A reflection on the knowledge flows of the Cynefin framework and their applicability within the context of new rolling stock introduction in the Netherlands Railways

As stated by Kurtz & Snowden, (2003) the Cynefin framework “originated in the practice of knowledge management as a means of distinguishing between formal and informal communities, and as a means of talking about the interaction of both with structured processes and uncertain conditions”. The framework provides a comprehensive overview of organizational knowledge exchange. Furthermore, it also identifies four main knowledge flows namely complex to knowable; knowable to chaotic; knowable to known; and chaotic to complex for more information see here. This post briefly elaborates these knowledge flows. In addition to this, the post also reflects on their applicability within the context of managing knowledge for new rolling stock introduction projects in the Netherlands Railways. Next section is distributed such that the first paragraph provides the elaboration and second the reflection on the applicability of each knowledge flow within the stated context.  

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Defining a railway system

There is no denying the rapid growth of the complex systems that continuously emerge in the world around us. Technological advancements spawn system after system, each increasing in interdependence on other systems that have come before. Systems, if ever they were separated are vigorously moving towards interconnectedness (Arnold & Wade, 2015). As systems continue to grow in scale and complexity, system integration (SI) has become a key concern (Madni & Sievers, 2014). (ISO 15288 Committee, 2015) states that the purpose of integration is: “to synthesize a set of system elements into a realized system (product or service) that satisfies system requirements, architecture, and design”. Moreover, SI involves interfacing and enabling the interactions of component elements to collectively provide the functionality needed by the system to accomplish its goals. SI increases in complexity when there are legacy systems that need to be integrated, and when humans are an integral part of the system (Madni & Sievers, 2014). All  of which is the case for the railway  operating network.

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Using Models to Assure Emergent Behaviors in Railway Transportation Networks

Abstract

Railway transportation networks are complex systems of systems which exhibit emerging behaviors. Emergence is the property that distinguishes a collection of things from a system which provides a behavior not attainable by any subset of system constituents. At the railway network level, the necessity of proving the desired emerging behaviors, revealing undesired patterns of behavior, and providing evidence that the rate of occurrence of a particular undesired one is below a specific threshold is stated in the RAMS standard. This short paper briefly discusses different aspects of this approach and elaborates on a successful example.

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Review: Rasmussen’s 1997 paper – Risk management in a dynamic society: A modeling problem

The article by Rasmussen (1997) begins with an introduction that risk management is treated differently across all relevant hierarchical levels of a socio-technical system. However, due to the dynamics of the system, treating risk-related decision-making in isolation does not enable us to recognize when we cross the boundary of safe operation. Thus, when assessing risks in a complex socio-technical system, we have to include the layers of legislation, management, work planners and system operators. As a result, we need to touch upon risk models of the disciplines varying from economics, organizational theories and cognitive psychology to engineering.

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