One set of loops that can help us better understand the basic interactions between a company and its marketplace is the supply/demand structure. Most everyone is familiar with the basic law of supply and demand: if demand rises, price tends to go up (all else remaining the same), and conversely, as supply goes down, price tends to go up (again, all else remaining equal). From a systems thinking perspective, this dynamic can be simply described by two coupled balancing loops that attempt to stabilize around a particular variable—in this case, price.

Supply and Demand: A Generic View

If we look at the supply/demand structure from a more generic perspective, we can use it to describe any situation in which an ability to supply a good or service is being balanced with the demand, utilization, or consumption of that product or service. This structure acts like a see-saw, with supply on one side, demand on the other, and some pivot point in the middle (such as quality, price, availability, or service) that links the consumer actions and the company’s decisions (see “Balancing Loops with Delays: Teeter-Tottering on See-Saws,” June/July 1990). The central variable serves as the “adjusting variable” because it is the signal that causes players on both sides of the see-saw to adjust the imbalance between supply and demand (see “Generic Structure”). These dynamics can occur between the company and the market-place or within an organization, where an internal function or unit (such as training or l.S.) is supplying services to other parts of the company.

For example, in the medical industry, one common adjusting process revolves around waiting time to get an appointment with a physician. On the demand side, if the wait time to see a particular physician becomes too long, patients might either try to find another provider, put off receiving care (in the hopes that the problem will “take care of itself”), or, if the problem is serious enough, go to the emergency room. If enough patients find alternate solutions, this leads to a decline in the physician’s utilization rate, which then eases the pressure on the physician’s schedule so that the wait time is reduced (B1 in “Medical Supply/Demand,” page 8). Physicians, for their part, might try to reduce the wait time for care by processing patients faster, adding physicians to their practice, or asking ancillary staff (such as nurse practitioners) to play a more significant role in patient care. All of these actions would increase the patient capacity and reduce the wait (B2).

What is important to note is that both balancing actions are usually happening simultaneously—that is, at the same time that the physicians are looking for ways to ease the patient bottle-neck, the patients are already taking action to relieve that pressure by seeking alternate providers or finding other ways to take care of themselves. Because demand is falling at the same time that capacity is rising, these actions will create another imbalance this time, with more available capacity for seeing patients than the actual demand for appointments. When this occurs, both parties will once again take action to close the gap (patients will return to their original provider because of the reduced wait time, while the physician’s practice might ease scheduling pressure) and the see-saw invariably tips in the other direction.

Seeking a Balance

This same see-saw structure of balancing capacity and demand shows up in a variety of contexts, such as service quality (hospitals, banks, car-rental shops, fast-food restaurants, I.S., training) or product availability (retail stores, specialty products, manufacturers).

Of course, most companies would like to find a way to strike exactly the right balance between the demand in the marketplace and their ability to service that demand. Unfortunately, that rarely happens. As the medical example shows, what is more likely is a pattern of oscillation as the two sides overshoot each other, adjust, and overshoot again.

In part, this behavior occurs because of several significant delays in the system: customer perception delay, company perception delay, and capacity addition delay.

• Customer Perception. It takes time for word to get around that a company cannot provide a particular product or service (this signal usually comes in the form pf rising prices, lengthening delivery delays, or declining quality). It also takes time for people to alter their usage or consumption patterns. Similarly, once a company has added capacity, it takes time for that signal to make it into the marketplace and draw customers back.
• Company Perception. Just as it takes time for customers to realize that a company can no longer meet their needs, it takes time for the company to recognize that demand for its product or service is declining. This delay is often exacerbated because companies do not act upon the information immediately, believing that the drop off in demand is either temporary or due to factors other than capacity shortfall.
• Capacity Additions. Once the company has recognized the imbalance between the marketplace demand and its ability to meet that demand, there is a further delay while the company adds the needed capacity. The length of this delay depends on the nature of the capacity being added—for example, it takes a lot longer to add capital equipment than to increase customer service representatives or improve a process.

Using the Structure

The generic supply/demand causal loop structure provides a useful starting point for exploring how internal actions and marketplace decisions are intertwined. To see how the structure can be applied to a specific problem, let’s take a look at the example of ZSearch, a research company that specializes in tracking down research articles in the biochemical industry. ZSearch had built its reputation on the quality and timeliness of its response to its customers’ inquiries. However, the company’s managers have become concerned about two recent trends: customer surveys have ranked the company below its competitors in terms of customer service, and they have noticed a drop-off in the overall number of research requests per day.

1. Define the Variables. To begin mapping out the system, first define the different parts of the see-saw: what is being “supplied,” what is being “demanded,” and what is the fulcrum around which the imbalances between the two are resolved.

In ZSearch’s case, the “supply” would be the number of customer service representatives, the “demand” would be the number of requests from customers, and the “fulcrum” would be the wait time for service. If the number of requests coming in outstrips the available capacity, an imbalance appears in the system. Customers who are stuck on the phone waiting for a customer service rep might be inclined to hang up and call one of ZSearch’s competitors, thus decreasing the wait time for service (B1 in “ZSearch’s Balancing Act”). On the other side of the see-saw, once ZSearch gets the signal that it needs more capacity, it can respond by increasing the number of service reps or raking other actions that would likewise decrease wait time (B2).

2. Identify Delays. Once you have identified the fundamental balancing loops, it is important to identify and quantify the relevant delays. In ZSearch’s case, the customer perception delay may be fairly short—it doesn’t take lung for customers to get a busy signal, put down the phone, and call a competitor (although it does take time to establish new supplier relationships).

On ZSearch’s side, there might be a long perceptual delay before ZSearch identifies the source of the drop-off in call volume and how to respond to it. At this point, it would be easy for them to blame external forces, such as aggressive competitors, rather think examining how their own policies might be contributing to the decline. However, ZSearch’s managers felt that the problem might stem from a shortage of trained service reps. They knew they could case this burden in the short term by increasing the work hours of their current staff, though they acknowledged that it would take several months to hire and train the new reps.

3. Design Interventions. When considering any potential solution, it is important Lu evaluate the action in terms of both its internal consequences and its impact on the marketplace. In particular, look for ways you can more directly influence the customers’ behavior (the demand loop), rather than simply reacting after-the-fact (the supply loop).

At first, ZSearch’s managers were at a loss as to how they could have any direct influence on their customer’s decision to hang up and call a competitor. But after some thought, they came up with with a program that they called the “superior customer service guarantee.” They promised that any customer who waited longer than 60 seconds for an available representative would receive a 40% discount on the order. It was a costly gamble, but it paid off—the guarantee not only boosted ZSearch’s reputation in the field, bur on three occasions that the demand outstripped capacity, customers were willing to wait the extra time (to get the discount) and ZSearch retained the sale.

More importantly, ZSearch received timely, valuable feedback about their response time without risking losing customers. Knowing that they now had a strong system in place for tracking their call volume and service turnaround (the demand side of the diagram), they could focus their attention on the supply side of the diagram—finding ways to keep their staffing up to optimal levels.

Larger Implications

Many organizational “crises”—poor sales, quality problems, slipping delivery times–can be traced back to the mismatch between supply and demand and how this disequilibrium is corrected. Within organizations, this plays out in pressures to outsource in order to improve service or reduce costs. But it also occurs in whole industries, as poor service or high prices attract new competitors and innovators to the industry. This is the very mechanism by which customers see quality rise as prices decline over rime in an industry.

Michael Goodman is vice president of Innovation Associates, Inc. (Waltham MA) and heads IA’s Systems Thinking Group.

Colleen Lannon Is co-founder of Pegasus Communications and managing editor of The Systems Thinker•.

Balancing Act

If the number of incoming requests outstrips capacity, an imbalance appears. This imbalance can be resolved in one of two ways: (1) customer calls drop off due to the long wait (B1); or (2) customer service reps are added in order to reduce the time it takes to process requests (B2).

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In fact, such an event did occur in Sweden in 1992, when the OK Petroleum company successfully lobbied for an increase in the country’s tax on leaded gasoline. This surprising action stemmed from OK’s development of a high-octane (98) lead-free automobile fuel, which burned cleaner than other fuels while still maintaining high performance. The Swedish government agreed to the tax because it was in alignment with its own clean air policies and with international conventions that it supported. Since OK had the only lead-free product on the market, the gas tax gave the company a significant price advantage at the pumps. “The competition was forced to follow suit,” explained OK’s Per Wadstein, leading to cleaner air for all of Sweden.

Economy vs. Ecology

Economy and ecology arc often pitted against each other in the “profitability versus environment” debate. There is a perception that companies can either prosper financially or take care of the earth, but not both. However, as OK Petroleum showed, these pursuits do not have to be mutually exclusive. In fact, ecology and economy derive from the same Greek root, eco, meaning house. (Ecoloqy stands for “study of the house,” and economy means “management of the house.”) This etymology suggests that the two concepts are not contradictory, but actually part of the same larger idea. I low, then, can we study and manage our “house” (the earth) in ways that benefit both industry and society over the long term?

The “Systems ‘Thinking for a Sustainable Future” initiative, based at the MIT Center for Organizational Learning, provides a set of principles, practices, and processes that recognize and reinforce the synergistic link between long-term economic and ecological development. It seeks to provide industrial decision-makers with both a conceptual, framework stud practical tools for building financially healthy companies that arc also ecologically sustainable. In addition, the initiative attempts to foster learning environments in which various stakeholders can grapple with the larger issues of the day. The hope is that within these settings, the participants will create presently unimaginable solutions to some of the world’s most intractable problems.

Sustainabillty

What do we mean by “sustainable”? A sustainable society is one that is self-perpetuating over the long term—meaning that it uses resources at a rare that does not exceed the rare at which they can be replenished, and that it produces waste materials at a pace that does not exceed the rare at which they can be reabsorbed by the environment. Within this framework, a sustainable organization can be described as a company that provides customers with goods and services for living a satisfying life, while maintaining both a healthy balance sheet and a healthy balance with the natural world.

Creating environmentally sustain-able business practices used to be considered a choice for businesses—an optional activity for those companies that had the time, energy, and interest. But now it is becoming a more mainstream concern, due to several trends:

• The marketplace is demanding “greener” products that reflect environmentally responsible management. Supermarket aisles are filled with products that proclaim their eco-friendliness—from phosphate-free detergent and acid-free paper to recycled cardboard and “dolphin-safe” tuna.
• Material resources are becoming more scarce, resulting in a rise in production costs in many industries. For example, integrated steel producers virtually disappeared in the U.S. during the 1980s because the costs of mining iron ore grew financially prohibitive as the availability of that resource decreased.
• Regulatory compliance is becoming an increasingly costly concern. One petroleum company’s environmental compliance costs topped $1 billion in 1994—a figure that exceeded the company’s net profit for the year.

How can business managers think systemically about a sustainable future? How can they balance needs for economic prosperity and ecological survival? To address these challenges, companies need to expand their current strategic thinking to include economic and ecological concerns—creating what W. Edward Stead and Jean Garner Stead call “sustainability strategies.”

A Conceptual Framework

The Natural Step movement. which originated in Sweden, offers clear conceptual framework for creating such sustainability strategies. Lei Dr. Karl-Henrik Robert. The Natural Step has proven to be one the most effective sustainability movements in the world, aligning diverse social business and ecological interests around fundamental scientific principles of natural systems. The Natural step process has been studies: and practiced by corporate managers, urban community members, youth at risk, and schoolchildren; it has been shared via books, audiotape, board game, or CD-ROM with every household in Sweden. It is an approach that does not blame any one sector of society for our current problems, but rather encourages all of us to find ways to contribute CO effective solutions.

The guiding principles of The Natural Step, known as the “four systems conditions,” are derived from the basic hews of thermodynamics: matter cannot disappear, and matter tends to dispense (see “The Four Systems Conditions”). By using the four systems conditions to evaluate whether their products and services are economically and ecologically sustainable, some of Sweden’s largest corporations have produced significant changes in their business strategies.

For example, the ICA supermarket chain in Sweden was asked frequently by its customers whether its refrigerators and freezers emitted CFCs, which are linked to ozone layer damage. After familiarizing themselves with the four systems conditions, ICA’s leadership engaged in a conversation with Electrolux (Eureka in the U.S.), their primary vendor of refrigeration products. Aware that CFCs, a non-biodegradable, unnatural compound, violated systems condition 2, ICA’s leaders asked Electrolux what it would cost to eliminate this compound from their existing inventory. After some technical hedging, Electrolux designers answered that it would take 1 billion Swedish crowns (approximately $140 million) to convert to soft freons—another persistent and unnatural compound, but one that is thought to be less damaging than CFCs. The CEO’s response was, “You want me to invest 1 billion crowns in a product, of which the only thing I know for sure is that it is doomed to failure?! Please come up with a more suitable alternative.”

Electrolux, which had not previously encountered The Natural Step, subsequently phoned Dr. Robert and asked him to come “talk about your damned systems conditions.” A short time later, the Electrolux team announced the development of an interim compound that does not harm the ozone and that is now successfully being manufactured and marketed as a “green” refrigerant. The company is also well on its way to producing a refrigerant that is biologically harmless. As a result of its work with Dr. Robert and his colleagues, Electrolux has begun employing The Natural Step method throughout the company, and is now using the four systems conditions as a framework for its strategic planning process.

Integrating Sustainability Strategies and Organizational Learning

While the four systems conditions offer a basic conceptual framework for creating sustainable business strategies, they do not provide a specific process whereby those principles can be used to develop and implement such strategies. This is where the disciplines and tools of organizational learning can help. For example, the tools and methodology of systems thinking provide a means to test the long-term implications of policy decisions on the wider environmental system.

Systems thinking can also provide an overarching framework for understanding the industrial, governmental, and environmental interactions that play a role in sustainable development (see “The Sustainability Challenge”). An overall increase in industrial productivity (such as the U.S. has experienced for most of the 20th century) leads to a reinforcing cycle of economic growth and profitability (R1), but it can also lead to an accumulation of industrial wastes in the environment. In the U.S., this has led to heightened regulatory pressures designed to reduced waste.

At the same time, increased consumer awareness of the environmental impact of production is leading to emerging new market opportunities in terms of “clean” technologies (B3), which, for those companies that invest in them, can lead to profitable alternatives to unsustainable production techniques (R4). However, the subsequent increase in regulatory compliance costs can constrain profits (B2), which can potentially limit industry’s ability to invest in “clean” technologies (R4).

The disciplines of team learning and mental models also have much to offer in that they can help generate more informed, productive conversations. In the ecology/economy debate, dialogue skills of genuine inquiry, deep listening, displaying one’s own line of reasoning, and respect for other view-points are critical, as are the ability to surface our mental models and to inquire into those of other people (see “The Power of Mental Models”). Through the use of dialogue and role-playing, we can gain deeper understanding of diverse points of view and bring out new ideas and solutions that a single point of view might not have produced.

In a recent learning laboratory at a petroleum company, for example, role-reversal, dialogue, and consensus-building tools were used to develop a new framework for environmental leadership. As part of the workshop, employees from the environmental engineering division took turns role-playing the traditional contestants in the environmental debate: “Government Bureaucrats,” “Tree-Hugging Environmentalists,” and “Big Bad Business.” By humorously taking on their worst perceptions of each other, participants were able to see beyond the stereotypes that they had placed on their professional adversaries.

The Sustainability Challenge

Heightened consumer awareness of accumulated industrial wastes has led to heightened regulatory pressures designed to reduce waste. However, the subsequent increase In regulatory compliance costs can constrain profits (B2) which can potentially limit Industry’s Investment in “clean” technologies (R4).

In the dialogue that followed, the engineers gained insights into the motivation, logic, and humanity of the various stakeholders, and were better able to understand the validity and utility of each point of view, even if the perspective challenged their own position. The engineers found that their subsequent meetings with EPA representatives on a difficult Clean Air Act project were significantly enhanced in terms of quality of communications, creativity of thinking, and efficacy of solution generated—all as a result of their experience in the workshop.

Organizational Learning for a Sustainable Future Integrating sustainability strategies and organizational learning—one approach focused on content (where we need to. go) and the other focused on process (how we’ll get there)—may hold unprecedented potential for producing sustainable ecological and economic development. We have termed this synergy Sustainable Organizational Learning (SOL). Although the development of SOL is only in its initial stages, we can imagine a variety of learning practices through which SOL practitioners will work toward long-term economic and ecological sustainability:

• Aligning industrial cycles and natural systems. Conversations around strategy and future planning will include the question, “What business activities should we engage in that will be aligned with the systems conditions for sustainability?” The answers to this question will strongly influence investment decisions with respect to new products and services. In this way, SOL practitioners will begin to align their company’s industrial cycles with natural systems.
• Building cross-company consortiums. By building consortiums of companies engaged in a similar inquiry, sustainable learning organizations will participate in company-to-company conversations that will enable them to learn from each other’s challenges and successes in the pursuit of sustainability strategies.
• Engaging in ongoing practice. By studying and practicing the disciplines of SOL, practitioners will foster new learning in themselves, their compa

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In the Knowledge Era, products and services will be composed of more intellect and less labor, material, and capital. The primary source of wealth creation will be the human imagination, something that cannot be managed using traditional methods. Thus, organizations will require a different set of operating assumptions for managing and monitoring their operations. Companies that create infrastructures to promote innovation and continual learning will flourish; those that do not respond effectively may find themselves struggling for survival.

Historical Perspective

From a historical perspective, industrialized Western societies have moved through two major eras that set the stage for the Knowledge Era (see “The Shifting Economic Base”). In the Agricultural Era, the main engines of wealth creation were land and labor. Organizations were primarily concerned with the production, movement, and storage of agricultural products within rural communities. Organizational structures were relatively simple, and land and labor were the key “raw materials.”

In the Industrial Era, labor continued to be important, but land’s contribution to wealth creation was surpassed by a new source—capital. Capital was required to fund the large purchases of plant and equipment, as well as to invest in research and development. Hierarchical structures, financial markets, production techniques (such as mass production), and scientific management philosophies were created to maximize the return on invested capital.

In what Savage calls the Knowledge Era, the traditional engines of wealth creation will be eclipsed by the importance of intellect. The sheer force of knowledge and knowledge creation in all its forms—know-how, know-who, know-what, know-why, know-where, and know-when—will dominate all other means for creating wealth. The shifts in managing for knowledge creation will be as profound as those experienced in the Industrial Era. In fact, the term “managing” may not even apply as companies experiment with new approaches for unleashing the potential and creative capability of all members of organization.

While the full impact of the Knowledge Era has yet to be seen, the shift has already begun. Consider these recent illustrations of how knowledge is becoming a basis for creating competitive advantage:

• Ryder Systems is considering selling its core business of truck leasing to concentrate on its knowledge-based logistics services.
• Software accounts for one-third of the total cost in a Minolta camera. This knowledge-based component makes the product easier to use and more valuable to the consumer.
• Microsoft has declared that its factories are “the human imagination.” Its stock sells at significantly over book value because of the market perception of its future earnings capability—its ability to innovate.
• IBM’s $3.5 billion purchase of Lotus Development included a substantial premium over book value owing to IBM’s belief in Lotus’ ability to continue to innovate and create exciting technology and groupware solutions.

As we enter the knowledge economy, intellectual components will be integrated into more products and services. This will require businesses to fundamentally rethink their past assumptions about management, and to build infrastructure geared toward capitalizing on the collective knowledge and learning capability of all members of the organization.

Managing the Learning Enterprise

What do we mean by “knowledge”? And how can companies create explicit processes for increasing knowledge and learning throughout the company? We define knowledge as the collective experience of the organization, which consists of the interaction between two primary elements: tacit and explicit knowledge.

Tacit knowledge includes things the organization knows, as well as those things it knows how to do but cannot express and codify. For example, one of the highest paid positions in a Dominos Pizza shop is the dough kneader. Attempts to proceduralize or codify how these “kneaders” perform their job has proved fruitless for Dominos. Therefore, the organization has instead focused on training new workers through apprenticeship programs, as well as retaining experienced “kneader” through higher salaries.

Explicit knowledge consists of the valuable information that can be expressed, communicated verbally, or codified through organizational artifacts such as knowledge repositories, policy manuals, user guides, visioning documents, etc. The process of learning through experience increases tacit knowledge, while codifying these experiences increases explicit knowledge. If we look at knowledge management from a structural perspective, the “learning rate” (of both tacit and explicit knowledge) is the inflow that increases overall organizational knowledge (see “Knowledge: A Structural View”). “Knowledge decay” (in the form of technology obsolescence or innovations that outdate the organization’s work) and “knowledge loss” (in the form of people leaving the organization or moving to other areas in the firm) are outflows that decrease the overall level of knowledge. Maximizing organizational knowledge, therefore, involves designing processes that in-crease the rates of learning and codification, and finding ways to decrease knowledge decay and loss.

But how can organizations design explicit knowledge-management processes? We have identified four specific areas of activity that can aid in this process:

1. Clearly articulate the purpose of creating organizational knowledge and how knowledge fits into the company’s overall business strategy.

2. Develop explicit knowledge and learning strategies that will enable the company to achieve its purpose.

3. Build organizational learning and knowledge-leveraging structures to implement the strategies.

4. Create feedback systems to measure the successes and shortcomings of the efforts, and provide data for continually modifying the strategies.

Together, these four elements make up an overall learning process—one that must be deliberately crafted with the same vigor as any key management strategy. Let’s briefly explore what this process looks like in more detail.

1. Clearly articulate the purpose of creating organizational knowledge.

In his book The Knowledge-Creating Company, Ikujiro Nonaka describes the Japanese view that “a company is not a machine but a living organism, and much like an individual, it can have a collective sense of identity and fundamental purpose. This is the organizational equivalent of self-knowledge—a shared understanding of what the company stands for, where it is going, and what world it wants to live in, and, most importantly, how it intends to make the world a reality.”

Without a clear understanding of how creating structures to improve learning and knowledge creation will provide value to customers and stake-holders, an organization’s efforts will be diffused and ineffective. A sense of a larger purpose creates the underlying impetus that will drive and sustain the process.

Developing this deep sense of purpose involves continuous conversations within the organization about what we want to achieve. It begins by creating a space in which these conversations can take place, which can continue throughout the next steps of the process. This awareness of the organization’s larger purpose, coupled with a clear understanding of current reality, provides the overall context for organizational learning.

2. Develop explicit knowledge and learning strategies.

Exploring the larger purpose of learning activities explains why an organization should develop a learning orientation; creating learning strategies focuses on how the organization will achieve its learning objectives. A company’s unique learning strategy will depend on the organization’s purpose and overall business strategy. A company that is committed to being “first to market” in order to generate high-margin sales from early adopters will have a dramatically different knowledge strategy than an organization that thrives on producing low-cost, mass-produced imitations.

Several frameworks exist for helping companies develop knowledge strategies. One such framework is the Organizational Learning Inventory (OLI), developed by Edwin Nevis, Anthony DiBella, and Janet Gould, who are affiliated with the MIT Center for Organizational Learning. The OLI was created through detailed studies of several companies in the U.S., Europe, and Asia to identify specific actions that promote organizational learning.

Knowledge: A Structural View

From a structural perspective, maximizing organizational knowledge Involves designing processes that Increase the learning rate, and finding ways to decrease knowledge decay and loss.

The cornerstone of the OLI is a set of 10 Facilitating Factors and seven Learning Orientations. Facilitating Factors are those activities or attitudes (such as environmental scanning and an experimental mindset) that promote or inhibit learning, while Learning Orientations describe stylistic differences in the ways companies approach learning (such as focusing on breakthrough thinking versus incremental improvements). By using the OLI to identify their overall learning system, companies can develop ways to manage their learning processes more explicitly.

Another framework is the Knowledge Management Assessment Tool (KMAT), created by Arthur Andersen and the American Productivity Quality Center. The KMAT was developed with input from 21 companies and has been used by more than 100 additional companies. It is used to help managers identify knowledge-management areas in their organization that require greater attention, as well as those knowledge-management practices in which the company excels (see “The KMAT Method”).

While both the KMAT and OLI can facilitate the process of designing a learning strategy, they approach it in different ways. The KMAT was originally developed as an external benchmarking tool. It allows participants to rate their performance on 24 emerging knowledge-management practices, as well as determine the importance of these practices to their organization. The OLI, on the other hand, looks at internal capabilities. It is designed to assess an organizational unit as a learning system—identifying the unique strengths and learning style of the work unit, and developing an organizational learning plan that capitalizes on those abilities. The OLI looks at the cultural side of organizational learning, and can be used very effectively as a framework for change management.

Both the OLI and the KMAT provide a structured process for clarifying an organization’s learning strategy, which will ultimately evolve beyond the confines of the tools themselves.

3. Build organizational learning and knowledge-leveraging structures.

Once the company has articulated its knowledge strategy, it can create action plans to close the gaps between the organization’s knowledge vision and its current performance. These action steps often involve building structures to promote effective knowledge creation and application.

For example, a successful oil and gas company was developing a new strategy to drive its future growth efforts, and the management team wanted to understand the systemic relationships between the different parts of the plan. One of the primary elements of the strategy was a shift in focus from growing through acquisitions to investing in exploration and production. However, they also believed that they needed to be the low-cost producer, and that they could only achieve this if they significantly reduced general and administrative expenses.

When they used causal loop diagrams to map the interrelationships among various elements of their strategy, they found that these two objectives were in conflict. In order to expand their exploration and production activities, they would have to increase the number of geologists and related staff to perform the increased work volume. But these salaries were considered part of general and administrative costs, and the pressure to become a low-cost producer would probably necessitate minimizing staff headcounts.

This conflict was resolved when the management team examined industry benchmarking data and found that they already had the lowest general and administrative costs in their peer group. Their margin was large enough that they could increase the staff headcount and still have excellent cost performance. The causal loop diagrams were then used to develop a simulation model that the management team used throughout the organization to create a greater understanding of how the shift in strategy would support the company’s long-term objectives.

Another client created a deliberate communication structure to provide a framework for validating and modifying key assumptions about the business. A cell structure was created for this 700-person organization, consisting of 50 cells of 10-20 employees each. Members of each cell came together in a series of workshops designed to allow them to share their insights about the organization and to learn from one another. The OLI was used to focus the process and engage employees in a meaningful conversation about the company’s particular learning style. Through this process, people found ways to capitalize on the firm’s learning strengths, and to leverage these capabilities throughout the organization.

A third company decided to begin its organizational learning strategy by introducing its employees to the basic concepts of organizational learning and leadership. As a symbol of its commitment to organizational learning, the company created a learning center.

The goal of the center’s staff is to expose the entire management team—approximately 2,000 people—to the disciplines of personal mastery, mental models, team learning, systems thinking, and shared vision. The concepts out-lined in The Fifth Discipline are providing the basic framework for this work. By raising the awareness of the theory and tools of organizational learning through-out the company, management hopes to leverage its core competencies into knowledge-based products and services that are valued by customers.

4. Create feedback systems to monitor progress.

Ongoing feedback on how well the organization is creating and implementing learning structures is critical to the successful implementation of any knowledge strategy. Key questions include:

• Did we get what we expected from building our infrastructure?
• Did we close the gaps identified in our strategy articulation process?
• What new elements do we need to consider in our strategy?
• How are we contributing to our inability to close the gaps identified?

Continual evaluation is crucial for keeping an organization’s learning process relevant in a changing business environment. Action strategies become less useful over time and must be continually revised and adjusted to meet emerging marker challenges.

Moving Forward

As we move into the turbulent and unpredictable Knowledge Era, we will need to engage in new and more meaningful ways in order to become effective at creating the results that we truly desire. Unraveling our old assumptions will take time, patience, and clear direction from every member of the organization. By reexamining our fundamental assumptions about learning and working together, we can begin to create deliberate and thoughtful structures for building upon the knowledge generated at all levels of the organization.

Rian M. Gorey and David R. Dobat are senior managers at Arthur Andersen Business Consulting, and are responsible for the Identification design, and delivery of Arthur Andersen’s Knowledge Services.

Editorial support for this article was provided by Colleen Lannon. Additional contributions were made by Tom Eisenbrook, Sam Israelit, and Lisa Kelley of Arthur Andersen.

Further reading:

Charles Savage, Fifth Generation Management (Boston: Butterworth-Heinemann) 1996.
Ikujro Nonaka and Hirotaka Takeuchi, The Knowledge-Creating Company (New York: Oxford University Press) 1995.

The Coming of the Knowledge-Based Business.” Stan Davis and Jim Botkin, Harvard Business Review, September/October 1994. Notes:

The discussion of tacit and explicit knowledge is based on Nonaka and Takeuchi, The Knowledge-Creating Company.

For more on the Organizational Learning Inventory tool, see “Charting a Corporate Learning Strategy” by Marilyn Darling and Gregory Hennessy (The Systems Thinker, December 1995/January 1996) and “Organizations as Learning Systems” by Janet Gould. Tony DiBella, and Ed Nevis (The Systems Thinker. October 1993).

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Following are summaries of three of the keynote talks. Recordings of some keynote and parallel sessions are also available on audio and/or videotape as part of the Systems Thinking in Action Conference Collection.

—Colleen P. Lannon

Peter Senge—Creating Transformational Knowledge

The concept of the learning organization first became prominent about six years ago. It is only now becoming clear, however, that this concept is missing something fundamental. We are now learning that what goes on in any creative process isn’t about organization, it’s about community. The absence of effective learning communities keeps our organizations from being able to learn from our most clear, demonstrated breakthroughs. Although individual learning occurs all the time in organizations, it often has little or no impact on the larger system. Learning communities provide the infrastructure and support to expand learning beyond the individual level.

The three core activities of the learning community are practice, research, and capacity building (see “Core Activities” on p. 2). Practice is anything that people do to produce an outcome or result. Practitioners can be line managers, a product development team, a sales team, or front-line manufacturing people. Research, on the other hand, is any disciplined approach to discovering and understanding, with a commitment to share what’s learned. The institution we associate most often with research is the university. Capacity building is carried out by coaches and mentors, who help people develop the capacity to do something they couldn’t do before. Consulting, or the HR function within an organization, is the institution most often associated with capacity building.

Unfortunately, in the real world these three activities rarely overlap. But if we were to get rid of the imaginary boxes that separate these areas, we would actually begin to see a system for producing theory, methods, tools, and practical know-how. This is the essence of a learning community.

Fragmentation

The fragmentation among these three areas of activity is at the heart of many problems we face today. One reason we are powerless to deal with our environmental problems or can’t help our large institutions change in fundamental ways is that the system whereby we collectively learn and alter our conditions is deeply fragmented. Walls have been built around the three areas of activity. Capacity builders such as consulting institutions, for example, undermine the knowledge-creating process because they have almost no incentive to share their insights with others. How free are they really to deal with the toughest issues of the client system? What if the person paying the bill is the problem? Can they tell him or her?

Then there are the walls between the university and other parts of the system. A typical article from an academic journal is full of jargon, referencing thousands of ideas that only a handful of people know about. These experts employ what Donald Schorr calls “technical rationality,” which separates theory from application: first you get the theory, then you apply it. This disconnection also appears in organizations, where the executives operate by technical rationality while the people on the front lines are the ones who actually have to put theory into practice.

Once we let go of technical rationality, we can ask: How does real learning occur? What happens in a community that integrates these areas? Artistic communities, for example, show that a different way of working together is possible. MIT’s Eric von Hipple, a world leader on product design, cites another example of a learning community. He notes that a lot of terrific new products are created by the customer, not by the company. In his view, companies that form different relationships with their customers can be extraordinarily more competent in product innovation—an example of how companies can form a learning community.

AutoCo: Learning Community in Action

Another example of a learning community is the AutoCo case, which has been the subject of a three-and-a-half year project at the MIT Center for Organizational Learning (OLC). It has been documented through a series of interviews that tell the story of this product development team’s journey—a story that chronicles fascinating change among individuals that occurred as they developed new capacities to work together. As one of the team leaders explained, “(Now) everybody says what’s really on their mind. All our problems are thrown on the table. It looks like chaos, but issues really get sorted out. We don’t wait until we have the answer to bring up the problem.”

In an engineering culture, this directly contradicts a basic ground rule: bring up the problem only after you have solved it. But by the end of the project, this team wasn’t operating that way anymore. They had found a new way of working together—one that proved extraordinarily successful and broke many company records. Clearly, this is a powerful story of the interaction between capacity building and practice.

However, the activities and mindset of the team were viewed as so foreign by the larger bureaucracy that the team was seen as “out of control.” After a global reorganization, the senior team members were not offered compelling positions, so they left the company within a few months of the product’s release.

Core Activities

[drop]T[/drop]he three core activities of the learning community Involve practice, research, and capacity building. By integrating these areas, we can begin to create a system for producing theory, methods, tools, and practical know-how.

There is a postscript to this story. Today, almost two years later, there are thousands of people involved in learning organization projects at AutoCo. Somehow, what seemed like an enormous setback at the time—the loss of several senior team members—did not hamper the overall process. And, per-haps even more surprising, AutoCo’s senior managers recently decided to publicly disseminate the learning history document, which tells the story of the team’s successes and failures. Why? Because it was consistent with their overall vision of making the link between research and practice. Until this disciplined approach to “discovery and understanding with a commitment to sharing” is present, the toughest issues that arise in innovative practices will often remain submerged.

Creating Learning Communities

How do we create learning communities? First, as in the AutoCo case, we must let our story out—even the parts of it that we do not like. Second, we need to be clear about our larger purpose. What are we committed to? If we are focused only on producing practical results, our efforts will never be truly successful. The knowledge-creating process must be broader than that; it must embrace all three areas. Without these multiple perspectives and commitments, brilliant innovations will not spread.

Finally, we have to find new ways of governing. At the MIT Center for Organizational Learning, we’re moving toward having a governing council that is elected by all the members of the community. This approach is radical, because in almost all nonprofit organizations the council appoints its own successors. But we believe that a democratic system, in essence, should invest more power in underlying ideas than in institutions.

In a democratic community, theory, tools, and practical knowledge are like a tree. The roots of the tree are theory, the branches are tools, and the fruit is practical knowledge. If you just eat all the fruit (take all the practical know-how, apply it, make lots of money) but don’t reinvest some of that fruit and let it reseed, you’ll have no more theory, no more trees.

At the heart of this tree is a transformational process: photosynthesis. The ideas that are drawn up through the roots (the theory) interact with the outside environment through the leaves (the tools) that create the fruit of practical knowledge. This system is transformational, and knowledge of the whole system might be called transformational knowledge.

But this transformational knowledge– of the knowledge-creating process—is not held by any one individual or group. It exists as collective knowledge held only by a community, a learning community. Thus, as we learn how to develop such communities, we may come to a much deeper appreciation of democracy, “a great word,” as Walt Whitman said, “whose … history has yet to be enacted.”

—Edited by Joy Sobeck

Robert Fritz—The Power and Beauty of Structure: Moving Organizations from Oscillation to Advancement

I studied at a conservatory of music, which is something I usually don’t mention in business settings. When people hear that you are in the arts, they immediately assume that you don’t know anything about business. But it strikes me that, in some ways, an organization is really no different from a piece of music. No organization is more structurally complex than, for example, Stravinsky’s Rite of Spring. In fact, if our organizations functioned like great orchestras, they would work very well—far better than many of them currently do. But we must include design as well as execution in our analogy—the composition is as important as the performance, if not more so.

The key to optimal performance—both in organizations and in the arts—lies in understanding and working with structure. Structure is an essential element in artistic pieces, and it can also work for or against change in organizations. If we focus on altering those fundamental structures that don’t work, we can accomplish the changes we want. However, if we don’t take structure into consideration, any change effort, no matter how valuable, may be doomed to failure.

The key to optimal performance—both in organizations and in the arts—lies in understanding and working with structure.

What Is Structure? The first characteristic of structure is that it consists of individual elements. These elements form relationships in which the combination of the elements causes the elements to behave in particular ways. The relationships, taken together, form a kind of unified entity. So structure is not simply various elements that have relationships with each other; it is the overall entity formed by these particular causal relationships.

In the arts, structure is based on tension/resolution systems. Tension is caused by a discrepancy between two things (light/dark, loud/soft, protagonist/antagonist, etc.), and it produces a desire for resolution. Artists manage tensions and resolutions quite consciously. To a filmmaker, the audience’s feelings are predictable, controllable. Alfred Hitchcock, for example, was a master at understanding how structural relationships cause particular patterns of behavior. He could make a film in which he determined exactly what the audience would feel at any moment of the film. If we, like Hitchcock, can understand structure, we can create a structure that is bound CO go in a particular direction. For an organization, this principle can help people form structures that lead to predictable and wanted changes, rather than unintended consequences and neutralization of success.

For example, a pivotal moment in the movie Casablanca occurs when Ilsa and Victor Laslow walk into Rick’s cafe. They’re sitting at a table chatting, and Rick looks over at Ilsa. Their eyes meet, and in that moment, we know we have a triangle. We have a woman who loves two men. We have a movie!

To determine if these relationships are structural, let’s test them. If we change the elements, do any of the dynamics change? Let’s say that Rick is in his cafe and Ilsa comes in alone. Does that change the dynamics? How about if Ilsa and Victor come into Rick’s cafe, but Rick has gone to Chicago, so he’s not there? Or, Rick is at the cafe and Victor comes in, but Ilsa’s not with him? It’s simply not the same—the tension that is set up between those three people dissipates the moment one of them is taken out of the scene. As soon as we change the structure of the relationships, the tendency for behavior changes.

As this scene illustrates, a structural relationship is one in which there’s a tendency for behavior to move in a particular direction. At the beginning of the film, Rick says, “I stick my neck out for nobody.” But at the end he sends the woman he loves off with another man for the well-being of humanity. Now that’s movement!

Organizational Structures

We can see similar tension/resolution systems operating within organizations. This type of system produces either oscillation or advancement (also called resolution). Obviously, we would like our companies to advance, but we often get stuck in oscillating patterns. Why? It has to do with the conflict that is set up when there are two competing tension/resolution structures operating in the same system.

To understand how conflict plays out, let’s say I’ve got a rubber band tied around my waist and anchored to a wall that represents change. This sets up a tension/resolution system—the tension in the rubber band will naturally resolve as I move toward the desired change. But suppose I’ve got another rubber band around my waist that anchors me to the opposite wall, representing stability and continuity. As I start moving toward change, the rubber band in front of me becomes slack, but the rubber band behind me becomes more tense. At a certain point, no matter how much I believe in the change, the tension produced by the desire for stability will overcome the desire for change. At this point, I will move toward continuity and away from change.

This is the type of trap that many organizations find themselves in when they are caught in competing tension/ resolution systems. In our example, there is a need for both continuity and change, but if these two tension resolution systems are in the same structure, they must compete. It isn’t that people by nature are resistant to change, but that there has to be an underlying structural motivation for us to resolve tension in the direction in which we want to go.

Moving Toward Resolution

Obviously, we want to structure our organizations to enable resolution rather than oscillation—to move from where we are to where we want to be and, having moved there, be able to move to yet another place. So how can we prevent ourselves and our companies from getting stuck in competing structures? By creating structures that can “resolve,” thus moving us toward advancement and success.

One way to sort out these conflicts is to establish hierarchies of importance in values, which can enable us to create structural tension— structures that are capable of resolution and advancement. When thinking about capitalizing a business, for example, the goals of building the company and managing short-term stock-market performance can become conflicting. If a leader in a company doesn’t sort out what’s more important—building the business or focusing on the return on the stock market—every time the employees move in a direction that will build long-term growth and sustainability, they will be pulled away from that because the company’s share price went down. In contrast, if a company understands the principle of structural tension, organizes itself around what matters to it most (in contrast with its current reality), and then takes actions that move it in that direction, it will move toward resolution rather than oscillation.

In a way, this process is like creating music. As a composer takes a theme and begins to develop it throughout a piece, all the parts coordinate and play together to create a comprehensive whole. It’s the same way in a well-designed company—by understanding and working with the concept of tension/resolution systems, individuals and departments can work together to continually evolve their capacity to design and then create their future.

—Edited by Joy Sobeck

Margaret Wheatley—Understanding Organizations as Living Systems

Most of us are pathfinders. We are trying to understand organizations as systems. But there are profound differences between cybernetic systems and living ones. The path of living systems requires that we entertain some startling and disturbing concepts—ideas that call into question our present approaches to systems study.

An organization is not just a system, it is a living system. Life is always new and surprising, constantly creating further complications and mystery as it unfolds. These characteristics of life do not sit well with our desire for control. Yet life creates such dense and entangled webs that it is impossible for us to predict its behavior or to understand it through mapping. Graphic depictions deceive us into believing that we can truly understand a system. In truth, every time we develop precision in our understanding of something—including causal loops and system maps—we lose the rest of the system. Every act of defining loses more information than it gains. The relevancy is actually in the messy, never-ending complexity of relationships.

Our desire for control leads us not just to maps, but to a reverence for techniques. We substitute the messiness of meaning for the elegance of techniques. Dialogue is an example. We took this valuable idea and turned it into a matter of technical skill, focusing on the techniques of dialogue at the expense of its essence. In this way, our desire for control can turn vital ideas into approaches that endanger and even destroy the good that we are trying to create in organizations.

Organizational Identity

A system is alive only if it can give birth to itself. This means that all organizations create themselves, spin themselves into existence. They become more dense and complex as they generate endless webs of connections. Organizations create themselves around questions of identity—i.e., what is the organization? Any changes that we hope to accomplish in the workplace must therefore occur at this deep level of identity.

To create learning organizations, we must understand the underlying agreements we have made about how we will be together. Instead of focusing on training programs or structures related to organizational learning, we first need to explore the agreements people have used to organize themselves, since it is within such agreements that our organizations take form. What is the cost, the price, of belonging to this system?

Failure to address these kinds of beliefs leaves us tinkering at the level of structure and form rather than at the organization’s core. An organization cannot be changed at the level of what we see, but only at the level where its identity is forming itself. Therefore, we cannot expect a learning structure to work unless the organization’s agreement of belonging is about learning. We cannot train people to be life-long learners if the agreements of belonging dictate keeping their mouths shut and “never making the boss look bad.”

The Autonomy of Living Systems

A living system is also autonomous—free to choose what it wants to recognize, regardless of what we explain to it or show it. Only if the system finds what we have to say interesting and meaningful will it open itself to new information. Thus we can never direct a living system; we can only disturb it. To truly understand an organization as a living system, we need to determine what the system finds meaningful. One way to do this is to think of our “interventions” as indications of what the system notices. This method can reveal a lot about what is going on inside the system—what motivates and inspires it, and how information moves through it. If we try to change an organization and it pushes back by ignoring us or moving in another direction, we need to see these responses as a window onto how the system works, rather than as a personal failure.

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It is the farmers’ fault. . . . They have neglected to plant new oaks. It takes ten years for truffles to appear under them in edible size. Truffles have dwindled. The price, in 30 years, has tripled. . . . Now the French taxpayer is paying for research into just how the truffle is born.”

–Ruffling France’s Truffles,’ The Economist, May 4, 1996

France’s Truffle Crisis

Why should anyone other than gourmets care about the fate of the black truffle? French government officials are concerned because the declining truffle harvest is opening the way for foreign competition. Though France dismisses the truffles grown by other countries as being of “dreadfully inferior quality,” some restaurateurs say that you cannot tell the difference—which could mean a big difference in terms of France’s hold on the worldwide truffle market.

But systems thinkers can also find something of interest in France’s “truffle crisis.” The issue—complete with long time delays and interesting behavior over time—lends itself well to a systemic perspective, and can provide a good opportunity for practicing drawing casual theories of a problem. Since most news articles are event oriented, drawing causal diagrams of news stories provides excellent practice in moving from events to patterns over time to systemic structure.

Creating a Causal Theory

The first step in creating a causal theory is to identify the problem and map key behaviors over time. In this case, the Economist article described the “problem” as a declining truffle harvest and a tripling of the price of truffles over the last 30 years (see “Declining Truffle Harvest”).

We know from the article that the truffle harvest is dependent on the truffle population, which in turn is based on the number of appropriately aged oak trees under which the truffles grow. The dynamics suggested by the Economist is that farmers are planting fewer oak trees, leading to less nurturing oak trees, a fall in the truffle population, lower truffle harvest, and finally, climbing prices (see “Simple Explanation”).

We can expand on this rather linear worldview by looking for plausible feedback loops. For example, a simple balancing loop governs the relationship between population and harvest—increasing population allows for a greater harvest, but each year the harvest reduces the live population (B1 in “Closing Feedback Loops” on p. 8). Based on general commodities theory, as the price goes up, we would expect people to put more effort into gathering truffles, which would lead to an increase in the truffle harvest and an eventual decrease in the price (B2). Notice, however, that the two balancing loops could act as a reinforcing loop—the higher price leading to a greater harvest, potentially lowering the population beyond its ability to regenerate, which would lower the harvest and raise the price even further in the future.

Exploring Solutions

To close the gap between the actual truffle harvest and the desired truffle harvest, the French government is investing in research to find ways to increase the truffle harvest without relying on oak trees. The implicit thinking behind this strategy is that putting more money into research will (after a time delay) result in effective alternatives for increasing the truffle harvest, and thus reduce the price to reasonable levels (B3 in “Looking for ‘Solutions’ “).

Technological Solutions

Investing in research can be described as a technological solution—an attempt to solve a problem by developing alternatives that bypass the cause of the problem. This is often faster and cheaper than finding a more fundamental structural solution. Without a clear understanding of the root causes, however, this approach risks creating unintended consequences that could set off a cascade of additional problems.

While the Economist article focused on potential technological solutions that would bypass the need for oak trees, other solutions could also be found by examining why the oak tree population itself is declining, and what can be done about it. Traditionally, oak trees in France have been planted by farmers in the course of their normal farm maintenance. Since most farmers lived on their farm for life, the 10-year delay between planting and truffle harvest was negligible. Over the last century, however, more farmers have been seeking employment in the city, as the attractiveness of city jobs relative to working on the farm has increased.

The question this raises for the French government is whether the oak tree population can be increased without relying on the farmers, or whether the farmers can be encouraged to take up farming (and oak planting) again. Because of the 10-year time delay between planting an oak tree and harvesting the truffles, it would be difficult to rely on short-term market forces to encourage the planting of trees. Through public programs, the French government could (and actually is) encouraging children to plant trees—but this is not likely to be a sustainable long-term solution, because it will likely stop as soon as the government push ends.

To ensure the long-term oak tree supply, the French government might therefore need to examine the forces that have made it so attractive to leave the farm for the city, and potentially create incentives to encourage more domestic farming (B4). Since the dynamics around this migration is complex and the delays long, it is not hard to imagine why the government is hoping for a technological solution rather than trying to influence a major social dynamic.

Using Articles for Practice

Using the truffle example, we have tried to illustrate how one might practice developing causal theories using stories found in magazine or newspaper articles. In summary, the process would be to:

1. Look for articles that talk about a problem over time. Avoid specific cases (Joe lost his job today) and hunt for trends (more and more people are losing their jobs).

2. Draw out the behavior over time. This provides a reference point of behavior that the causal theory should be able to explain.

3. Map out the problem as described in the article, first limiting yourself to the data directly mentioned. Then add other variables or feedback loops that you would hypothesize are driving forces behind the problem.

4. Map out any proposed solutions, and then look for unintended consequences or other alternatives.

Peter Senge has said, “We only learn what we want to learn.” By using real-life news items as the starting point for developing theories, the practice of systems thinking becomes more than an academic exercise. It can serve as a true exploration of issues that are important to us.

Linda Booth Sweeney is an educator consultant, and associate of the MR Center for Organizational Learning.

Don Seville is an associate with GKA Incorporated and is affiliated with Sustainable Solutions.

Editorial support for this article was provided by Colleen Lannon.

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But if we are not going to define “leanness” in financial terms, how should we define it? I believe we need to expand our thinking to include the application of employee competency to achieve leanness. I strongly believe that people are a company’s only long-term competitive advantage. As such, we should view them as assets and resources to be developed, rather than as line-item expenses to be controlled. By taking this approach, we might discover value-added activities that would enable us to keep employees on the payroll even during tight times.

Business Process

Within Harley-Davidson’s motorcycle operations, we are trying to establish a business process that will accommodate such thinking. At the top of our business process diagram is an umbrella that sets the context for our work (see “Business Process: Setting the Context”). Under this umbrella, we identify the values, issues, and stakeholders that are the basis for our vision statement, which is to be “a leader of continuous improvement in the quality of mutually beneficial relationships with all of our stakeholders.” We measure our progress in achieving that vision against the following statement: “The key to our success is to balance stakeholder interests through empowered employees focused on value-added activities.”

These statements could be viewed as esoteric rhetoric. But we hope they will operate instead as a guiding light toward effective leanness. If we adopt this view, then we can start to utilize the workforce as a resource, creating an environment in which all employees seek to apply their competencies to value-added activities that can result in employment security.

In this context, “employment security” is dramatically different from conventionally stated job security. In employment security, the employee’s focus is on ensuring that the company survives, while job security centers on ensuring that he or she continues to do the same thing day in and day out. If all employees focus on creating employment security by providing value-added activities (in conjunction with others with complementary competencies), the result will be a lean organization. In addition, their work will generate additional resources to develop the company further, enabling the company to become more externally and future oriented.

The Role of Financial Measurement

Defining leanness in terms of value-added activities does not eliminate the need for financial measures. In order for the company to survive over the long term, it must be financially viable, and all of the employees must understand this. A primary measure of employee effectiveness is the company’s financial results. Those results come only when employees deliver value-added activities that are recognized as such by the customers. Therefore, if customers are not purchasing our products, it is up to all employees to seek ways to apply their competencies toward creating new products or services that will ensure the long-term financial viability of the company. If this strategy is not recognized and adopted by all employees, it is likely that leanness will have to be associated with meanness in the form of down-sizing efforts that have cost reduction as their only objective.

In order to survive, Harley-Davidson had to experience such a downsizing. In 1982, we reduced our workforce by 40%. It was not an easy decision, but we did it as humanely as possible—far more humanely than our bankers thought necessary. However, this approach put us in good stead with the people inside the company, because they knew that we were in crisis and they put forth the extra effort to help the company recover.

While that result sounds great, I can’t help but speculate that if we had consciously worked on having all employees focus on value-added activities, the solutions to our problems could have been identified much earlier. By redefining our approach to leanness, we are hopefully putting ourselves on the right path to prevent a recurrence of that difficult experience.

Rich Teerlink is president and chief executive officer of Harley-Davidson. Inc.

Reprinted with permission from Collective Intelligence (Vol. 1 . No.1) May 1995. ©MIT Center for Organizational Learning. All rights reserved.

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

Demand Trend

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

Structure-Behavior Pairs

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

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

Creating a Causal Theory

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

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

Capturing Historical Trends

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

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

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

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

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

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

Interrelated Patterns of Behavior

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

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

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These activities perhaps contain more optimism than realism. Learning is, at its heart, a complex and difficult process—a source of joy when it works, but a source of pain and tension when it does not. Learning forces us to fundamentally rethink the way we view the world—a process that is difficult in part because our cultural assumptions predispose us to take certain things for granted, rather than to re-examine them continually. Since learning and culture are so closely interrelated, it is incumbent upon us to understand more about the interaction of the two, and to identify, if possible, what elements of a culture might truly facilitate learning to learn.

Two Kinds of Learning

To understand the important role that organizational culture plays in learning, we need to first make a distinction between two types of learning: “adaptive” and “generative.” (The term “generative learning” comes from Peter Senge. However, the same process has been labeled by Chris Argyris and Donald Schön as “double-loop learning,” while Don Michael, Gregory Bateson, and others have identified it as “learning how to learn.”)

Adaptive learning is usually a fairly straightforward process. We identify a problem or a gap between where we are and where we want to be, and we set about to solve the problem and close the gap. Generative learning, on the other hand, comes into play when we discover that the identification of the problem or gap itself is contingent on learning new ways of perceiving and thinking about our problems (i.e., rethinking cultural assumptions and norms).

For example, from an adaptive point of view, we may decide that we have to replace corporate hierarchies with flat networks in order to reduce costs and increase coordination. From a generative point of view, however, we might instead begin by examining our mental models and considering how hierarchies and networks might be integrated into a more effective corporate design. From “either this or that” thinking, we might have to develop the capacity to think about “this and that.”

Two Kinds of Anxiety

The very process of identifying problems, seeing new possibilities, and changing the routines by which we adapt or cope requires rethinking and redesign, because we have to unlearn some things before new things can be learned. Thus, generative learning, by its very nature, asks us to question our mental models, our personal ways of thinking and acting, and our relationships with each other. This deep level of change can produce two kinds of anxiety.

The first is the fear of something new. Instability or unpredictability is uncomfortable and arouses anxiety— what I have called “Change Anxiety,” or the fear of changing—based on a fear of the unknown. Adaptive learning, whether it be in individuals, groups, or organizations, tends toward stability. We seek to institutionalize those things that work. Indeed, it is the stable routines and habits of thought and perception that we call “culture.” We seek novelty only when most of our surroundings are stable and under control.

However, learning how to learn may require deliberately seeking out unstable, less predictable, and possibly less meaningful situations. It may also require perpetual learning, which opens up the possibility of being continually subject to Change Anxiety. This is a situation most of us would prefer to avoid.

But if, as many people anticipate, the economic, political, technological, and sociocultural global environment will itself become more turbulent and unpredictable, then new problems will constantly emerge and past solutions will constantly become inadequate. This brings us to a second type of anxiety, which I call “Survival Anxiety”—the uncomfortable realization that in order to survive and thrive, we must change.

In order for learning to occur, somehow we must reach a psychological point where the fear of not learning (Survival Anxiety) is greater than the fear associated with entering the unknown and unpredictable (Change Anxiety).

As teachers, coaches, and managers, how then do we make sure that Survival Anxiety is greater than Change Anxiety? One method is to increase Survival Anxiety until the fear of not changing is so great that it overwhelms the fear of changing. We can do this by threatening the learner in various ways, or by providing strong incentives for learning. For example, if employees feel that they will not get promoted in the organization if they don’t use electronic mail or conduct their meetings with the latest groupware, it would seem logical that they would want to keep up with new technology.

However, humans don’t always do what logic dictates. If an employee’s Change Anxiety becomes too high, he or she may instead become defensive, misperceive the situation, deny reality, or rationalize his or her current behavior. Change agents often come up against this type of resistance to organizational change and retreat to the rationalization that “it’s simply human to resist change.”

Perhaps a more effective way to initiate change is to reduce Change Anxiety so that it is less than Survival Anxiety. We can do this by concentrating on making the learner feel more comfortable about the learning process, about trying new things, and about entering the perpetual unknown (see “Reducing Change Anxiety”).

Addressing the anxiety caused by learning and change is certainly a good way to begin the learning process, at least at the individual and small group levels. But how can we apply the generative learning process across various organizational boundaries and sustain the learning process over longer periods of time? This requires the creation of an organizational culture that supports perpetual learning at the individual, group, and organizational levels.

A Learning Culture

What would such a culture look like? Learning cultures share at least even basic elements:

1. A concern for people, which takes the form of an equal concern for all of the company’s stakeholders—customers, employees, suppliers, the community, and stockholders. No one group dominates management’s thinking because it is recognized that any one group can slow down or destroy the organization.
2. A belief that people can and will learn. It takes a certain amount of idealism about human nature to create a learning culture.
3. A shared belief that people have the capacity to change their environment, and that they ultimately make their own fate. If we believe that the world around us cannot be changed, what is the point of learning to learn?
4. Some amount of slack time available for generative learning, and enough diversity in the people, groups, and subcultures to provide creative alternatives. “Lean and mean” is not a good prescription for organizational learning.
5. A shared commitment to open and extensive communication. This does not mean that all channels in a fully connected network must be used all the time, but it does mean that such channels must be available and the organization must have spent time developing a common vocabulary so that communication can occur.
6. A shared commitment to learning to think systemically in terms of multiple forces, events being over-determined, short- and long-term consequences, feedback loops, and other systemic phenomena. Linear cause-and-effect thinking will prevent accurate diagnosis and, therefore, undermine learning.
7. Interdependent coordination and cooperation. As interdependence increases, the need for teamwork increases. Therefore, organizations must share a belief that teams can and will be effective, and that individualistic competition is not the answer to all questions.

Inhibitors to Learning

Culture is about shared mental models—shared ways of perceiving the world, sorting out that information, reacting to it, and ultimately understanding it. Therefore, in order to understand what prevents us from creating learning cultures, we need to explore the shared assumptions that act as inhibitors to learning. If we look at western (particularly U.S.) organizational and managerial cultures, there are several shared assumptions or myths that prevent organizations from developing the kind of learning culture I have described.

Generative learning, by its very nature, asks us to question our mental models, our personal ways of thinking and acting, and our relationships with each other.

Human history has left us with a legacy of patriarchy and hierarchy, and a myth of the “superiority” of our leaders based on the view of the leader as warrior and protector. This has created almost a state of “arrested development” in our organizations, in the sense that we have very limited models of how humans can and should relate to each other in organizational settings. The traditional hierarchical model is virtually the only one we have.

One consequence of this rigid model is that managers start with a self-image of needing to be completely in control—decisive, certain, and dominant. Neither the leader nor the follower wants the leader to be uncertain, to admit to not knowing or not being in control, or to embrace error rather than to defensively deny it. Of course, in reality leaders know that they do not have all of the answers, but few are willing to admit it. And since subordinates demand a public sense of certainty from their leaders, they reinforce this facade. Yet if organizational learning is to occur, leaders themselves must become learners, and in that process, begin to acknowledge their own vulnerability and uncertainty.

In the U.S., we have the additional cultural myth of “rugged individualism” that makes the lone problem-solver the hero. The interdependent, cooperative team player is not typically viewed as a “hero.” In fact, competition between organizational members is viewed as natural and desirable, as a way to identify talent (“the cream will rise to the top”). After all, if teamwork were more natural, would it be such a popular topic in organization development literature? For the most part, teamwork is viewed as a practical necessity, not an intrinsically desirable condition.

Another myth that has developed among managerial circles might be called the “divine rights of managers.” Management is believed to have certain prerogatives and obligations that are intrinsic and are, in a sense, the reward for having worked oneself up into the management ranks. The relatively young and egalitarian social structure of the U.S. exacerbates this problem by emphasizing achievement over formal status. We have no clear class structure that provides people with a clear position in society. Hence, they often rely instead on earned position, title, and visible status symbols (cars, homes, etc.) as a way of displaying rank. The competition based work hierarchy then ultimately becomes the main source of security and status, and higher level managers are expected to act in a more decisive and controlling manner to express that status.

Another barrier to learning is the fact that work roles and tasks are very compartmentalized in the U.S., and are separated from family and self-development concerns. These roles are expected to be treated in an emotionally neutral and objective manner, which makes it very hard to examine the pros and cons of organizational practices that put more emphasis on relationships and feelings. Even talking about anxiety in the workplace is often taboo. This creates an inherent dilemma: how can we effectively address learning-produced anxiety if we cannot discuss it?

Within the work context we have the further problem that task issues are always given primacy over relationship issues. Everyone pays lip service to the notion that people and relationships are important, but our society’s basic assumptions are that the real work of managers lies with quantitative data, money, and bottom lines. Within this framework, people can seem like nothing more than another resource to be “deployed” or “controlled.” If we have any doubts about the reality of this viewpoint, consider how many performance appraisal systems tend to reduce performance to numbers rather than deal with qualitative descriptions of performance and leadership potential.

In reality, leaders know that they do not have all of the answers, but few are willing to admit it. And since subordinates demand a public sense of certainty from their leaders, they reinforce this facade.

The bias toward viewing organizations in quantitative terms shows up most clearly in graduate schools of business, where the popularity of quantitative courses in finance, marketing, and production is much greater than qualitative courses in leadership, group dynamics, or communication. Associated with this myth that management is only about “hard” things is the focus on short time horizons. Driven by our current reporting systems, managers learn early on to pay closer attention to the short-term trends in their financial numbers than to the long-term morale or development of their employees. Creating an environment for learning is a long-range task, yet few managers feel that they have the luxury to plan for people and learning processes.

The combination of this task focus, preference for hard numbers, and short-run orientation all conspire to make systems thinking difficult. Systems are ultimately messy, and they cannot really be understood without taking a longer range point of view, as system dynamics has convincingly demonstrated.

Articulating the Challenge

Creating a learning culture from this set of assumptions is very difficult. It is one thing to specify what it will take for us to become effective learners; it is quite another thing to get there, given these strong cultural inhibitors. But the first and most necessary step is always a frank appraisal of reality. If we understand our cultural biases, we can either set out to overcome them slowly, or, better yet, figure out how to harness them for more effective learning.

But we first must acknowledge the difficulty of our task. Culture is about shared tacit ways of being. Because it operates outside of our awareness, we are often quite ignorant of the degree to which our culture influences us. Therefore, we cannot expect that we can just set about to create whatever culture we want, as if it were the same as creating espoused principles and values. Only shared successes in using a new way of thinking, perceiving, or valuing will create this new approach, and that takes time.

I believe one mechanism by which cultures change is to re-prioritize some of the shared assumptions that conflict with others. For example, as we discover that competition and rugged individualism fail in solving important problems, we will experiment more with other forms of organizing and coordinating. Initially we may do it only because it is pragmatically necessary. But gradually we will discover the power of relationships and teams to complete tasks more effectively and to improve learning. This “proactive pragmatism” will eventually force us to create a learning culture and, in that process, produce new and quite different 21st century organizations.

Edgar H. Schein is Sloan Fellows professor of management emeritus and a senior lecturer at the Sloan School of Management. He chairs the board of the MIT Center for Organizational Learning and is the author of numerous books on organization development.

Editorial support for this article was provided by Colleen Lannon.

The post Can Learning Cultures Evolve? appeared first on The Systems Thinker.

That’s why the push toward state-supported school choice is so insidious. The “choice” it gives every parent—do what’s best for society in the long term or for my kid right now—can only be made one way. My kid right now.

School choice promoters don’t think they’re creating that dilemma. They believe that giving every child a voucher worth a fixed amount to be used at any school would force bad schools to shape up. I think it would drain away from bad schools most of the resources necessary for shaping up. It would swamp good schools with applicants, so they could pick out the best students. It would subsidize rich families who already send their kids to expensive private schools, and it would encourage intolerance as parents pick schools that accept only “Their Kind.” The poorest families would be left to bestow minimal-value vouchers on the poorest schools. For them, there would be no choice.

This kind of school system would set up a vicious circle that systems thinkers call “Success to t because the Successful.”

Such a system inefficiencies and injustices not because people are bad, but because people are smart enough to see that altruism is fatal in this game.

If you’ve played Monopoly'”, you’ve experienced “Success to is fatal in the Successful.” Everyone starts out equal. By chance, some players land on and buy up valuable properties for which they can charge rent. They use the rent money to build hotels, with which they can extract even more rent. The game is supposed to end when one person has bankrupted everyone else, but most adults quit long before that point has , been reached. The game gets too predictable and boring when the “hotels to the hotel-owners” stage kicks in.

Once our neighborhood offered a $100 reward for the most impressive display of Christmas lights. The winning family the first year spent the prize money on more lights. After they had won three years in a row, the contest was suspended.

“Success to the Successful” is no fun.

To him that hath shall it be given: lower electric rates for big users than for small ones; lower postage rates for bulk mailers than ordinary folks; and lower taxes on capital gains than on earned income. Incinerators, dumps, and polluting factories located disproportionately in low-income neighborhoods. The poorest kids get the worst healthcare and the worst schools.

“Success to the Successful” is not fair, though the successful work had to believe that they deserve the favors the system accords them.

Bill Gates’s Windows software dominates the superior Macintosh system because Microsoft and IBM have more marketing muscle than Apple. Big companies can afford more advertising, investment, researchers, accountants, lawyers. They can lean on distributors, suppliers, workers, communities, politicians. The politicians create a system in which no one can run for office without being rich or courting the rich.

“Success to the Successful” can destroy both market competition and democracy.

The problem is the structure of the system, not the morals of the people in it. “Success to the Successful” rewards the winner of a competition with the means to win again. It is especially perverse if it also penalizes losers. Such a system produces inefficiencies and injustices not because people are bad, but because people are smart enough to see that altruism is fatal in this game. It only takes parents who want the best for their children to ensure that other people’s children will be Monopoly losers for life, always paying rent, never collecting it, never seeing the board cleared or the opportunities opened, until things get so predictable, hopeless and degrading that they either drop out of the game or kick over the board.

To avoid such explosions and to keep games interesting, the world of sports has hundreds of devices for interrupting the “Success to the Successful” cycle and leveling the playing field: handicaps for weaker players; switching sides so the wind doesn’t always blow against you and the sun isn’t always in your eyes; loser chooses; starting new games with the score even.

Societies also have ways to break the cycle. Private property and democracy were invented to escape the terrible “Success to the Successful” traps of feudalism and monarchy. In modern times, we have come up with such leveling devices as progressive income taxes, inheritance taxes, anti-trust laws, securities trading laws, social safety nets, competence testing for jobs, affirmative action, and, the best invention of the lot, high-quality universal public education.

Our public school system has been one of the center posts of democracy and fairness in America. It was never as equitable as it should have been, but at least we honored it in concept and worked at it in practice. We had a shared commitment to each other’s children.

Now something has snapped. “Success to the Successful” is hailed as high wisdom. We refuse to pay for the education of other people’s children. Parents must choose between the best education for their own children right now and a future in which all children will grow up well educated.

That’s a choice no one should have to make.

Donella Meadows is a system dynamicist and an adjunct professor of environmental studies at Dartmouth College. She Is a MacArthur Fellow, and co-author of two best-selling books (the Limits to Growth and Beyond the Limits). She writes a weekly column for the Plainfield, NH Valley News.