Scope and Working Groups

The activities of the Manufacturing Modelling for Management and Control Technical Committee are devoted to promote the development of management decision-support systems in digital, resilient and sustainable manufacturing and supply chain systems in the era of Industry 4.0 based on combination of Industrial Engineering, OR and Data Science

The TC members are very active in the success of ten working groups: 

1- Working group  'Digital Supply Network Engineering and Management'

Chairs: Prof. Alexandre Dolgui and Prof. Dmitry Ivanov

The Working Group on Digital Supply Network Engineering and Management advances research and practice in the design, planning, control, and adaptation of supply networks in increasingly uncertain, interconnected, and digitally enabled environments. Modern supply chains are evolving from linear structures into intelligent, data-driven, and autonomous ecosystems that must simultaneously achieve efficiency, resilience, sustainability, and long-term viability. The WG promotes interdisciplinary research at the intersection of control theory, operations research, industrial engineering, artificial intelligence, data science, and digital technologies. Particular attention is devoted to the development of novel methods for managing disruptions, uncertainty, complexity, and systemic risks in global supply networks.

Key research themes include AI-enabled supply chain planning, control, and decision support; Digital supply chain twins and digital twin ecosystems for predictive, prescriptive, and autonomous management; Supply chain viability, resilience, adaptability, and recovery under disruptions and long-term crises; Intelligent orchestration of manufacturing, logistics, and service networks; Human–AI collaboration and autonomous decision-making in supply networks; Predictive analytics, machine learning, and generative AI applications in supply chain management; Risk analytics, disruption propagation, and resilience engineering; Control-theoretic approaches to supply chain dynamics and performance management; Sustainable, circular, and regenerative supply chain systems; Cyber-physical supply networks, industrial AI, and Industry 5.0/6.0 architectures; Platform ecosystems, supply chain ecosystems, and networked business models; Agentic AI, multi-agent systems and decentralized coordination mechanisms.

The WG seeks to establish scientific foundations and practical methods for the next generation of intelligent supply networks capable of sensing, predicting, adapting, and learning in real time. The ultimate vision is the development of viable supply chain ecosystems that leverage artificial intelligence, digital twins, and advanced control methods to ensure sustained value creation, robustness, sustainability, and societal well-being in the era of Industry 5.0 and emerging Industry 6.0 paradigms.

2- Working group 'Advanced multi-criteria applications in manufacturing and logistics'

Chairs: Prof. Lyes Benyoucef, Prof. Farouk Yalaoui

The increasing complexity, digital transformation, and sustainability requirements of modern manufacturing and logistics systems have intensified the need for advanced decision-making approaches capable of balancing multiple, often conflicting objectives. Global supply chains and industrial ecosystems must simultaneously address challenges related to cost, quality, flexibility, responsiveness, resilience, environmental impact, security, and human factors. In this context, multi-criteria modeling, multi-objective optimization, and intelligent decision-support methodologies provide essential tools for supporting strategic, tactical, and operational decisions in complex manufacturing and logistics environments.

The mission of the working group is to advance the theoretical foundations and practical applications of multi-criteria approaches for next-generation manufacturing and logistics systems. The WG promotes research on multi-objective system design and reconfiguration, production planning and scheduling, resource allocation, logistics and supply chain optimization, performance evaluation, sustainability assessment, and decision-making under uncertainty. It also explores emerging data-driven and human-centered approaches, integrating artificial intelligence, machine learning, digital technologies, and organizational aspects to develop intelligent, resilient, sustainable, and collaborative industrial systems.

The working group will focus on, but is not limited to, the following topics:

  • Multi-criteria modeling, optimization, decision-making, and intelligent decision-support methods for manufacturing and logistics systems.
  • Multi-objective design, configuration, and reconfiguration of manufacturing systems, supply chains, and logistics networks.
  • Multi-criteria performance evaluation, benchmarking, and sustainability assessment of manufacturing and logistics systems.
  • Multi-objective planning, scheduling, resource allocation, inventory management, and transportation optimization.
  • Decision-making under uncertainty, including risk assessment, resilience, robustness, and secure collaborative manufacturing and logistics networks.
  • Human-centered and data-driven multi-criteria approaches integrating artificial intelligence, machine learning, digital twins, and organizational or cultural factors in manufacturing and logistics decisions.

During last years, the working group has contributed to TC events such as:

IFAC World Congress 2020 at Berlin (Germany), IFAC-INCOM 2021 at Budapest (Hungaria), IFAC-MIM 2022 at Nantes (France), IFAC World Congress 2023 at Yokohama (Japan), IFAC-INCOM 2024 at Vienna (Austria), IFAC-MIM 2025 at Trondheim (Norway), and IFAC World Congress 2026 at Busan (South-Korea).

3- Working group ' Design and modelling of flexible and reconfigurable manufacturing systems'

Chairs: Dr. Olga Battaia, Dr. Xavier Delorme, Dr. Rita Gamberini and Prof. Manoj Kumar Tiwari

The working group investigates and develops novel modelling approaches for the design and management of reconfigurable, flexible or agile manufacturing systems. Nowadays, resilience is a key feature for manufacturing companies, which are facing more and more volatile markets and uncertain conditions. Reconfigurable Manufacturing Systems (RMSs) have been introduced for this purpose, with machine components, machines software’s or material handling units which can be added, removed, modified or interchanged as needed and when imposed by the necessity to react and respond rapidly and cost-effectively to changing requirements. In a reconfigurable manufacturing system, all the major components are modular (system, software, control, machines and process) to provide systems that can be easily scaled, converted, customized and diagnosed. Beyond resilience, the agility of RMSs also provides innovative ways to deal with new societal challenges such as sustainability (e.g. energy or life cycle management) and allows evolving toward new business models (e.g. cloud manufacturing or manufacturing as a service).

Key research topics include Conceptual frameworks to support RMS development; X-Network or Intertwined Supply Network; Optimization for the design or planning of Reconfigurable or Flexible Manufacturing Systems; Ramp-up and reconfiguration of manufacturing systems; Dynamic control and management of uncertainties in RMS; Reconfigurable digital twins; Lifecycle management of Manufacturing Systems; Impact of Industry 4.0 on Reconfigurability or Agility; Human-Robot Collaborative Manufacturing Systems.

The aim of this working group is to analyse scientific foundations and practical methods allowing RMSs to answer the emerging challenges of Industry 5.0 and support their adoption and development within the industry.

Deliverables in 2024-2026:

  • Invited Session at INCOM 2024: “Reconfigurable, Flexible or Agile Manufacturing Systems to Deal with a VUCA World” (2 parts, in total 10 papers presented)
  • Invited Session at MIM 2025: “Reconfigurability, Flexibility or Agility for Manufacturing Systems in a VUCA World” (5 papers presented)
  • Invited Session at IFAC WC 2026: “Challenges in Reconfigurable, Flexible or Agile Manufacturing Systems” (6 papers to be presented)

4- Working group "Zero-Defect Remanufacturing and Quality Management (ZDR-QM)"

Chairs: Dr. Foivos Psarommatis, Dr. Sotirios Panagou

The Working Group on Zero-Defect Remanufacturing and Quality Management (ZDR-QM) advances research and practice in the modelling, management, control, and optimization of remanufacturing and circular manufacturing systems. As industries transition towards more sustainable and circular production paradigms, remanufacturing is becoming a strategic enabler of resource efficiency, value retention, and industrial resilience. However, remanufacturing systems are characterized by significant uncertainty regarding product returns, quality conditions, process performance, and demand patterns, creating new challenges for planning, control, and decision-making.
The WG promotes interdisciplinary research at the intersection of manufacturing systems engineering, quality management, control theory, operations research, artificial intelligence, machine learning, circular economy, and Industry 5.0. Particular attention is devoted to the development of methods and technologies that enable defect prevention, uncertainty management, adaptive decision-making, and sustainable value recovery across product life cycles.

Key research themes include:

  • Zero-defect remanufacturing strategies and quality assurance systems;
  • Modelling and management of uncertainty in product returns, condition assessment, and remanufacturing processes;
  • AI-enabled decision support systems for planning, scheduling, control, and optimization of circular manufacturing operations;
  • Digital Product Passports, digital twins, and data-driven approaches for traceability and quality management;
  • Inspection, diagnostics, predictive maintenance, and adaptive process control for remanufacturing systems;
  • Reverse logistics, closed-loop supply chains, and circular value networks;
  • Human-centric and socio-technical approaches supporting operator involvement, explainability, and human-AI collaboration;
  • Sustainable and resilient manufacturing systems supporting circular economy objectives;
  • Integration of remanufacturing within Industry 5.0 and future intelligent manufacturing ecosystems.

The WG seeks to establish scientific foundations and practical methods for the next generation of intelligent circular manufacturing systems capable of sensing, predicting, adapting, and learning in real time. The ultimate vision is to enable resilient, sustainable, and human-centric remanufacturing ecosystems that leverage advanced analytics, artificial intelligence, digital technologies, and control methods to maximize resource utilization, product quality, and societal value creation.

5- Working group "Challenges and opportunities in applying Additive Manufacturing in Supply Chains"

Chairs: Associate Prof. Mirco Peron, Dr. Nils Knofius, Associate Prof. Francesco Lolli, Prof. Fabio Sgarbossa, Prof. Tsan-Ming Choi

Additive Manufacturing (AM) builds parts layer by layer directly from a digital model, which makes it possible to produce complex geometries and customised components that are difficult or costly to obtain with conventional manufacturing (CM) techniques such as casting, machining, or forming. After early adoption in the aerospace and automotive sectors, AM is now used in a wider range of applications, including patient-specific medical implants and the on-demand production of spare parts.

The effects of AM are not limited to the production stage; they extend to the structure and management of the supply chain. Because parts can be produced close to, or at, the point of use, AM allows production to be distributed across more locations, supply networks to be shortened, and part of the physical inventory of slow-moving or spare parts to be replaced by a digital inventory that is printed when needed. These changes are relevant to supply chain resilience and viability, because distributed, digitally based production can help a network keep supplying parts and adapt to changes in demand when conventional production or transport is disrupted. The same capability is of interest for humanitarian and emergency settings, where local production can shorten lead times in the early stages of a response.

Sustainability is a further motivation, but the effect is not always positive. AM can reduce material waste by adding material only where it is needed, lower transport-related emissions when production is localised, and support repair and component-level replacement that extend product life. At the same time, some AM processes are energy-intensive and feedstock production has its own footprint, so any environmental benefit has to be assessed over the full life cycle rather than assumed.

Despite these opportunities, the use of AM is still limited by several factors. Per-part costs are often higher than for conventional alternatives, the investment in machines and in part qualification can be significant, and the case for producing at the point of use depends strongly on the specific product and context. Deciding when AM is the better option, in economic, operational, and environmental terms, is more difficult once resilience is taken into account, since a more expensive AM-based configuration may still be justified if it reduces the risk of supply disruption.

The aim of this working group is to support well-founded decisions on the adoption of AM by clarifying the conditions under which its benefits outweigh its limitations. It welcomes contributions from operations research, industrial engineering, management science, and data-driven decision support, with the dual objective of developing methods and assessing their usefulness in real settings.

Topics may include, but are not limited to:

  • Conditions and decision frameworks for adopting AM in supply chains, including total cost of ownership
  • Effect of AM on supply chain design, including the localisation, regionalisation, and reshoring of production
  • Use of AM to improve supply chain resilience and viability under disruption and demand uncertainty
  • Spare-parts supply chains based on print-on-demand and digital inventories
  • AM service networks, shared capacity, and distributed production
  • AM in biomedical and patient-specific supply chains
  • AM in humanitarian and emergency supply chains
  • AM in support of circular supply chains, including repair, remanufacturing, and material reuse
  • Life-cycle assessment of the environmental footprint of AM, including process energy use and feedstock
  • Inventory, spare-parts, and maintenance policies enabled by on-demand production
  • Use of digital twins, data analytics, and AI to support AM production and supply chain decisions
  • Quality, qualification, certification, and standardisation issues that affect AM supply chains
  • Risk management, security, and protection of intellectual property in distributed AM supply chains
  • Human-centric and Industry 5.0 perspectives on AM-enabled supply networks

6- Working group  'Intelligent methods and systems supporting supply chain decision making'

Chairs: Prof. Michael Freitag, Prof. Enzo Morosini Frazzon, and Prof. Dr. Raphaël Oger

The working group focuses on the automation and digitalization of core supply chain functions, including production/manufacturing, warehousing, logistics, alongside their enabling services and technologies. Key research areas encompass the modelling, simulation, analysis, and control of manufacturing and logistic systems and processes; monitoring, diagnosis, and predictive maintenance; development and application of automation and digitalization solutions towards smart manufacturing and logistics. Emphasizing both scientific rigor and practical relevance, the group prioritizes actionable approaches that foster innovation across industrial supply chains.

During last 10 years, the working group has contributed to TC events as follows:

  • IFAC MIM 2016 (Invited session) in Troyes, France 
  • 20th IFAC World Congress 2017 (Invited session) in Toulouse, France 
  • IFAC INCOM 2018 (Open Invited Track) in Bergamo, Italy 
  • IFAC MIM 2019 (Open Invited Track and Invited Session) in Berlin, Germany
  • 21st IFAC World Congress 2020 (Invited Session) in Berlin, Germany
  • IFAC INCOM 2021 (Open Invited Track) in Budapest, Hungary
  • IFAC MIM 2022 (Invited Session) in Nante, France
  • 22nd IFAC World Congress 2023 (Open Invited Track) in Tokyo, Japan
  • IFAC symposium INCOM 2024 (Open Invited Track) in Wien, Austria 
  • IFAC conference MIM 2025 (Open Invited Track) in Trondheim, Norway
  • 23rd IFAC World Congress 2026 (Open Invited Track) in Busan, Korea

7- Working group  'Human factors and ergonomics in industrial and logistic system design and management'

Chairs: Prof. Daria Battini, Prof. Fabio Sgarbossa, Prof. Christoph Glock, Prof. Eric Grosse, Prof. Martina Calzavara

Despite the opportunities that the automation of industrial and logistic systems offer, many companies still rely on human work in many areas. Most models proposed in the past to support managerial decision-making in industrial and logistic systems have neglected the specific characteristics of human workers, which often leads to unrealistic planning outcomes or systems that underperform or may even be harmful to workers. To guarantee high productivity and efficiency and ensure that decision support models reflect reality as much as possible, it is necessary to consider human factors (synonymous with ergonomics) in designing industrial and logistic systems that are reliable, efficient, and safe workplaces. Even though recent research has started to integrate human factors issues into decision support models – for example by modeling learning effects or human energy expenditure – there is still a significant gap in the literature concerning the development of decision support models for industrial and logistic systems that take account of the interactions between the human worker and the design of the logistics system. The technical system can, unlike the worker, be comprehensively influenced by the system designer. Generally, human factors (perceptual, cognitive, physical and psychosocial aspects in the workplace) determine the performance of industrial and logistics systems to a large extent if human operators are employed. This aspect becomes more challenging in light of an aging workforce, which will likely put human factors-related issues in logistics – such as the risk of making errors at work or developing musculoskeletal disorders – on top of the agendas in many companies. In addition, the consequences of using Industry 4.0 technologies that substitute or assist operators in their manual work, such as augmented reality, adaptable workstations or cobots, are not yet fully understood in light of human performance, errors, work motivation, and technology acceptance. However, research in this area is an inevitable and important step toward the vision of Industry 5.0, emphasizing human-centered work, environmental sustainability, and system resilience.

This working group aims at investigating the development of innovative approaches for the integration of human factors in industrial and logistic system design. Topics may include, but are not limited to:

  • Human-centricity in Industry 5.0 and Resilient Operator 5.0
  • Opportunities to utilize human factors in Industry 4.0 for human-centered production and logistics systems
  • Human factors in Logistics 4.0 and Logistics 5.0
  • Technology adoption, reliability and maintainability
  • Behavioral issues and the interactions of humans and new technologies and AI in production and logistics
  • The impact, chances and challenges of using technical assistance systems (wearables, AR, exoskeletons etc.) in manual industrial work
  • Physical, cognitive and psychosocial human factors in operations and logistics management
  • Learning and forgetting in industrial systems
  • The impact of system design on human errors
  • Reduction of injury risks in manual operations
  • The impact of demographic changes/ an aging workforce on industrial system performance and safety

During last years, the working group has contributed to TC events such as

IFAC conference MIM 2016 (special Tracks and Sessions) in Troyes, France http://mim2016.utt.fr/

IFAC World Congress 2017 (Open Invited Tracks and Special Sessions) in Toulouse, France http://www.ifac2017.org

IFAC symposium INCOM 2018 (special Tracks and Sessions) in Bergamo, Italy http://www.incom2018.org/

IFAC conference MIM 2019 (special Tracks and Sessions) in Berlin, Germany http://blog.hwr-berlin.de/mim2019

IFAC World Congress 2020 (invited session,associate editors)

Forward thinking paper published by the WG chairs: Human factors in production and logistics systems of the future, Annual Reviews in Control 49 (2020) 295–305.

IFAC INCOM 2021 (invited session, associate editors)

IFAC MIM 2022 (invited track, associate editors)

IFAC World Congress 2023 (invited session,associate editors)

IFAC INCOM 2024 (invited session, associate editors)

IFAC MIM 2025 (invited sessions, tracks, associate editors, best paper award committee)

IFAC World congress 2026 (associate editors)

8- Working group  'Smart, Reliable and Sustainable Manufacturing-Distribution Systems'

Chairs: Dr. Abdelhakim Khatab, Prof. Lyes Benyoucef, Prof. Claver Diallo, Prof. El Houssaine Aghezzaf, Prof. Uday Venkatadri 

For companies to thrive in today’s highly competitive markets, their manufacturing and distribution systems must be cost-effective, time-efficient, reliable, resilient, agile, and sustainable. Sustainability concerns encompass material and energy consumption, greenhouse gas emissions, and environmental impacts throughout the entire value chain, from raw material extraction and production processes to distribution, consumption, and end-of-life activities. Integrating sustainability into manufacturing and distribution systems requires a comprehensive consideration of all elements of the value chain, including materials, products, manufacturing processes, logistics networks, design methodologies, remanufacturing activities, and supply chain operations.

The growing complexity and uncertainty of global supply chains, driven by fluctuating demand, renewable energy integration, geopolitical events, climate-related risks, and resource constraints, call for advanced approaches to decision-making under uncertainty. Research in this area spans stochastic and robust optimization, simulation-based optimization, reinforcement learning, Bayesian decision-making, multistate reliability modelling, uncertainty quantification, and risk-aware planning, supporting the design and operation of manufacturing-distribution systems that remain reliable and resilient under varying conditions and disruptive events.

Recent advances in digital technologies such as artificial intelligence (AI), and digital twins have created new opportunities for the development of data-driven models that enhance the reliability, resilience, efficiency, and sustainability of manufacturing-distribution systems. Such models enable predictive decision-making, real-time monitoring, optimization under uncertainty, risk assessment, disruption management, and adaptive system control. Increasingly, agentic AI systems and autonomous decision-making frameworks are enabling manufacturing and supply chain systems to perceive, reason, learn, and act proactively in dynamic environments. These intelligent agents can continuously evaluate operational conditions, coordinate decisions across multiple stakeholders, anticipate disruptions, and autonomously implement corrective actions to maintain system performance and sustainability objectives.

This Working Group's objectives align with Goal 8 (Decent Work and Economic Growth), Goal 9 (Industry, Innovation and Infrastructure), and Goal 12 (Responsible Consumption and Production) of the UN SDGs. Areas of particular interest include agentic optimization, autonomous and human-in-the-loop decision-making, digital twins, predictive and prescriptive analytics, reliability and resilience engineering, sustainable supply chain design, disruption management, cyber-physical production systems, circular manufacturing, smart logistics, AI-enabled operations management, and decision support under uncertainty. WG8 will also explore how autonomous agents can collaborate with human experts to achieve adaptive, transparent, and trustworthy decision-making while balancing economic, environmental, and social objectives.

List of topics:

All problems and approaches dedicated to sustainable and smart manufacturing: Optimization of manufacturing and remanufacturing systems, Design of green supply chains, Optimal predictive maintenance for productivity and remanufacturing, Industry 4.0 and smart factories, Sequential decision making, and Agentic AI production systems.

The working group has contributed to recent IFAC events with 20 IFAC Conference Papers, 1 Young Author Award, 1 Commended Paper Award, and 4 Special sessions. 

9- Working group  'Digital Twins in Manufacturing and Logistics Systems'

Chairs: Prof. Serena Finco, Prof. Mirco Peron, Prof. Audrey Cerqueus, Prof. Olga Battaïa, Prof. Xavier Delorme, Prof. Daria Battini

Digitalization in Manufacturing and Logistics (M&L) systems is a crucial driver of higher levels of productivity, resilience, sustainability, and flexibility. Digital technologies, such as the Internet of Things, Cloud Computing, Artificial Intelligence, Virtual Reality, Augmented Reality, and the new generation of Information Technologies, enable intelligent integration and interconnection among all actors involved in M&L processes. These technologies enable real-time monitoring, control, and data collection, as well as the development of cyber-physical systems that integrate physical and virtual environments.
In this context, the Digital Twin (DT) concept is an emerging and rapidly evolving research topic in M&L systems. DT is defined in several ways depending on the application domain; however, in the context of M&L systems, it can be described as a dynamic virtual representation of physical assets, processes, or systems, designed to evaluate, predict, and optimize their states and future behavior. DTs integrate bidirectional data flows between physical and virtual entities, meaning that a change in the physical system can influence the virtual model and, conversely, insights generated in the virtual environment can trigger decisions or actions in the physical system.
Through this continuous synchronization, DTs process historical data, monitor the present in real time, and support predictive and prescriptive decision-making.
The rapid development of Artificial Intelligence (AI) has recently opened new perspectives for the evolution of DT-enabled M&L systems, particularly through the emerging paradigm of Agentic Artificial Intelligence. This paradigm introduces intelligent software agents capable of autonomously reasoning, planning actions, interacting with digital environments, and supporting complex decision-making processes. When integrated with Digital Twins, agentic AI systems can continuously analyze real-time data streams generated by DT models, interpret system states, and proactively suggest or implement operational adjustments. This collaboration is especially powerful because DTs provide accurate and continuously updated representations of physical systems, while agentic AI systems interpret these representations to autonomously support operational decisions such as production scheduling, resource allocation, maintenance planning, and logistics coordination.
This integration is particularly relevant in the current industrial context, where companies operate in highly dynamic, uncertain, and interconnected environments. Manufacturing and logistics systems must therefore become increasingly reactive, agile, and adaptive to respond quickly to disruptions, demand variability, and operational disturbances. Moreover, agentic AI can assist managers by continuously monitoring operational data through DT infrastructures, detecting anomalies, identifying optimization opportunities, and generating decision alternatives in real time. These intelligent agents can collaborate with human decision-makers, augmenting managerial capabilities and enabling more informed, data-driven, and timely decisions. As a result, the DT–agentic AI integration supports a transition from traditional reactive management approaches toward proactive and adaptive operational control.
The relevance of DT in M&L is well established in the scientific literature, with applications spanning production planning and control, workpiece quality prediction, machine and human-robot collaboration, real-time monitoring of M&L systems, product traceability, performance prediction, and supply chain resilience. The integration of agentic AI further extends these capabilities, enabling autonomous or semi-autonomous decision support across these domains in complex, dynamic operational contexts.
Despite these opportunities, several critical challenges must be addressed to fully realize the potential of DT-enabled intelligent systems. These include difficulties in sharing DT infrastructures across multiple application systems and stakeholders, challenges in efficiently storing, processing and analyzing large volumes of heterogeneous data, and ensuring reliability, robustness and trustworthiness of DT and AI-based decision-support systems. The heterogeneity of digital technologies further complicates strategic decision-making around DT and AI adoption, as companies struggle to evaluate investments and align technological choices with organizational goals. Additional research challenges include the modelling of complex manufacturing systems, the integration of human factors, and the consideration of internal and external factors affecting machine degradation, workforce skills, and organizational adaptability.
This Working Group directly addresses these open challenges by investigating the development of original and innovative studies that implement Digital Twin concepts in M&L systems, with particular attention to the integration of advanced Artificial Intelligence paradigms such as agentic AI. Contributions are sought across a broad multidisciplinary spectrum (including statistics, Artificial Intelligence, computer science, operations research, industrial engineering, and management science) with the dual objective of advancing technical solutions and critically evaluating their benefits and limitations in real operational contexts.

Topics may include, but are not limited to:
• Using new emerging technologies for the DT implementation in M&L systems
• Conceptual frameworks to support DT and agentic AI development in M&L systems
• DT architectures in M&L systems
• Integration of Digital Twins and agentic AI for intelligent decision support
• Autonomous and collaborative AI agents for manufacturing and logistics management
• Real-time based models and algorithms for assembly line design, balancing and rebalancing techniques
• Simulation and optimization models based on real-time data for production planning and control in flexible manufacturing systems
• Real-time job scheduling and sequencing for complex M&L systems supported by intelligent agents
• DT models for improving M&L systems layout
• DT models for analyzing machine-to-machine interaction and human-robot collaboration
• DT methods and models to improve human factors in M&L systems
• Using DT concepts to improve M&L systems reliability, availability and efficiency
• DT as a tool to improve M&L system resilience
• Agentic AI for adaptive production planning, scheduling, and operational management
• DT-enabled intelligent monitoring and anomaly detection in M&L systems
• Quantitative and qualitative analysis concerning the implementation of DT in M&L systems
• DT and agentic AI applications from real M&L systems

10- Working group  'Smart intralogistics for warehousing and material handling in manufacturing and distribution systems'

Chairs: Prof. Martina Calzavara, Prof. Eric Grosse, Dr. Dominic Loske, Prof. Elena Tappia, Prof. Ilenia Zennaro

Recent market trends demand for an increasing variety of goods that have to be produced and delivered in ever shorter times. Since global markets continuously change, especially referring to the emerging e-commerce channel, industries need to be able to respond quickly and appropriately to these needs, working with constant uncertainties and aiming to be flexible and resilient at the same time. These aspects, associated with new material handling technologies, digital technologies that allow the introduction of new data-driven approaches for decision making, and the importance of a human-centric perspective, lead to challenges and trade-offs that have an important impact on the management and control of intralogistics activities, including material handling, warehousing, parts feeding, and products distribution. Therefore, the need of designing intralogistics systems that are flexible, synchronized, effective, and resilient emerges.

A rigorous design of intralogistics systems includes, for example, the feeding of the items to the assembly area, the right setting of the material handling system, the level of automation, and the location of the storage areas and the warehouse zones, including the appropriate transportation and products distribution. Moreover, the adoption of new technologies and assistive devices can relieve workers from high workload and enabler to ease and speed up manual activities, as well as warrant higher quality, reliability, traceability and sustainability of the intralogistics processes.

Technological developments towards smart warehouses enable new and increasingly competitive solutions for automation, information provision and integration, and worker assistance. However, there are still many open questions about the application of technology, for example with regard to cost-benefit analyses, decision support, system design, and human-technology interaction.

This working group aims at providing the opportunity of sharing new ideas, methods and technologies useful for the development and improvement of smart intralogistics for warehousing, material handling, and distribution systems. Topics of interest include (but are not limited to) the proposal of solutions and technologies as well as design, analysis, and evaluation methodologies for:

  • Storing and warehousing
  • Warehouse order picking
  • Material handling systems
  • Part feeding for manufacturing and assembly systems
  • Materials distribution strategies and warehouse locations
  • Delivery and transportation policies
  • Intralogistics systems and strategies
  • Forward-reverse logistics management
  • Smart, automated, robotized warehousing
  • Logistics 4.0
  • Human-technology interaction in intralogistics
  • Individual and group behavior in intralogistics
  • Digital twins of intralogistics systems
  • Data-driven evaluation of new technologies in intralogistics
  • Digital nudges and human behavior in intralogistics
  • Intersections of warehousing and transportation

During last years, the working group has contributed to TC events such as:

IFAC INCOM 2024 (invited session, associate editors)

MIM 2025 (invited sessions, associate editors, best paper award committee)

IFAC World congress 2026 (associate editors)