- Email: [email protected]
Networked manufacturing control: An industrial case
CIRP Journal of Manufacturing Science and Technology 4 (2011) 324–326
Contents lists available at ScienceDirect
CIRP Journal of Manufacturing Science and Technology journal homepage: www.elsevier.com/locate/cirpj
Networked manufacturing control: An industrial case Bart Saint Germain *, Paul Valckenaers, Hendrik Van Brussel, Jan Van Belle Katholieke Universiteit Leuven, Celestijnenlaan 300, Leuven, Belgium
A R T I C L E I N F O
A B S T R A C T
Keywords: Manufacturing Agent Holonic
The European Project MABE has investigated the application of a holonic manufacturing execution system (HMES) to networked production. This HMES implements the PROSA reference architecture [1]. Intelligent products manage their production in cooperation with intelligent resources. These intelligent products generate swarms of lightweight smart objects that respectively explore for suitable routings and reserve capacity at the intelligent resources [2]. The system is a self-organizing design. The research results revealed that the HMES design scales, without much effort, from a single plant control toward the control of a manufacturing network. Moreover, the HMES design was able to handle the part flows for the heat and surface treatment. Additionally, MABE has triggered research on trust to enable the HMES to cope with networks comprising independent organizations. Its results target cooperation in semi-closed networks. The research allows using track records during subsequent interactions, for instance to decide about slack values in schedules. ß 2011 CIRP.
1. Introduction Nowadays, supply chains running across a network of manufacturing systems characterize production activities. Increasingly, end-user products require the coordination of manufacturing activities of multiple production sites. Consequently, singleplant coordination and control no longer suffices for performance optimization. Moreover, those manufacturing supply chains exist in semi-open networks. Access to these networks is only granted to qualified partners and network membership will be terminated in case of poor performance (or worse). In other words, the interactions within those manufacturing networks are repetitive and failure to perform is penalized in subsequent interactions, whereas strong performance is likely to be rewarded provided the performance concerns services that will be needed in future activities. Therefore, the coordination and control mechanisms are not forced to account for worst cases concerning the performance of other network members. When the granularity of the commitments is small, any failure to deliver or perform can be absorbed while the incentive to perform consists of a continued participation (and profit sharing) in lucrative supply chains. This paper discusses how a holonic manufacturing execution system (HMES) is able to coordinate manufacturing and transportation activities within networked production. It reveals that its scalable design is intrinsically capable of coordination across multiple production sites. Moreover, this paper presents a trust framework, which was added to the original HMES, delivering a mechanism to
* Corresponding author. E-mail address: [email protected] (B. Saint Germain). 1755-5817/$ – see front matter ß 2011 CIRP. doi:10.1016/j.cirpj.2011.03.008
cope with multiple organizations and independent centers of control within the network. Trust mechanisms are used to make performance visible and transparent such that poor performance and strong performance will have the proper consequences. Trust mechanisms allow to strengthen lucrative supply chains and to fade-out poor-performing ones. Importantly, it ensures that network participants know that cheating will be noticed. At the same time, the HMES avoids the significant costs and delays of playing it safe (in a worst case) because of the small granularity of the interactions and commitments in combination with a highly repetitive game situation. 2. Industrial case The research results, discussed in this paper, originate from the EU project MABE and follow-up academic research on trust. The MABE team applied HMES [2] technology to a networked production system. 2.1. Virtual enterprise To focus research efforts and ensure industrial relevance, the EU project MABE addressed an industrial case: a virtual enterprise. This virtual enterprise consists of nine SME-sized companies, where the exact number of companies is likely to vary and grow over time. New companies are dynamically joining and leaving the group and new processes and equipment are introduced as needed. The main objective for forming a virtual enterprise was the optimized usage of information as well as the physical resources. The contribution of an HMES for this virtual enterprise is to enable the optimization without the need to
B. Saint Germain et al. / CIRP Journal of Manufacturing Science and Technology 4 (2011) 324–326
transform a network of SME-sized organizations into a single larger organization. Indeed, such reorganization is risky and costly. The HMES facilitates resource sharing at the level of the entire network and provides access to and usage of all relevant information throughout this network. From the HMES perspective, this network presents itself as a production system with at least two levels of organization: the network level and the factory level. In principle, the HMES based on PROSA [1] is a fractal design mirroring the organization of the underlying production system(s). The MABE case was offering an opportunity to demonstrate this capability. 2.2. Network nodes: heat treatment of metallic materials The nodes in the network of production systems are factories performing heat treatment of metallic materials, which also includes surface treatment. The available processes in this production network include: case hardening; vacuum hardening; salt baths. As in any modern production facility, the company offers services that extend the above processes with functionalities such as quality control or (robotized) shape correction. The factories comprise a mix of production organizations. Some departments are highly automated, a job shop with an automatic storage and retrieval system. Other production departments have less automation, are highly automated flow lines dedicated to specific (high volume) products, are flexible/programmable automated flow shops or are job shops treating very large parts. In other words, the variability is large, has emerged historically and is likely to continue existing because of the pressures from the customer markets and demands. 2.3. Network level The customer knows and orders to the virtual enterprise. Within the virtual enterprise, orders should be managed in such way to optimize the virtual enterprise as a whole. Good distribution of orders guarantees the efficiency of the virtual enterprise by creating batches and sequences. The HMES delivers an organization performing a network-wide coordination and facilitating optimization, from order entry until delivery. Information processing by the HMES accounts for the processing capabilities and capacity availabilities of the complete network. Moreover, the system supports the sales department predicting how fast and at what costs new orders might be launched into the network. 3. Networked HMES 3.1. Network nodes: on-site HMES Inside a single factory, the MABE team applied the PROSA+ANTS design for the HMES [2]. PROSA is a reference architecture for manufacturing control systems structuring the society of holons (agents) that implement a decentralized control. In an ANTS design, these holons use swarms of lightweight agents to explore and inform their environment on their behalf (note that this approach was inspired by ant colony foraging). This resulted in a system that mirrors the: resources: processing equipment and part handling facilities; products: process plans of the product types; orders: production activities.
325
Overall, this yields a digital factory on which the order holons (activities) virtually execute their intentions. The resulting HMES coordinates the manufacturing activities and generates short-term forecasts [2]. Specific for the industrial case is the batching of parts with compatible process temperature trajectories and environmental conditions. This batching, when properly executed, has significant impact on the performance of these capital-intensive production systems. Indeed, a fully loaded furnace and a partially loaded one operate almost at identical cost whereas the output differs significantly. The PROSA architecture turned out to be highly suited for this challenge. The product holons provide the facility to check for compatible trajectories whereas the order holons use a delegate MAS [3] to discover batching opportunities within the short-term forecasts or, alternatively, to trigger the build-up of such batches. Moreover, the MABE team developed an accurate model of a multi-chamber oven. This development demonstrated how the HMES is able to cope with complex part flows through production equipment. In particular, the HMES manages the technical issues of transitions between loads: preventing unwanted contaminations from occurring (e.g. carbon); allowing the next load to enter into the furnace (after a safety delay) while the previous load still is in the oil bath (allow overlap). In short, MABE assessed and validated the ability of the HMES to operate as close to the manufacturing equipment as desired or required. The experience revealed that the implementation efforts mostly consist of creating executable models of the equipment and processes. The MABE project triggered a follow-up development of a software tool to speed up the creation of such executable models [4]. The HMES approach permits the manufacturing execution system to adapt to particularities in production equipment and processes (where such peculiarities often are essential for the productivity). PROSA makes this possible by not limiting itself to data models in which the world-of-interest needs to be squeezed. Instead, the world-of-interest is mirrored by interacting computing processes and their developers enjoy much freedom concerning the formats used to exchange information. Relevant for this paper is this ability of the HMES to scale into more detail. Is the HMES also capable of scaling in the other direction? 3.2. Network level: multi-site HMES To the HMES, this network level constitutes an additional level. In a network of factories, the transport operations with the trucks, the storage at the factories and customer sites, the production processes offered by the factories, they are all indistinguishable from similar operations at lower levels within single factories. The HMES is a fractal design, which repeats itself at the various levels of the underlying (networked) production system. The mechanisms that cope with the presence of departments within a factory also cope with factories in a network of factories. Executable models resulting in a digital factory network address the differences between the HMES services and functionalities offered at the respective levels. These models reflect the corresponding parts of the world-of-interest. Given those models, the HMES selforganizes and copes with this additional network level without further modification or extension. The holonic manufacturing execution system scales without effort. In fact, these higher levels in the network are easy because products and parts are storable and transportable in between processing steps, whereas close to the equipment more challenging constraints and demands are more common. Moreover, at these higher levels, simpler models suffice. Most importantly, the presence of both resource and order
326
B. Saint Germain et al. / CIRP Journal of Manufacturing Science and Technology 4 (2011) 324–326
holons is crucial. Alternative approaches that, for instance, only comprise intelligent resources intrinsically struggle to deliver such adaptability and scalability [5]. Consider todays non-automated supply chains. The HMES design resembles the organization in which premium-paying customers have a human assistant/butler/ agent who manages their production orders on their behalf. This manager interacts with service providers to organize production without waiting for these providers to upgrade or adapt. Such upgrading and adaptation will rather occur in single organizations as a continuous improvement process to bolster competitiveness, regardless of and unrelated to specific production orders. In contrast, current production systems aimed at mass-customization only cope with situations for which the script was known at their design time. When a new product model not covered in one of the scripts is introduced, the production lines need upgrading and the human workers receive training. Importantly, in the latter case, the adaptation needs to be orchestrated. Such orchestration is likely to become very difficult when more than two links (enterprises) in a supply chain need to be managed. The HMES avoids such scaling issues. 3.3. Network level: semi-open systems Scaling the manufacturing execution system to the network level revealed to be easy as far as the functionalities in an existing single-factory MES are concerned. However, production and supply networks lack a single command and control center; they are semi-open systems. Therefore, the research on a networked HMES addressed generic challenges originating from such semiopen organizations. These are two-fold:
non-disclosure of sensitive information; trust issues: non-performance, deception, non-compliance. The HMES handles the first challenge by the creation of holons (computer processes). When order holons at supply chain level virtually arrive at a factory, the HMES creates representative internal order holons to act on their behalf. These internal order holons only disclose and release relevant information to the network level. Conversely, the network level only communicates relevant information to the factory-level holons. It is analogous to human customers entering a factory store where human workers take care of the orders without disclosing the internal operations. The second challenge triggered research on trust, which revealed to be unexplored territory in case of semi-open organizations such as in networked production (most research-by-others focuses on e-Bay). The research resulted in a decision support framework based on track records of the holons (resources, orders) in their interactions [6]. In this framework, an expectations module predicts future performance based on past performance. The trust level indicates how well the inference in this module is able to predict the real outcome. Therefore, the input for the trust model in the framework is how well the expectations matched the actual outcomes. Or in other words, what was the level of surprise in the interaction history. This trust framework enables decision making in a semi-reliable environment. Consider the following application. When an exploring ant agent [2] constructs its candidate routing, this agent requests the resource holons on its route to estimate processing times. The ant agent adds such time to its clock, i.e.
virtually executes a processing step and continues its journey. In order to construct robust routings, this agent must add an adequate amount of slack time. The expectations module infers from estimates received from the resource holons what is most likely to happen. For given slack values, the confidence module, which is using a trust model, estimates the certainty that processing steps have been executed. This mechanism distinguishes reliable network members from less reliable ones, reliable services from less reliable services (possibly of the same network member), and intrinsically repeatable processes from unpredictable ones. Another application is to distinguish routings that have been used in the past from routings that are feasible in theory but have no track record to prove this. Details are in Ref. [6]. 4. Conclusion Within a single network node, the HMES ability to cope with all the relevant specifics of the manufacturing processes and equipment is crucial. In contrast, at the network level the required executable models become simpler and fewer. However, the presence of both order holons (intelligent products) and the resource holons (intelligent resources) is essential. Alternatives attempting to capture all product-related aspects in an information format (document, recipe, routing sheet) struggle to scale across a production network, particularly when this requires orchestration. Moreover, the semi-open nature of networked production introduces new challenges. Handling of sensitive information enjoys the presence of order holons as first-class citizens, which allowed automating a common solution applied when humans manage operations. Finally, a trust framework infers expected behavior and confidence levels to enable decision making in a partially uncontrolled environment. Acknowledgements This paper presents work funded by the Research Fund of the K.U.Leuven Concerted Research Action on Autonomic Computing for Distributed Production Systems and the EU GROWTH project MABE. This paper presents work funded by the Research Fund of the K.U.Leuven -Concerted Research Action on Autonomic computing for Distributed Production Systems – and the EU growth project MABE
References [1] Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P., 1998, Reference Architecture for Holonic Manufacturing Systems: PROSA, Computers in Industry, 37:255–274. [2] Valckenaers, P., Van Brussel, H., 2005, Holonic Manufacturing Execution Systems, CIRP Annals—Manufacturing Technology, 54:427–432. [3] Holvoet, T., Valckenaers, P., 2007, Exploiting the Environment for Coordinating Agent Intentions, in: Proceedings of the 3rd International Conference on Environments for Multi-agent Systems III, E4MAS’06, Springer-Verlag, pp. pp.51–66. [4] Verstraete, P., Saint Germain, B., Valckenaers, P., Van Brussel, H., Hadeli Van Belle, J., 2008, Engineering Manufacturing Control Systems Using PROSA and Delegate MAS, International Journal of Agent-Oriented Software Engineering, 2/ 28: 62–89. [5] Sihn, W., Palm, D., 2004, The 5-Days-Car: Increasing Agility in the Automotive Supply Chain, DAAAM, 415–416. [6] Saint Germain, B., Distributed Coordination and Control for Networked Production Systems, Ph.D. Thesis, K.U. Leuven, 2010.
Contents lists available at ScienceDirect
CIRP Journal of Manufacturing Science and Technology journal homepage: www.elsevier.com/locate/cirpj
Networked manufacturing control: An industrial case Bart Saint Germain *, Paul Valckenaers, Hendrik Van Brussel, Jan Van Belle Katholieke Universiteit Leuven, Celestijnenlaan 300, Leuven, Belgium
A R T I C L E I N F O
A B S T R A C T
Keywords: Manufacturing Agent Holonic
The European Project MABE has investigated the application of a holonic manufacturing execution system (HMES) to networked production. This HMES implements the PROSA reference architecture [1]. Intelligent products manage their production in cooperation with intelligent resources. These intelligent products generate swarms of lightweight smart objects that respectively explore for suitable routings and reserve capacity at the intelligent resources [2]. The system is a self-organizing design. The research results revealed that the HMES design scales, without much effort, from a single plant control toward the control of a manufacturing network. Moreover, the HMES design was able to handle the part flows for the heat and surface treatment. Additionally, MABE has triggered research on trust to enable the HMES to cope with networks comprising independent organizations. Its results target cooperation in semi-closed networks. The research allows using track records during subsequent interactions, for instance to decide about slack values in schedules. ß 2011 CIRP.
1. Introduction Nowadays, supply chains running across a network of manufacturing systems characterize production activities. Increasingly, end-user products require the coordination of manufacturing activities of multiple production sites. Consequently, singleplant coordination and control no longer suffices for performance optimization. Moreover, those manufacturing supply chains exist in semi-open networks. Access to these networks is only granted to qualified partners and network membership will be terminated in case of poor performance (or worse). In other words, the interactions within those manufacturing networks are repetitive and failure to perform is penalized in subsequent interactions, whereas strong performance is likely to be rewarded provided the performance concerns services that will be needed in future activities. Therefore, the coordination and control mechanisms are not forced to account for worst cases concerning the performance of other network members. When the granularity of the commitments is small, any failure to deliver or perform can be absorbed while the incentive to perform consists of a continued participation (and profit sharing) in lucrative supply chains. This paper discusses how a holonic manufacturing execution system (HMES) is able to coordinate manufacturing and transportation activities within networked production. It reveals that its scalable design is intrinsically capable of coordination across multiple production sites. Moreover, this paper presents a trust framework, which was added to the original HMES, delivering a mechanism to
* Corresponding author. E-mail address: [email protected] (B. Saint Germain). 1755-5817/$ – see front matter ß 2011 CIRP. doi:10.1016/j.cirpj.2011.03.008
cope with multiple organizations and independent centers of control within the network. Trust mechanisms are used to make performance visible and transparent such that poor performance and strong performance will have the proper consequences. Trust mechanisms allow to strengthen lucrative supply chains and to fade-out poor-performing ones. Importantly, it ensures that network participants know that cheating will be noticed. At the same time, the HMES avoids the significant costs and delays of playing it safe (in a worst case) because of the small granularity of the interactions and commitments in combination with a highly repetitive game situation. 2. Industrial case The research results, discussed in this paper, originate from the EU project MABE and follow-up academic research on trust. The MABE team applied HMES [2] technology to a networked production system. 2.1. Virtual enterprise To focus research efforts and ensure industrial relevance, the EU project MABE addressed an industrial case: a virtual enterprise. This virtual enterprise consists of nine SME-sized companies, where the exact number of companies is likely to vary and grow over time. New companies are dynamically joining and leaving the group and new processes and equipment are introduced as needed. The main objective for forming a virtual enterprise was the optimized usage of information as well as the physical resources. The contribution of an HMES for this virtual enterprise is to enable the optimization without the need to
B. Saint Germain et al. / CIRP Journal of Manufacturing Science and Technology 4 (2011) 324–326
transform a network of SME-sized organizations into a single larger organization. Indeed, such reorganization is risky and costly. The HMES facilitates resource sharing at the level of the entire network and provides access to and usage of all relevant information throughout this network. From the HMES perspective, this network presents itself as a production system with at least two levels of organization: the network level and the factory level. In principle, the HMES based on PROSA [1] is a fractal design mirroring the organization of the underlying production system(s). The MABE case was offering an opportunity to demonstrate this capability. 2.2. Network nodes: heat treatment of metallic materials The nodes in the network of production systems are factories performing heat treatment of metallic materials, which also includes surface treatment. The available processes in this production network include: case hardening; vacuum hardening; salt baths. As in any modern production facility, the company offers services that extend the above processes with functionalities such as quality control or (robotized) shape correction. The factories comprise a mix of production organizations. Some departments are highly automated, a job shop with an automatic storage and retrieval system. Other production departments have less automation, are highly automated flow lines dedicated to specific (high volume) products, are flexible/programmable automated flow shops or are job shops treating very large parts. In other words, the variability is large, has emerged historically and is likely to continue existing because of the pressures from the customer markets and demands. 2.3. Network level The customer knows and orders to the virtual enterprise. Within the virtual enterprise, orders should be managed in such way to optimize the virtual enterprise as a whole. Good distribution of orders guarantees the efficiency of the virtual enterprise by creating batches and sequences. The HMES delivers an organization performing a network-wide coordination and facilitating optimization, from order entry until delivery. Information processing by the HMES accounts for the processing capabilities and capacity availabilities of the complete network. Moreover, the system supports the sales department predicting how fast and at what costs new orders might be launched into the network. 3. Networked HMES 3.1. Network nodes: on-site HMES Inside a single factory, the MABE team applied the PROSA+ANTS design for the HMES [2]. PROSA is a reference architecture for manufacturing control systems structuring the society of holons (agents) that implement a decentralized control. In an ANTS design, these holons use swarms of lightweight agents to explore and inform their environment on their behalf (note that this approach was inspired by ant colony foraging). This resulted in a system that mirrors the: resources: processing equipment and part handling facilities; products: process plans of the product types; orders: production activities.
325
Overall, this yields a digital factory on which the order holons (activities) virtually execute their intentions. The resulting HMES coordinates the manufacturing activities and generates short-term forecasts [2]. Specific for the industrial case is the batching of parts with compatible process temperature trajectories and environmental conditions. This batching, when properly executed, has significant impact on the performance of these capital-intensive production systems. Indeed, a fully loaded furnace and a partially loaded one operate almost at identical cost whereas the output differs significantly. The PROSA architecture turned out to be highly suited for this challenge. The product holons provide the facility to check for compatible trajectories whereas the order holons use a delegate MAS [3] to discover batching opportunities within the short-term forecasts or, alternatively, to trigger the build-up of such batches. Moreover, the MABE team developed an accurate model of a multi-chamber oven. This development demonstrated how the HMES is able to cope with complex part flows through production equipment. In particular, the HMES manages the technical issues of transitions between loads: preventing unwanted contaminations from occurring (e.g. carbon); allowing the next load to enter into the furnace (after a safety delay) while the previous load still is in the oil bath (allow overlap). In short, MABE assessed and validated the ability of the HMES to operate as close to the manufacturing equipment as desired or required. The experience revealed that the implementation efforts mostly consist of creating executable models of the equipment and processes. The MABE project triggered a follow-up development of a software tool to speed up the creation of such executable models [4]. The HMES approach permits the manufacturing execution system to adapt to particularities in production equipment and processes (where such peculiarities often are essential for the productivity). PROSA makes this possible by not limiting itself to data models in which the world-of-interest needs to be squeezed. Instead, the world-of-interest is mirrored by interacting computing processes and their developers enjoy much freedom concerning the formats used to exchange information. Relevant for this paper is this ability of the HMES to scale into more detail. Is the HMES also capable of scaling in the other direction? 3.2. Network level: multi-site HMES To the HMES, this network level constitutes an additional level. In a network of factories, the transport operations with the trucks, the storage at the factories and customer sites, the production processes offered by the factories, they are all indistinguishable from similar operations at lower levels within single factories. The HMES is a fractal design, which repeats itself at the various levels of the underlying (networked) production system. The mechanisms that cope with the presence of departments within a factory also cope with factories in a network of factories. Executable models resulting in a digital factory network address the differences between the HMES services and functionalities offered at the respective levels. These models reflect the corresponding parts of the world-of-interest. Given those models, the HMES selforganizes and copes with this additional network level without further modification or extension. The holonic manufacturing execution system scales without effort. In fact, these higher levels in the network are easy because products and parts are storable and transportable in between processing steps, whereas close to the equipment more challenging constraints and demands are more common. Moreover, at these higher levels, simpler models suffice. Most importantly, the presence of both resource and order
326
B. Saint Germain et al. / CIRP Journal of Manufacturing Science and Technology 4 (2011) 324–326
holons is crucial. Alternative approaches that, for instance, only comprise intelligent resources intrinsically struggle to deliver such adaptability and scalability [5]. Consider todays non-automated supply chains. The HMES design resembles the organization in which premium-paying customers have a human assistant/butler/ agent who manages their production orders on their behalf. This manager interacts with service providers to organize production without waiting for these providers to upgrade or adapt. Such upgrading and adaptation will rather occur in single organizations as a continuous improvement process to bolster competitiveness, regardless of and unrelated to specific production orders. In contrast, current production systems aimed at mass-customization only cope with situations for which the script was known at their design time. When a new product model not covered in one of the scripts is introduced, the production lines need upgrading and the human workers receive training. Importantly, in the latter case, the adaptation needs to be orchestrated. Such orchestration is likely to become very difficult when more than two links (enterprises) in a supply chain need to be managed. The HMES avoids such scaling issues. 3.3. Network level: semi-open systems Scaling the manufacturing execution system to the network level revealed to be easy as far as the functionalities in an existing single-factory MES are concerned. However, production and supply networks lack a single command and control center; they are semi-open systems. Therefore, the research on a networked HMES addressed generic challenges originating from such semiopen organizations. These are two-fold:
non-disclosure of sensitive information; trust issues: non-performance, deception, non-compliance. The HMES handles the first challenge by the creation of holons (computer processes). When order holons at supply chain level virtually arrive at a factory, the HMES creates representative internal order holons to act on their behalf. These internal order holons only disclose and release relevant information to the network level. Conversely, the network level only communicates relevant information to the factory-level holons. It is analogous to human customers entering a factory store where human workers take care of the orders without disclosing the internal operations. The second challenge triggered research on trust, which revealed to be unexplored territory in case of semi-open organizations such as in networked production (most research-by-others focuses on e-Bay). The research resulted in a decision support framework based on track records of the holons (resources, orders) in their interactions [6]. In this framework, an expectations module predicts future performance based on past performance. The trust level indicates how well the inference in this module is able to predict the real outcome. Therefore, the input for the trust model in the framework is how well the expectations matched the actual outcomes. Or in other words, what was the level of surprise in the interaction history. This trust framework enables decision making in a semi-reliable environment. Consider the following application. When an exploring ant agent [2] constructs its candidate routing, this agent requests the resource holons on its route to estimate processing times. The ant agent adds such time to its clock, i.e.
virtually executes a processing step and continues its journey. In order to construct robust routings, this agent must add an adequate amount of slack time. The expectations module infers from estimates received from the resource holons what is most likely to happen. For given slack values, the confidence module, which is using a trust model, estimates the certainty that processing steps have been executed. This mechanism distinguishes reliable network members from less reliable ones, reliable services from less reliable services (possibly of the same network member), and intrinsically repeatable processes from unpredictable ones. Another application is to distinguish routings that have been used in the past from routings that are feasible in theory but have no track record to prove this. Details are in Ref. [6]. 4. Conclusion Within a single network node, the HMES ability to cope with all the relevant specifics of the manufacturing processes and equipment is crucial. In contrast, at the network level the required executable models become simpler and fewer. However, the presence of both order holons (intelligent products) and the resource holons (intelligent resources) is essential. Alternatives attempting to capture all product-related aspects in an information format (document, recipe, routing sheet) struggle to scale across a production network, particularly when this requires orchestration. Moreover, the semi-open nature of networked production introduces new challenges. Handling of sensitive information enjoys the presence of order holons as first-class citizens, which allowed automating a common solution applied when humans manage operations. Finally, a trust framework infers expected behavior and confidence levels to enable decision making in a partially uncontrolled environment. Acknowledgements This paper presents work funded by the Research Fund of the K.U.Leuven Concerted Research Action on Autonomic Computing for Distributed Production Systems and the EU GROWTH project MABE. This paper presents work funded by the Research Fund of the K.U.Leuven -Concerted Research Action on Autonomic computing for Distributed Production Systems – and the EU growth project MABE
References [1] Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P., 1998, Reference Architecture for Holonic Manufacturing Systems: PROSA, Computers in Industry, 37:255–274. [2] Valckenaers, P., Van Brussel, H., 2005, Holonic Manufacturing Execution Systems, CIRP Annals—Manufacturing Technology, 54:427–432. [3] Holvoet, T., Valckenaers, P., 2007, Exploiting the Environment for Coordinating Agent Intentions, in: Proceedings of the 3rd International Conference on Environments for Multi-agent Systems III, E4MAS’06, Springer-Verlag, pp. pp.51–66. [4] Verstraete, P., Saint Germain, B., Valckenaers, P., Van Brussel, H., Hadeli Van Belle, J., 2008, Engineering Manufacturing Control Systems Using PROSA and Delegate MAS, International Journal of Agent-Oriented Software Engineering, 2/ 28: 62–89. [5] Sihn, W., Palm, D., 2004, The 5-Days-Car: Increasing Agility in the Automotive Supply Chain, DAAAM, 415–416. [6] Saint Germain, B., Distributed Coordination and Control for Networked Production Systems, Ph.D. Thesis, K.U. Leuven, 2010.