduling optimization model. The developed simulation-optimization framework includes the conduction of a scenario analysis that compares production KPIs (in terms of average advance, delay and resource saturation) related to different scenarios that include or not one or more type of stochastic events (i.e. rush orders and/or delays in the expected critical components delivery date).