These developments bring new layers of complexity for leaders. At the same time, they present a unique opportunity for quality professionals to contribute in new ways and advance to higher levels of the organization.

Of particular interest in the report is the highlighting of systems thinking as one of the key methodologies needed to address these new challenges. Although systems thinking has been around for a long time, it has not become a permanent part of the toolbox for many quality practitioners. Systems thinking is exactly what many organizations need to understand these emerging complexities and develop and execute strategic plans that will positively impact the bottom line.

Systems thinking provides the lens to view the organization beyond the traditional sum of its internal parts; it positions the organization itself as part of a larger system. Using systems tools to understand the patterns and structures of that larger system can provide an enormous competitive advantage to any enterprise.

Synthesis over Analysis

Seeing the organization in a different light requires new thinking. For years Russ Ackoff, Peter Senge, and others have encouraged us to change the way we think about organizations in order to improve performance. Ackoff would say that most thinking over the last 400 years has relied on analysis. We tend to take things apart, examine each piece, and then put them back together in an attempt to understand the whole.

The problem with that approach is that it does little to drive whole system improvement and may in fact be detrimental, as people focus solely on optimizing each part. Analytical thinking leads to a better understanding of how things work, but does not provide much insight into how things work together.

An approach that involves synthesis may provide better answers. Synthesis, the opposite the analysis, asks “what is this part of?” as a means to explain the behavior of the whole. From this perspective, performance is derived from the interaction of the parts and not the function of each part separately. For example, when engineers design a bicycle, they design the overall bike first, then the parts, not the other way around. The objective is to build the best bike, not the best part.

Opportunities Ahead

Systems thinking offers a variety of tools and practices that can support seeing the whole over the parts and understanding the dynamics of a complex system. For quality professionals, these rich insights can help ensure that decision making addresses the organizational and performance challenges of tomorrow. The door is open, quality professionals; tune up your systems thinking skills and take advantage of the opportunities ahead.

Mark Alpert is president of Pegasus Communications. This piece originally appeared in the Leverage Points blog; for more blog entries, go to blog.pegasuscom.com.

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1. Select the Issue

The first step in making mental models explicit is to identify the system or event to be investigated and collect the appropriate data. It is often helpful to look for a significant problem that has persisted despite efforts to eliminate it, or one that is present in a variety of organizational settings and is therefore difficult to define.

For example, in one company, senior management had charged a group of managers with implementing TQM by forming a Quality Improvement Team (QIT). In principle, the QIT team understood the long-term business implications of investing in TQM, but the many daily demands on their time often kept them more focused on meeting their nonTQM responsibilities. Over time, a group norm developed that it was okay not to prepare for the meetings or to be interrupted when doing TQM work, and attendance at TQM meetings began to decline.

2. Force-Field Analysis

DEEP FORCE-FIELD ANALYSIS TEMPLATE

A force-field diagram provides deeper understanding of an issue by identifying those things that enable or hinder an objective and the assumptions behind those factors.

The next step in making individual mental models explicit is to conduct semi-structured interviews with people closely involved with the issue. This begins to engage people in the process of making their thinking explicit. In the interviews, ask people to:

• trace the chronology of significant events,
• clarify their role in the situation,
• do a modified force-field analysis of the situation.

Conducting a deep force-field analysis requires: (1) identifying factors that are seen as barriers and enablers to achieving the objective, (2) making explicit the assumptions behind each barrier and enabler, and (3) providing directly observable examples that illustrate each one (see “Deep Force-Field Analysis Template”). The interview data may need to be augmented or clarified with company records or other data sources.

3. Select Important Variables and “Scrub”

After gathering the initial round of data, the next step is to identify the variables that are mentioned frequently or those that are claimed to directly affect key performance measures. In the TQM example, relevant variables might include “Senior Management Attention” “TQM Importance,” “TQM Activity,” and “Event-Driven Activity.”

When extracting data from the interviews, watch for subjective wording of variables such as “poor management” or “unmotivated workers.” Avoid such subjectivity by making sure to “scrub” the verbal data—that is, strip away qualifying adjectives and remove any judgment or inference.

4. Describe Variable Relationships

The next step is to identify other variables that are directly linked to each significant variable. Try to find as many interconnected pairs as possible by looking for causes, consequences, and constraints that link variables together. For example, in our TQM story, we can link the variable “Sr. Management Attention,” to “TQM Importance” with an “s” (indicating change in the same direction) since the managers tried to raise the importance of TQM by launching a QIT. These “directed arc” pairs will become the building blocks for the causal structure of the story.

5. Name and Describe the Story

To begin developing causal loops from directed arc fragments, it helps to identify a common “story” that runs through several fragments. The story helps guide the construction process by providing a theme against which we can judge whether or not to include a particular fragment. These stories should be recurring or systemic in nature, containing feedback characteristics (as opposed to one-time events which tend to be linear).

In the TQM example, we could tell a story entitled “Directive without Commitment” to describe the dynamics of the group. The QIT effort failed due to a lack of commitment by those who were responsible for launching the activity. In their view, QIT activities took a “back seat” to more pressing daily demands.

6. Construct a Causal Loop

To begin structuring the story, start linking relevant fragments together in the form of a causal loop diagram. Use questions such as, “Why did this happen?” to uncover further upstream causality, and “What did this affect?” to determine further downstream causality. Continue to link the elements in a feedback or causal loop structure until the causal loop reflects the story being told. Be sure to insert delays between cause and effect where appropriate.

CONFLICTING PRIORITIES

The first causal loop diagram (top) describes the belief by senior management that awareness of the importance of TQM would increase TQM activity (R1). However, they were also focused on event-driven activities that would reduce a gap between period performance and overall business goals (B2 and B3). In the final diagram (bottom), loop B4 is added to capture the dynamics of work demands that took time away from TQM activities

In the TQM example, senior management believed that as awareness of the importance of TQM increased, TQM activity would increase. This would eventually lead to improved long-term business performance, which should further heighten the importance of TQM (loop R1 in “Conflicting Priorities”). But senior management was also concerned with making sure day-to-day activities were done. And as event-driven activity increases, it takes time away from TQM activity (as the QIT members experienced).

7. Check the Diagram with the Story

Once you have a sketch of a causal loop diagram, it is critical to check it against the story to make sure it actually describes what the story is telling. In the TQM example, this step made the group realize that the link between senior management attention and event-driven activity was not explicitly supported by the data. Be sure to make changes where appropriate to better reflect the dynamics of the story.

8. Fine-Tune the Loop Detail and Recheck the Story

To fine-tune the loop, trace the logical flow through the diagram to make sure that there are no interruptions or “leaps,” and confirm that every cause-and-effect relationship represents the logical next step in the development of the story. Then check the level of detail, or “abstraction level,” to make sure that all elements are telling the story at the same level.

In reviewing the TQM diagram from this perspective, “Senior Management Attention” should be pulled out of the loop because once they delegated the task, they were no longer part of the story. A link can be added, however, to a new variable, “Work Backlog,” to represent the pressures that accumulate whenever time is diverted from daily work. This balancing loop captures the dynamics of team members being interrupted and missing meetings when pressed by other urgent activities (B4).

If any changes result from fine-tuning the loop detail, recheck the story against the interviews to ensure that the story is still adequately represented by the causal loop diagram.

New Insights

When developing causal loop representations of mental models, we are not concerned with capturing the world “as it really is” but rather with accurately representing an individual’s beliefs or mental models of the world. This does not mean that the mapping process is simply a regurgitation of what was already known. Mapping can actually be a discovery process in which new insights are gained by structuring a person’s experience in a clear and coherent framework. And, as we will discuss next month, such maps can help build a better shared understanding of an issue.

Daniel H. Kim is co-founder of Pegasus Communications, founding publisher of The Systems Thinker newsletter, and a consultant, facilitator, teacher, and public speaker committed to helping problem-solving organizations transform into learning organizations.

The post Using Causal Loop Diagrams to Make Mental Models Explicit appeared first on The Systems Thinker.

Step 1: Build Integrated Map

To begin creating a shared mental model, the first step is to integrate individual stories into a common map. In this step, individual causal loop diagrams that have previously been developed are combined into a central story that clearly captures the dominant theme running through most of the stories. For example, a company that implemented a TQM program had difficulty sustaining its TQM

TRAININIG CAPACITY STORY

Initial TQM activity meets with success, thus increasing motivation to do more TQM activities (R1). However, if training capacity does not keep pace with the demand for training generated by the TQM activities, the TQM skill gap increases, limiting TQM activities. This dynamic traces out a “Limits to Success” archetype

activities because it required more training than was available. The main loop in this story is therefore a reinforcing loop that represents the company’s initial implementation efforts (loop R1 in “Training Capacity Story”).

Once the main loop has been defined, other themes can then be incorporated into the central story line. For example, the managers in the TQM story believed that a capacity constraint in training had a negative impact on its TQM efforts. Their individual diagrams showed that as TQM activities increase, the level of TQM skills required increases as people tackle more challenging projects. This increases the demand for training, which reduces the ability to deliver training if training capacity is not increased to meet the rising demand (B2). As TQM training decreases, the TQM skill gap increases, which will hamper TQM activities (B3). The central storyline, or main loop, is thus refined until it captures the essential elements of all the individual maps.

Step 2: Simplify Map Through Decomposition

A typical integrated diagram developed in Step 1 may contain anywhere from 10 to 30 loops. At that level of complexity, most people find it difficult to remember all of the individual loops and their impact on the system as a whole. Therefore, in this step, we simplify the diagram as much as possible by “decomposing” it into smaller, more manageable chunks:

SIMPLIFIED TRAINING CAPACITY STORY

To simplify the diagram, multiple links can be collapsed into a single link. The new link must maintain the meaning of the original relationship and preserve the polarity between the original variables.

• Identify Systems Archetypes. The systems archetypes can be very helpful in decomposition, because they provide a framework for capturing large “chunks” of the whole story. In the TQM example, the presence of a growing action (TQM activity) that is limited by a constraint (training capacity) suggests that a “Limits to Success” archetype may be applicable.

  The purpose of using archetypes, however, is not to “force fit” the maps but to see if any of the archetypes can provide us with a lens that will help simplify the story.

• Identify Significant Behavior. If an archetype is not apparent, we can begin the decomposition process by identifying a significant behavior pattern—one that is important to the issue we are studying. Behavior over Time diagrams capture the dynamic relationships between variables on one graph, giving a more explicit understanding of how these variables interrelate.

  In the training capacity story, for example, there were two behaviors that stood out: the time behavior of TQM activities and the ability to deliver training (see “Behavior Modes”). Identifying one or more relevant behavior modes helps guide the decomposition process, while enabling us to retain the relevant dynamics.

BEHAVIOR MODES



TQM activities grow for a while and then level off as the ability to deliver training decreases. This generic behavior is generally found in “Limits to Success” dynamics.

• Collapse Multiple Links into a Single Link. Although our causal loop diagram only has three feedback loops, it contains 11 variables. To simplify the diagram, we can try to reduce the number of variables. When reducing the number of variables and links in a diagram, we need to be sure that the replacement variable preserves the essential meaning of the individual variables, and the new link preserves the causal polarity between the old set of variables.

  For example, we can combine the two variables “Required TQM Skills” and “Demand for TQM Training” into a single variable, “Demand for TQM Skills Training,” because the TQM skill requirement is not as essential to the story as the demand it generates for the training. The net effect to “Ability to Deliver Training” is preserved by signing the arc with an “o.” The entire simplified diagram is shown in “Simplified Training Capacity Story.”

• Check That Loop Polarity Remains the Same. The final step in the decomposition process is to double-check that the loop polarity has not been changed by the simplification process—i.e., balancing loops should still be balancing loops and reinforcing loops should still be reinforcing loops.

Step 3: Share Decomposition with Others

To check for consistency between the basic storyline and the simplified causal loop diagram, the diagrams should be continually verified against the available data in the modeling process. The diagrams should also be verified with all people who were originally interviewed to check for “face validity”—whether the diagram reflects the dynamics of what they believe happened.

Step 4: Confirm and Test the Decomposition

In addition to circulating the diagrams to the initial group of interviewees, they should also be shown to other relevant players in the organization. The validation at this stage consists of two parts: one is to determine whether there is general agreement that the final diagram accurately describes what has happened; the second is whether there is a general agreement on the actions suggested by the diagram. There is one important caveat to this analysis, however. Such confirmation does not imply that the final diagrams or the prescribed actions are necessarily “correct.” This methodology only helps capture what people believe to be true based on their current understanding of the situation, i.e., their mental models.

The four-step process of making mental models explicit is not designed to capture “the reality.” Testing the accuracy of mental models is beyond the capability of simple pen and paper tools such as causal loop diagrams or systems archetypes, and requires the use of a computer simulation model.

Daniel H. Kim is co-founder of Pegasus Communications, founding publisher of The Systems Thinker newsletter, and a consultant, facilitator, teacher, and public speaker committed to helping problem-solving organizations transform into learning organizations.

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