Journal of Systems Thinking · Volume 3, Issue 1 · 2023-10-30

Introduction to Soft Systems Methodology

Giles Hindle

https://doi.org/10.54120/jost.000008

Abstract

The purpose of this chapter is to introduce Soft Systems Methodology (SSM). The paper does not include a comprehensive description of SSM, but does outline its history, its fundamental nature and discuss how it might be used in practice. The origins of SSM date back to Lancaster University in the UK in the 1960s. It was the output of an action research programme which applied systems engineering methodology to the type of problems normally faced by managers. SSM should be viewed as an experiential learning cycle rather than a decision-making approach or an engineering approach. There are four main elements to the learning cycle of SSM. The first is a real-world situation which is perceived as problematical by stakeholders. The aim is to express the situation “as is” as best we can; to take a holistic view of the situation, capture alternative viewpoints and identify key issues. The tool used is Rich Picturing. The second element involves purposeful activity modeling. These models are driven by the assumption all situations will contain people at all levels trying to take purposeful action.The third element of SSM is a structured discussion between participants, which is informed by elements one and two. This element seeks to develop ideas, agreement and action plans to take the situation forward. The fourth element of SSM is action to improve the situation. SSM requires action to be taken – it is an ongoing process of experiential learning. SSM can be difficult for inexperienced users because it represents a general set of principles, rather than a specified method. Also, the modeling language of SSM is unusual within the field of applied systems thinking. It is highly flexible and takes time to master.

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The Journal of Systems Thinking publishes research on how things are organized, from ideas in a mind to systems in the world. It is home to systems thinking and the systems sciences, and to the bridge between how we know (epistemology) and what is (ontology).

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