Modelling as learning: A consultancy methodology for enhancing learning in management teams

Lane, David C.

doi:10.1016/0377-2217(92)90007-v

Cited by 132 publications

(89 citation statements)

References 13 publications

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“…This distinction is a very common one in SD, see for example, Wolstenholme's overview in the special issue on SD in JORS in 1999. 29 Stage 1: Elicit qualitative mental models from management of perceived interrelationships using causal loop diagramming (CLDs), resulting in a refined version of a 'strategy map'. 12,13 From this map, distil key performance indicators (KPIs) and assign preliminary targets.…”

Section: System Dynamics As a Modelling Methods For Bsc Developmentmentioning

confidence: 99%

“…Not only is the distinction between qualitative and quantitative modelling a common one in the SD literature, but a two-stage approach as suggested here to system dynamics modelling is also the 'normal' way of conducting system dynamics interventions in organizations. [14][15][16][29][30][31][32][33] In this article, we show how this generic approach can be tailored to support the process of developing BSCs. We will illustrate how this approach yields advantages compared to more 'conventional approaches', advantages that stem from the systematic approach to 'strategy mapping' that SD offers, as well as advantages that originate from the smooth transition from qualitative models to quantified simulation.…”

Section: System Dynamics As a Modelling Methods For Bsc Developmentmentioning

confidence: 99%

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Relevance assumed: a case study of balanced scorecard development using system dynamics

Akkermans¹,

Oorschot²

2005

Journal of the Operational Research Society

127

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The balanced scorecard (BSC) has become a popular concept for performance measurement. It focuses attention of management on only a few performance measures and bridges different functional areas as it includes both financial and non-financial measures. However, doubts frequently arise regarding the quality of the BSCs developed as well as the quality of the process in which this development takes place. This article describes a case study in which system dynamics (SD) modelling and simulation was used to overcome both kinds of problems. In a two-stage modelling process (qualitative causal loop diagramming followed by quantitative simulation), a BSC was developed for management of one organizational unit of a leading Dutch insurer. This research illustrates how, through their involvement in this development process, management came to understand that seemingly contradictory goals such as customer satisfaction, employee satisfaction and employee productivity were, in fact, better seen as mutually reinforcing. Also, analysis of the SD model showed how, contrary to ex ante management intuition, performance would first have to drop further before significant improvements could be realized. Finally, the quantitative modelling process also helped to evaluate several improvement initiatives that were under consideration at the time, proving some of them to have unclear benefits, others to be very promising indeed.

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Section: System Dynamics As a Modelling Methods For Bsc Developmentmentioning

confidence: 99%

Section: System Dynamics As a Modelling Methods For Bsc Developmentmentioning

confidence: 99%

Relevance assumed: a case study of balanced scorecard development using system dynamics

Akkermans¹,

Oorschot²

2005

Journal of the Operational Research Society

127

View full text Add to dashboard Cite

show abstract

“…(1) the Reference Group approach (Stenberg 1980), (2) the Strategic Forum (Richmond 1997), (3) the stepwise approach (Wolstenholme 1992), (4) modelling as learning (Lane 1992), (5) strategy dynamics (Warren 1999;2005), and (6) the "standard method" of Hines (Otto & Struben 2004). All of these approaches share much in common and are examples of Friend's (2006) label proliferation tendency as well as Westcombe, Franco, and Shaw's (2006) search for PSMs integrated with hard modelling.…”

Section: A Short History Of Client Involvement In System Dynamicsmentioning

confidence: 99%

Group model building: problem structuring, policy simulation and decision support

Andersen

Vennix

Richardson

et al. 2007

Journal of the Operational Research Society

224

158

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to refer to a bundle of techniques used to construct system dynamics models working directly with client groups on key strategic decisions. We use facilitated face-to-face meetings to elicit model structure and to engage client teams directly in the process of model conceptualization, formulation, analysis, and decision making."Group Model Building" (GMB) as we use the term has a cosy, narrow, some might say parochial ring to it. In this brief title, we fail to mention that the models our groups build are always system dynamics models. Furthermore as we discuss below, GMB has much in common with six other named techniques for gaining client involvement in strategic problem finding and problem resolving activities, all using system dynamics models, none of which use the term Group Model Building. We also see our work as sharing many common concerns with the viewpoints expressed in JORS' previous symposium on Problem Structuring Methods. Eden and Ackermann (2006) list four similarities between PSMs: (1) use of a model as a transitional object, (2) increasing the overall productivity of group process, (3) attention to facilitation and effective group process, and (4) appreciation of the significance of facilitation skills. If we were to use Eden and Ackermann's (2006) four points of similarity as a loose definition of PSMs we would have to conclude that GMB is another PSM.We are probably an example of Westcombe, Franco, and Shaw's (2006) fifth challenge "to design a new PSM that explicitly integrates with a certain approach to hard modelling" while trying to avoid working "simply as a bolt-on to hard modelling".

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“…virtual world experimentation avoids many of the limitations of real world experimentation, such as uncontrolled biological variation, environmental uncertainty, inaccurate measurement, delayed feedback, high costs and political interference. as a result, participants' understanding of the system's dynamic behaviour, improvement of the virtual world model, and improvement of system management policy are all accelerated (Lane 1992).…”

Section: Participatory Model Design and Testingmentioning

confidence: 99%

Better simulation modelling to support farming systems innovation: Review and synthesis

Woodward

Romera

Beskow

et al. 2008

New Zealand Journal of Agricultural Research

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Simulation modelling is a methodology that appears highly suitable for use in farming systems innovation. However, if the aim is to improve farming systems by supporting farmer behaviour change, model-based approaches seem to have delivered surprisingly little observable benefit to date. This paper identifies the problems underlying this apparent lack of impact, and proposes better approaches to improve on-farm benefits from farming systems modelling. A key principle that has been neglected in farm simulation modelling is intimate involvement of clients (defined as "the individuals or groups whose approval is needed for change to be implemented") throughout the innovation process. We argue that client participation is essential in the problem definition, model design and testing and policy design and evaluation phases of model-based research projects. The role of a simulation model within the innovation process, then, is to be a jointlycreated "virtual world" wherein experiments may be conducted to facilitate learning about the relevant system. We argue that whole farm simulation models that use decision rules to specify alternative farm management strategies are the best available form of virtual world models of farming systems. Besides appropriate client input, high quality models require excellent software development practices and strenuous attention to building user confidence. The latter should include analysing the model to assess its stability and sensitivity properties, before using it to simulate experiments that compare several management alternatives under a range of environmental and local conditions. This approach allows estimation of the variations in farm system performance that are likely to result from interactions between initial farm state, farm management policy and future weather and markets. On the other hand, using simulation models to discover "optimal" farm systems would usually be inappropriate due to the complexity arising from multiple-stakeholder views, multiple-criteria, and the dynamic nature of farming systems problems. An improved systems modelling methodology is proposed that should be better able to provide benefits into farming practice.

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Modelling as learning: A consultancy methodology for enhancing learning in management teams

Cited by 132 publications

References 13 publications

Relevance assumed: a case study of balanced scorecard development using system dynamics

Relevance assumed: a case study of balanced scorecard development using system dynamics

Group model building: problem structuring, policy simulation and decision support

Better simulation modelling to support farming systems innovation: Review and synthesis

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