The system dynamics approach and the methodology for multicriteria decision aid as tools for organizational learning

The system dynamics approach and the methodology for multicriteria decision aid as tools for organizational learning

Intellectual Capital, Management Theory 1 Comment 447


The objective of the present work is to demonstrate that the joint utilization of the system dynamics approach and the methodology for multicriteria decision aid enables the organizational learning process improving therefore the decision processes in the organizations.

A scientific approach was developed based on real experiences grouped according two categories: simulation experiences and field experiences. The simulation experiences were done with university students. It was created a controlled experimental environment to use the ‘‘Beer Distribution Game’’. The original rules to evaluate the learning process were modified to test our hypothesis. The field experiences were based on a pharmaceutical company that was passing through a transformation process.

From these experiences we concluded that the joint utilization of the proposed tools present specifics advantages as:

The system dynamics approach facilitates the formalization and communication of knowledge due to the perception of causal links using simulation tools

The system dynamics simulations make evident the counter intuitive feedback impact in complex systems.

The methodology for multicriteria decision aid allows to make explicit and to address scientifically the desires and beliefs of organizational agents (stakeholders) in order to formalize them into the organizational strategy and objectives.

Key words: organizational learning, system dynamics approach, methodology for multicriteria decision aid, Beer Game, pharmaceutical companies.


This paper is a brief summary of a dedicated effort seeking to improve the decision processes in organizations. During several years of professional and academic practice, the system dynamics approach (SD) (WOLSTENHOLME 1990), and the multicriteria decision analysis (MCDA) (POMEROL & BARBA ROMERO 1993, ROY 1985) emerged as especially effective tools to improve the decision processes in organizations. The combined use of the two techniques profits from the problem understanding resources brought by the system dynamics approach (SD) and the objectiveness to support the decision process of the multicriteria decision analysis (MCDA).

The paper is structured in 4 parts: The presentation of the research problem, the conceptual framework, the experiences and the research conclusions. The first part set the scope, objectives and drivers of the research. The second sets the theoretical basis. Afterward, the experiences context is described, the decision framework explained, an analysis identifying and evaluating the evolution of the used knowledge is explored and finally the relationship between the knowledge in use and the resulting performance of the organization under study is analyzed. At the end, a set of conclusions related to the types of knowledge identified and the impact of the suggested techniques are made.

1. Organizational Learning problem definition

Organizational learning has been studied from different perspective and through all related fields: sociology, cognitive sciences, organizational behavior, and others (HUBER 1996, MIDLER 1992). Specific concepts have been defined like, knowledge, organizational memory, deuterolearning, and organizational cognition. However, major issues still requiring further research (ARGYRIS & SCHON 1996 pp. 200-201):

  • What does it means productive learning?
  • At what level of aggregations does it make sense to evaluate productive organizational learning?
  • How to manage the inherent barriers for organizational learning in real world organizations?
  • What kind of interventions can enable the organizational learning process?

From a decision aid sciences perspective[1] the organizations can be considered as entities whose behavior is driven by the decision processes of its stakeholders (MARCH 1994). Adding the decision perspective to the organizational learning problem brings three major inter-related research directions:

  • The organizational action as a result of a decision process based in the deployment of available knowledge;
  • The knowledge acquisition process inside the organization and types of knowledge deployed in the organizational decision process;
  • The supporting concepts: Organizational memory, problem representation and language.

These issues should be addressed in two dimensions:

  • Aggregation level at which knowledge is deployed to solve organizational problems;
  • Impact of knowledge on the different decision attributes.

In order to design real world interventions that facilitate organizational learning, a set of experimental tools can be assembled from the system dynamics approach (SD) and the multicriteria decision analysis (MCDA). From the SD it can be taken the simulation and representation tools to understand the decision context (WOLSTENHOLME 1990). From the MCDA, the criteria building and aggregation procedures can be taken to enable the final steps of the decision process[2] (POMEROL & BARBA ROMERO 1993, ROY 1985).

From this context, we proposed to focus in three major issues:

  • What elements of the organizational decision process can be addressed with the experimental tools?
  • What types of knowledge can be better addressed with the use of the proposed experimental tools?
  • How the experimental tools can be used as memorization instruments in the organizational learning process?

2. Conceptual framework

The research was based on three major conceptual definitions:

  • Decision process in the organizations;
  • Types of knowledge deployed by decision makers in the organizations;
  • Organizational Learning concept.

The decision process is build upon two dimensions: a problem solving axis and a consensus-building axis (see Figure 1 : Axis of the decision process in organizations):

Figure 1 : Axis of the decision process in organizations

One axis corresponds to the problem solving heuristic (POMEROL 1997) that focus on finding a satisficing[3] solution for a decision problem (ROY 1992). However, if the selected solution is not accepted by the organization it will not be implemented at an organizational level. Therefore, a second axis exists that drives the problem solving heuristic to a consensus building process looking to build ownership of the solution. According to the position of the outcome in the decision matrix, the decision process could be satisficing but not implemented in the organization, accepted by the organization but not effective (not satisficing) or effective and organizational enough to be feasible as an organizational action (ROSAS FLUNGER 1999 pp. 106-114).

The second major conceptual definition is the classification of the knowledge deployed in a Management Situation[4]. Three types of knowledge can be identified (HATCHUEL & WEIL 1992):

  • Know-How (Savoir-faire): Is the knowledge that expresses a transformation procedure through known actions. Can be represented using a sequence of procedural instructions that can be easily codified in a computer language;
  • Understanding (Savoir-comprendre): Is the knowledge set deployed to understand why a perceived situation doesn’t correspond with a desired situation. A typical example can be the maintenance engineering context where a dysfunctional situation is perceived and its causes identified;
  • Combination knowledge (Savoir-combiner): Is the set of knowledge deployed to combine resources and objectives in a project approach. Is the knowledge of the entrepreneur that is able to organize its resources and objectives to establish a business system.

Finally, we defined organizational learning as the durable modification of the elements of the decision process of stakeholders in a management situation oriented to the implementation of organizational actions with a problem-solving objective. The concept is based in a concrete set of assumptions (ROSAS FLUNGER 1999 PP. 138-141):

  • Learning is a process;
  • Learning is based on a modification of the objectives, representations and rationality of stakeholders;
  • Modifications should be saved in the “organizational memory” in order to become durable;
  • Modifications happens at individual level of stakeholders;
  • Stakeholders are evaluated by the outcome of their actions (management situation);
  • Learning is expressed through an organizational action;
  • The process starter is a perceived problem;
  • The learning process has the intention to solve a problem.

3. Experiences

3.1. Simulation experiences

3.1.1. Experience description

The simulation experiences were a modified version of the “Beer Distribution Game” (STERMAN 1994, 1982). As part of operations research courses, in two engineering schools, a supply chain system with 5 echelons was simulated with more than 100 students (4 experiences with more than 25 students each). The students were grouped in teams acting as the retailers, distributors and wholesalers. The moderators (professors) served as the clients and the plant of the system. Each team was evaluated against the performance of other teams in the same echelon of their chains and by the overall performance of the chain they belong. Therefore the teams had 2 objectives: as individuals and as a group. The students were encouraged to compete between them but at the same time they had the responsibility of improving their overall chain performance. Major differences with the original Beer Game were:

  • The demand curve was not a step over a constant order volume but a cyclical demand;
  • The students were allowed to talk. At the beginning of the experiences the silences paradigm prevailed as the experiences were made in a formal evaluation context. Once the students realized it was possible to talk, the key issue was the effectiveness of the communication and the team dynamics allowing an efficient resolution of the game.

3.1.2. Decision framework

The Beer Game demands from the students three main decision processes:

  • Organization: The students have to organize themselves inside the teams to be able to meet the time frame of the experience. The teams have very short time to place the orders and make the deliveries. In consequence, an internal organization is imperative to be efficient performing the game activities. Two level of organization are required in the game: team and game organization. First the teams have to allocate internally the game tasks. Secondly, once the teams have discovered they can communicate through the chain they have to define a communication model to solve the information delay issue. For the internal team organization three main options are available: functional, mix and no organization. At the chain level also three models are available: non communication, representative and integral models depending on the level and stakeholders involved in the communication processes;
  • Stock policy: The teams have to define a stock policy that drives the order process. The students were allowed to use all their resources (books, etc) to define the policy. They were formally asked in the experience protocol to make explicit their stock policy;
  • Order placement: At each game step the teams have to define the quantities in the orders and the quantities in the deliveries.

3.1.3. Knowledge assessment

Each identified decision process deployed a different type of knowledge:

  • Organization: To understand the tasks, objectives, resources and constraints is the organization major issue. The tasks can only be achieved deploying the combination knowledge (savoir-combiner). All the teams were not able to define or to implement a clear organizational model;
  • Stock policy: To understand the feedback processes in information and physical flows of the game is the major issue for this decision. Understanding type of knowledge (savoir-comprendre) is deployed for this decision. Build the causal loops and identify the critical parameters are the inherent tasks and knowledge required. A clear evolution of the knowledge involved was perceived through the different moments of the game. At the beginning a first policy was set up without considering any communication with the chain. Once it was realized communication was allowed, a second stock policy was defined. Finally, once the orders stabilized, the teams moved from a cooperative approach to a more competitive one to try to get better grades than the others. (See Figure 2 : Evolution of deviation average of orders from retailers, wholesalers and distributors with polynomial approximation (95%);
  • Order placement: Defining the quantities to be ordered and delivered is a procedural task based on knowing how to apply the defined stock policy. Correspond to a typical know how (savoir-faire) context.

Figure 2 : Evolution of deviation average of orders from retailers, wholesalers and distributors with polynomial approximation (95%)

3.2. Field experience

3.2.1. Experience description

A family owned pharmaceutical company was defining its strategy. After several decades of success as a niche player, good levels of profitability and financial stability the next generation of owners was questioning the sustainability of its business model. An organizational learning initiative was designed and implemented targeting to:

  • Create a common vision in a recently hired management team;
  • Develop an adapted business model sustainable in the new market conditions.

The intervention was designed as a set of workshops where high level causal models were defined and MCDA tools were used to support the required decisions.

3.2.2. Decision framework

During the initiatives 4 decision were identified as being the most critical:

  • Product classification: A management decision on product portfolio was required in the laboratory. A significant number of product, especially the two most important were classified and sold as pharmaceutical products whether it was possible through simple initiatives to transform them in over the counter (OTC) products. The change would also have an impact on the sales channels adding other markets as the food retail channel to the common pharmacies channel (usually strongly regulated and constrained). It was not a simple classification exercise but a management decision to be taken;
  • Performance assessment: A full set of performance indicators was defined to evaluate the performance of the laboratory and an assessment process was launched requiring a measurement effort in all areas of the company;
  • Mission and vision definition: In workshops with the top management and the owners, the new vision and mission were defined;
  • Objectives definition: The mission and vision were defined according to detailed performance goals with clear measures.

3.2.3. Knowledge assessment

The identified decisions were based on specific knowledge:

  • Product classification used the know-how of rules and laws that defines the current product portfolio according to industry standards. The MCDA tools were used to support the criteria definition and assessment to identify current portfolio positioning. The type of knowledge used was know-how;
  • Performance assessment used a set of knowledge targeting the understanding of the current performance of the company. The main issue was to understand why the current performance was at the perceived levels. The type of knowledge was understanding (savoir-comprendre);
  • Mission and vision definition: Using all knowledge developed about the firm, a clear understanding of available resources (coming form the performance evaluation) the new company goals were defined. This was the result of combining available competences with feasible objectives. The type of knowledge deployed was combination (savoir-combiner);
  • Objectives definition: Based on the vision and mission, the detailed resources and detailed objectives should be defined and deployed to achieve the desired goals. The type of knowledge in use was combination knowledge (savoir-combiner).

4. Conclusions

The modified beer game allowed to understand the proposed organizational learning concepts but not the effective use of the experimental tools. In the experience, it was clearly enough what types of knowledge were deployed on each decision. However, the time pressure and dynamics of the beer game did not allow to use simulation models neither MCDA procedures.

The conclusions driven from the simulation experience were:

  • Organizational models with clear allocation of responsibilities increased the performance of the teams;
  • Open but organized communications were critical to achieve high performance levels;
  • The use of explicit stock policies increased the performance levels;
  • The measured performance levels were proportionally related to the learning curve of the teams for the understanding, definition and execution of the stock policy.

The pharmaceutical company experience allowed the utilization of the experimental tools in a real world environment. The conclusions from the field experience were:

  • The clear allocation of responsibilities as well as the understanding of the allocated tasks improved the organization’s performance;
  • The openness generated by the joint workshops, where the top management interacted, improved the organizations performance – the experimental tools were critical to achieve a sufficient level of communication;
  • The MCDA allowed to improve the transparency and fairness of the performance evaluation process enhancing the consensus building axis of the decision process;
  • The utilization of simulation models and causal diagrams made possible the identification of critical issues improving the development of understanding knowledge (savoir-comprendre) and increasing the effectiveness of the problem solving axis of the decision process.

Further development must be made as the sample of the field research is limited to a single example (pharmaceutical laboratory) but gives important insights in the implementation of the 2 suggested techniques – experimental tools.


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HATCHUEL, Armand, WEIL, Bernard, (1992), L’Expert et le système, Economica, Paris, 1992.

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ROSAS FLUNGER, Rudolf. (1999), L´approche de la dynamique des systèmes et l´aide á la décision multicritère comme outils d´apprentissage organisationnel, Thèse de doctorat, Université Paris IX-Dauphine, Paris.

ROY, Bernard, (1992), Science de la décision ou science d’aide à la décision, Cahier du LAMSADE Nº 97, LAMSADE, Université Paris-IX Dauphine, Paris, février 1992.

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WOLSTENHOLME, Eric F., (1990), System enquiry: A system dynamics approach, John Wiley & Sons Ltd., Chichester, UK, 1990.

[1] For further discussions on the relevance of the concept decision sciences and decision aid sciences ROY 1992.

[2] For further details on the steps of the decision process SIMON 1997.

[3] A satisficing solution is a feasible action that can be implemented and is “good enough” for the decision-maker; The satisficing solution is based on the concept that human decisions are not optimum from a strict mathematical point of view. The decision maker does not evaluate all possible solutions to choose the “best” option. The decision maker will evaluate a finite and limited number of options to make a choice, being the result what is called “satisficing” solution. For further details see ROY 1992 and SIMON 1997.

[4] For a definition of management situation GIRIN 1990.

Author: Rudolf Rosas Flunger

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Rudolf Rosas Flunger

Dr. Rudolf Rosas Flunger is an experienced management consultant and business executive. He has a track record of more than 20 years delivering substantial business results ranging from bottom-line improvement to broad organizational transformation. His professional background includes extensive experience in corporate strategy, investments analysis, pricing, sales and distribution and supply chain. Dr. Rudolf Rosas Flunger has broad international experience with a special focus in Latin America as he has worked for clients in Brazil, Argentina, Chile, Colombia, Peru and Venezuela. Additionally, he has been Professor in several Engineering Schools and is an international speaker as well as a member of the IEEE, PMI, System Dynamics Society, INFORMS. Dr. Rosas Flunger holds an Industrial Engineering Degree from UCAB (Caracas) as well as a MSc and PhD in Management Sciences from Université Paris Dauphine (joint program with École Polytechnique and École des Mines de Paris).

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