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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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One of the reasons for this wide-spread institutional failure is that the knowledge-creating system, the method by which human beings collectively learn and by which society’s institutions improve and revitalize themselves, is deeply fragmented. This fragmentation has developed so gradually that few of us have noticed it; we take the disconnections between the branches of knowledge and between knowledge and practice as a given

A Knowledge-Creating System

Before we can address the issue of fragmentation, we need to establish what has been fragmented. In other words, what do we mean by a knowledge-creating system, and what does it mean to say it is fragmented?

THE CYCLE OF KNOWLEDGE-CREATION

Like theories, the tree’s roots are invisible, and yet the health of the root system determines the health of the tree. The branches are the methods and tools, which enable translation of theories into new capabilities and practical results. The fruit is that practical knowledge. The tree as a whole is a system.

We believe that human communities have always attempted to organize themselves to maximize the production, transmittal, and application of knowledge. In these activities, different individuals fulfill different roles, with varying degrees of success. For example, in indigenous cultures, elders articulate timeless principles grounded in their experience to guide their tribes’ future actions. “Doers, “whether warriors, growers, hunters, or nannies, try to learn how to do things better than before and continually improve their craft. And coaches and teachers help people develop their capacities to both perform their roles and grow as human beings. These three activities-which we can term theory-building, practice, and capacity-building-are intertwined and woven into the fabric of the community in a seamless process that restores and advances the knowledge of the tribe. One could argue that this interdependent knowledge-creating system is the only way that human beings collectively learn, generate new knowledge, and change their world.

We can view this system for producing knowledge as a cycle. People apply available knowledge to accomplish their goals. This practical application in turn provides experiential data from which new theories can be formulated to guide future action. New theories and principles then lead to new methods and tools that translate theory into practical know-how, the pursuit of new goals, and new experience-and the cycle continues.

Imagine that this cycle of knowledge-creation is a tree (see “The Cycle of Knowledge-Creation” on p.1). The tree’s roots are the theories. Like theories, the roots are invisible to most of the world, and yet the health of the root system to a large extent determines the health of the tree. The branches are the methods and tools, which enable translation of theories into new capabilities and practical results. The fruit is that practical knowledge. In a way, the whole system seems designed to produce the fruit. But, if you harvest and eat all the fruit from the tree, eventually there will be no more trees. So, some of the fruit must be used to provide the seeds for more trees. The tree as a whole is a system.

The tree is a wonderful metaphor, because it functions through a profound, amazing transformational process called photosynthesis. The roots absorb nutrients from the soil. Eventually, the nutrients flow through the trunk and into the branches and leaves. In the leaves, the nutrients interact with sunlight to create complex carbohydrates, which serve as the basis for development of the fruit.

So, what are the metaphorical equivalents that allow us to create fruits of practical knowledge in our organizations? We can view research activities as expanding the root system to build better and richer theories. Capacity-building activities extend the branches by translating the theories into usable methods and tools. The use of these methods and tools enhances people’s capabilities. The art of practice in a particular line of work transforms the theories, methods, and tools into usable knowledge as people apply their capabilities to practical tasks, much as the process of photosynthesis converts the nutrients into leaves, flowers, and fruit. In our society,

• Research represents any disciplined approach to discovery and understanding with a commitment to share what’s being learned. We’re not referring to white-coated scientists performing laboratory experiments; we mean research in the same way that a child asks, “What’s going on here?” By pursuing such questions, research-whether performed by academics or thoughtful managers or consultants reflecting on their experiences-continually generates new theories about how our world works.
• Practice is anything that a group of people does to produce a result. It’s the application of energy, tools, and effort to achieve something practical. An example is a product development team that wants to build a better product more quickly at a lower cost. By directly applying the available theory, tools, and methods in our work, we generate practical knowledge
• Capacity-building links research and practice. It is equally committed to discovery and understanding and to practical know-how and results. Every learning community includes coaches, mentors, and teachers – people who help others build skills and capabilities through developing new methods and tools that help make theories practical.

“The Stocks and Flows of Knowledge-Creation” shows how the various elements are linked together in a knowledge-creating system.

THE STOCKS AND FLOWS OF KNOWLEDGE-CREATION

Research activities build better and richer theories. Capacity-building functions translate the theories into usable methods and tools. The use of these methods and tools enhances people’s capabilities. The art of practice transforms the theories, methods, and tools into practical knowledge, as people apply their capabilities to practical tasks.

Institutionalized Fragmentation

If knowledge is best created by this type of integrated system, how did our current systems and institutions become so fragmented? To answer that question, we need to look at how research, practice, and capacity-building are institutionalized in our culture (see “The Fragmentation of Institutions”).

For example, what institution do we most associate with research Universities? What does the world of practice encompass? Corporations, schools, hospitals, and nonprofits. And what institution do we most associate with capacity-building-people helping people in the practical world? Consulting, or the HR function within an organization. Each of these institutions has made that particular activity its defining core. And, because research, practice, and capacity-building each operate within the walls of separate institutions, it is easy for the people within these institutions to feel cut off from each other, leading to suspicion, stereo typing, and an “us” versus “them” mindset.

This isolation leads to severe communication breakdown. For example, many people have argued that the academic community has evolved into a private club. Nobody understands what’s going on but the club members. They talk in ways that only members can understand. And the members only let in others like themselves.

Consulting institutions have also undermined the knowledge-creating process, by making knowledge proprietary, and by not sharing what they’ve learned. Many senior consultants have an incredible amount of knowledge about organizational change, yet they have almost no incentive to share it, except at market prices.

Finally, corporations have contributed to the fragmentation by their bottom-line orientation, which places the greatest value on those things that produce immediate, practical results. They have little patience for investing in research that may have payoffs over the long term or where payoffs cannot be specifically quantified.

Technical Rationality: One Root of Fragmentation

How did we reach this state of fragmentation? Over hundreds of years, we have developed a notion that knowledge is the province of the expert, the researcher, the academic. Often, the very term science is used to connote this kind of knowledge, as if the words that come out of the mouths of scientists are somehow inherently more truthful than everyone else’s words.

Donald Schon has called this concept of knowledge “technical rationality.” First you develop the theory, then you apply it. Or, first the experts come in and figure out what’s wrong, and then you use their advice to fix the problem. Of course, although the advice may be brilliant, sometimes we just can’t figure out how to implement it.

But maybe the problem isn’t in the advice. Maybe it’s in the basic assumption that this method is how learning or knowledge-creation actually works. Maybe the problem is really in this very way of thinking: that first you must get “the answer,” then you must apply it.

THE FRAGMENTATION OF INSTITUTIONS

Because research, practice, and capacity-building each operate within the walls of separate institutions, the people within these institutions feel cut off from each other, leading to suspicion, stereotyping, and an “us” versus “them” mindset.

The implicit notion of technical rationality often leads to conflict between executives and the front-line people in organizations. Executives often operate by the notion of technical rationality: In Western culture, being a boss means having all the answers. However, front-line people know much more than they can ever say about their jobs and about the organization. They actually have the capability to do something, not just talk about something. Technical rationality is great if all you ever have to do is talk.

Organizing for Learning

If we let go of this notion of technical rationality, we can then start asking more valuable questions, such as:

• How does real learning occur?
• How do new capabilities develop?
• How do learning communities that interconnect theory and practice, concept and capability come into being?
• How do they sustain themselves and grow?
• What forces can destroy them, undermine them, or cause them to wither?

Clearly, we need a theory, method, and set of tools for organizing the learning efforts of groups of people.

Real learning is often far more complex and more interesting than the theory of technical rationality suggests. We often develop significant new capabilities with only an incomplete idea of how we do what we do. As in skiing or learning to ride a bicycle, we “do it” before we really understand the actual concept. Similarly, practical know how often precedes new principles and general methods in organizational learning. Yet, this pattern of learning can also be problematic.

For example, teams within a large institution can produce significant innovations, but this new knowledge often fails to spread. Modest improvements may spread quickly, but real breakthroughs are difficult to diffuse. Brilliant innovations won’t spread if there is no way for them to spread; in other words, if there is no way for an organization to extract the general lessons from such innovations and develop new methods and tools for sharing those lessons. The problem is that wide diffusion of learning requires the same commitment to research and capacity-building as it does to practical results. Yet few businesses foster such commitment. Put differently, organizational learning requires a community that enhances research, capacity-building, and practice (see “Society for Organizational Learning” on p. 4)

Learning Communities

We believe that the absence of effective learning communities limits our ability to learn from each other, from what goes on within the organization, and from our most clearly demonstrated breakthroughs. Imagine a learning community as a group of people that bridges the worlds of research, practice, and capacity-building to produce the kind of knowledge that has the power to transform the way we operate, not merely make incremental improvements. If we are interested in innovation and in the vitality of large institutions, then we are interested in creating learning communities that integrate knowledge instead of fragment it.

In a learning community, people view each of the three functions-research, capacity-building, practice-as vital to the whole (see “A Learning Community”). Practice is crucial because it produces tangible results that show that the community has learned something. Capacity-building is important because it makes improvement possible. Research is also key because it provides a way to share learning with people in other parts of the organization and with future generations within the organization. In a learning community, people assume responsibility for the knowledge creating process.

Learning Communities in Action

To commit to this knowledge-creating process, we must first understand what a learning community looks like in action in our organizations. Imagine a typical change initiative in an organization; for example, a product development team trying a new approach to the way they handle engineering changes. Traditionally, such a team would be primarily interested in improving the results on their own projects. Team members probably wouldn’t pay as much attention to deepening their understanding of why a new approach works better, or to creating new methods and tools for others to use. Nor would they necessarily attempt to share their learnings as widely as possible – they might well see disseminating the information as someone else’s responsibility.

In a learning community, however, from the outset, the team conceives of the initiative as a way to maximize learning for itself as well as for other teams in the organization. Those involved in the research process are integral members of the team, not outsiders who poke at the system from a disconnected and fragmented perspective. The knowledge creating process functions in real time within the organization, in a seamless cycle of practice, research, and capacity-building.

Imagine if this were the way in which we approached learning and change in all of our major institutions. What impact might this approach have on the health of any of our institutions, and on society as a whole? Given the problems we face within our organizations and within the larger culture, do we have any choice but to seek new ways to work together to face the challenges of the future? We believe the time has come or us to begin the journey back from fragmentation to wholeness and integration. The time has come for true learning communities to emerge.

Peter M. Senge, best-selling author of The Fifth Discipline: The Art and Practice of the Learning Organization, is an international leader in the area of creating learning organizations. He is a senior lecturer in the Organizational Learning and Change Group at MIT. Peter has lectured throughout the world and written extensively on systems thinking, institutional learning, and leadership.

Daniel H. Kim is a co-founder of Pegasus Communications, Inc., and publisher of The Systems Thinker. He is a prolific author as well as an international public speaker, facilitator, and teacher of systems thinking and organizational learning

Editorial support for this article was provided by Janice Molloy and Lauren Johnson

A LEARNING COMMUNITY

In a learning community, people view each of the three functions—research, capacity-building,practice—as vital to the whole

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A third way to begin a systems thinking diagramming process is to look at the problem in terms of trends or behavior over time. Since systems thinking is focused on how systems change over time, it can often be useful to first identify the problem behavior, and then graph the structure that corresponds to that behavior.

Demand Trend

At CMI the number of customer orders over the last two years showed an oscillating pattern with an increased amplitude in the past six months.

Structure-Behavior Pairs

Behavior over time graphs (also called reference modes) allow us to build causal theories by using past history to gain insight into the causal structures underlying a problem. Beginning with observed data or trends allows the system to “speak for itself” and reduces the risk of “force-fitting” the problem into a particular archetype storyline.

Behavior trends also provide clues as to what type of structure might be producing the observed behavior. Over the years, a number of simple “structure-behavior pairs” (common behavior trends and corresponding causal loop structures) have been identified. This library of structure-behavior pairs can provide a useful starting point for building a diagram (see “Structure-Behavior Pairs”). For example, a basic reinforcing structure—which amplifies change in one direction with even more change—produces either exponential growth or decay. Thus, if you are struggling with a problem that involves rapid growth or decline, it is likely that a reinforcing structure is at the heart of the situation.

Creating a Causal Theory

To see how the structure-behavior pairs can be used to create a causal theory of a problem or issue, let’s look at the experience of Custom Manufacturing, Inc. (a fictional name). CMI specializes in taking a commodity material and customizing it to fit the needs of each client, who then turn the material into an end-product and sell it to consumers. Orders are generally placed on a monthly basis, and the company’s turn-around time is roughly two weeks per order. Using this strategy, CMI had successfully created a niche in a growing market, and had experienced steady growth over the last two years.

However, over the last six months the company had seen wild fluctuations in its demand. At first, the managers assumed that this signaled increasing turbulence in the marketplace due to new entrants in the specialty materials niche. They were concerned about remaining competitive in the industry, and hoped that by gaining a deeper understanding of the structural issues involved, they might discover some decisions or policies they could take to affect the order stream, rather than simply reacting to the market trends.

Capturing Historical Trends

To use the structure-behavior pairs, you want to begin by capturing the important historical trends related to the issue. The managers at CMI began by drawing a behavior chart for demand over the last two years (see “Demand Trend”). The graph showed a clear oscillating pattern, which had increased in the last six months. Since the oscillation pattern suggests a balancing process with delays, they spent the next few weeks gathering data and talking to customers to see what might be causing the oscillations in demand.

Based on this information, and using the balancing loop template as a starting point, they created a causal loop diagram that told the following story: As demand increased, the number of orders to be processed increased. However, processing each customized order at CMI takes specially trained machinists. So when the demand exceeded the capacity of the current staff, the backlog of orders grew, as did the delivery time. But CMI’s customers have their own stream of orders to fill. Therefore, when CMI’s delivery time extended beyond an acceptable period (usually three weeks), their customers would go to CMI’s higher-priced competitors to fill their orders, resulting in a decline in orders at CMI.

Each time the backlog hit a critical level, CMI’s managers responded by adding capacity on a temporary basis. The added capacity, combined with a decrease in incoming orders, enabled the company to work off its backlog, and the delivery time would return to the original two-week goal.

However, it took several weeks for CMI’s regular customers to learn of the improved delivery times and shift their orders back to CMI. Thus, the demand for products oscillated as a function of the company’s internal capacity to meet the growth demand (see “Balancing Capacity and Demand”).

Once they had completed the behavior chart and the structural diagram, CMI’s managers were able to see more clearly how their internal capacity was, indeed, affecting their order stream. Although they had previously assumed that periodic downturns in their orders were the result of competitive pressures or cyclical trends in the marketplace, their systems work suggested that their internal policies could be making the situation worse.

With their improved understanding of how the company’s internal capacity (in terms of the number of trained machinists) affected its order stream, the managers took steps to institute a flexible workforce policy and to cross-train machinists, in order to be prepared to meet the fluctuations in demand.

Interrelated Patterns of Behavior

In real life, behavior trends are rarely as simple as those listed in the structure-behavior chart. For example, your company may have experienced art overall growth in new customers over the last 18 months, but that growth may have been punctuated by periodic downturns. This is because in most systems there are many reinforcing and balancing processes occurring simultaneously, which produce mixed data. However, the structure-behavior chart provides a good starting point for developing a deeper understanding of a problem. As you dig deeper into the structures at work—and the behaviors they produce—you can enrich your diagram by drawing additional loops until you create an accurate representation of the issue at hand.

Colleen Lannon is co-founder of Pegasus Communications, Inc. and managing editor of The Systems Thinker.

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One may be tempted to conclude that substituting new people would be easier than replacing all the information and systems. But even in the most bureaucratic organization, with all its standard operating procedures and established protocols, there is much more about the firm that is unsaid and unwritten. In fact, numerical and verbal databases only capture a small fraction of the information that is in mental “databases.”

The essence of an organization is embodied in its people, not its systems. The intangible assets of a company reside in the individual mental models that contribute to the organization’s memory. Without these mental models—which include the subtle interconnections that have been developed among the members—an organization will be incapacitated in both learning and action. Yet in most organizations, individual mental databases are not “backed up,” nor is the transfer from individual to organizational learning well managed. A critical challenge for a learning organization is understanding the transfer process through which individual learning and knowledge (mental models) become embedded in an organization’s memory and structure. Once we have a clear understanding of this transfer process, we can actively manage organizational learning to be consistent with an organization’s goals, vision, and values.

“The essence of an organization is embodied in its people, not its systems. The intangible assets of a company reside in individual mental models…”

From Individual Learning…

In order to develop a framework for organizational learning, we must begin by understanding how individuals learn. The “Individual Learning Cycle” diagram shows a simplified model of individual learning. The diagram traces the process through which the brain assimilates some new data (environmental response), takes into account the memories of past experiences, comes to some conclusion about the new piece of information (individual learning), and then stores it away (individual mental models). After processing the new learning, one may choose to act or simply do nothing (individual action).

The processing stage has been labeled “individual mental models” because it represents much more than the traditional concept of memory. Memory connotes a rather static repository for knowledge, whereas mental models involve the active creation of new knowledge. Mental models represent a person’s view of the world, including both explicit and implicit understandings. They also provide the context in which to view and interpret new material, and they determine how stored information will be applied to a given situation.

…To Organizational Learning

In the early stages of an organization’s existence, organizational learning is often synonymous with individual learning since it usually involves a very small group of people and the organization has minimal structure. As an organization grows, however, a distinction between the two levels of learning emerges. Somewhere in that process, a system evolves for capturing learnings from its individual members.

There is little agreement on what constitutes “appropriate” learning—those individual actions or learnings that should be transferred from the individual into the organization’s memory. Standard operating procedures (SOP’s), for example, are viewed as an important part of an organization’s memory—a repository of past learning. But SOP’s can also be a roadblock to learning if an organization becomes locked into old procedures and avoids searching for entirely new modes of behavior. How does an organization decide when once-appropriate routines are no longer valid? Can an organizational anticipate obsolescence of its SOPs or must it always learn by first making inappropriate decisions in the face of changing conditions? These are the types of issues which a model of organizational learning must address.

Organizational Learning Cycles

By extending our model of individual learning to include organizational learning, we can begin to explore the transfer process between the two (see “A Simple Model of Organizational Learning”). This model represents the organizational learning cycle as a four-stage process, with organizational learning composed of three distinct sub-stages: individual learning, individual mental models, and organizational memory. Individual actions are taken based on individual mental models. These actions, in turn, translate into organizational action, and both actions produce some environmental response. The cycle is complete when the environmental response, in turn, leads to individual learning and affects individual mental models and organizational memory.

This simple model captures the transfer of individual learning to organizational memory via changes in individual mental models. Thus, organizational learning is separated from action (because all learning does not translate into taking new actions) and from environmental response (because all learning is not precipitated by the environment). The complete learning cycle, however, does include both the actions of the individual and the organization as well as the environmental response to those actions.

An Integrated Model

There are at least two fundamentally different levels of learning at which an organization must be equally adept—operational and conceptual. Operational learning deals with the changes in the way we actually do things–filling out entry forms, operating a piece of machinery, handling a switchboard, retooling a machine, etc. While operational learning emphasizes the how of doing things, conceptual learning, emphasizes the why of doing things—that is, it has do with the thinking behind why things are done in the first place. Conceptual learning deals with issues that challenge the very nature or existence of prevailing conditions or procedures. In order for organizational learning to be effective, however, conceptual learning must be operationalized into specific skills that can be learned and executed.

Individual Mental Models: Frameworks and Routines. Individual learning is captured in mental models through two different paths (see “Organizational Learning: An Integrated Model”). Operational learning produces new or revised routines that replace old or outworn ones. Conceptual learning leads to changes in frameworks, leading to new ways of looking at the world and new actions. For example, a design engineer may follow a six-step process for getting her drawings ready for a program review meeting. Through experience, she may learn to improve the process by stream-lining some of the steps involved (operational learning). As she rethinks the framework of her work—the context in which the drawings are being produced and what their use is—she may question the production of the drawings themselves and identify situations when the drawings may not be necessary (conceptual learning). Her revised mental models will contain both the new frameworks and routines as well as the knowledge about how the routines fit within the new framework.

Organizational Memory: Weltanschauung and SOP’s. The dual pathway continues from mental models to organizational memory. Over time, individual mental frameworks become embedded into the organization’s own weltanschauung, or worldview. An organization’s view of the world, in turn, affects how the individual interprets changes in the environment and how she translates her mental models into action. It also influences how the organization translates its organizational memory into action. For example, if an organization believes its ability to affect the environment is low, it will rely on standard routines and reactive behaviors. If, on the other hand, an organization assumes that it can take an active role in affecting its environment, this organization may approach everything in the spirit of experimentation, testing, and inventing.

In similar fashion, individual routines that are proven sound over time become a company’s standard operating procedures. The strength of the link between individual mental models and organizational memory depends how influential an individual or group is. In the case of a CEO or upper management, influence can be high due to the power inherent in the positions. Similarly, a united group of hourly workers can have a high degree of influence due to their size.

Incomplete Learning Cycles

Organizational learning requires completing the entire loop. If any of the links are either weak or broken, learning can be impaired. Situational learning, for example, occurs when the link between individual learning and individual mental models is severed: that is, the learning is situation-specific and does not change mental models. Crisis management is one example of situational learning in which each problem is solved but no learning is carried over to the next case.

When the link between individual models and organizational memory is broken, fragmented learning occurs. Individual mental models may change, but those changes are not reflected in the organization’s memory. When organizational learning is fragmented among isolated individuals (or groups), the loss of the individuals (through turnover or layoffs) means loss of knowledge as well.

The link between organizational memory and organizational action, if broken, can lead to opportunistic learning. This occurs when organizational actions are pursued without taking into account organizational memory or the organization’s values, culture, and SOP’s. Sometimes this is done purposely, when one wishes to bypass the features of an organization that may impede progress on a specific front. The use of “skunk works” to develop the IBM personal computer is a good example, as is General Motors’ creation of an entirely new car division, Saturn.

Managing the Whole Learning Cycle

Managing organizational learning means managing the complete cycle—explicitly. Improving each of the pieces is not enough—the links between the pieces must also be managed. This requires addressing each of the incomplete learning cycles described above.

Beyond Situational Learning.

Mental models are the critical pathway between individual learning and organizational memory. Mental models are the manager and arbiter of how new information will be acquired, retained, used, and deleted. Although a company can try to manage the flow of information, control the environment, or manipulate peoples’ learning environment in various ways, if a person’s view of the world remains unchanged, it is unlikely that any such actions will affect the quality of learning.

Therefore, closing the loop on situational learning—the link between individual learning and individual mental models—requires developing individuals’ ability to transfer specific insights into more general maps that will guide them in the future. In order to make mental models explicit, we nerd appropriate tools to capture the type of knowledge that is being mapped.

Dynamic systems, in particular, require a different set of tools for making mental models explicit. Systems archetypes (systemic structures that recur repeatedly in diverse settings) such as “Shifting the Burden” and “Tragedy of the Commons” can be very helpful for eliciting and capturing managers’ intuitive understanding of complex dynamic issues. Action maps are also useful for capturing the behavioral dynamics of a team or organization over time. They help managers see the larger patterns of behavior in which their specific actions are embedded. Together, these two methods can help surface and capture a great deal of tacit individual knowledge in a way in which it can be shared, challenged, and subject to change—thus transferring it to organizational memory.

From Fragmented to Organizational Learning. Capturing individual mental models alone is not sufficient to achieve organizational learning, however. There also needs to be a way to prevent fragmented learning among individuals and to spread the learning throughout the organization. One way to accomplish this is through the design and implementation of learning laboratories—managerial practice fields where teams of managers can practice and learn together (see page 5).

Learning laboratories can be designed, in part, around the learnings captured in systems archetypes and action maps. The spirit of the learning lab is one of active experimentation and inquiry, where everyone participates in surfacing and testing each other’s mental models. Through this process, a shared understanding of the key assumptions and inter-relationships of the organization can emerge. The use of an interactive computer management flight simulator (see “Flying People Express Again,” V IN6) offers the participants an opportunity to test their assumptions and to viscerally experience the consequences of their actions. The learning laboratory can be the vehicle through which organizational memory—via its weltanschauung and SOP’s—can be enriched over time.

Harnessing Opportunistic Learning. If the organization’s own culture and ways of doing things get in the way of learning, scenario planning and idealized designs can provide a way to break out of the norms. Royal Dutch Shell uses scenario planning to create alternative realities that stretch beyond what most managers in the company are likely to envision. By carefully constructing a multiple set of possible scenarios, Shell has been successful in anticipating and adapting to extremely volatile environments (see “Scenario Planning: Managing by Foresight,” V IN7).

Idealized designs, used by Russell Ackoff and his colleagues at Interact (Bala Cynwood, PA), can also minimize the amount of influence an organization’s current state has in determining its future. The principle idea is to start by crystallizing an ideal future without considering the current capabilities or organizational limitations. Thus, the planning process is “pulled” by where you want to be instead of “anchored” by where you are.

The Learning Challenge

The old model of a hierarchical corporation where the top thinks and the bottom acts is giving way to a new model where thinking and acting must occur at all levels. As organizations push for flatter structures and reduced bureaucracy, there will be increased reliance on the individuals to be the carriers of the organization’s knowledge. Instead of codifying rules and procedures in handbooks and policy manuals, the new challenge is to continually capture the emerging understanding of the organization wherever it unfolds. At the heart of it all is understanding the role individual mental models play in the organizational learning cycle and continually finding ways to manage the transfer from individual to organizational learning.

Further reading: Daniel H. Kim, “Individual and Organizational Learning: Where the Twain Shall Meet?” System Dynamics Group Working Paper #D-4114 (MIT Sloan School of Management, Cambridge, MA) 1989.

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When to Simulate

In general, simulation modeling is useful for understanding complex relationships, for developing and testing specific policies, and for understanding the implications of long time delays on a problem or issue (see “When to Simulate,” April 1995). While these general guidelines give you a sense of why modeling can be useful, they don’t tell you when a particular problem you’re working on can benefit from simulation modeling.

That is why experienced practitioners watch for certain signals that indicate when it is time to move into simulation modeling. One such entry point is to listen for organizational “dilemmas.”

A “dilemma” occurs when a group is aware of multiple consequences of a policy or strategy, but there is no clear agreement around which consequence is strongest at what point in time. In such a situation—where there are strong passions around a specific issue and the organization or team is “stuck” at its current level of understanding—simulation modeling has the potential to be very effective. The following six-step process describes how modeling can be used to resolve such dilemmas.

1. Identify the Dilemma

The first step is to listen for situations where there are two different theories about the consequences of a decision (for more on this process, see “Using Systems Thinking ‘On-Line’: Listening for Multiple Hypotheses,” August 1995). Dilemmas are often characterized by one party strongly advocating for a decision or strategy, followed by public disagreement or private mumbling about how that action will cause just the opposite of the intended outcome.

For example, the plant management team of a major component supplier wanted to “load the plant”—that is, continually push sales in order to maximize the plant’s capacity usage. But others expressed concern that loading the plant might create other problems that would affect both quality and service.

2. Map the Theories

Once you have identified the dilemma, it is important to clarify the issues involved by explicitly mapping out each of the viewpoints using causal loop diagrams or systems archetypes. For example, in the plant capacity issue, the strategy behind loading the plant was to increase sales in order to maximize the capacity utilization of the plant, until the available capacity falls to zero and no more sales can be filled (B1 in “Theory 1: Loading the Plant”). The benefit of this strategy is twofold: by increasing sales, the plant will boost profits (R3); and by increasing the capacity utilization, the cost per unit will fall, which increases profits and allows more investment in plant capacity (B2).

However, other people felt strongly that if plant utilization remained too high, the ability to respond to customer changes (flexibility) would go down, eventually hurting the company’s reputation as a supplier and leading to a decline in sales (B4 in “Theory 2: Unintended Side-Effects”). They also believed that loading the plant would cause the stress level in the organization to rise, eventually eroding quality and further hurting their reputation (B5).

3. Assess Dynamic Complexity

Some situations of “multiple hypotheses” resolve themselves when the different parties work together to map out each story and find that one is clearly more accurate. But in cases where both sets of interconnections are plausible and the uncertainty or disagreement still exists, further work is needed to resolve the dilemma. If the uncertainty is simply around a number (such as accurate cost data) or the probability of one outcome versus another, it is a static problem and techniques such as decision analysis may be appropriate. Simulation modeling, on the other hand, is most effective where there is some degree of dynamic complexity—where the link between cause and effect is subtle and the implications over time are not obvious.

To check for dynamic complexity, ask if the uncertainty or disagreement is around what feedback loop is dominant at what time—in other words, the long-term impact might be different than the short-term effects. If this is the case, then some degree of dynamic complexity exists and it is appropriate to move into simulation modeling. In the manufacturing example, a key area of uncertainty was the effect of various capacity utilization strategies on the company’s reputation (and therefore sales) over time. The importance of this time delay indicated a degree of dynamic complexity that could benefit from a simulation approach.

4. Developing the Simulation Model

At this point, you are now ready to move into the development of the model. First, you want to focus the model-building effort by asking, “What do we need to learn in order to be able to resolve the dilemma?” Stating the objective up front will guide the rest of the process.

Once you have established your objective, you can define the boundaries of the simulation model by identifying the key decisions (critical policies that the organization makes), the important indicators (what you need to see from the system to assess the decision), and the uncertainties (most fragile assumptions about the relationships or outside world) associated with the dilemma.

In the capacity utilization example, the parties recognized that the dilemma would be resolved when they knew both the short- and long-term impact of different capacity utilizations on sales and profits. Their primary decision was to select a particular utilization goal (desired production relative to capacity), which could be assessed by looking at the long-term behavior of sales and reputation. The company’s key uncertainties included demand and customer sensitivity, because they didn’t know exactly how the market would evolve, and the impact of different utilizations depended heavily on their assumptions about the customer’s sensitivity to price, quality, and flexibility.

Once you have established the focus and boundaries of the model, you are ready to build the simulation model by defining the relationships between important variables, such as the relationship between manufacturing flexibility and reputation in our plant capacity example. (For more on the actual mechanics of model building, see “From Causal Loop Diagrams to Computer Models—Part II,” August 1994).

5. Divergence: Testing the Assumptions

Once you have built the model, you can begin testing the different assumptions behind your causal theories to see the effect of those interrelationships over time. At this stage, you are attempting to be divergent—trying out many possible scenarios, any of which can lead to new questions and experiments.

Using the simulator they had developed, the management team was able to test different capacity utilization levels, using various assumptions about the market and customer “sensitivity.” In the first scenario, the group made two simplifying assumptions: that there would be no change in overall market size, and that the organization could not build new capacity. They then ran the simulator, testing four different capacity utilization “goals”: 70%, 75%, 78%, and 82% of maximum possible capacity.

When they ran the simulation, they discovered that when the plant is running at 70% or 75% of its maximum capacity, it is able to maintain a set level of production. Bur at 78% or 82%, an unexpected oscillation occurred after a short period of time, the number of orders began to drop dramatically, and then gradually rebounded (see “Scenario 1” in “Capacity Utilization Scenarios”).

From these results, the group hypothesized that at higher capacity utilizations the plant is less flexible in meeting customer demands, which affects the company’s reputation and leads to a decline in sales. Once the number of orders falls below the utilization goal, the plant then has time to improve flexibility and quality. Over tune, its reputation and sales gradually rebound until once again it is in a situation with high capacity utilization but low flexibility — and the cycle begins again.

In the second scenario, the group tested the same four capacity utilization goals, but with new assumptions: that the market would grow steadily, and that the company would expand capacity when the forecasted demand exceeded the current capacity (sec “Scenario 21. To their surprise, when they ran this simulation, they found that a lower utilization goal actually leads to far more sales in the long run. From this they hypothesized that at a lower utilization, there is a greater unfilled demand, which leads to more optimistic forecasting and investment, more plant capacity, and a better reputation as a growing reliable supplier.

6. Convergence: Resolving the Dilemma

Testing various scenarios allows you to explore assumptions and gather data. But understanding more about behavior over time is only useful if it helps move toward resolution of the dilemma. Therefore, the divergent phase should be followed by a convergent phase, in which the group closes in on the policies that produce the most desirable short-term and long-term behavior for the most likely future scenarios.

In the plant capacity example, the team discovered that there was an optimal capacity utilization level, above which the organization created undesired oscillation. The resolution to their dilemma was to set capacity utilization at a level that balanced the need to load the plant with the need u, maintain flexibility and a high company reputation. In this case, the use of a simulation model enabled the team to productively address an issue that had been a long-standing dispute in the plant, and to develop a policy that was acceptable to all of the involved parties.

Don Seville is a research affiliate at the Mir Center for Organizational Learning and an associate with GKA Incorporated.

Many of these ideas emerged from conversations with Jack Homer of Horner Consulting. Inc., who also collaborated on the design of the model.

Editorial support for this article was provided by Colleen P. Lannon.

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