TG13 Collaborative Supply Networks (CSN)

Led by Eduardo Saiz (IKERLAN) and Raul Poler (UPV)



Collaboration in Supply Networks is becoming essential for day-to-day operations of any manufacturing enterprise irrespective of its size. Both SMEs and large enterprises stand to gain from collaboration solutions in their Network. Manufacturing enterprises of the future and stakeholders in their holistic value chain should shy away from building additional functionalities on top of an already heavy enterprise software landscape. SMEs and large manufacturing enterprises are increasingly looking for enterprise applications that are agile, instant valued, real-time, easy to use, and platform agnostic. Innovation for the future should be focussed on making collaboration smart, such that decisions within the holistic value chain could be taken ‘on-the-fly’ irrespective of the location of the enterprise or the decision maker through easy to use and innovative manufacturing applications.

Manufacturing enterprises need to collaborate in global supply networks to cope with variable demands and highly complex products. They have to respond faster to demand and supply fluctuations and increase forecasting capability on the one hand and reducing cycle time and supply chain costs on the other. Network traceability would facilitate improved product genealogy and better identification of products for recalls and withdrawals. Furthermore, supply network planning and execution would lead to the assessment of supplier performance and identification of bottlenecks in the networks. To assist in making business decisions, research should correlate production KPIs and logistics KPIs for collaborative demand and supply optimisation and analyse cost implications for changes, exceptions, and bottlenecks.

Enterprises are increasingly facing complexity resulting from frequently changing designs and therefore need to collaborate as a single virtual organization to keep track of the requirements of product-service systems. This research priority focuses on increasing reactivity to demand and rapidly delivering new products leveraging business relationships and local expertise with focus on SME participation. Factories are evolving faster than in the past and becoming more complex, expensive, and geographically distributed. Commonly used IT-backend systems are neither widely interconnected nor interoperable which makes holistic representation, monitoring and management of the factories difficult.

Today’s markets ask for flexible on-demand capacities. Internet economy and individualisation needs are pushing, in particular, for fast product/service systems able to combine rapid and flexible production capabilities with enhanced product design capabilities and exploit minimal distribution lead-times to match supply with volatile demand. From a technological point of view demand driven manufacturing involves a synchronized, closed loop between customer orders, production scheduling, and manufacturing execution; all while simultaneously coordinating the flow of materials and information along the supply chain.

Modelling methods and tools are needed to support the configuration of production systems since the early conceptual design phase, where the selection of the production resources and the development of the entire automation system are tackled. After the implementation of the production system, the production system model and the corresponding performance evaluation methods and tools need to be continuously updated according to the actual system behaviour, so that the virtual factory environment can be continuously tuned along the evolution of the real factories.

Distributed simulation systems offer good local optimization outcomes but lack interoperability and holistic modelling options, especially for complex manufacturing systems. Improving quality and throughput should be analysed together with impact on environmental, economical and social performances. Modelling and simulation models that take into account interactions between the different stages of the production chain must be generated to consider the whole life cycle and be able to optimise the final part quality and throughput considering the effect of each manufacturing step.

New supply chains which address globalisation and the integrated offering of product with service will demand new approaches, which take into account movement of material, exploitation of clusters of manufacturing excellence alongside an ability for local customization. The production of highly customised products with short life cycles addressing volatile markets will require new structures and operation strategies of their supply chains, comprehending sourcing, including land management as regards sourcing of biomass, and the management of the management of the EOL phase. Future supply chains will need to re-configure dynamically as customer specific products will be based on an increasing number of specific components.


Missions & Objectives

  • Promote networking between association members
  • Join competences on TG research issues
  • Generate ideas for potentials proposals in this field



New technologies, structures and ICT systems are needed to establish the future Collaborative Supply Networks, that support decision makers in finding and establishing the best possible supply network solution and to manage the customer needs:

  • Mobile apps that bring enterprise and network information under one roof for better taking decisions across supply networks.
  • ICT tools under the cloud-computing paradigm as the basis for communication amongst stakeholders for exchanging data and information.
  • Interoperable and open interfaces to connect to systems across geographically dispersed competence centres.
  • Agile UIs and mobile apps for seamless collaboration by designers and customers without requiring complex configurations will complement the functional aspects with usability properties.
  • Tools and technologies to provide a coordinated and fast interaction between stakeholders to accelerate the product life cycle and reduce the production lead times by operating in variable supply networks.
  • Design tools to identify and transfer key consumer requirements and collected data from suppliers into automated process routines.
  • Tools for flexibly integrate manufacturing processes and design specifications into efficient operational routines by keeping a comparable throughput time in different configurations.
  • Tools for integrate and synchronize production with demand while stream-lining material-flow by the interoperability of Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), and Advanced Planning and Scheduling (APS).
  • Agile and evolvable Manufacturing Execution Systems (MES) to deal with this highly dynamic environment and more sustainable manufacturing through optimization of knowledge-based systems and synchronization with shop floor automation and supply chain management systems.
  • Closed loop simulation instruments, visualization tools, knowledge-based systems and optimization algorithms integrated according to the available set of data and the expected level of details of the configuration solution, both at factory and al supply network level.
  • Integrated multi-level simulation and analytics to facilitate enhanced factory modelling by enabling views and interpretations from different perspectives.
  • IoT-based continuous data collection from real-world resources (i.e. assets, devices, products) from the field and along the value chain in conjunction with appropriate simulation and data analytics tools will identify deviations between expected and actual results allowing early management of factory and production issues.
  • Integrated scalable and semantic factory models with multi-level access features, aggregation of data with different granularity, zoom in and out functionalities, and real-time data acquisition from all the factory resources
  • Tools to support decision-making processes, activity planning and operation controlling and facilitate faster ramp up through decreased time-to-market for future factories.
  • Semantic models, holistic in nature and be able to represent all levels of production functions and equipment.
  • Real-time data acquisition through the connectivity paradigm offered by the IoT complemented with mobile decision-making apps that will assist plant managers in getting a holistic overview of KPIs computed on collected data.



TG13 Members



Name Partners
DFI Pole Frank-Walter Jäkel FhG IPK
I3-VLab Fabio Floreani Consorzio Intellimech
Matteo Villa FINCONS Spa
Alessandro Canepa Fratelli Piacenza S.p.A.
Eva Cosia Holonix
Marco Boero Softeco Sismat Srl
Sergio Gusmeroli / Michele Sesana TXT e-solutions spa
Eugenia Marilungo Università Politecnica delle Marche
Ioan Toma University of Innsbruck
Jose Miguel Pinazo AINIA
Eduardo Saiz IK4-IKERLAN
Rubén de Juan Marín ITI – Instituto Tecnológico de Informática
Raul Poler UPV – Universitat Politècnica de València
PGSO Pole Matthieu Lauras Ecole des Mines d’Albi-Carmaux
Vincent Chapurlat Ecole des Mines d’Alès – LGI2P
David Chen University of Bordeaux
Grande Région Pole Yannick Naudet LIST
Sekhari Aicha University Lyon 2
Hervé Panetto University of Lorraine
PtRP Pole Ricardo Goncalves UNINOVA
TANET – UK Pole Colin Piddington / Gash Bhullar Control 2K
Keith Popplewell Coventry University
China Pole Liu Xiaofeng Harbin Institute of Technology




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