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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According to project manager Janet Gould, the research will address three basic questions:

• What does it mean to be competent in systems thinking?
• What skills must people acquire in order to become competent in systems thinking?
• What additional skills are necessary to become an active facilitator of systems thinking within an organization?

Much of the initial work in the project has been devoted to defining what should be included in a list of systems thinking competencies. The diagram at the right shows a proposed framework for addressing the issue. Deciding what specific skills fall under each matrix cell is a crucial aspect of the research and will re-main fluid for some time. Even the current definition of the axes is a tentative selection.

Although dialog about the course content continues, a few formats for delivery of the course have been suggested. One possibility is to conduct an intensive, five-day course which would immerse the participants in the principles of systems thinking. Such an experience, explains Tom Grimes of Hanover Insurance Company, a sponsoring company, might help participants retain the lessons from the course. “We have such a capacity to think linearly in our lives,” he explains, “that it’s going to take a major learning experience to turn it around.”

Another possible format would be to have three days of instruction followed by “refresher courses” held every few months. In between, participants would keep logs describing how systems thinking is affecting their work. The iterative process could continue for a year or more, notes Gould.

Regardless of its final format, an essential element of the course will be team learning. Groups of people from the same division of a company will be encouraged to go through the course together and to continue using the skills they have learned back in the workplace. Explains Gould, “We think the learning might last longer with this method, because the participants would be working with a group of people with whom they can continue talking about systems thinking, rather than being isolated.”

Initially, the content of the course will be modeled after two courses already available—the MIT Summer Session, a week-long introduction to systems thinking, and a five-day course designed by consultant David Kreutzer for his clients. Both courses teach participants simple feedback loops and how to build simple computer models of complex systems. A limitation of both course designs, however, is that the skills covered do not fully address all the cells of the research matrix.

Grimes hopes to address four main objectives with the course:

1) To raise awareness of the limits and some of the potential dangers of linear thinking.

2) To use systems thinking as a way of identifying the assumptions we make underlying our decisions.

3) To develop a common language for talking about systemic issues.

4) To critique and expand our view of reality without getting into issues of personality or emotionality.

A prototype course should be ready in a few months. At that point, the researchers will begin to implement it in four or five participating companies. But Gould emphasizes that the course design is only part of the research project. “We also need to know from a research standpoint whether this course is going to do anything for a company. Are people actually learning what we’re expecting?”

…an essential element of the course will be team learning.

In order to evaluate the course, participants will answer questionnaires that test how well they have assimilated key concepts. Not only will their answers help the researchers gauge the success of the course, but Gould notes that the participants will also be able to track their own progress.

Internal facilitators will also play a crucial role in implementing systems thinking in a company. “Essentially you need to build up internal expertise in systems thinking,” explains Dan Simpson, Director of Planning at The Clorox Company. “Without that internal expertise, it’s unlikely any new thinking mentality will infiltrate the organization very well.” Simpson adds that a separate, more intensive course may be necessary to train the facilitators who will continue the systems thinking learning process inside their companies. “These people will continually make the translation from what is an academic field of study into operational action inside an organization.”

Despite the questions on how well the course might implement systems thinking in companies, there is no doubt among the course planners that systems thinking is a valuable tool for organizations. As Simpson describes it, “Systems thinking helps practicing managers begin to think through the ‘ripple effects’ of their decisions. It’s often not clear when you make a decision as a practicing manager in one area that there are interactions with other areas, intended or not. Systems thinking offers a way to control—or at least consciously manage—the ripple effect, as opposed to just letting things happen.”

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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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When errors such as these surface, blaming seems to be a natural reflex in many organizations. Even those individuals who wish to learn from mistakes fall into naming culprits. Once we figure out who’s at fault, we then try to find out what is wrong with the supposed offenders. Only when we discover what is wrong with them do we feel we have grasped the problem. Clearly they are the problem, and changing or getting rid of them (or simply being angry at them) is the solution.

There’s a problem with this common scenario, however: Where there is blame, there is no learning. Where there is blame, open minds close, inquiry tends to cease, and the desire to understand the whole system diminishes. When people work in an atmosphere of blame, they naturally cover up their errors and hide their real concerns. And when energy goes into finger pointing, scapegoating, and denying responsibility, productivity suffers because the organization lacks information about the real state of affairs. It’s impossible to make good decisions with poor information.

In fact, blame costs money. When the vice president of marketing and the vice president of R&D are blaming each other for quality problems in product development, they can’t focus on working together to bring the best products to market. Their finger pointing results in lost sales potential.

Blame rarely enhances our understanding of our situation and often hampers effective problem solving. So how do we avoid the tendency to blame and create organizational environments where we turn less frequently to blame? Clarifying accountability is one option. This process of assigning responsibilities for a situation in advance can help create a culture of real learning.

Accountability comes from clear contracting, ongoing conversations, and an organizational commitment to support accountability rather than blame. The contracting focuses on tasks to be accomplished, roles to be taken, processes to be used, standards sought, and expected results. Periodic conversations over time review both explicit and tacit contracts in order to verify shared understanding. This communication becomes most useful when people are willing and able to discuss their common difficulties within a larger setting that values accountability.

The Differences Between Accountability and Blame

The dictionary helps clarify the differences between accountability and blame. To be accountable is “to be counted on or reckoned on.” To blame is “to find fault with, to censure, revile, reproach.” Accountability emphasizes keeping agreements and performing jobs in a respectful atmosphere; blaming is an emotional process that discredits the blamed.

A focus on accountability recognizes that everyone may make mistakes or fall short of commitments. Becoming aware of our own errors or shortfalls and viewing them as opportunities for learning and growth enable us to be more successful in the future. Accountability therefore creates conditions for ongoing, constructive conversations in which our awareness of current reality is sharpened and in which we work to seek root causes, understand the system better, and identify new actions and agreements. The qualities of accountability are respect, trust, inquiry, moderation, curiosity, and mutuality.

Blaming, on the other hand, is more than just a process of allocating fault. It is often a process of shaming others and searching for something wrong with them. Blaming provides an early and artificial solution to a complex problem. It provides a simplistic view of a complex reality: I know what the problem is, and you’re it. Blame thus makes inquiry difficult and reduces the chances of getting to the real root of a problem. Blame also generates fear and destroys trust. When we blame, we often believe that other people have bad intentions or lack ability. We tend to excuse our own actions, however, because we know firsthand the challenges we face. The qualities of blame are judgment, anger, fear, punishment, and self-righteousness.

The Organizational Consequences of Blame

Blame Slows Information Flow and Reduces Innovation. People sometimes use blame as a strategy to get others to take ownership of problems. But this approach often backfires because people begin to equate acknowledging mistakes and surfacing bad news with punishment. When this happens, two reinforcing sets of behaviors may emerge: one by managers who are ostensibly seeking information and then punishing those who bring bad news, and the other by groups of employees who hide information and try either to protect each other or to blame each other. People who feel compelled to protect themselves can’t admit mistakes-and therefore can’t learn from them. Under these conditions, individuals spend time denying problems rather than solving them, and

The Reinforcing Cycles of Blame

Blame causes fear, which increases cover-ups and reduces the flow of information. The lack of information hinders problem solving, creating more errors (R1). Fear also stifles risk taking and discourages innovation (R2).

As shown in “The Reinforcing Cycles of Blame,” blaming leads to fear, which increases cover-ups and reduces the flow of information by stopping productive conversation. The lack of timely and accurate information about an organization’s current reality hinders problem solving, leading to more errors and more blame (R1).

Blaming and the fear it generates also discourage innovation and creative solutions. Frightened people don’t take risks, which are essential for innovation. Lack of innovation, in turn, leads to an inability to solve problems effectively and an increase in errors (R2).

Blame “Shifts the Burden.” In a “Shifting the Burden” situation, a problem has multiple solutions. People often grab onto the most obvious, short-term fix rather than search for the fundamental source of the problem. The lack of a permanent, long-term solution reinforces the need for additional quick fixes. Blame is a fix that actually diverts the blamers’ attention away from long-term interpersonal or structural solutions to problems (see B1 in “The Addiction to Blame” on p. 3).  Although blame provides some immediate relief and a sense of having solved a problem (“It’s their fault”), it also erodes communication (R3) and shifts the focus even further from accountability (B2), the more fundamental solution.

Blaming can also be addictive, because it makes us feel powerful and keeps us from having to examine our own role in a situation. For example, Jim, a brewery manager, got word that things were slowing down on line 10, a new canning line. He left his office and headed to the plant floor. “Grady, you’ve got to get this line going. Get with it,” he told his line foreman. Grady replied, “Jim, you know those guys on the last shift always screw things up.”

This is a familiar conversation to both men. Each walks away thinking something is wrong with the other. Jim thinks, “That Grady, I give him responsibility and he just can’t get it together.” Grady thinks, “Why is he always on my case? Can’t he see this is a tough issue? He’s so simplistic and short-sighted.”

In this scenario, Jim can walk away feeling relieved because he knows what the problem is-Grady is a lousy supervisor and may need to be replaced. Grady, on the other hand, can blame Jim for being a shortsighted, run-the-plant-by-the-numbers manager. Both get some initial relief from blaming each other, but neither solves the ongoing problem.

Moving from Blame to Accountability

How, then, do we move from blame to accountability? Even within carefully designed systems, people may fail at their work. And even with a knowledge of system dynamics, we still often look for an individual’s failure as a way to explain a problem. One leverage point is to understand the organizational dynamics of blame as described above. There is also leverage in changing how we think about and conduct ourselves at work.

There are three levels of specific behavioral change in moving from blame to accountability-the individual level, the interpersonal level, and the group or organizational level. First, individuals must be willing to change their own thinking and feelings about blame. Second, people need to become skillful at making contracts with one another and holding each other accountable for results. Third, groups need to promote responsible and constructive conversations by developing norms for direct conflict resolution between individuals. These behavioral changes-and the use of systems thinking to focus on the structures involved and not the personalities-can help create a constructive organizational culture.

Individual Level

Below is a list of ways to start breaking the mental models we hold about blame. When you find yourself beginning to blame someone else for a chronic problem, refer to this list and to the sidebar “Distinctions Between Blame and Accountability” (see p. 4).

1. Remember that others are acting rationally from their own perspective. Given what they know, the pressures they are under, and the organizational structures that are influencing them, they are doing the best they can. Give others the benefit of the doubt.

2. Realize that you probably have a role in the situation.Your behavior may be influencing this person’s behavior and may be producing some unintended effects. Keep in mind that you will tend to justify your own actions and point of view and discount the other person’s perspective.

3. Remind yourself that judgment and criticism make it very difficult to see clearly. Judgments are mental models that limit the ability to take in new data. They tend to increase the likelihood of anger and make it difficult to learn. The following questions may help stretch your thinking and ease angry feelings. Ask yourself:

• What information am I missing that would help me understand this person’s behavior?
• How might this behavior make sense?
• What pressures is he or she under?
• What systems or structures might be influencing this behavior?

4. Use a systems thinking perspective to explore the pressures on the players involved. Notice that there are some larger forces at work that are probably having an impact on both of you. For example, when organizational goals, strategies, and values aren’t clear, groups will sometimes work toward different objectives. A group that values customer service over cost will conflict with a group that is trying to lower expenditures. Identify some key variables and their interrelationships, and ask, Is this situation an example of a vicious cycle, “Shifting the Burden,” or “Accidental Adversaries”?

5. Be willing to be held accountable. This means that, when an issue comes up, you are willing to consider whether you have lived up to your end of an agreement or expectation. Ask yourself:

• Did I have a role in this situation?
• Did I take some actions that seemed right at the time, but that had unintended consequences?

6.Work constructively with your anger. Sustained anger may point to personal issues that have been triggered by the current situation. Broken agreements, mistakes, and blame all have difficult associations for most people. However, in a learning environment, constructive resolution of conflict can also lead to significant personal growth. The guiding questions here are:

• What am I learning about myself in this situation?
• What does this remind me of?
• What new behaviors or thoughts does this situation call for that may be a stretch for me?

Interpersonal Level

Initial Contracting. At the beginning of any working relationship, it’s vital to come to some basic agreements defining the nature and scope of the work, specific and yet-to-be-defined tasks, deadlines and related outcomes, processes or methods to be used, interim checkpoints and expectations at those checkpoints, standards, and roles.

It’s also helpful to discuss what to do in the event of a misunderstanding, a lapse in communication, or a failure to keep an agreement. Imagine possible breakdowns and design a process for handling them. If breakdowns do occur, be prepared to remind others of the plan you had prepared.

When lapses do take place, they need to be brought to the collective attention as soon as possible. Misunderstandings and broken agreements often promote anger, frustration, and blame. Allowing unaddressed misunderstandings to fester can hamper productive conversations. By contrast, raising issues early can minimize escalation of problems.

Accountability Conversations. Once any project or working relationship is under way, it’s useful to check in periodically on the state of the partnership through accountability conversations. You may or may not have clear recollections of the initial contract regarding the task, roles, standards, processes, and expected results. Either way, it’s productive to establish or reestablish these agreements and explore what is working or not working as you take action together to create envisioned results.

Accountability conversations aren’t always easy. However, the skills they require can be applied and developed over time. Some of the basic tools of learning organizations come into play here-the ladder of inference, for example, can be used to create a conversation of inquiry rather than inquisition. The accountability conversation is also the perfect setting for practicing left-hand column skills to surface assumptions blocking honest and productive discourse. In addition, admitting the tendency to

Distinctions between blame and accountability

Here are steps for initiating an accountability conversation:

1. Find out whether the person you are working with is interested in seeing problems as learning opportunities. If so, when a problem occurs, include other people who are also interested in the situation. Other people’s perspectives can be helpful because often two people in conflict are actually mirroring the conflict of a larger system within the organization.

2. Create a setting that is conducive to learning.

• Allow plenty of time to address the issues.
• Reaffirm with each other that the goal is to learn, not blame.
• Establish confidentiality.
• Be truly open-minded.
• Listen hard to the other person’s perspective

3. Have a conversation in which the two (or more) of you

• Clarify your intention for the meeting.
• Identify the data and any assumptions or conclusions you have drawn based on that data.
• Identify the pressures each of you is experiencing in the situation.
• Identify any stated or unstated expectations. If implicit agreements were not jointly understood, this is a good time to clarify and reestablish shared agreements.
• Analyze the problem from a systems perspective. Clarify how your mutual beliefs and actions might be related and are perhaps reinforcing each other.
• Identify some new ways to address the problem.

Group Level

How people talk about one another in an organization affects the levels of accountability and trust. Often, because people are reluctant to discuss accountability issues directly, they go to a third party to relieve their discomfort and get support for their point of view. The complaint does not get resolved this way, however, although the person with the complaint gains some relief. Bringing a complaint to a third party to clarify a situation can be a much more productive alternative.

To see how this works, let’s take a situation where Tony is angry with Lee because Lee wasn’t fully supportive in a meeting. Tony complains to Robin that Lee is unreliable. Robin sympathizes with Tony and agrees that Lee is unreliable. Tony and Robin now feel closer because they share this point of view. Lee does not yet know that Tony has a complaint. Later, though, Robin, busy with other projects, puts off one of Tony’s requests. Now Tony complains about Robin to Lee, and Robin doesn’t get the necessary feedback. Over time, all of these relationships will erode.

What is the alternative to this kind of dysfunctional blaming and resentment? The solution is a deep commitment on the part of all these people to work through their reluctance to give and receive difficult feedback. In addition, they need to learn how to hold one another accountable in an ongoing way. Now, when Tony is angry with Lee and goes to Robin, the purpose is to get coaching on how to raise the issue with Lee, not to get Robin’s agreement on what is wrong with Lee. In addition, Robin’s role is to make sure that Tony follows through on raising the concern directly with Lee.

To resolve conflict directly:

1.Bring your complaints about someone else to a third person to get coaching on how to raise your concerns.
Valuable questions from the coach include:

• Tell me about the situation.
• What results do you want?
• What’s another way of explaining the other person’s actions?
• How might the other person describe the situation?
• What was your role in creating the situation?
• What requests or complaints do you need to bring to the other person?
• How will you state them in order to get the results you want?
• What do you think your learning is in this situation?

2. Raise your concerns directly with the other person. Reaffirm your commitment to maintaining a good working relationship and find a way to express your fundamental respect for the person. The ladder of inference can be a helpful tool for focusing on the problem. Start by identifying the data that is the source of your concern. Then spell out the assumptions you made as you observed the data and any feelings you have about the situation. Finally, articulate your requests for change. During the conversation, remind the other person that reviewing the concern is part of learning to work together better

3. Let the coach know what happened.

4. Outside of this framework, refrain from making negative comments about people

5. For listeners who frequently hear complaints about a third party and want to create a learning setting, it can be helpful to say something like: “I’d like to help, but only if you want to create a constructive situation. We can explore these questions; otherwise, I prefer not to listen to your complaints.”

Organizational Accountability: The IS Story

Systems thinking provides useful tools for surfacing and breaking reinforcing cycles of blame within an organization. In the story below, a group was able to use causal loop diagrams to help them move beyond blame and craft a constructive, long-term solution.

The Information Systems group of a manufacturing plant was meeting to discuss their lack of progress on a large project to overhaul the department. Initially, the IS group decided that top management’s actions caused the group’s ineffectiveness. The plant management team (PMT) kept adding projects to the group’s already full plate. Members of the PMT responded to “squeaky wheels” by giving otherwise low-priority projects the force of their support. Also, the PMT didn’t reinforce plant wide policies the IS group had developed. Most important, the team didn’t give group members the support they needed to stick to the IS overhaul they had committed to, and wouldn’t give them the budget to hire the additional staff they sorely needed.

But when the group mapped out their current situation in a causal loop diagram, they gained a new perspective on the problem. They found that the situation resembled a “Success to the Successful” story, in which two or more projects or groups compete for limited resources.

The diagram “Success to the Squeaky Wheel” shows how, in this case, the IS group’s attention to urgent requests diverted resources away from prioritized items. Because rewards for completing urgent requests were heightened, the urgent tasks continued to receive greater attention (R2).  At the same time, the rewards for and focus on prioritized tasks decreased (R1). Finally, as people realized that urgent requests received greater attention than prioritized items, the number of “squeaky wheels”-or people promoting their own agenda items to management-proliferated. This development was followed by an increase in management’s efforts to get action on those agenda items, which further promoted urgent items over prioritized ones (R3).

After examining the causal loop diagrams, the group realized that they had played a role in the stalled progress on the overhaul project. Although IS team members encouraged each other to blame the PMT, no one in the group had given the PMT feedback concerning the impact of their requests and lack of support.

Armed with a systems view, the group identified several actions they could take to shift these unproductive dynamics. They decided to tell the PMT that they recognized that the IS overhaul was a top priority for the plant as a whole. They would point out that they couldn’t make progress on the overhaul if they continued to respond to “squeaky wheels. “The group would also let the PMT know that when they received additional requests, they would ask:

• How much of a priority is this request for you?
• Are you aware that there is a tradeoff in priorities?

The group concluded that they would issue a memo to the PMT describing their priorities and soliciting the PMT’s support of those priorities. They would also request that the PMT clearly communicate the priorities to the rest of the plant. In the memo, they would indicate the tradeoffs they were making and identify how their choices would help the company as a whole. The group felt that, with the PMT’s support, they would have the authority to focus on the prioritized project instead of responding to urgent requests.

Conclusion

Developing accountability skills is challenging; it takes courage and the willingness to learn new ways of thinking and acting. So why is moving from blame to accountability worthwhile? Because blame is like sugar – it produces a brief boost and then a let-down. It doesn’t serve the system’s long-term needs and can actually prevent it from functioning effectively. On the other hand, developing accountability skills and habits on every level of your organization can be an important element in maintaining your organization’s long-term health.

Marilyn Paul, PhD, is an independent organizational consultant affiliated with Innovation Associates, an Arthur D. Little company. She has sixteen years of experience facilitating organizational change. One focus of her work is peer mentoring and capacity development.

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

STEPS IN THE SYSTEMS THINKING METHOD

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

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

The Systems Thinking Method

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

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

The Seven Critical Thinking Skills

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

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

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

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

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

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

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

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

EXTENSIVE AND INTENSIVE MODEL BOUNDARIES

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

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

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

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

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

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

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

TWO REPRESENTATIONS OF THE LEARNING PROCESS

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

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

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

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

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

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

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

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

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

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

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

A Divide and Conquer Strategy

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

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

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

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As we will see, most organizations and people have mastered the ability to adapt to new situations and challenges. They can learn and improve, as long as the basic causes of their success do not change. But sometimes managers and enterprises become addicted to old ways of operating and making decisions, and thus fail to function well in a new environment. The result is decline. To understand the powerful dynamics that cause this turn of events, we must investigate the systems within which these organizations and managers operate.

Adaptation Versus Addiction

Adaptation and addiction differ in subtle ways. Adaptation takes place when we observe the symptoms of a problem and then take an action that counteracts the problem. Addiction occurs when we observe the symptoms of a problem and then take an action that suppresses the symptoms of the problem but makes the actual problem worse.

For example, lei’s say that you have just moved to a new area and find yourself spending a great deal of time alone. The number and quality of your social relationships are important indicators of the health of your personal system. Moving causes a decline in your system’s health when it leads to loneliness. An adaptive response to your loneliness could be to get involved with activities in your new community, to make connections with people at your new workplace, or to join a few clubs with members you find compatible.

The cause-effect mechanisms at work in this process are illustrated in the diagram “Adaptation” (p. 2). If the quality of your social life is important to you, then any change that causes loneliness in effect decreases the health of the system. After a delay, perceived health also goes down. When perceived health declines, the gap between perceived and desired health—between where you think your health is and where you want it to be—increases. So you take action to close the gap. In an adaptive system, the consequence of an action counteracts the problem and restores the health of the system. The whole process is a balancing loop that holds perceived health close to the level of health you actually desire.

But let’s look at another scenario, one of addiction. Say that when you find yourself feeling lonely, rather than trying to meet people, you have a few drinks. Drinking alcohol depresses the emotional center in your brain that causes you to experience loneliness. Thus, over the short term, the alcohol suppresses the symptoms of loneliness (sadness, self-pity, and so forth). But when you drink to an extreme, the quality of your social life deteriorates even further, making you even more lonely. So the action you took to ease the problem eventually only worsens it.

The cause-effect relations involved in addiction have two subtle differences from those associated with adaptation (see “Addiction” on p. 3). First, we take an action whose consequence raises our perceptions of the health of the system, but not the actual health. Second, our action actually damages the system’s real health.

Addiction, therefore, is a process by which an external problem can send us into a damaging cycle that quickly feeds on itself. Eventually, we don’t even need an external problem to spur us to take action; we simply generate our own internal problems through our addictive behavior—like someone who drinks salt water to quench his or her thirst.

Unfortunately, it is fairly easy to slide from adaptation into addiction, because it is usually difficult to measure the true health of any system. We often rely on symptoms—indirect measures—to determine the perceived health of the system. But information about symptoms typically comes to us only after a delay. The information also may contain deliberate biases or random errors. As a result, we take an action – that will eventually damage our true health, because the short-term symptoms cause us to feel better than we did before. A classic example of this pattern is smoking cigarettes, which can bring us immediate pleasure, but will also damage our health in the long run.

An Addictive Response in Organizations

Enterprises often become addicted to patterns of behavior that have brought them success in the past. They persist in pursuing policies that are no longer productive, until there is some sort of collapse within the organization, such as excessive outsourcing of technology until there is virtually no internal capacity left. This failure can happen when the feedback loops governing the firm’s success manifest a phenomenon called shifting dominance.

The “dominant” loop in a system is the one that principally controls the system’s behavior over a certain, often extended, period of time. When one loop dominates for a decade or more, a whole generation of managers, a set of control systems, and even a mythology grow up around the lever points that activate the loop governing the enterprise’s success; for example, “Marketing promotions are always the answer to a sales slump.” The company leaders see these lever points as the keys to their prosperity and act in ways that reinforce them.

But eventually, any loop will lose its dominance; another set of causal mechanisms will become more important. Then the usual lever points no longer lead to success, and the managers are left with a heritage of ineffective policies and irrelevant myths. At this point, the firm should drastically revise its policies. But often it simply redefines its measures of success so that the old policies still appear attractive. Why does this occur, and what can we do to prevent it from happening?

The Market Growth Model: Shifting Dominance at Ace Electronics

The concept of shifting dominance first became real to me in the 1960s when I encountered a model created by David Packer, an early member of the Industrial Dynamics Group at the Massachusetts Institute of Technology’s Sloan School of Management. Out of the group’s investigations evolved an elegant theory, later called the Market Growth Model, that illustrates the mechanics and importance of shifting dominance.

Packer and his colleagues applied system dynamics to a firm I will call Ace Electronics. In its earliest days, Ace had an enormously superior product. Its sales were limited only by the company’s capacity to market and sell the product. The dominant loop governing the firm’s profits was composed of its expanding sales force, growing orders and backlogs, swelling production capacity, and increasing deliveries (R4 in “Market Growth”). Because the budget for the marketing and sales department was a percentage of sales income, its budgets expanded, and the sales force grew even more. This loop produced rapid growth.

For a long time at Ace, the market growth loop was dominant, and a group of people who knew how to make this loop operate moved up through the firm’s ranks. However, eventually the dominance shifted within the system (B5 in “Market Growth”). The sales force booked far more orders than the factory could produce, so the order backlog started to increase. When the sales force could not promise timely delivery in a technologically sophisticated and rapidly changing market, its effectiveness in booking new orders declined. Sales began to drop. Before, expanding the sales force increased profits; now it cut into them.

When we find ourselves unknowingly caught up in a situation of shifting dominance, we often blame each other for our faltering fortunes. Ace is a perfect example of this phenomenon. We can imagine that the company leaders agonized over why the sales staff wasn’t as good as it used to be, what kind of new incentives were needed to whip the sales staff back into shape, and so forth. But in shifting dominance, the problem actually originates within the system in the form of an addiction to the old ways of doing things. Managers can push a sales force as hard as they like and still fail to revive sales—the system simply doesn’t respond to this kind of force when the control has shifted to a different loop.

As one particularly destructive result of Ace’s failure to adapt to change, the company eventually developed an addiction to a new, short-term “solution”: downsizing. Many companies fall into the trap of firing people in order to make the bottom line look good on the next quarterly report. Downsizing lowers costs and temporarily kicks up profits. But if it’s not done well—and often it isn’t—downsizing also drastically reduces the quality and size of the staff and dulls a company’s competitive edge. As its niche shrinks, the company has to fire even more people in order to boost its profits. The addictive cycle of downsizing takes over.

The Difficulties of Breaking Addiction

If the pitfalls of addiction seem so obvious, why is it so difficult to break out of addictive processes? There are three main forces that work against an individual or organization seeking to break the cycle of addiction.

The Pain of Withdrawal. One reason is that withdrawal is extremely painful. Remember that perceived health, which drives our actions in the addictive system, is affected by two factors: actual health and the consequences of the actions we take (see “Addiction” on p. 3). These addictive consequences progressively damage actual health, which means that we have to take more and more of the addictive action to offset the consequences. The process becomes a spiral of increasing use.

Codependency. When we get ourselves into the trap of addiction, it’s astonishing how the various components of the system work in collusion to sustain the addictive behavior. This subtle reorganization of the system to support the addictive action is called codependency and is another reason that breaking an addiction is so difficult.

Drifting Goals. Addiction has many forms. One interesting variant of the addictive structure occurs with the addition of a causal link that produces what is commonly known as “Drifting Goals.” If we don’t get what we want, we start to want what we get. When we lower our aspirations, the addiction causes progressive deterioration of our goal.

If we don’t get what we want, we start to want what we get.

For example, imagine a firm that borrows more and more in order to finance its operations. One symptom of a company’s health is its debt-equity ratio; there are industry standards that indicate the appropriate ratio of debt to equity in a healthy firm. When debt rises above this level, a company will undertake efforts to increase profitability or sell off assets to reduce debt. But if these efforts fail and the debt-equity ratio remains high, management may get used to the higher levels of debt and stop trying to reduce them. Spokespeople for the firm may even develop elaborate rationalizations indicating why the higher levels are acceptable. Of course, over the long term, high levels of debt can be fatal to an organization.

Understanding and Changing Systemic Structure

Addictive behaviors, with their self-perpetuating, destructive cycles, can seem particularly stubborn. But cycles of addiction can be broken, allowing us to respond more adaptively to situations of shifting dominance.

What is the key to breaking addictive responses in organizations? One place to begin is to familiarize ourselves with the laws of systemic behavior and learn to work with these laws (see The Fifth Discipline by Peter Senge). Most of the principles of systemic behavior apply directly to the process of addiction and contain the seeds of a solution (see “Moving Beyond Organizational Addictions” on p. 4).

The most effective way to combat organizational addiction is to learn to understand the system. When we do that, we can anticipate an imminent shift in dominance and prepare ourselves for it; in other words, we can design an adaptive instead of an addictive response. We can also identify opportunities to create new feedback loops that let us catalyze a desirable shift in dominance. But beware of spending too much time creating loops that aren’t going to dominate. The key is to make a change that will grip the system and take it down a different path.

To beat personal addictions, we often must place our trust in the potential of the system to change. Likewise, in organizations, if we build up confidence in a group’s ability to work together, to stay committed to each other, and to cope with problems in a way that will produce satisfactory results in the long run, we can get through withdrawal together. With the high turnover rates the business world is experiencing under downsizing, it has become harder for workers to place their faith in anyone or to adopt long-term perspectives. However, only trust can help an organization establish a sense of stability. Despite all the pressures to do otherwise, we must work to cultivate a culture of trust.

Herman Daly, a leader among economists in analyzing sustainable development, once made a statement that is profoundly applicable to the challenges discussed in this article: “The paths to sustainability are unknown, not because they’re hard to find, but because we never looked.” Let’s start looking for long-term solutions to organizational addictions.

Dennis Meadows is director of the Institute for Policy and Social Science at the University of New Hampshire. He directed the system dynamics graduate program at Dartmouth College for 16 years. He has written eight books that apply systems thinking to social and corporate issues.

Editorial support for this article was provided by the editorial staff and Joy Sobeck.

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The potential for measurement error is therefore an important modeling consideration. Traditional analytical approaches often leave out qualitative factors because they are hard to measure and reduce the precision of a model’s results. But the importance of measurement error diminishes when the investigative focus shifts from concern over the system’s current state to understanding the system’s behavior over time, which is often the purpose of a system dynamics model.

Measurement Error

In traditional business analysis, conceptual (pen-and-paper) models might contain a mix of quantitative and qualitative factors, but such models rarely advance past the theoretical stage. Traditional formal (computer) models usually either bury qualitative factors within more quantitative assumptions or leave them out entirely. As a result, organizations over the years have focused much more of their analytical attention on easily measurable “hard” factors (for example, production output, lines of code, or cash flow) than on “soft” variables (for example, employee morale, efficiency of code, or customer satisfaction). But these soft factors are important components of the system structure: They can strongly influence the performance of the system. So one of the key steps to understanding dynamic social systems is crafting and using simple but explicit and sensible measures for qualitative variables.

It is important to keep in mind that any measurement will contain some degree of error. For variables that have well-established units—feet, pounds, liters, volts—the measurement error is usually a function of the accuracy and precision of the measuring device. For example, if the smallest gradation on a ruler is 1/32nd of an inch, then any measurement taken with that tool may be off by as much as 1/64th of an inch. Whether this fundamental type of error is significant depends on what is being measured and the purpose of the measurement.

For soft variables that don’t have well-established units of measure, measurement error arises from two additional sources: the definition of a unit of measure and the creation of a measurement tool. For instance, when trying to measure customer satisfaction, we might invent a unit of measure, such as the customer satisfaction index. Through survey instruments, focus groups, and other tools, we could then come up with a number to represent our best estimate of current, actual customer satisfaction. Both of these steps introduce the potential for error, in addition to the fundamental error described above. And people often cite these additional opportunities for error as justification for excluding qualitative variables from a computer modeling effort.

The Dangers of Excluding Qualitative Variables

Although qualitative factors are generally more prone to measurement error than quantitative variables, we shouldn’t exclude them on that basis alone. How well a model recreates the system’s performance—and thereby the model’s usefulness—depends on much more than measurement precision.

Another perspective is that measurement error is an important but usually static source of error in models. For that reason, the measurement error in one time period does not affect (or has a limited effect on) the measurement error in the next. For instance, the measurement error associated with this month’s plant utilization, operating hours divided by total hours in the month, is not affected by last month’s measurement error, nor will it affect next month’s. Also, well-designed measurement standards are unbiased: They tend to overestimate values as often as they underestimate them. A static and unbiased measurement error will have a relatively small impact on the depiction of a system’s dynamic behavior (behavior over time). Leaving out a qualitative factor entirely, on the other hand, means potentially omitting an influential feedback loop, and thereby creating a dynamic source of model error. Dynamic errors, unlike measurement errors, compound over time, causing the model to lose validity very quickly.

For example, consider the variable “Employee Morale” (see “Interacting Hard and Soft Variables”). As profitability drops, management intervention increases. In response to greater management controls, employee morale decreases, leading to a decline in productivity, quality, and, ultimately, profitability. Leaving this variable out of the model ignores serious reinforcing processes and quickly leads to unacceptable levels of total model error. Can we precisely measure employee morale and its impact on related variables? No. Are the processes described real? Absolutely. Although we can worry about whether a system dynamics model, with all its qualitative factors, is generating precise output, one thing is certain: A model that leaves soft variables out entirely is definitely off the mark.

Quantitative Scales for Qualitative Variables

If qualitative factors are so important, how do we incorporate them into a computer model? Although most managers can give a ballpark estimate of qualitative variables such as market focus, they often feel uncomfortable about encoding this kind of estimate in a computer model. But for many qualitative factors, a ballpark estimate by an experienced management team is the only data available and is usually a pretty good reflection of the system’s state. At the very least, this data is important because it represents the mental models of the managers involved in the model-building process.

Indexed Variables. The most straightforward way to capture qualitative variables in a model is to create an indexed variable. To do this, we typically set the value of the variable equal to “1” at some given point in time, usually the start of the simulation. We can then identify the factors that affect the indexed variable and establish mathematical or graphical relationships to cause the indexed variable to change over time.

For example, suppose we create an index for customer satisfaction that has a value of “1” at the outset of the simulation. Notice that we are not attempting to say that the current level of satisfaction is high or low; we are simply establishing a starting point. Next, we determine that the ratio of customers to telephone representatives is a key driver of customer satisfaction. Third, we ask what ratio (everything else being equal) would maintain customer satisfaction at “1” and what would happen if the ratio changed? For instance, if the “steady state” ratio were 200 customers per telephone representative, the management team could estimate the impact of letting the ratio slip to 300: satisfaction might fall by 20 percent. When we run the model, we will then know whether satisfaction goes up, down, oscillates, or remains constant, based on the behavior of the indexed variable at any given time and its value relative to “1.”

Formulating an explicit equation or graphical relationship between a qualitative variable and its drivers provides a management team with the opportunity to share mental models about the business and to try to achieve a mutual understanding. Through modeling, differing assumptions about the strength of such relationships can be tested.

Assessing Models

Interacting Hard and Soft Variables

The qualitative variable “Employee Morale” Is a key component of these reinforcing loops. As profitability drops, management intervention increases. In response to greater management controls, employee morale decreases, leading to a decline in productivity, quality, and, ultimately, profitability.

If you are responsible for building and managing models, present the decision-maker with alternatives: the old way of model-building or a new way that captures the impact of qualitative variables. Use both kinds of models concurrently to explore the thinking and assumptions that went into each one, and to uncover new insights about the organization and business. See if you can move senior management from using modeling as a forecasting tool to using modeling as a way to test ideas, explore strategies, and learn how the system works.

If you are the decision-maker, ask tough questions about the assumptions going into the models you use, especially with respect to qualitative factors. For instance, ask whether employee morale and its impact on productivity are addressed in your business plan. If not, why not? Last but not least, figure out ways to free your team from the tyranny of quantitative spreadsheet thinking. Spreadsheets are an important and helpful tool, but today’s management teams need to have a variety of tools at their disposal and must know how to include qualitative factors in their thinking.

Gregory Hennessy Is co-founder and managing director of Dynamic Strategies, a collaboration of advisors working with clients to Improve organizational effectiveness, apply system dynamics, and develop organizational learning capabilities.

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What Is Organizational Anxiety?

Anxiety can be an insidious force: Not only does it sap energy levels and damage our health, it also eats away at job performance and stifles innovation and creativity. Like individuals, organizations can also suffer from symptoms of anxiety. Over the long run, anxiety can reduce an enterprise’s strategic adaptability and effectiveness.

In recent years, researchers have looked at anxiety from an intriguing new perspective. As they see it, the origin of anxiety is the struggle between life and death. This struggle that rages within individuals also takes place in work groups and organizations. Of course, organizations do not experience death in the same way that individuals do; however, they do face the very real possibility of financial or operational demise. Organizations can cease to exist through bankruptcy, takeover, mergers, and so forth. As a result, they experience their own brand of anxiety

Defenses Against Anxiety

Literature abounds on how work-groups and organizations try to cope with the destructive feelings of anxiety. According to one theory, some companies resort to a form of defense that combines three tactics called splitting, projection, and introjection that individuals often use to fend off anxiety. Splitting happens when we separate the “good” aspects of our lives from the “bad.” We then project “bad” qualities onto others and introject “good” qualities into ourselves. This tactic helps us to feel more in control of our panic, because we turn our attention to judging and trying to control others.

For example, an anxious manager might split good and bad by considering himself all-powerful (he introjects good into himself) while at the same time dismissing subordinates as unworthy (he projects bad onto others). Even worse, a manager in this frame of mind might be compelled to act on these projected feelings by punishing workers with extra work, impossible schedules, unreachable goals, and so forth. Companies tend to “institutionalize” this kind of behavior. Employees may quit and new ones may be hired, but the tough schedules and unattainable goals persist regardless of the individuals employed at any given time.

Groups or organizations that are leaderless can suffer more anxiety than most. For example, self-directed work teams may have difficulty making decisions if no leader steps forward. The team may become ineffective as it struggles to search for a leader, thus creating what can be paralyzing anxiety.

In these cases, the people involved often defend themselves against fearful emotions in three ways:

• Dependency. The group stops trying to solve its problems and instead waits for a “messiah” to save it.
• Pairing. Two individuals related to the group (for example, two group members, or one member and an outside consultant) combine to try to oust someone they consider a “bad” member.
• Flight/Fight. Group members blame all problems on an outside cause, or they pretend that no problem exists.

Defense mechanisms are neither good nor bad, and indeed can help protect us from emotional overload. But, the way these mechanisms are stitched together in an organization’s mental model can create the exact opposite of what the group wants and needs: Instead of reducing the anxiety, the behavior only worsens it. And mental models are notorious for leading to self-fulfilling prophecies: We see only what we expect to see, and then we act in ways that bring about results that confirm our assumptions.

When anxiety lodges itself in a company’s collective mental model of how things work, it will continue to perpetuate itself until the organization’s behavior changes to balance or reduce the increasing anxiety. For example, many organizations pride themselves on their “heroic” acts. When crises strike, creating high levels of anxiety, a few heroes step forward to “save the day.” The organization rewards the heroes. At the same time, by giving rewards, the organization inadvertently encourages the creation of future crises, which will lead to more anxiety and then to additional rewards for heroic action. This behavior is a perfect example of self-perpetuating anxiety.

Anxiety Amplified

Defensive actions can trigger reinforcing processes that serve to amplify and perpetuate anxiety. Here are examples of three reinforcing loops that can sustain or even worsen anxiety in organizations. Although these loops were created by a work group at a large company, they reflect dynamics experienced by many organizations.

The “Messiah” Loop. In this dynamic, if Anxiety about the organization’s performance intensifies, employees look for a “messiah”(Search for Savior). This search diminishes workers’ Accountability, in turn reducing their Perceived Ability to Succeed. The diminishing of workers’ self-esteem then leads to an increase in Anxiety.

The loop contains a bitter irony: The group searches for a savior to ease its anxiety, but waiting for a “messiah” only leads to more anxiety. The team could design a more fundamental, enduring solution to their anxiety by focusing instead on learning and performing. Sadly, however, the “quick fix” of seeking a savior diminishes the organization’s need for—and thus its ability to apply—a more fundamental solution.

THE “MESSIAH” LOOP

if Anxiety about the organization’s performance intensifies, employees Search for a Savior. This search diminishes workers’ Accountability, in turn reducing their Perceived Ability to Succeed and increasing Anxiety.

The “Manic Defense” Loop. In this reinforcing process, anxious managers project the organization’s problems onto their subordinates and then try to punish them. To justify this punishment, the managers focus obsessively on quantitative measurements, slavishly using them to control the action around them. Through this emphasis on metrics, the managers deplete the organization of the physical, financial, and—perhaps most important—psychological resources the team members need to succeed. All of this ultimately leads to even more intense anxiety.

In the “Manic Defense” loop, increased Anxiety leads to more Focus on Metrics, which in turn causes Resources Used for Measuring to go up. As Resources Used for measuring rises, Resources Available for Projects diminishes, which in turn increases Resources Requested. The diminished resource base for projects puts added pressure on those trying to complete projects. The project manager then requests more resources in order to complete the projects. As Resources Requested increases, Percentage of Resources Received is reduced because of the multiple demands on the system created by the additional resources requested for measurement.

This development further cuts into Perceived Ability to Succeed and ultimately heightens Anxiety.

The “Fight” Loop. We call the third reinforcing process the “Fight” loop because it captures the way anxiety sparks conflict within the team and encourages an aggressive desire to have one’s own viewpoints and decisions prevail. Increased Anxiety leads to increased Internal Competition, which leads to a greater Need to Be Right. Intensifying the Need to Be Right reduces the level of inquiry (Questions), which also lowers Understanding and increases the Resources Used for Making Decisions. More employee time and energy is needed to make decisions when there is little understanding of the issues facing the organization. The rest of the loop follows the “Manic Defense” loop, ultimately creating even more Anxiety.

Understanding Our Own Role

As we look at the three reinforcing loops, we can begin to see how team members themselves might create and intensify their own anxiety. Often, factors viewed as external causes for anxiety, such as perceptions of failure or layoffs, could really be internally driven. To surface these factors, we might ask, “Who is perceiving this failure—our own organization, stockholders, or customers?” If it is our own organization, we can begin to search for ways to change that perception. If we have suffered layoffs, could it be that our business is cyclical? If so, how is our organization perpetuating industry cycles? Many organizations aren’t aware of the role they play in perpetuating not just their own business cycles, but those of the entire industry.

Thus, often what an organization views as “not our problem” is just that. The organization tries to behave in a way that will produce positive results, but inadvertently creates undesired outcomes. This is an example of what Jay Forrester called “the counter-intuitive behavior of social systems.” Realizing that we often cause our own problems may be embarrassing, but it is also good news, for whatever we create in a system, we may be able to change if we gain insight into it.

In Search of Balancing Loops

The dynamics shown in the three loops present a grim image of the system of organizational anxiety. The picture is particularly discouraging because all the loops are reinforcing, creating a vicious cycle. But the picture does not have to remain grim: Reinforcing processes are not all necessarily bad. Just as the reinforcing loops in the diagrams can heighten anxiety exponentially, they can also reduce anxiety, if they are turned around to become virtuous cycles.

The lack of balancing loops is another important piece of information about the systems the diagrams depict. Without balancing loops, there are no processes in place for returning the system to equilibrium after a disturbance caused by the reinforcing loops. All three loops amplify the central variable—Anxiety—and no loops have been identified that keep it under control.

When drawing their system of anxiety, teams often neglect to build balancing loops into their models, perhaps because people tend to notice things that create rapid change (R loops) more than forces that keep things stable (B loops). Also, when addressing a specific problem, team members may focus on how their anxiety is worsening, not on how it might be alleviated.

Clearly, though, balancing loops have to exist in every organization; otherwise, the place would unravel toward anxiety-induced paralysis, anarchy, or some other extreme endpoint of a reinforcing process. Some sort of balancing dynamic often subtly works to keep the situation relatively under control. In fact, these hidden loops can create the sense of oscillating, persistent anxiety experienced by the staff.

Balancing loops that might control anxiety could include coping mechanisms such as open communication, flexible work hours, and personal leave time. Unfortunately, if the reinforcing loop around anxiety dominates the system, these coping mechanisms may never be able to balance out the increasing anxiety. Communication may open up and temporarily reduce anxiety, but then a sudden crisis may shut down communication and thereby increase anxiety again. This pattern causes the organization to ride the waves of anxiety time and again.

A team can also balance their anxiety by linking a new, outside force to Anxiety in a way that will ease feelings of fear rather than heighten them. If, for instance, the members of a group develop coping mechanisms in their private lives (loving families, close-knit communities, and so forth), they might be able to calm their collective anxiety, as shown in “Reversing Anxiety.” As the Use of Private-Life Coping Mechanisms increases, Anxiety and the Search for a Savior decrease. Account-ability and the group’s Perceived Ability to Succeed are then enhanced, leading to less of a need for reliance on the coping mechanisms.

REVERSING ANXIETY

As the Use of Private-Life Coping Mechanisms increases, Anxiety and the Search for a Savior decrease. Accountability and the group’s Perceived Ability to Succeed are enhanced, leading to less of a reliance on coping mechanism.

A Systemic Makeover

According to the field of System Dynamics, there are two main ways of actually changing a system: Shifting Loop Dominance or Direction, and Changing Loop Structure so as to alter the flow of feedback through the system. Here are some additional strategies for breaking the cycle of anxiety.

Shifting Loop Dominance or Direction. Often, the main loops in a system all lead to greater anxiety. For this reason, teams may want to explore how they can weaken those loops and reshape the system. For example, the “Messiah” loop could be reversed if team members gave up the search for a savior and instead enhanced their own empowerment and accountability. A team could weaken the “Manic Defense” loop by consciously reducing the focus on metrics. To do this, management could cut back on the number of metrics used, employ other ways of measuring the company’s performance, emphasize customer service over internal metrics, streamline bureaucracy, free up resources used for measuring, and so forth. Finally, a team could disarm the “Fight” loop by finding ways to reduce internal competition and the need to be right, by promoting inquiry skills (Questions), and by lessening resources used for making decisions.

Changing Loop Structure.We can actually reshape a systemic structure by incorporating new variables and links and removing others. By making these changes, we can alter the pathway by which feedback flows throughout the system. There are many possibilities for creating new links. In dealing with a system of organizational anxiety, one valuable addition might be the use of inquiry skills. Inquiry skills include methods of conversing that can overcome barriers to understanding and learning, whether the barriers are organizational or interpersonal. Thinkers such as Chris Argyris, David Bohm, and William Isaacs have written extensively about this set of skills. In the case of an anxious team, as the group gets more and more practice in using inquiry—and begins to achieve some success—it will learn to use these tools more readily in response to a surge in anxiety

Looking Ahead

Of course, a causal loop diagram is only an early step in the process of solving a systemic problem. Actually changing a systemic structure takes a lot more than just redrawing links. To reshape the way they do things, a group will need to think about what the links in its drawings mean.

For example, the more managers understand the anxiety-intensifying system that they’ve helped to create, the more motivated they may feel to restructure that especially irksome “Manic Defense” loop. Instead of projecting their anxiety onto “bad” subordinates, they could learn to recognize both the good and the bad in the way their organization operates. In a difficult but profoundly healthier process, the team members would examine things in a far more systemic way than the traditional short-term perspective on metrics allows, and would join together to do the hard work essential for improving their performance.

Making attitude changes isn’t simple or easy, and the team will need to dig even deeper to find the best leverage points for change. However, altering some key mental pictures of how things work is an organization’s best hope for pulling itself out of the anxiety-ridden system it has created. Talking about their anxiety system and drawing causal depictions of it can give a team vital insight into how they are creating their own problems.

The team might learn more at this stage if they also used a computer simulation model of their anxiety system. Modeling their system would require them to identify the things they think most strongly drive the loops, and it would give them a way to test the insights that they found while drawing the loops. In addition, modeling would make it easier for them to redesign the problematic structures in their system.

The group could then design interventions that apply pressure to any leverage points it identifies in the earlier steps. In many cases, the most powerful interventions would involve using new tools—particularly inquiry skills—for deepening the organization’s collective knowledge about itself. If all goes well, the team will grow less dependent on self-defeating defense mechanisms and rely increasingly on its own strengths, knowledge, skills, and resources.

An Example: ABC, Inc

A computer manufacturer, ABC, Inc., had suffered some significant business failures that generated a tremendous level of anxiety in the organization. Arguments over how to price products became the focus of people’s anxiety. The “old-timers” thought that the company should maintain its high prices to reflect its image as a pioneer in the industry and as a producer of high-quality products. On the other hand, the “newcomers” thought that customers were becoming more price sensitive because of the lower prices offered by ABC’s competitors.

The first step to resolving the impasse was to have both sides share their mental models of what was creating the anxiety over pricing. Using tools such as the ladder of inference, the groups discussed their own interpretations of the data they used to make pricing decisions. One manager reported, “Our data show that our best customers are more concerned about quality and are willing to pay the higher price.” Another stated, “All our customers care about is a low price. We are being destroyed by our competition.” Each side held tightly to its position and blamed the other group for undermining ABC’s success.

The groups then developed causal loops that captured the two perspectives. Through this process, they found that price was not the key issue; the real issue was defining what type of organization ABC would be in the future. Would ABC be an innovative producer of high-quality products, or would it be a mass producer of relatively high quality, but less innovative goods?

At this point, the company created a computer simulation to test the financial impact of the two scenarios. The simulation revealed that the innovative strategy would result in a loss of customers. However, by charging more per unit, the company could make up much of the lost revenue. Further investigation showed that customers who buy lower-priced products tend to demand more technical services, cutting further into revenues. This finding made the mass-production scenario less appealing in the long run.

By using causal loops and simulation in this way, ABC diffused the anxiety within the organization and took the focus away from blaming individuals for the company’s troubles. ABC was also able to make more informed decisions regarding its pricing and long-term business strategy.

Eradicating Anxiety

It is easy to view organizational anxiety as something that is out of a group’s—or anyone’s—control. But the discussion above shows how we can play a role in creating our own anxiety. Managers and employees often become trapped in mental models that only worsen their anxiety. Yet the team is far from helpless to control its behavior. We all possess the power to change our attitudes and behaviors in order to reshape dynamics that we ourselves have created. With this enhanced understanding, we can then take intelligent steps to manage or even eradicate anxiety and thereby enhance our effectiveness.

For references and further reading, please see Anxiety in the Workplace: Using Systems Thinking to Deepen Understanding (Pegasus Communications, 1998).

Janet M. Gould Wilkinson is director of the Organizations as Learning Systems project at the Massachusetts Institute of Technology.

John J. Voyer is associate professor of business administration and co-director of the MBA program at the School of Business, University of Southern Maine.

David N. Ford is an associate professor in the system dynamics program at the University of Bergen in Norway and a visiting professor in the School of the Management of Technology and Economics at the Chalmers University of Technology in Sweden.

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Though Dynamic Thinking is one of the easiest of the systems thinking skills to master, it does not come naturally for most people. What seems more common is Static Thinking. For Static Thinkers, the starting point for understanding change is where they are right now; that is, the current state. These thinkers tend to see change as “jumping” from the current state to a future goal in a rather straightforward way. The historical trajectory leading up to the current state and the unfurling of the pathway from the current state to the future condition typically don’t garner much attention.

In the Pits

The trajectories indicate that there are several different ways to reach a current crisis point. Static Thinkers commonly project the path from “current crisis” to “future condition” as a straight line. Dynamic Thinkers chart paths that are longer and less linear, incorporating a “worse-before-better segment.

The Benefits of Dynamic Thinking

Why embrace Dynamic Thinking? Let’s look at some of the problems associated with the alternative and then see what opportunities Dynamic Thinking provides for improving performance.

In describing what ails their organizations, people tend to focus on the current crisis—profit margins are razor thin, the turnover rate is too high, customer satisfaction is in the pits. “Victory” is then defined as boosting profit margins to some higher level, lowering the turnover rate to a certain mark, or raising customer satisfaction to a particular degree. This type of focus, which is based in Static Thinking, has two basic problems.

The first problem is that the observation “customer satisfaction is in the pits” says nothing about the path it followed to get there. As the figure “In the Pits” illustrates, there are several different ways to reach a current crisis point.

If leaders and managers want to embark on a type of initiative that can successfully move a system from its current state to a desired future state, they must investigate the nature of the relationships that carried the system to where it is now (and may be holding it there!). Dynamic Thinking encourages people to use the historical trajectory for stimulating and guiding inquiry into the underlying relationships that produced it. The insights that stem from such an inquiry can help us design an initiative that successfully leverages the desired change in performance.

The second problem with Static Thinking is that the course of the “path forward” gets relatively little attention. As the illustration indicates, people commonly project the pathway from “current crisis” to “future condition” as a straight line, assuming that improvement will proceed at a steady pace in one direction. The assumption underlying such a projection is that improvement can be “engineered”—that the system is a “mechanism” and hence will passively accept change.

By contrast, those employing Dynamic Thinking skills carefully consider the shape and duration of the path forward. The assumption is “organization as organism”: The system will both adapt to and resist change. As a result, the paths forward charted by Dynamic Thinkers are typically longer and less linear than those traced by Static Thinkers. In particular, they often incorporate a “worse-before-better” segment—reflecting the idea that in order to improve a situation, you have to first invest something in the effort. Investing, in turn, usually implies enduring some sort of short-term “hit.”

Honing Dynamic Thinking with Reference Behavior Patterns

The most useful tool for honing Dynamic Thinking skills is the Reference Behavior Pattern (RBP), a kind of behavior over time graph. An RBP is a graph over time of the variable that best captures the issue or challenge of concern. Developing an RBP at the outset of any performance improvement or strategy design effort is one of the best ways to focus a group’s energy, while also encouraging a Dynamic Thinking perspective. Here are examples of how to use this tool most effectively.

Example 1: ‘World-Class” Teams. A group of senior managers from a hardware product group within a high-technology company was searching for a solution to performance problems in their group. In a meeting, they came to a consensus that the answer was to develop “world-class” teams. To explore this question, the group needed to address several other questions: How “world class” were the group’s teams at that moment? How had “world classness” changed over time? By how much did they think they could improve this variable and over what time frame? All of these questions fell flat as long as the group was unable to frame the challenge as a dynamic problem.

The question that got the managers thinking dynamically was: How would you know if you had world-class teams (that is, what performance indicators would characterize such teams)? This query led the group to identify a series of operational measures—like product-development cycle times, manufacturing defect rates, and so forth—that they could chart over time to reveal a historical trajectory, assign a current state, and use to imagine future trajectories. The insights gleaned from the RBPs enabled the team to think in non-abstract terms about initiatives they could implement to improve performance. Voila!

Example 2: Declining Revenues. The second example involves a group at a financial services company where the number of cardholders, amount of revenues, and number of transactions were all growing., Initially, RBPs of almost all the company’s key measures sloped upward. Things got interesting, though, when the group divided annual revenues by the number of cardholders. That curve rose for a few years, but then turned downward and continued to fall for the last five years. The decline of revenues per cardholder suggested that the company was gaining customers who felt less inclined to use their cards or who had little discretionary income—both signs of potential market saturation. This example indicates something else that’s important to remember in constructing RBPs. Often it is useful to focus on a relative rather than absolute performance indicator. “Dividing through” reveals relative changes that often stimulate insights.

These examples make it clear that the time axis plays a large role in the usefulness of RBPs. In constructing one of these graphs, therefore, think carefully about whether the issue in question is unfolding in minutes, weeks, or years. Electric utility people, for example, “live” with hour-to-hour load fluctuations and associated purchase price swings. But the long-term economic viability of a utility depends on capacity decisions that can play out with a yearly rhythm. It doesn’t make sense to cast an RBP in hours when you want to examine trends over a number of months or years! Paying close attention to the time units in an RBP is a great way to keep tactical and strategic aspects in proper perspective—and to generate vastly clearer insights about ways to improve performance.

A “Path Forward”

Dynamic Thinking, by focusing attention on historical trajectories, encourages you to look at underlying systemic relationships, and provides a first clue as to the nature of these relationships. This skill also guides attention to the shape and timing of the “path forward,” stimulating you to think about the many possible problems that may befall any change effort. By using Reference Behavior Pattern graphs, you can hone your Dynamic Thinking skills to a fine point. The new perspective that results from this kind of thinking can then help you develop high-leverage improvement initiatives.

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

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The Paradox of KSFs

Most of us approach a large, complex issue by breaking it down into manageable parts. By focusing on a few aspects at a time, we sometimes succeed in improving the parts, but we often fail to address the problem as a whole. In the long run, this approach robs us of resources that we could have used to look at an issue from a systemic perspective.

We can find ample evidence of the limits to a factors approach in medical literature. In Sweden, for example, researchers tried to reduce cardiovascular risk factors in 3,490 business executives. After five years of intervention and 11 years of follow-up, the executives had reduced their risk factors by an average of 46 percent, yet they had a higher death rate than members of a control group. A similar study in the U.S. produced comparable results.

We might dismiss these studies as statistical flukes if the consequences weren’t so serious. The sad reality is that these results probably reflect many of our efforts, not just in healthcare but in virtually every facet of our organizations. Although we focus time and again on improving single factors, we fail to acknowledge that the health of most individuals—and most systems—is greatly determined by the relationships among critical loops. The line “the operation was successful, but the patient died” sums up the pitfalls of the factors approach to complex systems.

Beyond Factors to Loops

To create long-lasting success, we need to extend our factors approach and identify the interrelationships among the factors that drive the dynamics of the system—in short, to identify the Key Success Loops (KSLs).When we take a systemic approach, we realize that the lowest meaningful units of analysis are loops, not individual factors—and we no longer initiate actions on any factors until we distinguish the critical loop or loops involved.

Now, imagine being given the same charge as before from corporate (see “Key Success Loop Approach”). We begin in the same way, by brainstorming and then prioritizing KSFs (Step 1). But instead of leaping into action by assigning the factors to teams, we take each of the high-priority factors and identify at least one reinforcing loop that will make the factor self-sustaining without continued external investments (Steps 2 and 3). We integrate all of the loops into a single diagram, in which the individual loops are connected by the factors they have in common (for example, B and D in Step 4).We then look at the diagram as a whole and decide where to make the investments that would help support the success of the entire system (Step 5). Only after we have developed a sufficient understanding of the system will we assign teams to implement specific success loops. Each team then collaborate closely with those teams whose loops are directly connected to theirs (Step 6).

Launching a New Venture

Let’s walk through a simplified example of a Key Success Loop approach. Suppose we want to launch a new business venture in our organization (see “New Business Venture Success Loops”).We begin by brainstorming a list of KSFs that we believe are important to our success, such as number of new products, skilled people, profit, and ability to meet customer needs (Step 1).

We then focus on the first factor and try to identify a key loop that would make it self-reinforcing. We can ask either “What would an increase in the number of new products cause?” or “What would be an important driver of growth in the number of new products? ”The first question leads us downstream in the arrow flow to “Revenues,” while the second takes us in the upstream direction to “Acquisitions.” Either way, we try to create a reinforcing loop around the original factor (Step 2).We then repeat the process with the remaining factors (Step 3).

After we have created a loop for each KSF, we look for common variables in the individual loops. In this example, loops R1 and R2 can be linked through “Revenues” and “# of New Products” (Step 4). Once we have a diagram that maps the key linkages, we can begin to identify the best places to make high-leverage investments (Step 5). Now we are ready to assign teams to focus on each of the loops through a collaborative effort in which each team understands its loop in the context of the larger system (Step 6).

Benefits of KSLs

Moving beyond Key Success Factors to Key Success Loops offers a number of advantages. First, because the loop approach links you to a broader set of variables, you reduce the risk of focusing on the wrong factors. Even if you initially pick the wrong factors, the process of mapping the loops increases the likelihood that you will include the most important ones. Also, identifying the loops decreases competition for limited resources. When everyone can see the interconnections, teams are less likely to “pump up” their own factors without regard for the effect on others. Loops can also provide a clearer picture of where investing in one point could positively affect multiple factors. Finally, rather than being stuck in the “Ready, Fire, Aim” syndrome that many organizations experience, emphasizing KSLs can actually give you a viable “Ready, Aim, Fire” approach.

Daniel H. Kim is a co-founder of Pegasus Communications, Inc., and publisher of The Systems Thinker.

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