—Colleen P. Lannon

Peter Block—Stewardship: A Governance Strategy for the Learning Organization

As we begin to develop new infrastructures for organizational learning, at some point we must address the ineffectiveness of our current governance systems. Peter Block argues that nothing short of political reform at the institutional level will provide us with the systems and structures needed to stew-ard the learning organization into the future. His exploration of the concept of stewardship provides a foundation for creating institutional structures that engage each individual in the process of moving a company toward its desired future.

—CPL

Over the years, we have tried to humanize and soften our organizational structures. But all we have learned to do is adapt more effectively to what is essentially a corrupt and autocratic system. What we really need is political reform at the level of institutional structure that addresses the larger issue of power—who is “in charge.” Ultimately, we must ask how we can create institutions where citizens and citizenship are rediscovered. How can we create a culture where we are all accountable for what is happening?

Accountability and Patriarchy

To be accountable means to carry the well-being of an institution in one’s hands. Such a change in thinking demands a redistribution of power. But given the political structures in which we now live, such redistribution is almost impossible. Our current structures are highly controlling and deeply patriarchal. Unfortunately, we all collude in maintaining that structure. We treat top management as if it is more important than other areas of the company, and we continue to express the belief that learning must start at the top (e.g., “leadership sets the vision”).

In fact, most of our management practices are “colonial” strategies designed to maintain consistency, control, and predictability. If we are serious about creating learning organizations—places where surprise, discovery, and genuine contact have meaning—then we have to do something about these artifacts of sovereignty and colonialism.

The economic motivation for change is driven by customers. They are demanding a unique response to their needs, which our current structures are incapable of providing. For example, look at the difference between Federal Express and the U.S. Post Office. At Federal Express, if I give them my last name and zip code, they know exactly who 1 am. More importantly, they know my needs and preferences as a customer. The post office, on the other hand, identifies me as “Current Resident.” Even though they come to my house every day, they do not know me as a unique customer. It’s not that the people in the post office care less about their customers than Federal Express does, it is just that Federal Express is organized to allow the customer to control the relationship. In order to make that happen in all organizations, we need to redesign our structures so that everyone feels responsible and accountable for meeting the customer’s needs.

Social Architecture

So how can we design new social architecture to support accountability and responsibility? Our task is to build the capacity of local units to redesign and reconfigure themselves—whether it is a neighborhood, school, or department. That redesign should focus on five areas: job design, staff roles, human resource practices, pay practices, and financial practices.

Redesigning Jobs. How do we engage people in restructuring their jobs to ensure that they meet the needs of a market?

Staff Roles. Many of our staff functions—finance, human resources, training and development, etc.—still operate as if top management is their customer. But we cannot have an empowered workforce if we still have staff groups that serve in a policing role. By allowing line management a choice of staff services, we can remove the power from the hands of staff groups and have them serve local units.

Human Resource Practices. How can we redesign our human resource practices to promote partnership? One way is to put power and choice in the hands of those doing the actual work by enabling peers to do the hiring, the scheduling, and the feedback of each other.

Pay Practices. Most institutions have two pay systems: executive compensation and regular compensation. The goal for executive compensation is to pay the people at the top as much as possible, while regular compensation is targeted at suppressing labor costs. How can we create a partnership when we have a system as divisive as that?

Pay should be based on success or failure in the marketplace. If a supervisor determines pay increases, that insulates individuals from the marketplace. We need to pay according to real business outcomes, rather than approval ratings.

Financial Practices. How can our financial practices create ownership at the center and at the bottom of the organization? The problem with high-control systems is that they steal accountability away from people. If management decides our pay, if others organize our efforts, if we look to “the top” to define the future, we are simply reinforcing the notion that we are not responsible. By creating a culture of accountability, and by redistributing choice and power throughout the organization, we can create large, whole systems change.

—Edited by Elisabeth Bowman

Danah Zohar—A Quantum Vision for Building the Learning Organization

The top-down control that has characterized traditional management structures is no longer effective in an age of accelerating uncertainty and rapid change. The new physics of the 20th century–particularly quantum physics—offers a new model for creating the integrative, cooperative, and constantly inventive infrastructures necessary for the learning organization. In her presentation, Danah Zohar explores the implications of the Newtonian paradigm for our society and our organizations, and describes the new possibilities that present themselves when we begin to view our organizations through the lens of quantum physics.

—CPL

Our paradigm—our deeply held set of unconscious assumptions—structures our experiences without us even realizing it. Our environment shapes this paradigm, and the paradigm, in turn, focuses our attention. It determines the questions we will ask, the expectations we will have, and the experiments we will do in our lives and in our organizations.

In fact, our brains can’t help making mental models based on our paradigms. The purpose of the self-organizing system in the brain is to make patterns out of our experience. Without this pattern-making process, we would be completely scattered. The downside, however, is that our paradigms can trap us. We can get “paradigm paralysis,” where we only know how to ask the questions that our paradigm allows us to ask.

Part of breaking out of paradigm paralysis is learning to ask new questions. In complexity and chaos physics today, there is this idea of being “at the edge,” meaning that we are poised like a tightrope walker between too much order and too much chaos. If we can learn to poise ourselves at the edge, that is where we can be most creative and begin to ask new questions that will lead to new mental models and patterns.

Newtonian Physics

All of the concepts, language, expectations, and images of our culture have come down to us filtered through the lens of Newtonian physics. Newton said that the physical world consists ultimately of atoms. Each atom is impenetrable and is related to every other by way of forces of action and reaction. When one atom touches another, it knocks the other off its path. If it doesn’t want the other off its path, the best it can do is avoid the other atom—it can “compromise.”

Freud modeled his psychology of object relations after Newtonian atomism. He said, “You’re an object to me, and I’m an object to you. When we meet, all we can do is bounce into each other, conflict, and go our separate ways. Or we find avoidance strategies.” This idea is the basis of our notion that the individual is the primary unit of society. It has led, unfortunately, to an emphasis on fragmentation. We divide our organizations into units, and these units compete and bounce against each other.

The quantum model, on the other hand, tells us that everything in the universe is interwoven with everything else. The quantum universe says that the world doesn’t consist of separate interacting parts; it consists of sets of systems that are so interwoven that they take their identity from their relationship. For example, the way I relate to you changes me. The environment in which your organization operates changes the potentiality and the whole agenda for your organization.

Uncertainty in the Quantum Organization

A quantum organization therefore stresses dynamic integration—cooperation rather than competition. In quantum physics, C always equals more than A+B. You have to bring A and B into interrelationship to get that larger C. For example, I am an individual, and I make decisions as an individual. But I am also in relationship to others, and part of me is being evoked by participation in that field. By engaging with another person in relationship, I realize an aspect of myself to which I did not have access before.

If you have a Newtonian particle at A, and it wants to get to location B, there’s one best path for that particle—it will follow the path of a straight line and go directly to B. Now in quantum systems, if you have a particle at A, you don’t even know where B is or what B is. It’s only eventually, when B comes into focus, that we see retroactively the particular path A rook to get to B. Quantum physics thus says we can’t predict anything, and that there’s no single “best path.”

Thus, the leading principle of 20th-century science is this idea of uncertainty. For our organizations, this means that we need to develop infrastructures that will allow us to surface all our potentiality and actually thrive on uncertainty. If I come into a situation with the belief that I know what I want to do, I will just get the result I am looking for. But if I come to a situation with an attitude of inquiry—questioning what might be the best way forward or what insights others can offer—then new possibilities will slowly evolve and I will get a result I never imagined possible.

Dialogue

The larger question we need to address as individuals and organizations is, “How can we dip into that rich field of potentiality and develop a whole that is greater than the sum of the parts?” Dialogue is one way to do this, because we come to a dialogue with a willingness to share our uncertainty, our pain, and our expectations. Through that process, something rather magical happens. Suddenly, everything comes together, and new ideas emerge. With those new ideas, our present position evolves—not through a Newtonian perspective, but through questioning and uncertainty. And from that experience, we arrive at a new way of thinking.

—Edited by Kellie Wardman O’Reilly

Karl-Henrik Robert—The Natural Step: A Framework for Large-Scale Change

Moving from fragmentation to wholeness means expanding our perspective to include the larger system. In his talk, Karl-Henrik Robert describes The Natural Step, a large-scale social and environmental movement that is based on the following premise: “If you want a large number of people to work together in a coordinated way, they must share an image of the system of which they are a part.” His story provides an illustration of how a common shared vision can become the catalyst for effecting large-scale change.

—CPI..

The Natural Step is a federation of professional associations in Sweden—economists, doctors, business leaders, lawyers, entertainers, etc.—that are working toward developing a sustainable society. There are approximately 10,000 people participating in The Natural Step, working together on cooperative projects. What binds our group together is a collective under-standing of the larger system of which we are a part.

A system is like a tree—the trunk and the branches are the underlying principles that give form and structure to the system, while the leaves represent the various efforts we can take to meet the principles. If we look at our work in The Natural Step from this perspective, we can see that the various associations—the engineers and scientists, doctors and lawyers—are each operating as the leaves, providing input from their background, while the trunk provides an overarching unity to our work. Because we are operating out of a shared mental model of the system as a whole, we are able to operate effectively as a team, rather than simply a collection of individuals. By working cooperatively toward the same overall principles of sustainability, we believe we can create large-scale change.

There Is No “Away”

We know from physics—from the principal of the conservation of matter—that the Earth cannot expand in volume or size to support its inhabitants. Matter doesn’t disappear on Earth, but it does change forms. That is the core of our dilemma: we are systematically turning our natural resources into garbage. We are consuming resources and turning them into dispersed waste faster than they can be reconstituted back into resources.

Our whole biosphere operates as a system of natural cycles. For over two million years, the human species took part in those cycles, utilizing resources in a manner that was sustainable. Then we identified concentrated energy, such as fossil fuels and nuclear power, which gave us access to tremendous flows of matter. Now that we have the power to utilize these resources, we are flooding our own ecosystem. We are turning back the evolutionary clock and making our species extinct. This is the global challenge that we face.

Toward Sustainability

So what are some overall principles for sustainability? Clearly, a sustainable society must integrate itself into the natural cycles of the Earth. Since matter cannot disappear, the sum of the living resources must equal the waste that is emitted back into the system. With this in mind, it is not difficult to identify the overall principle for sustainability in our whole ecosphere: there must be a balance in these flows. The basic principles can be summarized in four system conditions:

1. Extracted substances from the Earth’s crust must not systematically increase in nature. Nature cannot sustain a systematic increase of dispersed junk from the Earth’s crust. Why? Because substances disperse, but they do not disappear. Every substance becomes a toxin if its concentration is too high.

2. Substances produced by society must not systematically increase in nature. For the same reason as above, we must not produce unnatural, persistent substances such as DDT, PCB, or freons, which contribute to a systematic increase of man-made compounds. When we produce more compounds than can be handled in the system, they naturally increase in concentration and become deleterious to the system as a whole.

3. The physical basis for the productivity and diversity of nature must not be systematically deteriorated. This principle refers to the Earth’s system itself—its physical needs. We cannot keep digging up the earth, eliminating forests, and destroying the species that coexist in this system.

4. We must have a fair and efficient use of energy and other resources. If one billion people starve while another billion have a definitive over-production of goods, this cannot be perceived as a fair and efficient use of resources to meet human needs.

Creating a Sustainable Society

Thus, the four system conditions make up the trunk of the tree—the absolute, bottom-line conditions for the entire system. If we want to create a sustain-able society, we must live in agreement with these basic principles. For businesses, operating according to these basic principles is also a way of saving money and becoming more efficient.

As part of our work in The Natural Step, we work with businesses to identify the systemic consequences of their actions. By referring to the four basic system conditions, we identify the consequences of current practices, and offer professional advice on how to operate within those principles as well as pros-per from them. We train businesses to make investments that help them improve their image in the short term and set the stage for greater profitability in the long term. For example, if we continually convert non-renewable resources into garbage, the prices of those resources will inevitably go up.

We also prompt businesses to ask themselves this critical question: “Are we systematically making ourselves less economically dependent on resources or practices that have no futures’ For example, suppose we are trying to decide if we should rely more or less on mining a particular substance. If there is very little room for more mining in the system because it will violate a system condition, it is not a good long-term strategy. Any smart team understands that you will be hit by the future market or by future legislation if you systematically depend on something that has no future.

So the rules of the game for the future involve making ourselves economically independent of violating the four system conditions. If we do not succeed in this effort, the consequences are obvious—we are our own Titanic. If we go down, we all die together. The laws of nature supersede manmade laws, and they will impose themselves on us whether we want it or not—it’s just a matter of time. In realizing this, we can make a choice—to continue to follow unsustainable practices that we will pay for in the long term, or begin to profit now from smart investments that take into account the natural infrastructure of which we are a part.

—Edited by Diane J. Reed

Peter M. Senge—Building Learning Infrastructures

Sustaining large-scale change requires more than a one-time shift in structures and habits—it requires deeply embedded infrastructures that enable the continued creation and dissemination of new knowledge. Peter Senge discusses the recent innovations in infrastructure that are snaking the learning organization a sustainable phenomenon. His discussion of infrastructure then becomes a springboard for exploring the role of storytelling in creating a larger context and meaning for our work.

—CPL

There has been a lot of emphasis in business lately on the importance of infrastructure. Reengineering, business process redesign, and rethinking performance measures all have to do with the infrastructure of organizations—what wires things together. I believe that fundamental innovations in infrastructure are important in order to create an environment where the work we are doing can continue. These innovations in infrastructure fall into two categories: (1) rethinking and redesigning existing infrastructures; and (2) creating new infrastructures to support learning.

Redesigning Existing Infrastructure. One area of potential leverage involves redesigning existing organizational infrastructures—the process that currently hold together organizations. For example, Shell International Petroleum Company’s rise from a mediocre position in the world oil industry to preeminence was the redesign of a critical infrastructure—its planning process. Shell’s planners discovered that having a single plan was becoming irrelevant in a world of unpredictability and change. But the planning process itself—the act of bringing people together to develop strategies in response to various scenarios—was increasingly important. Shell’s scenario planning process, which was eventually named “planning as learning,” represents an extraordinarily elegant strategy for creating new learning capabilities in organizations.

Creating New Infrastructures. In addition to rethinking the elements of infrastructure that have always existed, over the last few years a whole host of new innovations in learning infrastructure have emerged. For example, coaching networks have become an important part of team development. Coaching can take the form of educational initiatives, diagnosis, intervention design, facilitation, and core process consultation. At EDS there are now about 100 “transformational coaches” who have gone through a one-year training program in these skills, and several other companies are developing similar networks of internal coaches.

Another area of infrastructure development centers around redesigning the work environment so that working and learning become inseparable. This includes innovations such as learning laboratories and applied practice fields. For example, at Ford Motor Company, the 1995 Lincoln Continental team created a new car development learning laboratory. Federal Express has also developed a global sales learning laboratory, and there are many other learning laboratories being used in other companies.

These individual learning experiences have had some well-documented successes. But the next challenge is how to share the insights from individual teams throughout the respective companies. We are gradually coming to realize that there is no infrastructure in our organizations to enable serious analysis and reflection on what is being learned. One possible way to do this is through learning histories—a formal process that is being developed for capturing data on critical learning incidents. As one example, for 150 years the U.S. Army has had learning historians who provide it with a rich sense of its own history and its ability to learn from the past.

Why Talk about Infrastructure?

We can get very excited about the new infrastructures that are being developed, but building infrastructure is not an end in and of itself. Organizational learning infrastructures are part of a larger group of elements that are essential for designing a learning organization—what I have termed “organizational architecture” (see “Framework for the Learning Organization”). And organizational architecture really functions in the service of a larger purpose, which is to create an environment in which the “deep learning cycle” can be initiated, energized, and sustained. The deep learning cycle involves developing fundamental new skills and capabilities, which lead people to see the world differently and then to develop fundamental new attitudes and beliefs.

Essentially, we need to develop sufficient organizational architecture in order to begin to sustain this deep learning cycle—to be able to create some degree of critical mass of new collective capacity. As this capacity develops, it further expands our collective ability to listen to the larger pattern of what is emerging—the “implicate order” that I referred to in my original presentation of this framework at the 1993 Systems Thinking in Action”‘ Conference. This brings us to a new point in the cycle, as we reflect on the emerging story—a new, deeper set of “guiding ideas.”

Framework for the Learning Organization

Innovations in infrastructure are part of a larger group of elements–organizational architecture–that are essential for designing a learning organization. Organizational architecture really functions in the service of a larger purpose. which is to create an environment in which the “deep learning cycle” can be initiated, energized, and sustained. The deep learning cycle involves developing fundamental new skills and capabilities, which lead people to see the world differently and to develop fundamental new attitudes and beliefs.

What Is Our Story?

I think this brings us to the question, “What is our new cultural story?” Cultures ultimately need a story in order to be vital. But as a society and as a culture, we have lost our story. The old story—the account of how the world came to be and how we fit into it—sustained us for a long period of time. It shaped our emotional attitudes, provided us with life purpose, and energized our actions. But it is no longer functioning properly, and we have not learned a new one.

Dee Hock, the founder of VISA International, says that “we are living in an era of massive institutional failure on every front.” The mismatch between our large institutions and the deeply complex interdependent world we live in is evident in our current environmental crises, in the chaos and perpetual crises of businesses, in our paralysis in confronting national political issues, in the breakdown of our societal infrastructure and civic spirit, etc. There is not a single critical institution that is not failing in the eyes of the public.

As the cycle moves another turn, it’s time for a new set of guiding ideas. It’s time for a new story of how human beings and human institutions can rediscover our place in a larger natural order. As Sarita Chawla asked, “What is the story our grandchildren would want us to be telling today?”

—Edited by Colleen P. Lannon

Peter Block is a consultant and speaker whose work focuses on ways to create empowering organizations. He is the author of Stewardship and The Empowered Manager

Danah Zohar is a physicist and philosopher who teaches at Oxford Brooks University in England. She is the author of The Quantum Self and The Quantum Society.

Karl-Henrik Robert is the founder and working chairman of The Natural Step, a federation of professional associations in Sweden that cooperate on projects to benefit the environment.
Peter M. Senge is the director of the MIT Center for Organizational Learning, and author of The Fifth Discipline: The Art and Practice of the Learning Organization.

The post Building Organizational Learning Infrastructures appeared first on The Systems Thinker.

In 1982, however, a period of massive change began. The president of the college was forced to resign, and the college subsequently went through four presidents over a span of 13 years. After the most recent departure, an interim CEO was brought in to “clean up the mess” while the board of directors searched for yet another replacement.

Although the interim president was considered highly competent, he had a reputation for being more like Atilla the Hun than Stephen Covey in terms of his leadership style. And despite the board’s assurances that any interim replacement would not be eligible for the position, the acting president was eventually hired permanently. This decision, on top of years of change and instability, sent the organization into a state of shock. Daily rumors circulated about potential firings, and few people in the college felt secure enough to take risks. In order to regain our effectiveness as an organization, we needed to somehow work on rebuilding our community. But first, we needed to address the underlying issues that had bred a culture of fear and mistrust.

Examining the Culture

In September 1994, I discovered an article in The Systems Thinker by Greg Zlevor entitled “Creating a New Work-place.” The article asserted that all organizations operate at some point along a “community continuum”: somewhere between “disciety” (dysfunctional society) and “community.” It seemed to me that in order to improve our organizational climate, we first needed to identify where we were on the continuum.

I shared the article with the director of research at MATC, and together we decided to conduct a “quick-and-dirty” survey based on Zlevor’s model to get a sense of how our colleagues viewed our organization (see “MATC Community Survey”). Once it was complete, we mailed the survey to the entire management council of the college (over 125 people).

To our surprise, we were inundated with phone calls the next morning. Many of the callers were struck by the candor of the statements, which were considered “undiscussables” in the organization. (The statements were taken verbatim from Zlevor’s description of the different positions on the continuum.) Some callers had questions about confidentiality (their names were inadvertently included on the back of the survey, due to the internal mail routing labels). Several callers wanted to know if the new president was behind the survey. Still others were relieved that our organization was beginning to talk about these issues:

Amazingly, we received more than an 85% return rate on the surveys. We separated the responses into five piles, each representing a point along Zlevor’s continuum. The results were almost perfectly bimodal: people either saw the college as dysfunctional (“This place is so political”) or formative (“We have our ups and downs, but mostly ups”). We surmised that because there was no shared sense of the community as a whole, people’s experience of the college depended to a large extent on the ups and downs of their daily experience.

We brought our data to the next meeting of the senior administrators (all of whom had been recipients of the survey) in order to explore the results. The dynamics of the ensuing discussion were as revealing as the survey results had been. Some people immediately demanded to know, “Why was my name put on the back of the survey?” Others became defensive, wondering, “Why wasn’t I told about the original article?” The group as a whole seemed to attack the validity of the survey itself, asking, “Why was this even done?” Their reactions seemed to reflect the overall climate of the organization—one of fear, mistrust, and well-entrenched defensive routines. At the conclusion of the meeting, they recommended that the entire survey episode be put to rest. However, it was not going to be forgotten that easily.

MATC Vision Deployment Matrix

To get a better picture of current reality at the college, and to paint a picture of the desired future. the STOL group used a tool called the Vision Deployment Matrix!”. This diagram shows the collective responses of the STOL group to the first two columns of the matrix.

Reframing the Work

Earlier that year, a small group of people representing a cross-section of management began meeting regularly to learn more about systems thinking concepts and tools. The official title for the group was STOL—for Systems Thinking and Organizational Learning—but we jokingly referred to our get-togethers as “Systems Thinking over Lunch.” Since our group had been using different case studies to hone our skills, I brought up the survey as a good opportunity to explore the larger dynamics at play in the organization. However, we quickly realized that the implications of this project were larger than any of our previous case studies—it really involved reframing how we thought about the nature of our entire organization.

As one of the ways to provide a framework for this effort, we decided to use the Vision Deployment Matrix TM, a tool developed by Daniel Kim for helping groups articulate an action plan for moving from current reality toward a shared vision (see “Vision Deployment Matrix T”: A Framework for Large-Scale Change,” February 1995). The nine members of our STOL group filled out the Vision Deployment Matrix individually, then worked together to weave the individual perspectives into a collective matrix (see “MATC Vision Deployment Matrix”). After we filled out the first two vertical columns of the matrix—”Desired Future Reality” and “Current Reality”—we decided to get the president’s input to see how his perceptions compared to our own.

After hearing a short explanation of the matrix, the president also filled out the first two columns. Interestingly, his responses were similar to ours. For example, in the box that indicated the systemic structures needed to achieve the vision, the STOL group had noted a need for “shared decision-making” and “effective communications,” while the president expressed a desire for “more constructive meetings.” This gave the STOL group confidence that the president shared our understanding of the vision and current reality of the college. In addition, his willingness to participate sent an important signal that he supported our efforts to examine and improve our organizational culture.

Improving Communication

Through the process of developing our matrix, we began to realize that one of our biggest obstacles to achieving our vision of improved community was the unspoken mental models held by members of the college—the untested assumptions that were preventing open and effective communication. This became clear at the next meeting of the Management Council, when the president gave a presentation on the issues facing the organization. After his talk, the STOL group then conducted a “left-hand column” exercise, in which the participants wrote down on the right side of the page what the president said, and on the left side they voiced what they thought or felt in reaction to his comments.

What the group discovered through the process was that we all tend to hear what we expect to hear. For example, the people who anticipated hearing only “bad news” heard precisely that. Those who expected to see a “tough guy” in the president had their predictions confirmed. And, intriguingly, the people who were open to organizational change saw the shifts that were occurring as a positive development for the college (see “Left-Hand Column: One Perspective” for an example of this exercise). This exercise opened up our awareness of the significant role our mental models play in selecting what we hear and don’t hear, and it had the desired effect of opening the group up to a deeper level of conversation. Our work in developing a deeper level of community was beginning to take hold.

Preliminary Results

When the STOL group developed its Vision Deployment Matrix, we noted that one of the indicators of progress toward developing community would be an openness in communication throughout the administration of the college, as well as an increased ability as a group to suspend our assumptions and inquire more deeply into each other’s reasoning. The area where we have seen the greatest progress toward this goal has been in the Management Council meetings. In the past, they were full-day sessions that consisted primarily of lectures given by the president and/or his direct reports. The attendees often felt “talked at” for hours on end. There was very little participation, and many attendees passed the time by surreptitiously doing paperwork. When we did a quick analysis of the cost of the meetings, we discovered that the college was spending approximately $100,000 per year on a function that yielded very little benefit.

We decided, therefore, to use the Management Council meetings as an opportunity to work on developing better communication, and to begin to tap into the collective intelligence of the members. We shortened the meetings to half-day sessions, eliminated the speakers, and refocused the agenda on working together in small groups to tackle some of the serious issues facing our institution. At the first of the redesigned management meetings, two college-wide issues that were generally considered to be undiscussables were addressed: (1) how to better implement the entire CQI process; and (2) how to productively examine the positive and negative effects of the changes that occurred within the organization during the last several years.

In order to facilitate more productive communication at the meeting, we assigned people to small groups, each of which represented a cross-section of the college. As the groups were invited to share their insights with the entire council, previously undiscussable issues were sufficed, and some very productive conversations ensued. For example, the “undiscussable” issue of a compensation and benefits inequity between union and non-union employees was raised, and specific recommendations were made for further action. After the meeting, we shared the outputs with the president (who chose not to be present during the meeting so as not to inhibit open communication), and we forwarded the results to the CQI Steering Committee of the college.

Left-Hand Column: One Perspective

After a talk by the president to the Management Council the STOL group conducted a left-hand column” exercise. In order to surface the mental models operating in the group.

Our Ongoing Work

The evaluations from our first redesigned Management Council meeting were very positive. Many people commented that the college was “finally moving forward.” But even as we are celebrating this modest success, we recognize that we have a long way to go toward our goal of developing a healthy community at MATC. In order to continue our work on organizational integration and community building, the STOL group has identified four areas for further action:

• continue to work on building communication and trust
• make systems thinking courses and materials available to others at the college
• continue to develop systemic solutions for problems at the college, working with the president to effect high-leverage changes
• re-survey the Management Council to accurately assess current reality at the college

As we develop our skills in community building and in creating structures that will sustain that community, we believe we can make a profound difference in the organizational culture. With the help of organizational learning tools, we are confident that our culture will continue to move toward openness and community.

James B. Rieley directs the Center for Continuous Quality improvement at Milwaukee Area Technical College. He also consults with business and industry, government, and educational institutions. Editorial support for this article was provided by Diane J. Reed and Colleen P. Lannon.

This story was presented at the 1995 Systems Thinking in Action”‘ Conference.

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Whenever a manager says something like, “Our goal is to make a profit,” he or she has just defined the organization’s purpose in accounting terms. Stating that profit is the goal of a business is like saying the purpose of life is breathing. Certainly people must breathe in order to stay alive, just as businesses must earn a profit in order to survive. But reducing life or business to such mundane necessities drains them of all human significance.

Unfortunately, since the 1950s large numbers of managers have done just that, defining their organizations’ purposes in terms of accounting results. This trend has also led to the use of accounting targets to control people’s work—a practice commonly referred to as “managing by results,” and one that was condemned by Dr. W. Edwards Deming as a surefire way to weaken a business system and to increase variation in performance.

Although recent advancements in accounting systems have improved our ability to measure results more accurately, they’ve done nothing to address the inherent shortcomings in the underlying philosophy. Activity-based cost management (ABC/ABM), business process reengineering, balanced score-cards, and other schemes for measuring vital signs all hold to the idea that business results are improved by manipulating independent quantitative targets. They define improvement as little more than moving faster on the same track—a view that would be appropriate if business were on the right track and we only needed to improve the status quo. Unfortunately, that is not the case (see “Tracing Accounting’s Influence since the 1950s” on p. 2).

The Accounting Worldview

Our current accounting practice is based on the Cartesian/Newtonian worldview, which originated in Western Europe in the late 15th century. Double entry bookkeeping and the systems of income and wealth measurement that have evolved from it are predicated on the belief that any result is the linear sum of infinitely divisible independent causes. It is not surprising, therefore, that 20th-century accounting depicts reality as though it were the summation of independent parts that interact with each other only through the influence of external forces. According to that system, the whole—defined as bottom-line results—is merely the linear sum of its parts. Therefore, a change in any part (cause) is automatically reflected as an equal, linear change in the whole (effect). For example, if profit equals revenue minus cost, then changing any cost by one unit presumably changes profit by the same amount.

The scientific community that gave us this mechanistic worldview has, of course, adopted a new position in the 20th century. Quantum physicists and evolutionary biologists now believe that reality is best described as a web of interconnected relationships that give rise to an evolving universe of objects that we perceive only partially with our limited senses.

Even though scientists no longer portray the universe as a giant clock, most executives operate as if organizations behave like machines. Mechanistic concepts such as “management by objective,” “managing by the numbers,” and “remote-control management” became even more prevalent in business after the 1950s—when large numbers of executives trained in accounting and finance rose to commanding positions in U.S. business. If our accountants and businesses were to adopt the new scientific worldview, they would probably begin to question their ability to describe organizational activity with a language that is based on the double-entry system of recording and measurement.

Accounting as Systemic Inquiry

It is tempting to consider what the last few decades of business would have been like if managers had viewed accounting as a tool that promotes inquiry about an organization’s purpose (see “Using Accounting to Promote Inquiry” on page 4). Managers with a more systemic perspective might have viewed the company’s purpose differently: to nurture their employees’ and suppliers’ capacity to serve customers. In such companies, profit—like breathing— would have been the natural condition of a healthy system, not an obsessive pursuit that drives the system to imbalance.

Two world-class companies that have adopted this view of accounting are the Swedish truck maker Scania and the auto maker Toyota. Both companies maintain excellent accounting systems, but neither sees management’s job as trying to control parts of the organization with accounting-driven targets. Instead, they focus their attention on the disciplined mastery of process or “pattern”—a source of meaning that undergirds and aligns all decisions and actions, like the underlying order that many evolutionary biologists and physicists see pervading the entire universe.

As a result of this different emphasis, both companies have enjoyed uninterrupted profitability, without layoffs, since at least 1960—a record unmatched by any competitor. Moreover, each company is generally acknowledged in its respective industry as the lowest cost producer of the highest quality products. But underlying each company’s high performance is a remarkable capacity to focus everyone’s attention on mastering a deeply shared pattern—at Scania, in product design, and at Toyota, in operations management.

Scania and Toyota

Scania is the world’s fifth-largest maker of heavy-duty trucks (U.S. Class 8). Based in Sweden for over 100 years, the company now generates over 95 percent of its revenue outside of Sweden—primarily in Western Europe, South America, and Asia. It is the only heavy truck maker that has chosen to grow from within, organically, rather than by mergers or diversification. Scania has never believed in financial “synergies” of acquisition, an attitude that has been vindicated by the recent financial setbacks suffered by Scania’s European competitors as a result of mergers. In contrast, Scania has focused its growth exclusively on the highly integrated production of custom-made heavy trucks, including manufacture of all critical components such as engines, gearboxes, axle assemblies, frames, and cabs.

The key to Scania’s high performance is a modular product design strategy begun in the late 1950s and put into full production by 1980. The goal of this system is to transcend the trade-off between meeting individual customer demands and achieving satisfactory profitability. Scania has been able to achieve both goals simultaneously by adhering to a modular design pattern that delivers “rich ends from simple means.” By enabling standardization and interchangeability of parts, they meet the widest possible customer requests with the least number of components. Designers can thus create a diverse array of products by varying only those features that affect the final result. For example, among the thousands of variants in cabs that appear on Scania trucks, there is only one windshield, one driver’s compartment and only three different door shapes.

Thanks in large part to this strategy, Scania has consistently generated far more profit than any of its competitors. For example, Volvo, Scania’s closest competitor in Europe, sold approximately the same number of vehicles in the 1980s but required about twice as many parts—so Scania earned about 1 billion Swedish kroner (approximately $140 million) more in operating income per year.

It is worth noting that Scania does not drive managers to meet cost targets by reducing their part number count. Many companies that employ ABC systems today view part number count as a “cost driver,” and they use it as a weapon to control design decisions. However, this approach merely tells managers what to do—it does not trigger inquiry into the best means for achieving the desired result. It is not surprising that managers focus on short-term “quick fixes” to satisfy accounting-based targets, rather than on long-term mastery of a robust discipline such as modular product design. As Scania’s history shows, achieving the lowest overall cost does not necessarily result from focusing people’s attention on cutting costs of individual parts.

Toyota, like Scania, set out over 30 years ago to bridge the apparent trade-off between meeting individual customer demands and achieving satisfactory company profitability. However, unlike Scania’s focus on product design, Toyota focused its attention on mastering a disciplined pattern of work that is known as the Toyota Production System (TPS). TPS has enabled Toyota to produce greater varieties of higher quality cars at lower cost than any other auto maker in the world. Using this system, Toyota has become the world benchmark for low cost—yet it does not use cost information to drive managers or workers. In fact, Toyota financial executives say that their company has never had, nor intends to have, a standard cost accounting system.

If our accountants and businesses were to adopt the new scientific worldview, they would probably begin to question their ability to describe organizational activity with a language that is based on the double-entry system of recording and measurement.

This is not to say that Toyota is not concerned about costs. The central message of the TPS has always been to identify and eliminate waste—whether in the form of time, resources, space, energy, human potential, or customer dissatisfaction. But Toyota’s strategy for eliminating waste—and reducing cost—is to instill in everyone in the organization (including suppliers) a deep dedication to mastering a disciplined approach to work.

A key principle underlying the TPS is the need to make visible to workers what is normal and what is abnormal in any work they do. That way a worker can stop and correct an abnormal situation the moment it occurs, and can take measures to prevent it from happening again. For example, on the production line, Toyota workers perform repetitive work according to a standard rhythm (called “takt time”) that dictates the rate at which product flows into final demand. Thus, if all work stations along the line are paced to work in 60-second cycles, then autos flow off an assembly line at the rate of 60 per hour. During the course of a day, workers rotate among three or four different stations in order to avoid monotony and to balance ergonomic requirements. But in any station the worker will perform exactly the same steps, the same way, in order to assure quality and safety. If something is abnormal, a worker sees it immediately and can stop the line to correct it.

Never are Toyota managers encouraged to “speed up” the line in order to achieve cost targets. If output falls behind schedule because workers have had to stop the line, the plant makes up the difference by going into overtime, not by changing the rhythm of work. Speeding up the line defeats the whole purpose of attaining a standard rhythm that will preserve quality and safety. This policy clearly enables Toyota to continuously improve its ability to produce exactly what customers want, precisely when they want it, at the lowest possible cost.

A New Vision for Accounting

The Scania and Toyota cases reveal what is possible when accounting is used in the service of an overall systemic approach to business. Both companies steadfastly define their purpose in terms of a holistic pattern that transcends financial results in order to assure both customer satisfaction and company survival. Functioning as a part of the larger system, every person and unit of the organization derives meaning from the organizational strategy. In a quantum sense, the whole in both companies is not defined by its parts; the parts derive their meaning from the whole.

Instead of rushing to intervene every time results fall short of a desired financial target, managers in Scania and Toyota trust that satisfactory results will occur if everyone continues to pursue mastery of their special customer-focused discipline. It is as though they believe the result is already there and that their job is to master the pattern that brings forth that result. This is a deeply quantum and systemic attitude that brings to mind Henry Miller’s saying: “The world is not to be put in order, the world is order incarnate. It is for us to put ourselves in unison with this order.”

Before managers attempt to go further in making accounting a positive force for systemic inquiry, they must first understand the place of variation in nature. A useful place to begin this journey would be to master the discipline of managing variation, as Toyota has done. At Toyota, this process began over 40 years ago, with their work on managing variation through statistical process control (SPC). Today, Toyota’s mastery of the concept has reached the point where virtually all processes are maintained in control without the explicit use of SPC. But Toyota reached this point only after years of disciplined attention to setting standards, mastering those standards, and developing fail-safe processes.

It is clear that we need to create new accounting systems for the 21st century—approaches that are compatible with organizational learning. Whatever those new forms may be, they will not preclude accounting’s important role as the primary source of after-the-fact results measurement. Even though life is not about breathing, it is still important to measure respiration rates from time to time.

However, accounting must go beyond providing measurements of results. By providing a means for exploring the assumptions and worldview that drive behavior in an organization, accounting can serve the larger organizational purpose of promoting inquiry into the relationships and patterns that give rise to the results we see.

H. Thomas Johnson is the Retzlaff Chair in Quality Management at Portland State University’s School of Business Administration (Portland, OR).

This story was co-presented with Anders Bröms at the 1995 Systems Thinking in Action Conference. The story about Scania was developed jointly with Anders Bröms and his colleagues at SAM Samarbetande Konsulter AB in Stockholm, Sweden.

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Part of the difficulty stems from the fact that organizational learning, like company culture, is controlled only indirectly by those in charge of the organization. The management levers that drive organizational learning are numerous, spread throughout the organization, and often work only after substantial time delays. And since every organization is unique, the levers that will enhance learning vary from company to company. As a result, it is difficult to identify a single action plan for building organizational learning capability. Instead, what is needed is a set of guide-posts that can help managers identify their own learning objectives, evaluate their current capabilities, and create a customized plan to move toward those goals.

The Organizational Learning Inventory

The Organizational Learning Inventory is an assessment tool that helps managers identify specific actions that can promote organizational learning in their company (see “Organizations as Learning Systems,” October 1993). This tool addresses the questions: What can we do to improve our organization’s ability to learn? How can we create an integrated strategy for learning? And how can we assess how well that strategy is working?

The cornerstone of the Organizational Learning Inventory is a framework of ten Facilitating Factors and seven Learning Orientations (see “The Organizational Learning Inventory” on page 3). Facilitating Factors are those activities or attitudes that promote or inhibit learning (e.g., environmental scanning or an experimental mindset), while Learning Orientations describe stylistic differences in the ways companies approach learning (e.g., focusing on breakthrough learning versus incremental improvements). Together, these Facilitating Factors and Learning Orientations describe an organization’s overall learning system. By using the Inventory, managers can better assess their company’s learning capabilities and develop a plan to manage their learning processes more explicitly.

The management levers that drive organizational learning are numerous, spread throughout the organization, and often work only after substantial time delays.

The Inventory is not meant to be simply filled out and tabulated in a report. It is most effective when used in a work session that engages a group in identifying their company’s specific learning objectives and analyzing the organization’s barriers to learning. The real value of an Organizational Learning Inventory Work Session thus lies in the quality of conversation it promotes, which can lead to shared mental models and a shared learning strategy. The more specific and “actionable” that strategy is, the more likely it is to succeed.

Steps in a Work Session on Organizational Learning

The Work Session consists of four stages, from assessing the organization’s current learning capabilities to creating an action plan to meet the company’s learning objectives (see “The Process”).

In the first stage, participants address the question, “Where does our organization fall along the dimensions identified in the Inventory?” As each criterion is introduced, the participants evaluate the organization’s capability or preferences in that area. For example, as they discuss the Facilitating Factors, group members may rank how much evidence there is that the company is investing in activities such as determining performance gaps, involving leadership in learning initiatives, and promoting continuous education. The current state assessment is best accomplished in a focus group or working session, rather than through surveys or interviews, because this gives the team an opportunity to develop a shared understanding of the company’s learning strengths and weaknesses. The dialogue that emerges from the assessment helps create momentum to make the transition from generic guideposts to organization-specific action steps.

After the current state assessment is complete, attention turns to exploring the desired state (stage two). At this point, the participants discuss the learning capabilities they think will be necessary to support the organization’s strategic business objectives. The group can use the Learning Orientation section of the Inventory to explore the company’s particular “style” of learning, and to consider how the company’s unique strengths can be used as a source of competitive advantage.

Once the group has laid out the company’s current capabilities and learning objectives, it then assesses the gaps between the two (stage three) and develops an action plan to close those gaps (stage four). One possible approach is to simply go through each dimension, look at the size of the performance gap, and determine an action strategy to reduce or eliminate the gap. But since it is likely that performance gaps will exist along several of the Inventory dimensions, such a process might become overwhelming. Instead, it may be more useful for the group to focus its efforts on the three or four most critical areas, and develop action plans to address those gaps. Over time, as the team makes headway on these initial issues, the focus may be drawn to other gaps.

Learning about Learning: The inventory in Action

Just as Royal Dutch/Shell discovered through its “planning as learning” process that the act of creating a plan can be more important than the actual plan, the Inventory is of value to decision-makers because of the assessment process, not because of the tool itself. This was the experience of a group at the Harvard Law School Library, who used the Inventory to create their own learning strategy.

The Process

An OLS Work Session consists of a four-stage process: (1) assessing the company’s current capabilities, (2) identifying the learning objectives, (3) analyzing the gaps, and (4) creating a plan to move toward those goals.

Harvard Law School Library (HLSL), with its staff of 90 employees, faces several strategic challenges in the next few years. Beginning with the June 1996 commencement, HLSL will close its physical plant for 14 months while the entire facility is renovated. In the process, the staff must move an inventory of 1.7 million volumes of legal information and somehow continue to provide services and limited access to a demanding faculty and student body.

In addition, HLSL is facing the same challenges as other research and professional libraries, as technology transforms how they maintain their assets and provide access to them. In fact, the very definition of an “asset” is changing. It is beginning to include electronic records, databases, and other multimedia information, as well as books and other traditional media. As a result, the concept of “access” is also changing — from physical location and retrieval to electronic access via networks. Along with this comes the need to train the library’s patrons how to make the best use of these electronic tools.

Terry Martin, professor of law and librarian of the Law Library, was visionary enough to recognize the parallels between the short-term challenge of the renovation and the long-term strategic shift required by the technological changes. The question he saw the library facing was, “How can we use our physical renovation as a laboratory for learning how to provide our services in this new ‘virtual’ marketplace of legal research?” To address this issue, he and his staff used the Inventory to develop “A Framework for Learning”—their own customized learning strategy that will help them prepare for the future.

The HLSL Experience

The first step in the HLSL Work Session was to invite all members of the Library’s staff to a brief introductory session to discuss the concepts of organizational learning and the process of using the inventory. To assist in this process, books and articles on organizational learning were also made available through the administrative offices.

Next, they recruited a solid cross-section of interested staff members to participate in the Work Session. In particular, the assessment-to-action nature of this process made it especially important to include those individuals who were most interested in, and able to assist in, implementation. This broad representation had the added benefit of helping to educate newer staff on how other departments conduct their operations.

Over the course of one week, HLSL worked through the Inventory in five groups — four cross-departmental teams of four to five staff members, and one leadership team. Later that week, all of the participants came together to review their assessments and share key pieces of their visions for where the Library will be in the year 2005.

In the ensuing conversation, they focused specifically on “weak links” in HLSL’s capabilities, the impact of changing trends in library research, how their preferred learning style as a group might need to change to reflect these trends, and the key leverage points that would help them make significant changes in their learning profile. For example, they recognized the need to continue to break down traditional functional barriers by sharing critical information across boundaries. In addition, they articulated the need for strong leadership to set priorities, and they acknowledged how they could enhance their problem-solving if they took other perspectives into account.

From this discussion, the HLSL group identified four key “fields” or characteristics from the Inventory on which to focus their “Framework for Learning”:

• Involved Leadership (to transform vision into action)
• Formal Dissemination (to ensure the rapid and consistent dissemination of important information)
• Team Learning (to move from a traditional focus on individual skill-building to becoming skilled at working in teams)
• Systems Perspective (to break out of traditional functional perspectives).

Establishing Goals and Objectives

Rather than proceeding from the key goals to a linear set of objectives, the Library staff used graphical facilitation to creatively explore the relationships between these four goals, to see what larger issues might emerge. For example, pairing “Team Learning” and “Systems Perspective” drew out “learn larger processes and context” as one objective. Through this work, the group developed a cluster of larger objectives surrounding the four goals, which included “become skilled at team interactions,” “share important information consistently,” and “strengthen tie between vision and implementation” (see “Relationships between Key Goals”).

This cluster will become the ground from which, twice a year, a rotating team of volunteer “organizational learning stewards” will identify a new set of commitments for each of the three groups at HLSL: the institution, the working teams, and the individuals. For example, one commitment asked of the institution is to “reinstitute orientation training and materials to give new staff members an understanding of the fundamental processes and goals of HLSL, and to give experienced staff up-to-date information about who is responsible for what information and processes.” For working teams, the initial plan calls for participants in departmental meetings to ask, “Who else is involved in this issue? Who else needs to be informed about our plans?” And individuals are asked to “take responsibility to identify skills that I will need [next year] and get them included as part of my annual performance review this spring.”

In order to keep the process fresh and manageable, the team will throw out each set of commitments after six months and create a new list from the goal cluster, asking themselves, “What are the best things we could do now to accomplish these goals?” If a previous commitment makes it back onto the list, it will be because it remains a significant element of HLSL’s learning strategy. This ongoing process is a self-generated, self-renewable learning strategy based on an assessment that is specific to HLSL’s unique situation and needs.

Aligning Corporate Strategy with Learning Strategy

Developing an organization’s learning capacity requires a broad base of support and understanding. When the HLSL project started, some members of the staff were curious about organizational learning, and several people even had a vague notion of what organizational learning meant, but they had no real idea about how to begin. Within a week, however, the Library staff had developed a learning strategy that reflected its current strategic situation, and its employees had discovered for themselves the value of using a systemic perspective to plan and solve problems.

As the HLSL story illustrates, one of the greatest values of the Organizational Learning Inventory is the explicit link it creates between an organization’s learning strategy and its overall business strategy. Creating a set of learning initiatives that will support future activities and create a competitive advantage is a major innovation at most organizations. For example, the very fact that a management team may conclude, “We need to invest in scanning our environment” represents significant progress in an organization’s strategy for learning.

In most cases a team will likely discover that their company’s business strategy suggests a specific learning path. That is, for a given Learning Orientation, they might find that their business strategy calls for positioning the company at a particular place along the spectrum. Or their business strategy may call for particular emphasis on some Facilitating Factors over others. At HLSL, for example, the renovation and the changes in technology required a radical shift in orientation from individual toward team learning.

As the team discusses how the business strategy translates to each dimension on the Inventory, both the business strategy and the learning strategy become clearer and the link between them is made increasingly explicit. In fact, because the Inventory provides a concrete framework for what has historically been one of the “softer” components of an organization’s strategy, the discussion surrounding the future state of learning capability may lead to a refinement in the business strategy.

Together, strategy and organizational learning cover a lot of conceptual ground. Some teams may be fortunate enough to have access to someone who is knowledgeable about both strategy and organizational learning, but generally this is not the case. Most will have to forge ahead and bridge the gap on their own. With practice and a clear set of guideposts, however, a team can develop the skills necessary to align its learning objectives with its business strategy.

Marilyn Darling is an associate at GICA Incorporated and also a principal of Signet Consulting Group. Gregory Hennessy is an associate at GKA Incorporated. They are both certified facilitators of the Organizational Learning Inventory.

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

References: Arie deGeus. “Nanning as Learning,” Harvard Business Review. Mar-Apr 1988.

Relationships between Key Goals

The HLSL team grouped their four goals into a cluster and then explored the relationships among them In order to Identify the larger themes and Issues. This duster will become the basis from which, twice a year, a rotating team of “organizational learning stewards” will Identify a new set of commitments for achieving HLSL’s learning objectives.

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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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Why are people being more frugal about their credit card purchases? It may be that consumers are so busy paying off their current credit card debt that they don’t have much cash left for additional purchases. The Fortune article notes that the percent of delinquent credit card accounts — accounts that are 30 or more days overdue — has risen from 2.4% in 1994 to about 3.2% in 1995. They also report that the Consumer Credit Counseling Service, a nonprofit organization that helps people in credit trouble, has a caseload of over 800,000 clients “whose consumer-debt loads average $20,000 — no, that’s not a typo — on incomes of just $24,000.”

These and other reasons for the sudden fall in spending can be seen through the lens of the “Limits to Success” archetype (see “Spending & Credit Loop”). The reinforcing loop of this story describes the “spending begets more spending” orientation of our consumer society. As we buy more “stuff,” we increase our standard of living and grow accustomed to the comfort and status of our new spending level. Over time, this increases our desired standard of living, which leads to a desire to buy more things, further increasing our spending (R1).

Optimistic marketers would like to believe that this reinforcing loop is never-ending — that consumers will continually escalate their spending to achieve ever-higher living standards.

But most of us do have limits — in the form of funds available for spending. As our spending increases, our funds available decreases, which requires us to decrease our spending (B2). There are, however, two primary ways to increase the pool of funds available: find ways to increase real income, or simply borrow the money.

The favored path in recent years has been to borrow money by using credit cards, which temporarily bypasses the balancing loop through another reinforcing loop of credit line “funds” (R3). But if the real limit, which is the person’s income, has not increased to support the higher spending levels, then at some point spending not only must fall, but it must fall below the long-term sustainable level. This is inevitable, because when we use credit card debt to increase our current consumption, we have done so at the expense of our future consumption power.

Living the “High Life”—with High Debts

So, what’s wrong with borrowing from the future to live better today? If it will all balance out in the long run, why not enjoy certain purchases now rather than wait until tomorrow?

If you pay off your entire balance each month and incur no finance charges, then credit cards can serve as a convenient tool for managing short-term cash flows. But if they are used to expand current spending through increasing amounts of debt, a reinforcing cycle kicks in that will eventually force a drop in spending, as a greater percentage of current spending must go toward paying off the debt.

To understand how credit card debt can get so out of control — and how it impacts both current and future spending — we need to look more closely at a stock and flow structure of credit card debt.

Basically, credit cards expand our current spending capability because spending (in the form of credit card charges) is carried in a credit card debt accumulator and does not affect current spending until credit card payments are made (B4 in “Credit Card Debt Structure”). At first glance, it appears that we have simply substituted the original “Funds Available” balancing loop (B2) with the “Credit Card Debt” loop (B4) — a seemingly innocuous change that simply delays payment of purchases. Of course, carrying a balance means that we will have to pay finance charges, so delaying payment comes at some cost. But what is less obvious is how that delay structure distorts our perception of funds available.

When we carry a balance, most credit card companies (with the exception of American Express) only require us to pay off a fraction of the total balance. This allows us to shift our attention away from paying off the total debt toward simply meeting the minimum monthly payments, which makes it look like we have more spending money available than we actually do. In a way, the credit card accumulator helps us “forget” that we made a $1000 purchase, since • our monthly minimum payment has only gone up by $100.

At some point, however, the debt balance gets large enough that the monthly payments themselves are no longer affordable. In addition, the accumulation of finance charges (R5) means that a greater percentage of the monthly payments are going toward paying off interest charges, not toward paying for the actual goods purchased. We can temporarily “fix” this problem by acquiring new credit cards, which prolong the illusion of greater wealth (R3). But sooner or later, the credit card games must end — either we reach our maxi-mum spending limit or begin defaulting on payments. At that point, the debt problem itself must be addressed.

A Debtor Society

Unfortunately, the problem of debt is not isolated to a few overzealous consumers. The U.S. federal debt illustrates the same structure on a much larger scale. Like credit card debt, the federal debt allows the government to decouple current spending from current funds available, and shifts attention to the payments on the debt rather than on the debt itself. But unlike credit card holders, who cannot expand their own credit lines at will, the federal government can simply choose to raise its own credit limit rather than restrain spending (e.g., by selling more Treasury Bills, issuing more government bonds, or raising taxes).

Given both the consumer and national debt crises, it would seem that the United States has become a debtor nation at both the micro- and macro-level. In fact, we seem to have adopted the mentality that being in debt is normal — even desirable. At a national level, it is almost inevitable that our standard of living will fall, as more of our future wealth goes toward paying “finance charges” to the foreign countries that have been giving us their “line of credit.” Just as families are shifting their wealth to the bankers and the retailers, the U.S. as a whole is shifting wealth to Japan, Germany, and other countries.

A Sustainable Future

Of course, there is an obvious solution to both debt crises—stop spending and start saving. Rather than funding current spending with future income, we could instead save current income to be used for future spending. This approach has the added benefit of actually expanding our future spending ability, through the accumulation of interest payments.

In the “Shift from Debt to Savings” diagram, the credit card debt structure has been replaced with a savings structure. In this situation, some of the current spending is devoted to deposits, which can be withdrawn later to in-crease the funds available at that time (R7). Also, the vicious cycle of ever-increasing finance charges (R5) has been replaced by a virtuous cycle of interest payments (R6). The delay also plays a major role in the dynamics of building savings — once savings reaches a certain level, withdrawals can be made indefinitely without ever touching the principal.

Although the structures are basically the same, the outcome is completely different. While the savings situation provides a sustainable spending approach for the long term, the debt structure does not. Shifting from operating in the debt structure to the savings structure is doubly hard, however, since not only do you have to cut current spending to get out of the debt structure, but you have to cut it even further to begin the savings structure.

Outlining a map of the structure — complete with all the relevant numbers — may help us see more explicitly the full costs and benefits of savings. For example, putting money into savings rather than into credit card purchases results in a double payoff because you avoid finance charges and gain interest payments. One hundred dollars in savings, for example, earns 5% interest and saves 18% in finance charges.

Ultimately, making the shift from the debt structure to the savings structure — both as individuals and as a nation— requires a clear choice. To end the credit game, we need to decide that creating a sustainable future is more important than satisfying current consumption. Such a clear and compelling vision can provide the momentum to see the strategy through — even during the long delay as we pull out of debt and begin the savings structure. Unfortunately, given the short-term focus of our government officials, the prospects of that happening for the U.S. government seem a lot less optimistic than it does for individual consumers.

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Recognizing this challenge, we began to streamline our product definition, design, and development processes. Our group vice president, Bill Johnson, instituted a formal process to review key projects and programs. While solving project issues did result in improved quality and quicker time-to-market, the data gathered in this phase gave us an indication that we were facing much deeper issues. We began to see that our problems were linked to long-term dynamics such as changing customer demands and the increasing complexity of our business environment, which would require a different approach than we had used in the past.

This was the start of a long journey for our group. Our path took many turns as we met new challenges and discovered new resources along the way, uncovering deeper and deeper levels of obstacles to our business success. The process involved people with varied roles who were willing to work together to try new approaches, learn from their mistakes, and try again. Our story will hopefully offer some guidelines for others in the middle of a similar discovery process (see “The Journey: Going Deeper into Causes”).

A Systems Approach

The first phase of our journey was to address the immediate issues of customer satisfaction and quality. Customers were moving away from Digital proprietary computing environments, and were more often demanding multi-vendor “system solutions” — families of products that worked together to solve their business problems. As one key customer said, “We want to choose the best solutions regardless of who makes it. And we want everything to work together just as if it came from a single vendor.”

To meet this need, we began experimenting with a systems approach to product design and delivery, which meant paying as much attention to the relationship between products as to the products themselves. This approach caused us to focus in a more disciplined and structured manner on actual market and customer requirements, forcing us to surface our assumptions about trends in the marketplace, the industry, the technologies, the customer, and the competitive environments.

For example, we evolved a simple but powerful process called “Customer-Based Requirements Dialogue.” Rather than beginning with the question, “What product should we build?” we began by exploring our marketplace and customer environment assumptions. Only after we had acknowledged and explicitly surfaced our assumptions did we begin talking about the market requirements: the problems we were trying to solve and the opportunities we were addressing. Finally, we defined the specifications for the system and the features for each component product. We found this new process difficult because we were more used to prescribing solutions than describing the marketplace and customer environment in which the products would be used. Although these descriptions were about future states, which meant that most of them were educated guesses, this process proved to be more effective and efficient than our traditional methods.

Our systems approach also required us to work more actively across functional and hierarchical boundaries within the networks group, because it became increasingly clear that the answers we needed were located throughout the organization, not just at the top or in one area. We began to consciously open up our decision-making processes to include diagonal slices of the organization. We also made necessary job changes to meet the needs of our new approach, creating new roles such as systems technical leadership, systems business management, and systems project leadership. These changes addressed the need to manage the relationships between products, people, projects, and processes.

Focusing on the system also forced us to reevaluate our relationships with other companies. We acknowledged that if we were moving toward open solutions in response to the customer’s changing demands, then cross-company collaboration was required. An early result was that long-time competitor Apollo Computer (now Hewlett-Packard) became a new partner in the design and development of products.

From Systems Engineering to Systems Thinking

As we continued with our fledgling attempts at a “systems” approach to solving customer problems, we discovered two important books that helped bring clarity and structure to our efforts. The first was Peter Checkland’s Systems Thinking, Systems Practice, which clearly articulated our uphill struggle as we moved from “component thinking” to “systems thinking.”

Checkland writes, “Rene Descartes taught Western civilization that the thing to do with complexity was to break it up into component parts and tackle them separately…. Systems thinking, however, starts from noticing the unquestioned Cartesian assumption.., that a component part is the same when separated out as it is when part of a whole…. The Cartesian legacy provides us with an unnoticed frame-work — a set of intellectual pigeon-holes — into which we place the new knowledge we acquire. Systems thinking does not drop into its pigeon-hole, it changes the shape and structure of the whole framework of pigeon-holes. This questioning of previously unnoticed assumptions can be painful, and many people resist it energetically.”

With this new understanding, we renamed what we had been calling “Systems Engineering” and began calling it “Systems Thinking.” This new term reflected our recognition that we had to apply a systems thinking approach to all aspects of the way we did business, not just engineering. As a result, we developed a brief, 20-question guideline for people to use to begin applying systems thinking to any problem or situation they were facing. We also developed a systems thinking work-shop to meet requests for this type of work in other parts of the company. One such seminar was delivered regularly at a management development program, which reached hundreds of middle managers and technical leaders worldwide.

Then, in late 1990, we discovered Peter Senge’s The Fifth Discipline: The Art and Practice of the Learning Organization. This wonderful book made sense out of the experiences of the preceding three years, and gave us a clear set of constructs, language, and guidelines to bolster our efforts. Now we knew we were not in this alone — there was plenty of help and knowledge available that was based on a vast body of research and practice.

Servant Leadership and Decision Making

As we improved our ability to deliver at the product level, it became clearer that we needed to gain clarity at the larger “umbrella” level of shared vision. In order to do this, we needed to explore our individual “mental models”— our internal assumptions and beliefs about the way the world works — and come to some shared understanding of the larger issues we were facing. Therefore, in the early spring of 1991, we began looking for ways to surface, examine, and systematically break through our mental models of the marketplace and industry trends.

We began with a process called “FutureMapping,” a scenario planning methodology brought to us by North-east Consulting Resources Inc. of Boston. Through this process, we developed scenarios that described industry, competitive, and product trends from the present through 1997. Over the course of the next 15 months, we held one-day working sessions that engaged over 350 key people across the group in a modified version of this process. The key benefit was that we were forced to explicitly describe our industry, market, competitive, and customer “systems,” and to identify and monitor those five or six critical assumptions that represented “forks in the road”—decision points where we had to make tough choices.

As we did this work, it became clearer that the most difficult points for us in all of these new systems processes were the points of decision making. Working with teams that crossed functions, groups, hierarchies, and companies, it was often difficult to establish clear leadership roles and points of decision-making accountability. In addition, every manager, engineer, project leader, and technical leader had to balance the need for rapid execution and delivery against the desire to stay open to new information. It was all too easy to flip to one extreme or the other — to go for consensus and remain in dialogue forever, or to make quick decisions in an authoritarian manner. Our core challenge was (and still is) to manage this balance, and learn how to live productively within this dilemma.

To address the challenge of making decisions among diverse groups of people, we embarked upon some experiments with new leadership styles. With help from Robert Greenleaf’s book, Servant Leadership, we began moving toward developing “servant leaders”— or as Peter Senge puts it, leaders whose job is not so much to have the answer, but to instill confidence in those around them so that together they will come up with the answer at the time it is needed. We received some very positive feedback from this work — leaders told us they now found it easier to make decisions and that product development moved forward more quickly. But we also learned that the decisions were only as good as the information that was placed “on the table.”

Facing Undiscussables

It seemed that the next challenge we faced was how to bring more data to the table — including those issues that people did not feel comfortable raising. So, we embarked on a series of carefully designed and facilitated dialogues between senior management and several hundred engineers, technical writers, project leaders, and supervisors to discuss the obstacles we faced in quality and time-to-market. These meetings began to surface some of the difficult issues people found hard to raise because they felt “unsafe.” Harvard Professor Chris Argyris calls these unsurfaced issues the “undiscussables” that prevent real learning in organizations. Using the framework presented in his book Overcoming Organizational Defenses, we began to acknowledge the presence of “undiscussables,” but were left with the dilemma of how to raise and resolve them productively.

Change and Upheaval

At the same time that we were facing the challenge of how to deal effectively with “undiscussables,” a major change took place in the company as a whole. In October of 1992, Digital founder and CEO Ken Olsen was succeeded by Robert Palmer. This transition heralded two years of massive downsizing and almost continuous restructuring within the company. At that point, Digital as a whole was losing approximately $3 million per day, and had absorbed more than $3 billion in losses over the previous three years.

The immediate impact on NaC was that it merged with other networking entities under a new vice president, and continued to be reorganized and reshaped into many forms. As a result, people felt very insecure, morale plummeted, and attrition rose. It was simply not the time to begin the difficult and soul-searching work of connecting at the level of integrity, honesty, and respect espoused by Chris Argyris. Although we knew how important this work was for our long-term success, we just couldn’t begin it, given the constant instability and growing state of anxiety within the group. It was challenging enough just to continue to design, develop, and deliver products and systems. So what next? As with many of the breakthroughs we had in the past, the answer was close at hand.

Bringing in the “People”

Aspect In June of 1992, I presented at a conference on organizational learning, where 1 met Sandra Seagal and David Home. Their work, Human Dynamics, offers a framework for understanding differences in the way we learn, communicate, relate, and develop as human beings. With support and funding from John Adams, now vice president and technical director of Networks Integration Software, I attended a five-day training course in Human Dynamics and became convinced that this was the missing piece that could move our work forward. Human Dynamics offered a systemic approach to the complexities and wonders of human functioning that was clear, logical, and structured, yet broad and flexible enough to encompass the infinite nuances that make us each unique human beings (for more on the Human Dynamics methodology, see “Human Dynamics: A Foundation for the Learning Organization,” May 1994).

Back in the Networks Group, we saw Human Dynamics as the technology that would enable us to rebuild the trust, safety, and empowerment we knew was desperately needed. Over the next two years, almost 500 people across the company received training in Human Dynamics. Much of this was accomplished under the auspices of the Engineering Excellence Program led by Corporate Consulting Engineer Peter Conklin. Human Dynamics was seen as a foundational technology that would enhance our ability to make the changes needed in our engineering processes. We also began to dovetail Human Dynamics with Human Systems Change, a technology from Options Consulting, Inc. (Reading, MA) that was helping us create a systemic language for the human/cultural side of change so that we could name and over-come perceived resistance.

As with the efforts of the preceding seven years, we have had some good results from our application of Human Dynamics. While it is difficult to measure them quantitatively, there are many anecdotal accounts of improved efficiency and effectiveness in interpersonal communication and team productivity. As with many new technologies, our challenge now is making this practice part of our everyday way of doing business.

Valuing the Relationships

Despite the difficulties in Digital as a whole over the seven years of this story, our portfolio of networking products has continued to be profitable. While we cannot prove a direct connection between our ongoing efforts and our continued profitability, it is clear that it was a contributing factor.

As a networking group, we have learned that relationships are equally important as products — after all, connectivity is the essence of our business. In fact, we are beginning to believe that a root cause of many of our business problems lies in the breakdown of personal relationships. Although our first inclination in business is to blame profitability problems on poorly executed strategy or a lack of management skills, we believe that the cause may well be the absence, avoidance, or breakdown of authentic connection and communication between human beings.

Peter Block puts it very well in his 1994 book Stewardship: “Money is a symptom, money is never the real issue…. An economic crisis for any organization means it is failing in its market-place. In some fundamental way it is unable to serve its customers. And if it is unable to serve its customers, it means it has failed to serve its own internal people.” We have learned that serving the most basic needs of our people — to connect, communicate, and grow in a supportive environment — does indeed produce a profitable business.

Our seven-year journey has brought us far in our ability to learn and work together in more effective ways. In some sense, we have worked “back to front.” If we were to start over again, we would without a doubt begin with the fundamental technology of Human Dynamics and proceed from there. We would then know that we were building on the most solid foundation there is—people who are aware of themselves as fully empowered human systems, learning and growing, and consciously nurturing themselves and each other in order to produce the results they most desire.

Chris Strutt holds the title of Consulting Engineer. Systems Thinking Methods, In the Network Integration Software Segment of Digital Equipment Corporation.

In a future Issue, Chris Strutt will discuss In more detail the application of Human Dynamics technology at Digital. Editorial support for this article was pro-vided by Kellie T. Wardman.

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Deciding when to use computer models requires special attention, since they can require significant investments of time and money. While computer modeling is often a lengthy and intensive process, it can produce insights and action plans far beyond what is possible with pen-and-paper tools. So how do you know when to simulate? The following set of simple guidelines can help in that decision.

Modeling a Specific Issue

It is important to have a specific problem or issue in mind before you begin modeling. If you are working on a particular issue that has a clear purpose, you will have more success in setting appropriate boundaries for the model and determining the amount of detail you will need. If you try to model your whole organization, you will quickly get bogged down.

If you are not sure exactly where to start, the early steps of model building (identifying the important variables and how they relate) can help you flesh out some of the important issues. Starting with simple diagrams and building from there can also help you determine what to include in the final model.

Example: Attempting to model your entire manufacturing process without a clear sense of purpose can be difficult. Knowing, for example, that you want to assess the impact of hidden manufacturing delays can help you determine whether to include factors like purchasing or suppliers, or whether to chart information on a weekly or minute-by-minute basis.

Understanding Complex Behavior

Humans are very good at understanding and articulating relationships. We can describe, for example, how marketing, production, and sales are related. We are not as adept, however, at simulating how those relationships play out over time. If we increase marketing by 15%, for example, what will happen to sales and production in the next year? Computer models can take such complex, non-linear relationships and show how they play out over time.

Computer models offer vivid illustrations of how the structure of a system creates the behavior we observe. In essence, modeling means developing a structural picture of the problem and then simulating the behavior of the system under those assumptions. A model can also aid in linking past and present behavior by showing how both can be described by the same structure.

Modeling can be very useful if long time delays are a key part of the problem or issue. While tools such as causal loop diagrams cannot adequately quantify the impact of delays in the system, computer models can clearly identify different kinds of delays and show how they affect a system’s overall behavior.

Example: In order to investigate the rising cost of insurance claims, one property and casualty insurer built a model of its claims adjusting process. The managers involved in the process surfaced several non-linear connections between time pressure, productivity, and quality—all of which in turn had long-term effects on overall costs. Mapping and simulating these relationships revealed how a short-term focus on cutting costs led to a long-term erosion of quality—and ultimately higher settlement costs.

Formulating and Testing Policies

Computer models can be very effective for developing and testing specific policies. For example, a computer model can allow you to test the results of different hiring, marketing, or inventory management strategies. Testing your ideas and assumptions about critical relationships can help you better assess the results of the policy interventions you make.

Most policies have both short-term and long-term implications. Without some understanding of the long-term ramifications of a specific policy, we tend to favor decisions that will benefit us in the short term. Unfortunately, those short-term actions often undermine long-term sustainability or profitability. Modeling can reveal those tradeoffs by making the long-term consequences just as real and present as the short-term ones.

Example: One heavy equipment manufacturer had a policy of adding additional plant capacity only when its backlog grew to six months. By the time the new capacity came on line, however, order volume had generally decreased (due to the long shipping delays) and the company was saddled with over-capacity until its order backlog grew again. This would spark another round of capacity additions, and the whole dynamic would repeat itself. When the company’s managers built a simulation model, they discovered that their own capacity decisions were in large part responsible for the order swings. Testing different policies suggested that their conservative approach to capacity expansion might actually be putting the company at the greatest risk of losing customers over the long term, and might be unnecessarily constraining their growth. Simulation modeling is generally most effective when it is applied to a specific, focused problem. There are, however, particular situations where the lack of specific focus is the problem. In such cases, the process of modeling itself can help you gain a clearer understanding of a particular problem or issue (see “From Causal Loop Diagrams to Computer Models—Part 11,” August 1994). Since model building is a highly iterative process, as you cycle through the steps you can come to a greater level of clarity about what the most critical issues are. At that point, you will be in a better place to assess whether or not you should go further in the simulation process.

Kelile T. Wardman is publications director at Pegasus Communications and an editor of The Systems Thinker”4.

Daniel H. Kim is the publisher of The Systems Thinker’ and co-founder of the MIT Organizational Learning Center where he directs the Learning Lab Research Project.

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But assessment strikes fear in most people’s hearts. The word itself draws forth a strong, gut-level memory of being evaluated and measured, whether through grades in school, ranking in competitions, or promotions on the job. As writer Sue Miller Hurst has pointed out, most people have an intrinsic ability to judge their progress. But schools and workplaces subjugate that natural assessment to the judgment of teachers, supervisors, and other “experts,” whose appraisals determine promotions, wealth, status, and, ultimately, self-esteem.

Assessing Learning

Is it possible to use assessment in the service of learning? Can assessment be used to provide guidance and support for improving performance, rather than elicit fear, resentment, and resignation? This has been a guiding question at the MIT Center for Organizational Learning for several years, as we have struggled to find a reasonable way to assess learning efforts. The motivations are essentially pragmatic: our corporate affiliates need some idea of the return on their investments, and we as researchers need a better understanding of our work.

To create a new system of assessment, we started by going back to the source — to the people who initiate and implement systems work, learning laboratories, or other pilot projects in large organizations. We then tried to capture and convey the experiences and understandings of these groups of people. The result is a much-needed document that moves beyond strict assessment into the realm of institutional memory. We call it a “learning history.”

The Roots of a New Storytelling

A learning history is a written document or series of documents that is disseminated to help an organization become better aware of its own learning efforts. The history includes not just reports of action and results, but also the underlying assumptions and reactions of a variety of people (including people who did not support the learning effort). No one individual view, not even that of senior managers, can encompass more than a fraction of what actually goes on in a complex project — and this reality is reflected in the learning history. All participants reading the history should feel that their own points of view were treated fairly and that they understand many other people’s perspectives.

A learning history draws upon theory and techniques from ethnography, journalism, action research, oral history, and theater. Ethnography provides the science and art of cultural investigation — primarily the systematic approach of participant observation, interviewing, and archival research. From journalism come the skills of getting to the heart of a story and presenting it in a way that draws people in. Action research brings to the learning history effective methods for developing the capacities of learners to reflect upon and assess the results of their efforts. Finally, the tradition of oral historians offers a data collection method for providing rich, natural descriptions of complex events, using the voice of a narrator who took part in the events. All of these techniques help the readers of a learning history understand how participants attributed meaning to their experience.

Each part of the learning history process — interviews, analysis, editing, circulating drafts, and follow-up — is intended to broaden and deepen learning throughout the organization by providing a forum for reflecting on the process and substantiating the results. This process can be beneficial not only for the original participants, but also for researchers and consultants who advised them — and ultimately for anyone in the organization who is interested in the organization’s learning process.

Insiders versus Outsiders

One goal of the learning history work is to develop managers’ abilities to reflect upon, articulate, and understand complex issues. The process helps people to hone their assessments more sharply by communicating them to others. And because a learning history forces people to include and analyze highly complex, dynamic interdependencies in their stories, people understand those interdependencies more clearly.

In addition, the approach of a learning history is different from that of traditional ethnographic research. While ethnographers define themselves as “outsiders” observing how those inside the cultural system make sense of their world, a learning history includes both an insider’s understanding and an outsider’s perspective.

Having an outside, “objective” observer is an essential element of the learning history. In any successful learning effort, people undergo a transformation. As they develop capabilities together, gain insights, and shift their shared mental models, they change their assumptions about work and interrelationships. This collective shift reorients them so that they see history differently. They can then find it difficult to communicate their learning to others who still hold the old frame of reference. An outside observer can help bridge this gap by adding comments in the history such as, “This situation is typical of many pilot projects,” or by asking questions such as, “How could the pilot team, given their enthusiasm, have prevented the rest of the organization from seeing them as some sort of cult?”

Similarly, retaining the subjective stance of the internal managers is important for making the learning history relevant to the organization. In most assessments, experts offer their judgment and the company managers receive it without gaining any ability to reflect and assess their own efforts. The stance of a learning history, on the other hand, borrows from the concept of the “jointly told tale,” a device used by a number of ethnographers in which the story is “told” not by the external anthropologist or the “naive” native being studied, but by both together. For these reasons, the most successful learning history projects to date seem to involve teams of insiders (managers assigned to produce and facilitate the learning history) working closely with “outside” writers and researchers hired on a contractual basis.

Results versus Experience and Skills

Companies today don’t have a lot of slack resources or extra cash. Thus, in every learning effort, managers feel pressured to justify the expense and time of the effort by proving it led to concrete results. But a viable learning effort may not produce tangible results for several years, and the most important results may include new ways of thinking and behaving that appear dysfunctional at first to the rest of the organization. (More than one leader of a successful learning effort has been reprimanded for being “out of control.”) In today’s company environment of downsizing and re-engineering, this pressure for results undermines the essence of what a learning organization effort tries to achieve.

One goal of the learning history work is to develop managers’ abilities to reflect upon, articulate, and understand complex issues.

Yet incorporating results into the history is vital. How else can we think competently about the value of a learning effort? We might trace examples where a company took dramatically different actions because of its learning organization efforts, but it is difficult to construct rigorous data to show that an isolated example is typical. Alternatively, we might merely assess skills and experience. A learning historian might be satisfied, for instance, with saying, “The team now communicates much more effectively, and people can understand complex systems.” But that will be unpersuasive — indeed, almost meaningless — to outsiders.

In this context, assessment means listening to what people have to say, asking critical questions, and engaging people in their own inquiries: “How do we know we achieved something of value here? How much of that new innovation can we honestly link to the learning effort?” Different people often bring different perceptions of a “notable result” and its causes, and bringing those perceptions together leads to a common understanding with intrinsic validity.

For example, one corporation’s learning history described a new manufacturing prototype that was developed by the team. On the surface, this achievement was a matter of pure engineering, but it would not have been possible without the learning effort. Some team members had learned new skills to communicate effectively with outside contractors (who were key architects of the prototype), while others had gained the confidence to propose the prototype’s budget. Still others had learned to engage with each other across functional boundaries to make the prototype work. Until the stories of these half-dozen people were brought together, they were not aware of the common causes of each other’s contributions, and others in the company were unaware of the entire process. The learning history thus included a measurable “result” — the new prototype saved millions of dollars in rework costs — but simply reporting a recipe for constructing new prototypes would be of limited value. At best, it would help other teams mimic the original team, but it wouldn’t help them learn to create their own innovations. Only stories, which deal with intangibles such as creating an atmosphere of open inquiry, can convey the necessary knowledge to get the next team started on its own learning cycle.

The Strength of the Story

Some learning histories have been created after a project is over. Participants are interviewed retrospectively, and the results of the pilot project are more-or-less known and accepted. Other histories are researched while the story unfolds, and the learning historian sits in on key meetings and interviews people about events that may have taken place the day before. “Mini-histories” may be produced from these interviews, so that the team members can reflect on their own efforts as they go along and improve the learning effort while it is still underway. But such reflection carries a burden of added discipline: it adds to the pressure on the learning historian to “prove results” on the spot, to serve a political agenda, or to justify having a learning history in the first place.

To create an ongoing learning history, an organization must embrace a transformational approach to learning. Instead of simply learning to “do what we have always done a little bit better,” transformational learning involves re-examining everything we do—including how we think and see the world, and our role in it. This often means letting go of our existing knowledge and competencies, recognizing that they may prevent us from learning new things. This is a challenging and painful endeavor, and learning histories bring us face to face with it. When the learning history is being compiled simultaneously with the learning effort, then the challenge and pain of examining existing frameworks is continuous. But to make the best of a “real-time” learning history, admitting and publicizing mistakes must be seen as a sign of strength. Uncertainty can no longer be a sign of indecisiveness, because reflecting on a learning effort inevitably leads people to think about muddled, self-contradictory situations. Much work still needs to be done on setting the organizational context for an ongoing learning history so that it doesn’t set off flames that burn up the organization’s good will and resources.

Currently, there are almost a dozen learning history projects in progress at the Learning Center. In pursuing this work, we no longer talk about “assessing” our work. Instead, we talk about capturing the history of the learning process. It is amazing how this approach and new language changes the tenor of the project. People want to share what they have learned. They want others to know what they have done — not in a self-serving fashion, but so others know what worked and what didn’t work. They don’t want to be assessed. They want their story told.

George Roth is an organizational researcher with the MIT Center for Organizational Learning and a consultant active in the study of organizational culture, change, and new technology introduction.

Art Kleiner is co-author and editorial director of The Fifth Discipline Fieldbook, and author of the forthcoming The Age of Heretics, a history of the social movement to change large corporations for the better.

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My thinking about corporations has undergone a similar evolution. When I joined corporate America over 30 years ago, I entered a world that was still operating on organizational principles not unlike those of the traditional command-and-control model. People felt they understood the rules by which the corporation operated, and their roles within it. But that was before economic upheaval, global competition, and dynamic change forced companies to downsize, reengineer, and restructure. These changes, designed primarily to affect the way financial assets were used, had a profound and often unsettling impact on the relationship between employees and their companies.

These changes also prompted me to reexamine the role of CEO. Clearly, the model of traditional commander-in-chief was not adequate for the demands of an organization seeking to transform itself in a world of rapid change. It seemed to me that in organizations undergoing rapid change, the CEO must at times function rather like an ecologist. He or she needs to strive to create an environment that allows the separate parts of the whole to flourish as a single organic system, while also understanding how that system fits into the larger economic, social, and political systems of which it is a part.

The truth is that transformation is not about downsizing, reengineering, and restructuring. It’s about people — about raising their aspirations and unleashing their potential. It’s also about learning, which is the cornerstone of any successful transformation.

Those insights have influenced the transformation process at Shell Oil Company. They have also contributed to a transformational infrastructure in which learning plays a central role. That infrastructure is made up of five elements:

• Creating Mission, Vision, and Values. We began our transformation through a process designed to create a mission, vision, and values powerful enough to engage the minds and hearts of all 22,000 people in the corporation. The process — which is ongoing — encourages people to share their ideas about who we are, who we want to be, and where we fall short of those aspirations. The emerging dialogue from this process is producing a valuable dissonance that forces people to look deep within themselves and discover their personal visions for the company. This is important, since our transformation will not be complete until the personal visions of all our people converge into one collective vision.
• Developing and Implementing a Business Model. Because of the highly technical nature of our business, many of our managers have traditionally been more comfortable with differential equations than with balance sheets. To raise our business acumen to the level of our technical skills, we have developed a business model that gives us a new theory about our business as well as a new set of tools with which to shape our future. Our leaders are currently using this business model to build winning strategies by understanding where they can exert the greatest leverage and add the most value. But the business model has applications far beyond the executive suite. When it is fully implemented, it will give every Shell employee a better understanding of his or her contribution to our company.
• Creating a New System of Governance. To unleash the potential of all our people, we are moving to a new system of governance that disperses authority and responsibility throughout the organization. This new system distributes much of the power that formerly resided in the office of the CEO to our principal businesses. Eventually, a significant share of that power will devolve into the business units within those businesses. We expect this sharing of power to offer our people a more entrepreneurial environment, a greater sense of ownership, and enlarged opportunities for personal growth. And we expect it to propel our company to the level of financial performance that can only be achieved in a high-accountability culture.
• Developing New Concepts of Leadership. Until recently, leadership was considered the preserve of those at the “top” of organizations. But at Shell, we believe that everyone has both the opportunity and the responsibility to exercise leadership within his or her own area of expertise and sphere of influence.Are leaders born or made? Whatever the answer to that question, we do believe that leadership skills can be broadened and deepened. Through leadership development workshops, we are helping our managers understand their personal leadership potential and discover new ways of thinking and doing.
• Using Learning as a Fulcrum for Change. All of our transformational activities—from the creation of our mission, vision, and values to the development of our business model and our new system of governance—are taking place under the aegis of the Learning and Development Initiative. This initiative provides the framework within which both individual and collective learning takes place. Although theoretical learning has its place, we believe that the most powerful learning experiences—the ones that produce both the fastest and most lasting results—are those in which real people are engaged in finding real solutions to real problems.

Because learning is both the foundation of our transformation and a permanent part of our culture, we are also establishing a corporate learning center that will promote continuous learning and business excellence, immerse new leaders and new employees in our culture, and continue the process of change.

To accomplish our objectives, we are striving to weave learning into the very fabric of our culture at Shell Oil Company. We consider it the means by which we will achieve all our other ends. It is the way we make sense of a complex and interconnected world. In a period of rapid change, it is our only sustainable competitive advantage. And it is the infrastructure for the transformational change that will enable us to achieve world-class performance in all our businesses.

Philip J. Carroll Is the president and CEO of Shell Oil Company. Reprinted with permission from Collective Intelligence. Vol. I No. I (Cambridge. MA: MIT Center for Organizational Learning).

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