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.

The railway network is  large scale, complex socio-technical system. Hence, it does not comprise only of its technical components, but also of the interaction with the humans, who are among other things developing, operating and maintaining it. Depending on the social/political environment and the organizational/management structure of the railway system concerned, a number of stakeholders performing different functions, can be involved within the life cycle phases of the system. These can include: passengers, freight operators, infrastructure manager, maintainers, contractors, train operators, suppliers, governments, safety authorities etc. The roles and responsibilities of these stakeholders can be contracted out to several other stakeholders or sub-contractors, depending on the social, political or legal considerations, size and complexity of the system or subsystem concerned and economic, organizational or managerial considerations (CENELEC, 2015).

What is important to keep in mind, according to (Wilson, 2014), is the need to decide early on what level of the system we are gathering data at, and what level we might be implementing changes for. Within (CENELEC, 2015), the sequence “system, subsystem, component” is used to demonstrate the breakdown of a system into its constituent parts. The precise boundary of each element (system, subsystem and component), either physical or functional, will depend upon the system in question. The system itself is contained in an operational environment. The behavior and state of the system might change if there is a change to the functionality or interaction of a subsystem or component. A system responds to inputs to produce specified outputs, whilst interacting with an environment (CENELEC, 2015).

The use of the terms system and subsystem can depend on the point of view taken. Something which is regarded as a system by the people who developed it might be regarded as a subsystem by people who use it as part of their system. This difference of points of view is rationalized by the concept of nested systems in a system hierarchy (CENELEC, 2015). According to the nested systems concept, systems are themselves built up of smaller systems that themselves are built up of even smaller systems and so on. For convenience, multi-level nested systems are usually handled on the basis of groupings of systems at successive levels of a hierarchy. This provides visibility of different levels and enables consideration of:

 – the interactions and interfaces between the “system under consideration” and its “siblings” i.e. the inter-related subsystems / components, and

– the influences and interactions between the “system under consideration” and its environment (i.e. the “parent” or “containing system”)

 Other system layers may be interpreted as contextual, and although we need to understand a certain amount of them we would not be working at a  deep data collection level (Wilson, 2014). All of which, raises the question of what the system actually is, and what should be included (or not).

Currently, there are a lot of differences in the interpretation of “system”, by different communities and individuals, all of which can lead to miscommunication (Sillitto et al., 2017). According to (Sillitto et al., 2017), the term system serves different and important purposes, and misinterpretation should be avoided, because that can lead to adverse consequences. The aim is to synthesize a definition, which can serve as a backbone for all those who will use the term system. The definition should help to:

  • Create a common understanding
  • Create an understanding of all critical attributes which are required to define a system

In order to converge to a definition of “system”, numerous definitions were collected by surveying mainly engineering literature and (railway) relevant standards. These definitions were analyzed for differences and similarities. Based on this, key components could be extracted. In order to do this, text referring to the definitions was analyzed to identify frequently occurring words used to describe system and these could be grouped in the themes mentioned in the first row of Table 1. This division is based on the author’s interpretation of the academic literature (i.e. synonyms) and the keyword are intended to characterize the different parts which need to be included, in order to define a system.

  combination/collection elements/entities interrelationship/interacting purpose/objective behavior as whole organized/gathered boundary
(ISO 15288 Committee, 2015) x x x x   x  
(Boardman & Sauser, 2006) x x x   x x  
(Blanchard & Fabrycky, 2011) x x x x      
(CENELEC, 2015) x x x x x   x
(Hybertson, 2016) x x     x    
(Cameron, Crawley, & Selva, 2016) x x x   x    
(International Council on Systems engineering (INCOSE), 2015) x x x x      
(Wilson, 2014) x x x x x   x

Table 1: Comparison of system definitions

In all of these definitions, common elements mentioned to define a system include: collections, elements, interactions, defined purpose/objective, behavior as a whole, organized and boundary.

Synthesizing this information, “system” can be defined as: a combination of elements interacting/functioning together within a certain context to achieve a common goal. Moreover, the whole is greater than the sum of parts.

 The latter part refer to emergent behavior: the system performs functions and carries out purposes that do not reside in any component system. These behaviors are emergent properties of the entire system and cannot be localized to any component system (Maier, 1998).


Arnold, R. D., & Wade, J. P. (2015). A Definition of Systems Thinking: A Systems Approach. Procedia Computer Science, 44, 669–678. https://doi.org/10.1016/J.PROCS.2015.03.050

Blanchard, B., & Fabrycky, W. (2011). Systems engineering and analysis, Pearson. Boston.

Boardman, J., & Sauser, B. (2006). System of Systems – the meaning of of. 2006 IEEE/SMC International Conference on System of Systems Engineering, (April), 118–123. https://doi.org/10.1109/sysose.2006.1652284

Cameron, B., Crawley, E., & Selva, D. (2016). Systems Architecture. Strategy and product development for complex systems. Pearson Education.

CENELEC. (2015). NEN-EN 50126-1 Railway Applications – The Specification and Demonstration of Reliability, Availability, Maintainability and Safety (RAMS) – Part 1: Generic RAMS Process. 1.

Hybertson, D. W. (2016). Model-oriented systems engineering science: a unifying framework for traditional and complex systems. Auerbach Publications.

International Council on Systems engineering (INCOSE). (2015). Systems engineering handbook- A guide for system life cycle processes and activities (Cs. David d. Walden, eSeP Garry J. Roedler, eSeP kevin J. Forsberg, eSeP r. douglaS Hamelin Thomas m. Shortell, ed.). New Jersey: John Wiley & Sons, Inc. All.

ISO 15288 Committee. (2015). NEN-ISO/IEC/IEEE 15288 Systems and software engineering – System life cycle processes. Nederlands Normalisatie-instituut.

Madni, A. M., & Sievers, M. (2014). System of systems integration: Key considerations and challenges. Systems Engineering, 17(3), 330–347. https://doi.org/10.1002/sys.21272

Maier, M. W. (1998). Architecting principles for systems-of-systems. Systems Engineering, 1(4), 267–284. https://doi.org/10.1002/(SICI)1520-6858(1998)1:4<267::AID-SYS3>3.0.CO;2-D

Sillitto, H., Dori, D., Griego, R. M., Jackson, S., Krob, D., Godfrey, P., … McKinney, D. (2017). Defining “System”: a Comprehensive Approach. INCOSE International Symposium, 27(1), 170–186. https://doi.org/10.1002/j.2334-5837.2017.00352.x

Wilson, J. R. (2014). Fundamentals of systems ergonomics/human factors. Applied Ergonomics, 45(1), 5–13. https://doi.org/10.1016/j.apergo.2013.03.021

Using Models to Assure Emergent Behaviors in Railway Transportation Networks


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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Introduction-Mohsen Jafari

Research Fellow

Mohsen Jafari is Post-doc Research Fellow, who has joined the SIRA project since May 1st 2019. Prior to that, he was Research Fellow at the University of Twente (Faculty of Behavioural, Management and Social Sciences), and was involved in doing research and teaching on topics related to the economic evaluation of healthcare technologies.    

Mohsen has done both his BSc and MSc degrees in Industrial Engineering at Sharif University of Technology, and University of Tehran, respectively. 

Afterwards, he had been working as Project Coordinator, Planner, and Systems Analyst in different industries for 3-4 years in Tehran/Iran. Mohsen also holds a Ph.D. in Operations and Technology Management from the University of Melbourne (Australia). His research interests are innovation, technology management, product development, operations management, and computational social science. His e-mail address is m.jafarisonghori@utwente.nl.