Identifying Relationships of Interest in Complex Environments by Using Channel Theory

Complex environments show a high degree of dynamics caused by vital interactions between the objects within those environments and the alterations the set of objects and their characteristics within those environments go through over time. Manufacturing is an area where those kinds of dynamics are quite obvious as e.g. manufacturing companies increasingly have to manufacture new products and variants of products or to integrate new equipment or machinery to an existing production system. We show that we can tame the level of complexity in dynamic environments by identifying relationships of interest between the objects in such environments. Knowing about the relationships that are relevant to a particular task or of a particular interest between the objects in complex environments gives us insights on how those objects behave and interact with one another under specific circumstances. We can use this information to reveal regularities that govern such kind of behaviour and interaction between the objects and thus can predict the behaviour of the overall system when particular surrounding conditions are met. To identify a type of relationship of interest between the objects in a specific complex environment, we apply the theory of Information Flow (IF for short), also known as Channel Theory put forward by Barwise and Seligman in 1997, to the manufacturing area to find out which equipment and tooling might be used to conduct a particular production step. We chose the Channel Theory as it is a solid theory, well designed for distributed systems, and our application area of complex environments can perfectly be seen as such a distributed system. Furthermore, there exists a series of applications based on the Channel Theory that proves that this theory is able to find relationships between two or more sets of objects from different contexts in a distributed environment. While applying the Channel Theory in the application domain of manufacturing, we recognise that the so far known approaches from the literature that have been used in other application areas are not capable of addressing the higher level of complexity in this environment adequately. We observe that we should revise especially the usage of IF classifications, which are one of the main building blocks in constructing the channel to infer the relationships between objects. Furthermore, to cope with the high degree of complexity in our application domain, we introduced an iterative 2-step approach based on composite channels to derive the relationships of interest between the production steps that has to be conducted and the production capabilities of the available equipment. We enhance the way how the Channel Theory has been applied so far by using an iterative approach for finding out the relationships between product specifications and production capabilities. By introducing this iterative approach, we show with the help of an example from the manufacturing domain that the Channel Theory can also be applied successfully in complex environments. References Barwise, J., & Seligman, J. (1997). Information Flow: The Logic of Distributed Systems (reprinted). New York: Cambridge University Press. Checkland, P. B. (1981). Systems Thinking, Systems Practice. Chichester: John Wiley & Sons Ltd. Checkland, P. & Scholes, J. (1990). Soft Systems Methodology in Action. Chichester: John Wiley & Sons Ltd. Stowell. F. A. (2016) Soft not Vague. On Peter B.Checkland, Systems Thinking Systems Practice a 30 year retrospective, Chapter in, Schlüsselwerke der Systemtheorie, Ed D. Baeker, Published Springer, pp375-402