J.A.M. Vennix

Group model-building: tackling messy problems

Group model-building here refers to a system dynamics model-building process in which a client group is deeply involved in the process of model construction. The problem that is modelled can be reasonably well defined, but it can also take the form of an ill-defined or messy problem, i.e., a situation in which opinions in a management team differ considerably. These messy managerial situations are difficult to handle, primarily because thus far little theoretical work has been conducted to shed more light on the question why these messy situations exist and why it may be difficult for a management team to reach agreement. This article fills this theoretical gap by drawing on literature from sociology, (social) psychology and small-group research. Insights from this literature are discussed and translated into guidelines for conducting Group Model-Building projects for messy problems. The article ends with the conclusion that system dynamicists should include Group Model-Building and facilitation training in their teaching programs. Copyright

Group model building: adding more science to the craft

This article discusses the issue of making group model building interventions more of a science than an art by outlining a number of requirements of a research program. Important elements that are discussed are the various goals of group model building interventions and the components and scripts of an intervention. Then the problem of theory development is discussed, together with a number of hypotheses which the authors suggest need more investigation. The article also discusses issues related to the selection of an appropriate research design, as well as a number of thorny measurement problems.

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

The authors use the term, ‘Group Model Building’ (GMB) (Richardson and Andersen, 1995; Vennix, 1996, 1999) to refer to a bundle of techniques used to construct system dynamics models working direct...

Group Model-Building To Facilitate Organizational Change: An Exploratory Study

An important objective of most system dynamics modeling projects is to support strategic decision making. This paper describes a (qualitative) modeling project where the primary goal was to establish consensus regarding the problem situation and commitment to the action necessary for change. The project was conducted with a group of mid-level managers of a company at the beginning of a period of organizational change. This group of managers engaged in a series of group model-building sessions, facilitated by the authors. Extensive evaluation of the project results indicates that consensus and commitment with regard to the problem have been established, but that the project was not successful in creating a full consensus on the course of action.

Group model building: a decision room approach

In this article, the authors investigate the benefits and drawbacks of using group model building in a group decision room. An approach to group model building adapted for use in a group decision room is described. The authors then determine the feasibility of such an approach in a pilot study and compare the results of the traditional version with those of the electronic version used by a group of 32 graduate students. Specifically, the authors assess the effects on quality of communication, satisfaction, knowledge (of others’ opinions and of the problem), consensus, and commitment. The evaluation shows that the electronic version yields positive results similar to the nonelectronic version. This result strengthens the idea that electronic support can be used to avoid the direct costs of convening groups, as is necessary in the traditional form. A number of improvements to both the intervention and the evaluation procedure are discussed.

Building Consensus in Strategic Decision Making: System Dynamics as a Group Support System

System dynamics was originally founded as a method for modeling and simulating the behavior of industrial systems. In recent years it is increasingly employed as a Group Support System for strategic decision-making groups. The model is constructed in direct interaction with a management team, and the procedure is generally referred to as group model-building. The model can be conceptual (qualitative) or a full-blown (quantitative) computer simulation model. In this article, a case is described in which a qualitative system dynamics model was built to support strategic decision making in a Dutch government agency.Since people from different departments held strongly opposite viewpoints on the strategy, the agency had discussed its strategic problem for more than a year, but was obviously not able to reach consensus. The application of group model-building was successful in integrating opposite points of view, as well as in fostering consensus and creating commitment.The purpose of the article is twofold: first, to illustrate the process of group model-building with system dynamics; second, to evaluate why it was successful. Evaluation results reveal the importance of both systemic thinking through model-building and the role of the facilitator in catalyzing the strategic decision-making process.

Validity in SSM: neglected areas

Contrary to the prevailing notion in hard OR, in soft system methodology (SSM), validity seems to play a minor role. The primary reason for this is that SSM models are of a different type, they are not would-be descriptions of real-world situations. Therefore, establishing their validity, that is representativeness with regard to reality, is useless. However, in this paper, we employ a broader meaning of validity (well-groundedness) and demonstrate that this surfaces a couple of neglected areas in the discussion of validity in SSM. These relate to (a) the notion of SSM as a learning system, (b) the idea to improve real-world problem situations, and (c) learning about the effectiveness of SSM itself.

Causal loop diagrams as a de-escalation technique

Escalation of commitment, the tendency of decision makers to keep on investing in losing courses of action, has been shown to be a costly decision bias that affects many areas of decision making. Even though escalation is a widely studied phenomenon, there has been comparatively little research on how to avoid this bias. The present study focuses on de-escalation of commitment and proposes that causal loop diagrams (CLDs) can help to decrease escalating commitment to a failing course of action. By means of an experiment, this study shows that using a CLD decreases escalation tendencies.

Improving Operations Management by Synthesizing Participant Knowledge and System Data

With the advent of Enterprise Resource Planning (ERP) systems such as SAP and Oracle, availability of data is no longer the bottleneck to decision making in many organizations. Instead the reverse seems to apply more frequently. Integrated organization-wide computer systems overwhelm managers with data to such an extent that it becomes difficult to assess its relevance for managing operations. A number of methodologies attempt to help management to distill meaning from large amounts of data, such as the Balanced Scorecard, discrete event simulation, qualitative modeling approaches and system dynamics. These methodologies enable managers to identify multiple performance indicators and determine tradeoffs between effects of proposed improvements. Implementation of improvements however entails small or large scale organizational change. Methodologies are commonly used in an expert mode, which makes them prone to many of the potential traps of change management, such as lack of commitment. The approaches can often only be used by experienced consultants. Indeed the literature shows many cases in which ERP systems are not used to their full extent, even though expert consultants supervise implementation months or years after the initial roll-out. Alternative methodologies that enable problem owners to identify problems and combine knowledge and system data in solving these are available. Group model building, which combines system dynamics and active involvement of problem owners, may circumvent the traps to which expert methods are prone. In this chapter we explore three projects in which master students with limited experience used group model building to model and improve operations management. We address the context in which the projects were carried out and the process of client participation and model construction. We describe effects in terms of end products, quality of solutions, results as judged by the clients (such as commitment and implementation) and outcomes of system changes. Our contribution to the literature on ERP systems is a definition of the necessary conditions that need to be in place, in order for group model building to succeed in improving operations management. Our contribution to the group model building literature is a clarification of where the standard process of participative modeling needs to be adapted when used in operations management.

Non-Conscious vs. Deliberate Dynamic Decision-Making—A Pilot Experiment

The purpose of this paper is to explore the effects of non-conscious vs. deliberate ways of making decisions in a dynamic decision-making task. An experimental setting is used to study this question; three experimental groups are distinguished: immediate decision-making (only very limited time for deliberate cognitive processing), considerate decision-making (relatively long time for deliberate cognitive processing), and distracted decision-making (time for non-conscious cognitive processing only). As experimental stimulus, a simulator based on the Kaibab Plateau model was employed. With a sample size of more than 100 experimental participants, group differences are not significant for most data examined. Implications comprise the formulation of a framework to guide further research. The value of this paper lies in the fact that it connects to a recent discussion in psychology and transfers it to a domain in the core interest of the system community: decision-making in situations with dynamic complexity. Furthermore, it offers a range of improvement points for potential follow-up studies.