The project targets an inter-disciplinary approach towards complexity modelling and controlling of collaborative enterprise networks in production industry. For the purpose of closing a gap in network theory latest empirical surveys on successful network give strong hold to the hypothesis that there exist only two different paradigms of control in networks (see Fig. 3). All intermediary forms do either fail or evolve to either of the two forms over time. Namely, the first is the paradigm of "guided networks" which comprises features of hierarchical control in terms of first order cybernetics with the controller being a constituent element of the system. The second paradigm is the "self-organised, organic network" which is implicitly managed by Adam Smith’s "invisible hand" of an external context, not being explicitly a control element of the system (second order cybernetics). Hence, stability and instability issues of industrial networks might be driven by factors related to appropriate network control, although these driving factors have not been established, yet.
It is the objective of the COLL-PLEXITY proposal to bring together the recent insights from complexity science into the field of collaboration in production industry and allow for fully exploiting the benefits it may yield in comparison with traditional organisational paradigms. Despite the great complexity and variety of systems, universal laws and phenomena are essential to their inquiry and understanding. Scientific endeavour is based, to a greater or lesser degree, on the existence of universality, which manifests itself in diverse ways. In this context, the study of complex systems as a new endeavour strives to increase the ability to understand the universality that arises when systems are highly complex. A study of universal principles does not replace detailed description of particular complex systems. However, universal principles and tools guide and simplify inquiries into the study of specifics. For the study of complex systems, universal simplifications are particularly important. Sometimes universal principles are intuitively appreciated without being explicitly stated. However, a careful articulation of such principles can enable us to approach particular systems with a systematic guidance that is often absent in the study of complex systems.
After beginning to describe complex systems, a second step is to identify commonalities. One might make a list of some of the characteristics of complex systems and assign each of them some measure or attribute that can provide a first method of classification or description.
- Elements (and their number)
- Interactions (and their strength)
- Formation/Operation (and their time scales)
- Diversity/Variability
- Environment (and its demands)
- Activity(ies) (and its[their] objective[s])
Such descriptions of deterministic networks should be combined with the non-lineair approaches of complexity science complemented with the models of theoretical evolutionary biology, e.g. Adaptive Dynamics, Markov chains for dispersal.
Taking into consideration the different dimensions of complexity in socio-technical system, the two network- and control-paradigms entail different types and patterns of complexity and allow for tackling different types of collaborative problems. Ashby’s "Law of Requisite Variety" postulates that only complexity can absorb complexity and the project aims at abiding by this law in terms of matching the collaborative system’s variety (behaviour) with the complexity of the problem to be solved, henceforth increasing the Complexity Handling Capability. The project will contribute in an interdisciplinary way by bringing together the currently isolated research fluxes of complex problems and complex systems from different scientific disciplines.
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The project will contribute to the issues of interdisciplinary complexity definition (in order to bring different mental frameworks closer together), translate this definition into a model of problem complexity and imposed complexity (the topic of emergence) and a model of production system complexity on the other hand (internal complexity).
The paramount scientific objective is bringing together the two complexity perspectives of complex problems and complex socio-technical systems into one match-making "generic model of complexity". This will help determine the right network paradigm for different types of collaborative problems. According to the framework of complexity modelling outlined in the figure above the envisaged results of the project are:
- a classification scheme for complex problems and emergence as well as complex systems in production industry
- a case specific model of problem and system complexity
- a generic model of complexity for system coordination in collaborative production networks
- a framework for problem and system complexity matching
- an outlook towards complexity metrics for measuring and controlling complexity in dynamic networks
- a pilot site evaluations
The guiding research questions to be answered along the proposal’s proceedings are:
- What are the challenges in setting up an managing different types of networks in collaborative reality (production industry)
- How can complexity of an socio-technical system be described?
- What are the characteristics of complexity in different types of natural and artificial systems and networks?
- How can complexity in such systems be described and measured against the background of a holistic complexity management?
- What explanations are there for complexity management in different network architectures?
- Is there a correlation between the type and complexity of the collaborative problem and the most suitable underlying network structure for solving it?
These questions build the guiding structure for the proposal’s scientific impetus. Nevertheless, they contain a high degree of risk of being unanswerable in the project’s context. Therefore the project consortium restricts itself to focussing on complexity matching. The specification of quantitative complexity measures as a constituent controlling element to a holistic complexity management can therefore only be subject to subsequent research endeavours; relying on a strong basis of knowledge and understanding of complexity issues in collaborative systems.
However, with the field of complexity research still being a scattered patchwork of insights, the pragmatic and interdisciplinary approach of this proposal holds the potential of yielding valuable insights into complexity modelling in today’s networked production industry.
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