This volume explores these questions and introduces the principles and practice of a quietly growing field: systems thinking. With roots in disciplines as varied as biology, cybernetics, and ecology, systems thinking provides a way of looking at how the world works that differs markedly from the traditional reductionistic, analytic view. Why is a systemic perspective an important complement to analytic thinking? One reason is that understanding how systems work – and how we play a role in them – lets us function more effectively and proactively within them. The more we understand systemic behavior, the more we can anticipate that behavior and work with systems (rather than being controlled by them) to shape the quality of our lives.

It’s been said that systems thinking is one of the key management competencies for the 21st century. As our world becomes ever more tightly interwoven globally and as the pace of change continues to increase, we will all need to become increasingly “system-wise.” This volume gives you the language and tools you need to start applying systems thinking principles and practices in your own organization.

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From my work with both business and education professionals over the last 15 years, I have come to believe that systems thinking’s steep learning curve is related to the fact that the discipline requires mastering a whole package of thinking skills.

STEPS IN THE SYSTEMS THINKING METHOD

Begin by specifying the problem you want to address. Then construct hypotheses to explain the problem and test them using models. Only when you have a sufficient understanding of the situation should you begin to implement change.

Much like the accomplished basketball player who is unaware of the many separate skills needed to execute a lay-up under game conditions – such as dribbling while running and without looking at the ball, timing and positioning the take-off, extending the ball toward the rim with one hand while avoiding the blocking efforts of defenders – veteran systems thinkers are unaware of the full set of thinking skills that they deploy while executing their craft. By identifying these separate competencies, both new hoop legends and systems thinking wannabes can practice each skill in isolation. This approach can help you master each of the skills before you try to put them all together in an actual game situation.

The Systems Thinking Method

Before exploring these critical thinking skills, it’s important to have a clear picture of the iterative, four-step process used in applying systems thinking (see “Steps in the Systems Thinking Method”). In using this approach, you first specify the problem or issue you wish to explore or resolve. You then begin to construct hypotheses to explain the problem and test them using models whether mental models, pencil and paper models, or computer simulation models. When you are content that you have developed a workable hypothesis, you can then communicate your new found clarity to others and begin to implement change.

When we use the term “models” in this article, we are referring to something that represents a specifically defined set of assumptions about how the world works. We start from a premise that all models are wrong because they are incomplete representations of reality, but that some models are more useful than others (they help us understand reality better than others).  There is a tendency in the business world, however, to view models (especially computer-based models) as “answer generators;” we plug in a bunch of numbers and get out a set of answers. From a systems thinking perspective, however, we view models more as “assumptions and theory testers” we formulate our understanding and then rigorously test it. The bottom line is that all models are only as good as the quality of the thinking that went into creating them. Systems thinking, and its ensemble of seven critical thinking skills, plays an important role in improving the quality of our thinking.

The Seven Critical Thinking Skills

As you undertake a systems thinking process, you will find that the use of certain skills predominates in each step. I believe there are at least seven separate but interdependent thinking skills that seasoned systems thinkers master. The seven unfold in the following sequence when you apply a systems thinking approach: Dynamic Thinking, System-as-Cause Thinking, Forest Thinking, Operational Thinking, Closed-Loop Thinking, Quantitative Thinking, and Scientific Thinking.

The first of these skills, Dynamic Thinking, helps you define the problem you want to tackle. The next two, System-as-Cause Thinking and Forest Thinking, are invaluable in helping you to determine what aspects of the problem to include, and how detailed to be in representing each. The fourth through sixth skills, Operational Thinking, Closed-Loop Thinking, and Quantitative Thinking, are vital for representing the hypotheses (or mental models) that you are going to test. The final skill, Scientific Thinking, is useful in testing your models.

Each of these critical thinking skills serves a different purpose and brings something unique to a systems thinking analysis. Let’s explore these skills, identify how you can develop them, and determine what their “non-systems thinking” counterparts (which dominate in traditional thinking) look like.

Dynamic Thinking: Dynamic Thinking is essential for framing a problem or issue in terms of a pattern of behavior over time. Dynamic Thinking contrasts with Static Thinking, which leads people to focus on particular events. Problems or issues that unfold over time as opposed to one-time occurrences are most suitable for a systems thinking approach.

You can strengthen your Dynamic Thinking skills by practicing constructing graphs of behavior overtime. For example, take the columns of data in your company’s annual report and graph a few of the key variables over time. Divide one key variable by another (such as revenue or profit by number of employees), and then graph the results. Or pick up today’s news-paper and scan the head-lines for any attention-grabbing events. Then think about how you might see those events as merely one interesting point in a variable’s overall trajectory over time. The next time someone suggests that doing this-and-that will fix such-and-such, ask, “Over what time frame? How long will it take? What will happen to key variables over time?”

System-as-Cause Thinking: Dynamic Thinking positions your issue as a pattern of behavior over time. The next step is to construct a model to explain how the behavior arises, and then suggest ways to improve that behavior. System-as-Cause Thinking can help you determine the extensive boundary of your model, that is, what to include in your model and what to leave out (see “Extensive and Intensive Model Boundaries”). From a System-as-Cause Thinking approach, you should include only the elements and inter-relationships that are within the control of managers in the system and are capable of generating the behavior you seek to explain.

By contrast, the more common System-as-Effect Thinking views behavior generated by a system as “driven” by external forces. This perspective can lead you to include more variables in your model than are really necessary. System-as-Cause Thinking thus focuses your model more sharply, because it places the responsibility for the behavior on those who manage the policies and plumbing of the system itself.

To develop System-as-Cause Thinking, try turning each “They did it” or “It’s their fault” you encounter into a “How could we have been responsible?” It is always possible to see a situation as caused by “outside forces.” But it is also always possible to ask, “What did we do to make ourselves vulnerable to those forces that we could not control?”

EXTENSIVE AND INTENSIVE MODEL BOUNDARIES

The extensive boundary is the breadth or scope of what’s included in the model. The intensive boundary is the depth or level of detail at which the items included in the model are represented.

Forest Thinking: In many organizations, people assume that to really know something, they must focus on the details. This assumption is reinforced by day-to-day existence—we experience life as a sequence of detailed events. We can also think of this as Tree-by-Tree Thinking. Models that we create by applying Tree-by-Tree Thinking tend to be large and overly detailed; their intensive boundaries run deep. In using such models, we would want to know whether that particular red truck broke down on Tuesday before noon, as opposed to being interested in how frequently, on average, trucks break down. Forest Thinking–inspired models, by contrast, group the details to give us an “on average” picture of the system. To hone your Forest Thinking skills, practice focusing on similarities rather than differences. For example, although everyone in your organization is unique, each also shares some characteristics with others. While some are highly motivated to perform and others are not, all have the potential to make a contribution. Regardless of the individual, realizing potential within an organization comes from the same generic structure. For example, what is the relationship among factors that tends to govern an individual’s motivation?

Operational Thinking :Operational Thinking tries to get at causality—how is behavior actually generated? This thinking skill contrasts with Correlational or Factors Thinking. Steven Covey’s The Seven Habits of Highly Effective People, one of the most popular nonfiction books of all time, is a product of Factors Thinking. So are the multitude of lists of “Critical Success Factors” or “Key Drivers of the Business” that decorate the office walls (and mental models) of so many senior executives. We like to think in terms of lists of factors that influence or drive some result.

There are several problems with mental models bearing such list structures, however. For one thing, lists do not explain how each causal factor actually works its magic. They merely imply that each factor “influences,” or is “correlated with,” the corresponding result. But influence or correlation is not the same as causality.

For example, if you use Factors Thinking to analyze what influences learning, you can easily come up with a whole “laundry list” of factors (see “Two Representations of the Learning Process”). But if you use Operational Thinking, you might depict learning as a process that coincides with the building of experience. Operational Thinking captures the nature of the learning process by describing its structure, while Factors Thinking merely enumerates a set of factors that in some way “influence” the process.

To develop your Operational Thinking skills, you need to work your way through various activities that define how a business works examine phenomena such as hiring, producing, learning, motivating, quitting, and setting price. In each case, ask, “What is the nature of the process at work?” as opposed to “What are all of the factors that influence the process?”

Closed-Loop Thinking :Imagine discussing your company’s profitability situation with some of your coworkers. In most companies, the group would likely list things such as product quality, leadership, or competition as influences on profitability (see “A Straight-Line vs. a Closed-Loop View of Causality”). This tendency to list factors stems from Straight-Line Thinking. The assumptions behind this way of thinking are 1) that causality runs only one way—from “this set of causes” to “that effect,” and 2) that each cause is independent of all other causes. In reality, however, as the closed-loop part of the illustration shows, the “effect” usually feeds back to influence one or more of the “causes,” and the causes themselves affect each other. Closed-Loop Thinking skills therefore lead you to see causality as an ongoing process, rather than a one-time event.
To sharpen your Closed-Loop Thinking skills, take any laundry list that you encounter and think through the ways in which the driven drives and in which the drivers drive each other. Instead of viewing one variable as the most important driver and another one as the second most important, seek to understand how the dominance among the variables might shift over time.

TWO REPRESENTATIONS OF THE LEARNING PROCESS

Factors Thinking merely enumerates a set of factors that in some way “influence” the learning process. Operational Thinking captures the nature of the learning process by describing its structure.

Quantitative Thinking: In this phrase, “quantitative” is not synonymous with “measurable.” The two terms are often confused in practice, perhaps because of the presumption in the Western scientific world that “to know, one must measure precisely.” Although Heisenberg’s Uncertainty Principle caused physicists to back off a bit in their quest for numerical exactitude, business folk continue unabated in their search for perfectly measured data. There are many instances of analysis getting bogged down because of an obsession with “getting the numbers right.” Measurement Thinking continues to dominate!

There are a whole lot of things, however, that we will never be able to measure very precisely. These include “squishy,” or “soft,” variables, such as motivation, self-esteem, commitment, and resistance to change. Many so-called “hard” variables are also difficult to measure accurately, given the speed of change and the delays and imperfections in information systems.

A STRAIGHT-LINE VS.A CLOSED-LOOP VIEW OF CAUSALITY

The assumptions behind Straight-Line Thinking are that causality runs only one way and that each cause is independent of all other causes. Closed-Loop Thinking shows that the “effect” usually feeds back to influence one or more of the “causes,” and the causes themselves affect each other.

But let’s return to our “squishy” variables. Would anyone want to argue that an employee’s self-esteem is irrelevant to her performance? Who would propose that commitment is unimportant to a company’s success? Although few of us would subscribe to either argument, things like self-esteem and commitment rarely make it into the spreadsheets and other analytical tools that we use to drive analysis. Why? Because such variables can’t be measured. However, they can be quantified. If zero means a total absence of commitment, 100 means being as committed as possible. Are these numbers arbitrary? Yes. But are they ambiguous? Absolutely not! If you want your model to shed light on how to increase strength of commitment as opposed to predicting what value commitment will take on in the third-quarter of 1997—you can include strength of commitment as a variable with no apologies. You can always quantify, though you can’t always measure.

To improve your Quantitative Thinking skills, take any analysis that your company has crunched through over the last year and ask what key “soft” variables were omitted, such as employee motivation. Then, ruminate about the possible implications of including them systems thinking gives you the power to ascribe full-citizen status to such variables. You’ll give up the ability to achieve perfect measurement. But if you’re honest, you’ll see that you never really had that anyway.

Scientific Thinking: The final systems thinking skill is Scientific Thinking. I call its opposite Proving Truth Thinking. To understand Scientific Thinking, it is important to acknowledge that progress in science is measured by the discarding of falsehoods. The current prevailing wisdom is always regarded as merely an “entertainable hypothesis,” just waiting to be thrown out the window. On the other hand, too many business models are unscientific; yet business leaders revere them as truth and defend them to the death. Analysts make unrelenting efforts to show that their models track history and therefore must be “true.”

Seasoned systems thinkers continually resist the pressure to “validate” their models (that is, prove truth) by tracking history. Instead, they work hard to become aware of the falsehoods in their models and to communicate these to their team or clients. “All models are wrong,”” said W. Edwards Deming. “Some models are useful.” Deming was a smart guy, and clearly a systems thinker.

In using Scientific Thinking, systems thinkers worry less about outfitting their models with exact numbers and instead focus on choosing numbers that are simple, easy to understand, and make sense relative to one another. Systems thinkers also pay lots of attention to robustness they torture-test their models to death! They want to know under what circumstances their model “breaks down.” They also want to know, does it break down in a realistic fashion? What are the limits to my confidence that this model will be useful?

The easiest way to sharpen your Scientific Thinking skills is to start with a computer model that is “in balance” and then shock it. For example, transfer 90% of the sales force into manufacturing. Set price at 10 times competitor price. Triple the customer base in an instant. Then see how the model performs. Not only will you learn a lot about the range of utility of the model, but you also will likely gain insight into the location of that most holy of grails: high-leverage intervention points.

A Divide and Conquer Strategy

As the success of Peter Senge’s The Fifth Discipline: The Art & Practice of the Learning Organization has shown, systems thinking is both sexy and seductive. But applying it effectively is not so easy. One reason for this difficulty is that the thinking skills needed to do so are many in number and stand in stark contrast to the skill set that most of us currently use when we grapple with business issues (see “Traditional Business Thinking vs. Systems Thinking Skills”).

By separating and examining the seven skills required to apply systems thinking effectively, you can practice them one at a time. If you master the individual skills first, you stand a much better chance of being able to put them together in a game situation. So, practice . . . then take it to the hoop!

“Barry Richmond is the managing director and founder of High Performance Systems, Inc. He has a PhD in system dynamics from the MIT Sloan School of Management, an MS from Case Western Reserve, and an MBA from Columbia University”

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What Does Systems Thinking Involve?

Why Use Systems Thinking?

Systems thinking expands the range of choices available for solving a problem by broadening our thinking and helping us articulate problems in new and different ways. At the same time, the principles of systems thinking make us aware that there are no perfect solutions; the choices we make will have an impact on other parts of the system. By anticipating the impact of each trade-off, we can minimize its severity or even use it to our own advantage. Systems thinking therefore allows us to make informed choices.
Systems thinking is also valuable for telling compelling stories that describe how a system works. For example, the practice of drawing causal loop diagrams forces a team to develop shared pictures, or stories, of a situation. The tools are effective vehicles for identifying, describing, and communicating your understanding of systems, particularly in groups.

When Should We Use Systems Thinking?

Problems that are ideal for a systems thinking intervention have the following characteristics:

• The issue is important.
• The problem is chronic, not a one-time event.
• The problem is familiar and has a known history.
• People have unsuccessfully tried to solve the problem before.

Where Should We Start?

When you begin to address an issue, avoid assigning blame (which is a common place for teams to start a discussion!). Instead, focus on items that people seem to be glossing over and try to arouse the group’s curiosity about the problem under discussion. To focus the conversation, ask, “What is it about this problem that we don’t understand?”

In addition, to get the full story out, emphasize the iceberg framework. Have the group describe the problem from all three angles: events, patterns, and structure (see “The Iceberg”).
Finally, we often assume that everyone has the same picture of the past or knows the same information. It’s therefore important to get different perspectives in order to make sure that all viewpoints are represented and that solutions are accepted by the people who need to implement them. When investigating a problem, involve people from various departments or functional areas; you may be surprised to learn how different their mental models are from yours.

How Do We Use Systems Thinking Tools?

Causal Loop Diagrams. First, remember that less is better. Start small and simple; add more elements to the story as necessary. Show the story in parts. The number of elements in a loop should be determined by the needs of the story and of the people using the diagram. A simple description might be enough to stimulate dialogue and provide a new way to see a problem. In other situations, you may need more loops to clarify the causal relationships you are surfacing.

Also keep in mind that people often think that a diagram has to incorporate all possible variables from a story; this is not necessarily true. In some cases, there are external elements that don’t change, change very slowly, or whose changes are irrelevant to the problem at hand. You can unnecessarily complicate things by including such details, especially those over which you have little or no control. Some of the most effective loops reveal connections or relationships between parts of the organization or system that the group may not have noticed before.
And last, don’t worry about whether a loop is “right”; instead, ask yourself whether the loop accurately reflects the story your group is trying to depict. Loops are shorthand descriptions of what we perceive as current reality; if they reflect that perspective, they are “right” enough.

THE ICEBERG

How Do We Know That We’ve “Got It”?

Here’s how you can tell you’ve gotten a handle on systems thinking:

• You’re asking different kinds of questions than you asked before.
• You’re hearing “catchphrases” that raise cautionary flags. For example, you find yourself refocusing the discussion when someone says, “The problem is we need more (sales staff, revenue).”
• You’re beginning to detect the archetypes and balancing and reinforcing processes in stories you hear or read.
• You’re surfacing mental models (both your own and those of others).
• You’re recognizing the leverage points for the classic systems stories.

Once you’ve started to use systems thinking for inquiry and diagnosis, you may want to move on to more complex ways to model systems-accumulator and flow diagrams, management flight simulators, or simulation software. Or you may find that adopting a systems thinking perspective and using causal loop diagrams provide enough insights to help you tackle problems. However you proceed, systems thinking will forever change the way you think about the world and approach issues. Keep in mind the tips we’ve listed here, and you’re on your way!

Michael Goodman is principal at Innovation Associates Organizational Learning

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Managers often don’t have the time, perspective, or framework to learn from the successes and failures of their problem-solving efforts. As a result, organizations fall into a recurring pattern of temporary relief followed by a return of the same problems. If people do attempt to learn from the past, they frequently find themselves ill-prepared to make sense of their own experience. Even in cases where the solutions produce lasting results, managers often lack an understanding of why these approaches succeeded.

A CORE THEORY OF SUCCESS

As the quality of relationships rises, the quality of thinking improves, leading to an increase in the quality of actions and results. Achieving high-quality results has a positive effect on the quality of relationships, creating a reinforcing engine of success.

Limitations of Traditional Approaches

When attempting to determine why an initiative succeeded, most managers talk in terms of the individual factors they believe were critical to the success. This propensity to focus on factors in isolation rather than seeing them as interrelated sets is part of what Barry Richmond refers to as “traditional business thinking” (“The ‘Thinking’ in Systems Thinking: How Can We Make It Easier to Master?” March 1997). Indeed, many companies formulate their thinking about success as lists of important attributes or competencies, without identifying the key ways in which the items are connected.

For example, companies often begin their efforts to improve their organizations by listing critical success factors. They identify a goal (for example, industry leadership) and then list the factors that management agrees are essential to achieving this goal (such as desirable products and services, ability to deliver). They then prioritize the items on the list and assign the top priorities to special teams. This list-based approach poses several problems. First, people frequently treat the factors separately, in a “divide and conquer” strategy. The danger here is that they may not properly consider important interactions among the different factors. Hence, a marketing department may not warn manufacturing and customer service about the potential impact of a major marketing campaign.

Another problem is that if management reduces the initial investments after a key success factor (KSF)has reached a certain desired level, the success may prove temporary. Often, when we have achieved a certain desired level with KSF1, we declare victory and shift resources over to KSF2. As we build up KSF2 and then KSF3, KSF1 starts to deteriorate because of lack of continued investments. So, we shift some resources back to KSF1 as we declare victory on KSF2 and KSF3.

Unless managers develop a theory of how these factors are interrelated in creating ongoing success (or failure), they cannot put the data from their experiences together in a way that serves as a guide for future actions. Unfortunately, most approaches to helping organizations solve persistent problems focus on applying other people’s theories and methods to the organization and not on developing a specific theory about the organization’s own operations. Systems thinking and organizational learning can offer tools and methods for companies to begin developing such theories and for putting them into action.

The Importance of Theory

Regrettably, the corporate world has little appreciation for the importance and power of theory. Many managers associate theory with universities and research institutions, which they view as too insulated from the real world. Hence, managers often dismiss theory as too academic and irrelevant to the pragmatic conduct of business. But the American Heritage Dictionary, Standard Edition, defines theory as “systematically organized knowledge applicable in a relatively wide variety of circumstances, especially a system of assumptions, accepted principles, and rules of procedure devised to analyze, predict, or otherwise explain the nature or behavior of a specified set of phenomena.” This definition clearly shows that there is nothing strictly academic about the concept of theory at all.

Using this definition of theory, we can say that creating a long-lived, successful organization means managers must develop systematically organized knowledge that represents the system of assumptions, accepted principles, and procedural rules they use to make sense of their past experience and to predict the future. In this sense, theory building is about developing a better understanding of our organizations and improving our capacity to predict the future. In other words, theory-building has everything to do with running a successful business.

We have to be cautious when we use the word “prediction,” because it tends to be used interchangeably with the word “forecast.” Forecasting attempts to provide a specific kind of prediction; however, it usually focuses on calculating specific numerical data that we expect to occur at some point in the future. The main criterion of success for forecasts is the accuracy of the result, not the accuracy of the assumptions or the methods used to produce it.

When we talk about predictions based on theory, however, we are more interested in the accuracy of the underlying assumptions and less in the numerical accuracy of the predicted result. Why? Because, in a complex world that is inherently unforecastable (a basic tenet in the emerging science of chaos), only understanding interrelationships can guide us in making the course corrections inevitably required in an environment of rapid and continual change. All good theories therefore help provide guidance by increasing our predictive power about the future

Theory-Building: Shifting from Factors to Loop

So, responsible leaders should ask themselves, “What good theories do we have that provide practical guidance for ensuring our organization’s future success?” The more clearly you can articulate your organization’s theories about what leads to success, the more deliberate you can be about investing in the elements that are critical to that success. From a systems thinking perspective, having a core theory of success means moving beyond identifying individual success factors to seeing the linkages that create the reinforcing engines of success within the organization.

For example, once we have a list of key success factors, we can take the next step of identifying how each KSF is connected to a reinforcing loop (see “Shifting to a Loop Perspective”). The key success loop (KSL) identified in our example shows that by increasing desirable products and services, we increase sales revenues and boost the amount of money available for investment. With more money to invest, we can draw more technical talent and produce even more desirable products and services (R loop).

Shifting our formulation of theories from factors to loops is important for several reasons. First, it forces us to think through the logical chain of causal forces that ensure that the KSF becomes self-sustaining. Second, it shifts our emphasis away from the factor itself to the broader set of interrelationships in which it is embedded. Third, by mapping each of our KSFs into Key Success Loops, we are more likely to see the interconnections among all the KSFs. This approach requires shifting our worldview from one that sees factors as the lowest unit of analysis to one that recognizes loops as the basic building blocks of organizational systems.

Theory as an Intervention Guide

Having an explicit theory of success allows an organization to continually test the impact of planned actions and assess whether these actions are having a net positive or negative effect on the company’s overall success. So what might a theory of success look like in a learning organization?

One such core theory of success would be based on the premise that as the quality of the relationships among people who work together increases high team spirit, mutual respect, and trust), the quality of thinking improves (consider more facets of an issue and share a greater number of different perspectives) (see “A Core Theory of Success,” p. 1).When the level of thinking is heightened, the quality of actions is also likely to improve (better planning, greater coordination, and higher commitment). In turn, the quality of results increases as well. Achieving high-quality results as a team generally has a positive effect on the quality of relationships, thus creating a virtuous cycle of better and better results.

The most important point about this kind of systemic theory is that success is not derived from any one of the individual variables that make up the loop, but rather from the loop itself. All of the variables are important for the theory to work properly, because if one of them isn’t functioning, there in forcing process doesn’t exist. If we believe that this loop describes a relevant theory of success for our organization, it forces us to pay attention to how all the variables are doing and how each is affecting the others in the loop.

As an example, we can use this Core Theory of Success to trace the implications of a common occurrence in corporations—top-down organizational efforts to get quick, short-term results. When results fall short of expectations, management often “helps” the people below by undertaking efforts intended to improve the bottom line immediately (see “Applying the Accelerator and the Brakes”). The “accelerator” (say, downsizing) works and improves the quality of results we are looking for (better profit picture). But those same action scan also serve as “brakes,” or unintended consequences that counteract any beneficial actions. These action scans destroy the quality of relationships by creating mistrust and low morale, and thus ultimately decrease the quality of results. The end result may be a lot of wasted energy with no real improvement in overall results.

Without having a core theory, we might simply focus on the “accelerator” aspect of the intervention and declare victory when the results improve in the short term. We wouldn’t necessarily connect the long term negative consequences of the “braking” action to the original intervention. When the results deteriorate again, the pressure to improve results increases. We might respond by repeating the same efforts that we believe worked so well the last time. By having the theory and the accompanying loop, on the other hand, we can see how the top-down efforts may have a negative impact and implement additional measures to counter balance that effect.

To illustrate how this generalized accelerator-and-brakes dynamic might play out in a specific situation, let’s look at an example. Curtis Nelson, president and CEO of Carlson Hospitality Worldwide (the parent company of Radisson Hotels), wrote in their company magazine: “Take care of your people, inspire them, allow them to grow to their full potential and evoke their personality, and they will reach a higher level of job satisfaction. That in turn inspires greater commitment, which leads to greater guest satisfaction.”

Although Nelson did not draw a loop in his article, he articulated in words his core theory of success for this hotel and cruise business (see “Hotel Core Success Loop”). The diagram shows that investments in people’s potential enhances job satisfaction, which builds commitment and translates into higher guest satisfaction and higher revenues. An increase in revenues means a rise in profits, which leads to more investments in people.

Now, suppose something unexpected happens to decrease profits, such as a rise in airfares that reduces business travel. Top management might respond by calling for cost-cutting measures to improve the profit picture. In the short term, profits are likely to rise – the intended result. However, an unintended consequence of enacting such measures may be substantial decreases in the company’s investment in its people, leading to a decrease in job satisfaction. This decrease in job satisfaction will reduce profits in the longer term, because employees will be less committed, causing a decline in customer satisfaction. Lower profits would then provoke another wave of cost cutting, repeating the accelerator and brakes dynamic. In this way, a one-time disturbance from the outside can trigger an internal response that keeps cycling for a long time.

Again, by articulating our core theory of success, we will be more likely to pay attention to both the short-term and the long-term consequences of our actions. In particular, our theory can prevent us from inadvertently undermining the very loop, we depend on for our success.

Of course, in a real company setting, a core theory of success is likely to involve many loops, not just one. The various loops will be interconnected in many ways, and their dynamic behavior will not always be intuitively obvious. Building and understanding such theories requires more than a one-time investment in creating a quick overview map (like the ones in this article); it requires a shift in mindset that values theory-building as a vital ongoing activity of the organization.

Managers as Researchers and Theory-Builders

But in order to survive and thrive in the emerging economic order, organizations must focus on producing

long-term, sustainable results. Managers at every level need a broader perspective-a theory-of how their organization can create and maintain success. Theory-building can no longer be seen as a separate activity from the practice of management; it must become an integral part of a manager’s job. Managers must take on new roles as researchers and theory builders, which will require investment in the development of new skills and capabilities (see “Applying the Disciplines of the Learning Organization”). Just as we currently depend on accountants and financial statements to help us manage our complex enterprises, there may come a time when we will depend on our theory-builders and organizational maps and models to navigate the turbulent waters of tomorrow’s business environment.

Daniel H. Kim is a co-founder of Pegasus Communications, Inc., and a co-founder of the MIT Center for Organizational Learning.

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By following a systems thinking-based graphical facilitation process using hexagons, we can surface and focus on the issues that are most important in our organizations.This process can help shorten the amount of time it takes teams to move from ideas to action steps and can give us a more systemic view of the issues our organizations face.

The Hexagon Technique

The hexagon technique for brainstorming was created by Tony Hodgson and Gary Chicoine-Piper, British creativity and organizational development consultants. Unlike other brainstorming techniques, which focus on generating ideas, hexagons can also help us surface underlying assumptions. By writing people’s thoughts and ideas on hexagons and posting them in front of the entire team, we make those thoughts visible and separate the idea from the person. Because of their unique shape, hexagons can easily be grouped in a honeycomb structure; we can then move the hexagons into various configurations and combine ideas in different ways.

The following steps describe a brainstorming process using hexagons.

Unlike other brainstorming techniques, which focus on generating ideas, hexagons can also help us surface underlying assumptions.

Step One: Identifying Issues

Hexagon maps capture the event level—the world of who, what, when, where, and why. You want to start here, because you first have to connect with people’s understanding of their world. So begin by talking in everyday language. Surface team members’ concerns and mental models by asking questions such as: “What is really important to you?” “What issues do you feel are absolutely critical for success?” “What are your strongest opinions?” “What do you feel is holding us back?”

Summarize each response in three to six words and write it on a hexagon. Place the hexagons on the board in sequence, with one point at the top; don’t attempt to arrange the ideas at first. Number the hexagons sequentially for easy/reference. You can use color-coded hexagons (see “Color-Coding Hexagons”); for example, if you or someone else has a strong opinion, put it on a red hexagon.

But because your goal is to build a shared understanding, you have to go beyond simple brainstorming. You want to explore the reasoning—and the emotions—behind people’s statements. So, if someone says, “We need to raise prices,” you would ask, “Can you tell me more about that?” And they may respond, “If we don’t raise prices, this company is going to go under, because we won’t have enough revenues to support R&D!” The challenge is to capture of each person’s position in just a few words, so that the hexagon can serve as a vivid reminder for the entire group of that individual’s perspective. Separating an idea from the person who articulated it and creating a symbol for the idea that can be understood by the whole group is an important step in the process.

Step Two: Identifying Clusters

From this divergent step, move to convergence. Zero in on the key issues by asking, “Which hexagons seem to go together?” Find the hexagons that seem related, and then move them the same general area on the board. Once you have reviewed the positioning with the team, draw a circle around each grouping and give each cluster a name (see “Identifying Clusters”).

You may start by writing one title, and someone might say, “Let’s change it; that’s not quite right.” Remember, it’s an organic process through which the team starts to develop a common language. The titles of the most important clusters become the emergent agenda; these are the hot spots, according to the group.

Step Three: Identifying Variables

In this step, you begin to distinguish the most important trends and patterns of behavior over time within your organization. When you look for key variables, you’re really asking, “Can you tell me how something in this system might change over time?” Surface the key variables by asking a set of transition questions about the most important clusters on the board: “What do you really want?” “How would you know if you got it?” “Who are the key players?” “What do they want?” “How would they know if they got it?” “What are the key uncertainties?” You’re trying to shift to the systems point of view, and it takes some skill to navigate into that domain.

A variable is something that can increase or decrease over time. So, if you were concerned about the economy, you might say, “We might have a depression!” A depression, however, is not a variable; a variable is something that can go up or down, something you can measure. However, once you define a depression as eight consecutive quarters of declining GNP, you’ve found your variable. GNP is something that can go up or down. You can also identify “soft” variables— “morale” or “quality” can go up or down over time.

When identifying variables, choose brief, one- to two-word descriptions for each. Try not to use adverbs or lengthy adjectives. Write the variables on orange hexagons for easy identification.

IDENTIFYING CLUSTERS

After writing the issues on hexagons and numbering the hexagons sequentially for easy reference, cluster related issues. Draw a circle around each grouping and give each cluster a name. Then, surface the key variables by asking a set of transition questions about the most important clusters.

Next Steps

Once you’ve identified the trends and the variables, you’ll be amazed at how quickly you can move to a causal loop diagram by arranging the key variables. But for real breakthroughs, the maps must focus on the group’s most important problems and objectives. Otherwise, they can easily degenerate into meaningless “spaghetti” diagrams, or pictures of things that are connected but don’t necessarily have a cause-and-effect relationship. The steps outlined above should help your team make the transition from ideas to key variables.

Be sure to document your learning process for future reference. Take photographs of your hexagon maps, clusters, variables, and causal loops.

Beyond Hexagons

Using hexagons for brainstorming can be an engaging, dynamic way to move a group toward taking a systemic perspective. By allowing you to capture various viewpoints, hexagons provide a spring board for creative brainstorming and can help you elicit mental models. Finally, by helping you identify key issues, the hexagon technique enables teams to move from ideas to variables, and then on to causal loop and flow diagrams.

David Kreutzer is founder and president of GKA Incorporated, an international management consulting firm. The process described in this article is based on the initial steps of FASTBreak, a systems thinking-based facilitation methodology for moving from ideas to action designed by GKA Incorporated.

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