Modelling for Organisational Learning

By Wissam El Hachem
Currently located in Bergen, Norway
Master in System Dynamics, University of Bergen

Interests: Supply chain management, Network flow, Production management


This short essay will try to define and explain organizational learning, its importance, the importance of modelling and what kind of modelling fits well with this purpose.

First, what is Organizational Learning and why is it important? Managers within a firm are expected to control a complex web of variables so the output of such a system would meet the best interests of the company. This is organizational learning. It has different levels, such as individual learning, culture, process, creativity and knowledge management which are summarized in Wang & Ahmed (2002). Mental Models are the basic elements in any company, and they drive any change that a company undertakes. A definition from (Doyle & Ford, 1998) is that mental models are a dynamic system with “a relatively enduring and accessible, but limited, internal conceptual representation of an external system whose structure is analogous to the perceived structure of the system”. Mental models are simplistic compared to life and due to our attachments to them, they form a hurdle for a firm to develop into a learning entity.  An endogenization of the learning guaranteed by a company policy of feedback and open conversation would render the learning an integral part of the company’s culture, sustaining long term change. If properly handled, organizational learning is essential for a sustained competitive advantage. To be able to control organizational learning driven by mental models, a tangible set of indicators must be developed.

Second, why is modelling important? Building models is building a framework that translates the mental models, and specifically the set of indicators, into a computer model. This computer model is controllable and modifiable faster than our mental models. “Learning takes place when people discover for themselves contradictions between observed behaviour and their perceptions of how the ‘world’ should operate” (Morecroft, 1994).

Third, what kind of modelling fits this purpose well? System Dynamics (or SD) is a computer simulation methodology that investigates complex dynamic problems. In most cases, there are feedback, delays and non-linearity which rules out pure analytical reasoning, rendering computer simulation the only feasible option. Causal Loop Diagrams (or CLD) and Stocks & Flows (or S & F) are the two possible manifestations of the causal structure of the system under investigation. SD is perceived to be a match for modelling mental models. A major problem of this simulation approach is replication. Hence the need to explore potential methods that might systemize early stage analysis and variable selection in an SD context. Among others, there are Analytic Network Process (or ANP), Analytic Hierarchy Process (or AHP) and Principal Component Analysis (or PCA). Since PCA requires the assumption of linearity between variables and AHP assumes no interdependency between the variables, ANP is judged to be the best fit for the purpose of investigating and possibly reducing the initial pool of variables. For those interested  in reading more about ANP, it is a decision making methodology developed by Professor Thomas Saaty. A hybrid SD & ANP approach seem to be a good fit. Modular approach to system dynamics modelling is common, and ANP would be the perfect fit for conceptualizing and building these blocks.

For a more developed investigation of these topics and a list of references, please refer to this paper.

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