Modelling of Complex Supply Networks Georg Weichhart1, Chris Stary2, Stefan Oppl2 Profactor Produktionsforschungs GmbH, Im Stadtgut A2, 4407 Gleink-Steyr 2 Department of Business Informatics – Communications Engineering, University of Linz
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Abstract In order to capture the increased complexity of products and production processes as well as market requirements, collaboration models representing organisational networks enable improvements at a process level. To bridge the gap between divergent goals of partners and to support distributed decisionmaking, novel collaboration frameworks are necessary. In this paper, we identify several requirements for such frameworks.
1. Introduction Collaboration in organisational networks is seen as a means to respond to challenges imposed by the business environment. Since coordination of different legal entities with divergent goals is time- and resource-consuming, support for efficient and effective coordination in collaborative networks of enterprises is necessary. They aim to support sustainability on the network level. Companies working in the Manufacturing Domain, especially in Europe, are currently exposed to a variety of forces (cf. [1], [2]): * Globalisation (of Markets and the Supply Chain) * Increased Complexity (of Products and Processes e.g. an increased number of (personalised) features) * Increased Dynamics (including faster response and more frequent changes e.g. by shortened Product Life Cycles and decreased Time to Market) Management paradigms like “agile” or “lean” have tried to respond to these challenges. But still companies subscribing to these paradigms have failed to survive. Current research in the manufacturing domain indicates a need for technologies and methodologies enabling sustainability in economical terms [3].
Many (larger) companies respond to these challenges by focusing on core competencies and outsourcing other services. On the one hand, this behaviour simplifies the production related tasks for a single company. On the other hand this results in large complex organisational networks. The autonomy of different legal entities still working to achieve their internal goals and the size of today’s overarching supply chains require to consider organisational networks as complex (social) systems.
2. Enterprise Modelling Enterprise Modelling for Integration (EMI) is seen as a systemic approach to support enterprise interoperability [2],[4]. The entire enterprise system is modelled from a dedicated perspective. One of the first approaches to provide a framework capturing different perspectives has been the “Generalised Enterprise Reference Architecture and Methodology” (GERAM) [5]. A more recent approach towards a commonly accepted meta-model is the Unified Enterprise Modelling Language [6]. Other Frameworks for enterprise modelling, with special attention to information systems, include Zachman's Architecture Framework [7], and The Open Group Architecture Framework (TOGAF) [8]. A different approach is based on the idea of holonics. While it is also a systemic modelling paradigm [4], it has its roots in Intelligent Manufacturing. In contrast to those mentioned above, active elements (called agents or holons) exhibiting intelligent behaviour are used as a modelling construct. In this case, integration is less of an issue. However, research is performed with respect to coordination and negotiation between independent (intelligent) agents.
3. Structuring requirements for a modelling approach for complex supply networks
Supplier networks can be seen as a complex system. In a complex system, “great many independent agents are interacting with each other in a great many ways” [9]. Due to the independence of single agents, spontaneous self-organisation emerges, dependent on environmental states (ibidem). It also has to be noted that the overall behaviour of a complex system can’t be explained by summarising the individual behaviours of its agents. A certain amount of non-linearity and uncertainty exists in the overall system. Using centralised approaches individual views of the agents need to be integrated into a single large model, which in turn needs then to be communicated to all. It has also to be assumed that the interpretation of this single model is shared by all agents. In order to understand a model and the information implicitly contained within requires knowledge of the modelled context (being the context of one of the agents). Consequently, even more information needs to be communicated for successful cooperation. What still can’t be accounted for is that autonomous agents follow individual goals. Detailed models also cannot provide an overview in order to reach a common understanding of the global model [10]. To support the supply chain using a distributed modelling approach imposes requires keeping the autonomy of the suppliers in providing their individual models, and support for coordination between the suppliers via their models. Given them, we are able to identify several requirements for constructing and using a framework: * Providing the possibility to model the individual behaviour. * Monitoring and interpretation capabilities concerning the behaviour of network members. * Supporting the request for necessary but so far missing information from other agents. * Supporting of (local) coordination allowing decentralised and autonomous decision-making. A framework meeting the addressed requirements needs to increase the robustness of the overall collaboration. The adaptation to local disturbances should involve a minimal set of partners. The runtime support for exchanging information when needed could result in speeding up the design of models, as no large models have to be defined as wholes but rather local ones, which can be refreshed and updated when necessary. In this way, such a framework also has to accounts for uncertainty and dynamics exhibited by complex systems. More support at runtime is necessary to cope with dynamics, uncertainty, and decentralization. User-centred design of tools supporting complex systems is necessary to allow coordination between independent partners and support involved users in their individual style of working.
Acknowledgements Part of this work has been conducted in the course of the R&D projects CrossWork and I*PROMS Network of Excellence, funded by the EC.
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