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 business settings, corporations have tended to focus mainly on the socializing benefits of games — participating in “team building” exercises, for example, which were used to develop interpersonal skills and cooperation. Games, for the most part, were not considered managerial “skill builders.”

In recent years, however, a new variety of games have begun to appear in companies throughout the world. These “microworlds” or “management flight simulators” are helping managers share their mental models of strategic issues, teach decision making skills, improve strategic planning, and enhance corporate learning.

Gaming Interface

These new games are essentially system dynamics simulation models with an added “interface” that allows users to experiment with the simulation by trying different strategies and decisions. In a typical modeling project, the modeler often learns a great deal through the process of building a model, while the model remains a “black box” to the client. By making the modeling insights accessible through a gaming interface, a non-technical audience can explore the assumptions and causal links behind a system dynamics model. The black box then becomes a learning tool.

A good game design must include the following basic qualities: they must allow the player to learn the concepts that the model was designed to communicate; they must be easy to use and yet challenging; and they must be fun to play. For example, when I was asked to design a simulation model and interface for a computer exposition, I created a simulator that looked very much like a video arcade game. It had flashing lights, sound effects, and exciting warning and congratulatory messages sprinkled throughout. But embedded within the flashy exterior was an exploration of the dynamics of marketing — complete with a debriefing of the causal loop diagrams. The simulation was fun and exciting, but it also provided a learning experience.

Decision making vs. Policy making

In business, managers are constantly asked to make decisions, guided by company policies that govern various aspects of decision-making. For example, a manager may make ordering decisions weekly, but there may be a policy that requires the inventory level to always be above a certain minimum level. The manager exercises her own judgement guided by the policy of maintaining certain inventory reserves. Both Games and simulations are valuable for helping managers and policymakers gain experience by testing, failing, and retesting policies and decisions on simulated companies without risking real people and dollars.

From a modeling viewpoint, games support decision making while simulations support policy making. In a game, players must make decisions at the beginning of each and every round. Players interact with the computer period-by-period, allowing them to test innovative decisions and develop expertise in formulating rules for future decisions. In a simulation, users are asked to provide a set of policies at the beginning of the simulation. The users then watch as the effects of their policy deployments unfold over time. Through multiple simulations, they can test long term strategies and engage in scenario planning. They cannot, however, interact with the computer on a real-time basis.

With the advent of recent interface design tools, managers can now create software interfaces that convert their system dynamics models into games, which can then be used as corporate learning tools. Currently, there are a number of powerful interface design tools that are compatible with models written in either STELLA or Professional Dynamo (see “Interface Software” box).

Games as Corporate Memory

Like traditional games, system dynamics-based computer games can be used to teach as well as entertain. Managers can use simulation games to make their “mental models” explicit and hone their decision-making skills. Games can also serve as “corporate memory” by capturing the important insights generated by a modeling project or other experience. Such insights can then be passed along to new employees by allowing them to explore these microworlds. The game can give employees an overview of the organization, while at the same time helping them “bond” with the corporate culture.

Although you may not feel comfortable playing the “bloop, bloop” game with the CEO of an international corporation, games will continue to play an increasingly important role in preparing managers for the challenges of tomorrow. Let the games begin!

W. Brian Kreutzer is vice president of research and development at Gould-Kreutzer Associates, Inc., Cambridge. MA. He co-authored the second edition of Managing A Nation, a microcomputer software catalog, with Drs. Gerald Barney and Martha Garrett. He has designed a large number of software interfaces in conjunction with his consulting work.

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Similarly, two basic loops — reinforcing and balancing — can be seen as the equivalent building blocks of complex social and economic systems. These simple structures combine in an infinite variety of ways to produce the complex systems that we as managers are expected to control.

Engines of Growth and Decay

Reinforcing loops produce both growth and decay. That is, they compound change in one direction with even more change. For example, in the employee-supervisor reinforcing loop below, positive reinforcement from the supervisor is capable of producing good employee performance, while negative reinforcement can produce poor employee performance over time.

Goal-Seeking Processes

Of course, most things in life cannot continue growing forever. There are other forces — balancing loops — which resist further increases in a given direction. Balancing loops try to bring things to a desired state and keep them there, much like a thermostat regulates the temperature in a house.

An equivalent example in manufacturing involves maintaining buffer inventory levels between production stages. In this situation, there is a desired inventory level which is maintained by adjusting the actual inventory whenever there is too much or too little.

Using the Building Blocks

To see how these two basic loops can combine to form more complex structure-behavior pairs, let’s revisit the employee-supervisor feedback loop. Clearly the employee’s performance will not improve indefinitely just because the supervisor is supportive. The employee may have been putting Negative in longer hours in Reinforcement order to continue Time impressing the supervisor. Over a period of time, the increased work hours may begin to wear down the employee’s energy level. If this continues, at some point the supervisor’s supportive behavior will be eclipsed by the sheer energy drain of working long hours. Improved performance will gradually be offset by the effects of burnout, until finally the balancing loop connecting energy level and hours worked becomes dominant. At this point the employee’s performance will either plateau or decline.

Summary Points

All complex dynamic behavior is produced by two loops: reinforcing and balancing. Behind every growth or decay is at least one reinforcing loop. For every goal-seeking behavior, there is a balancing loop.

A period of growth followed by a slowdown in growth is usually caused by a shift in dominance from a reinforcing to a balancing loop.

Next issue: Balancing loops with delays.

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Rapid suburban development was a result of the need for new revenues in financially strapped cities, according to the article. Since Proposition 2-1/2 imposed limits on tax increases, cities have competed heavily for new development to increase their tax base.

To prevent another real estate crisis, the Metropolitan Area Planning Council (MAPC) wants to open a data bank for suburban developers and planners. It would provide information on vacancy rates, thereby helping them time construction to meet market demand. The theory is that more market data would prevent developers from overbuilding and creating another glut of commercial space.

The role model for the MAPC data bank is the system developed by the Boston Redevelopment Authority (BRA) in 1984. The BRA publishes quarterly reports of office vacancy rates based on staff research and information obtained by builders, building owners and real estate brokers. The BRA exercises further market control through its design review process, which can speed up or slow down approval for new projects as necessary. Because of the BRA’s control over project approval, developers have an incentive to cooperate and provide the BRA with the necessary information.

One potential problem for the MAPC project is that the data bank would require developers to supply information on new projects to the MAPC. In an industry where information is often the key to competitive success, such a cooperative venture might break down in a highly competitive market, just when such information would be most crucial for predicting and preventing overdevelopment. Even if developers had access to market data, there is no guarantee that they would adapt their long-term plans accordingly. Many developers believe their own project will succeed despite the market outlook.

Discussion Questions

• A “boom and bust” phenomena like the one that Boston developers experienced usually signals a negative feedback loop with delays at work. Can you trace out at least one loop that might be producing such behavior?
• Based on the loop(s) you drew above, do you think more information would have saved some developers, as the Globe article suggests?
• What was the driving force behind the overbuilding in the suburbs?
• What sort of city intervention would significantly help developers avoid a future cycle of overbuilding?

The Boston real estate market, like any other sector of a free market economy, is subject to the laws of supply and demand (See Toolbox, page 5). As demand for real estate increases and supply remains fixed — which usually happens in the short run — rents begin to rise. If they rise too high, they eventually bring down demand because the rent becomes unaffordable for some people.

Of course there is a supply side to this story. Rising rents increase the attractiveness of real estate and encourage more development. After an appropriate planning and approval delay, new construction begins and, after a construction delay, real estate supply eventually expands. But as new units come into the market and compete for tenants and buyers, rents begin to fall. Together, the supply and demand loops attempt to keep the real estate market in balance.

If all the delays — planning, approval, and construction — were short, perhaps on the order of weeks, then supply would adjust more rapidly and rents would not fluctuate as much.

The market would approach equilibrium, and there would be little discrepancy between supply and demand. In real life, unfortunately, supply adjustments take a long time while demand adjustments can be quick and brutal, as they have been during the latest downturn in Boston real estate.

In the Boston suburbs throughout the eighties, demand remained high and bid the rents to such heights that it drove developers into a “building frenzy.” Due to the delays in the system and poor market information, massive overbuilding occurred. Each developer continued to think his or her development would be the first or the best in the market.

By establishing a real estate data bank, the Metropolitan Area Planning Council (MAPC) hopes to prevent another “boom and bust” cycle by providing developers and planners with an accurate and early warning signal of market saturation. Overbuilding would be signaled by a rise in vacancy rates while a decrease in vacancy rates would indicate an impending space crunch. In theory, providing accurate information on vacancy rates would slow down construction by creating a new balancing loop: as developers saw vacancy rates rising, they would slow down or stop new construction and thus prevent overbuilding.

The success of the MAPC data bank program depends on at least two things: the accuracy of the information on supply and demand, and the credibility it has to affect developers’ decisions. If developers decide to build even though vacancy rates are on the rise, the balancing process will not work.

The reason why the BRA is reasonably successful at regulating development is that it exercises considerable control over the planning and approval process. This ensures that developers will cooperate and provide the BRA with accurate information. It also guarantees that developers will not be allowed to ignore market signals and build anyway. Without a regulatory component similar to the BRA, the MAPC would face considerable obstacles in trying to gain cooperation and credibility among developers.

The MAPC would face further difficulties if it tries to regulate development. Unlike the BRA, which only oversees Boston development, the MAPC needs the cooperation the suburban communities-101 “little fiefdoms,” as they have been called. With many of these communities feeling tremendous budget pressures, getting and maintaining an agreement may prove to be the toughest task. Armed with better information, some communities might try to entice more development for themselves by offering special incentives. The result could be a bidding war among the communities that would encourage even more development.

For the MAPC project to be effective, all the surrounding communities would have to agree to coordinate development efforts rather than compete for them. By itself, the MAPC data bank will not change the structural forces that are responsible for over-building — namely, the inherent delays in the system that cause rents to rise and remain high enough to entice developers to grab a “profit opportunity” and the other pressures in the system (such as taxes) that make new developments attractive. Without greater consideration of the structural dynamics of supply and demand in the real estate market, providing more data to developers would be a questionable “quick fix” solution. Further reading: “Texas in the Northeast?” Forbes, April 2. 1990 and “US property collapse could drag down world’s markets,” Sunday Morning Post, February 18, 1990.

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Enter ithink, the latest product from High Performance Systems — the STELLA people. STELLA, which first came out in 1985, is a visual diagramming tool for creating computer models of complex issues. It was considered a breakthrough in modeling, because it turned the spreadsheet concept inside-out: the relationships arc put on top, in the form of icons which are linked together, while the numbers are stored underneath the diagram elements.

ithink is the result of High Performance Systems’ recent decision to cleave STELLA into two distinct product lines: a new version of STELLA, which is geared toward an academic audience, and ithink, the business-oriented application. ithink retains the best of STELLA while adding some new features which make it even better at capturing complex issues and testing alternative scenarios.

From ‘Mental Models’ to Computer Models

Psychologists say we can only hold six or seven thoughts in our heads at one time. But since most systems we deal with have far more components, we can’t possibly carry around an accurate picture of those systems. ithink makes it possible to capture our mental models in a diagram, literally drawing a map of the important interconnections and relationships in a system. Using its simulation capabilities, it is then possible to study the dynamics that result from those interconnections.

Visual Tool

When creating a model with ithink, managers start by capturing the “big picture” of their business. Using the “camera” icon, they can insert a picture that represents the overview structure of the problem under study — a map of key markets or suppliers, for example. This picture then opens up to reveal a fuller representation of the system in the form of a diagram of the system’s “plumbing.”

ithink’s four tools — stocks, flows, converters, and connecters — are the building blocks for creating models (see the “Cash Flow” diagram). Stocks represent things that accumulate, such as water in a bathtub or, in the Cash Flow model, cash. “Soft” values can also accumulate, such as frustration or resentment. Flows, like the faucet and drain on a bathtub, determine what flows into or out of a stock. In the cash flow model, revenues flow into receivables, while cash flow drains receivables.

Converters and connector arrows help capture other relationships in a system that are not represented by stocks and flows. For example, in the Cash Flow model, a connector arrow is used to show that cash has an effect on cash constraint (a converter), which in turn affects expenses.

Once the “plumbing” of the diagram is laid out, it is time to fill in any unspecified equations. To help with this process, ithink has over fifty macro and data import capabilities. Or, using ithink’s graphic function, you can sketch in the relationship between two variables (see the cash/cash constraint graph).

You are now ready to bring your creation to life. ithink can automatically run a simulation of your model, giving you output in the form of graphs, comparative plots, or spreadsheets. If the numbers don’t seem right, you can easily change the diagram or the equations to create a more accurate picture of the system. You can also share the model with your colleagues, expanding and refining the model as your collective understanding of the system grows. This ability to quickly sketch out and share your mental picture of a system with others makes ithink well worth its price.

Multiple Scenarios

Once you have fine-tuned the model, you can put ithink’s advanced features to work for you by testing alternative policies or locating “leverage points” — areas in a system where small change can create dramatic results.

The comparative plot feature enables you to save the values of several different model runs and plot them on the same graph (see the “Profit Scenarios” graph). It is then very easy to compare the effect of different policies on key variables. Comparative plots make scenario planning even easier with ithink than STELLA — a big advantage for planning departments.

The sensitivity tools allow you to test a key variable with different values, charting its effect on other variables. Once you set the range of values, ithink automatically runs the model and displays the results on comparative plots. Using ithink’s sensitivity analysis feature, you can locate leverage points by determining which policy gives you the results you want over the long term.

ithink, like its predecessor STELLA, is an excellent tool for understanding complex systems and testing different policies within those systems. But ithink goes one step further, adding features such as sensitivity analysis and comparative plots which enhance its policy-testing capabilities. Although we may currently live in the age of spreadsheets, ithink goes a long way towards ushering in a new age of visual thinking tools.

W. Brian Kreutzer is vice president of research and development at Gould-Kreutzer Associates, Inc., Cambridge, MA. He co-authored the second edition of Managing A Nation, The Microcomputer Software Catalog, with Ors. Gerald Barney and Martha Garrett.

ithink is a product of High Performance Systems, Inc., (800) 332-1202.

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The Structure

Recognizing this paradox can help individuals and companies avoid the “Limits to Success” trap. In a typical scenario (see “Limits to Success Template”), a system’s performance continually improves as a direct result of certain efforts. As performance increases, the efforts are redoubled, leading to even further improvement (loop R1). When the performance begins to plateau, the natural reaction is to increase the same efforts that led to past gains. But the harder one pushes, the harder the system seems to push back: it has reached some limit or resistance which is preventing further improvements in the system (loop B1). The real leverage in a “Limits to Success” scenario doesn’t lie in pushing on the “engines of growth,” but finding and eliminating the factor(s) limiting success while you still have time and money to do so.

In a rapidly-growing company, for example, initial sales are spurred by a successful marketing program. As sales continue to grow, the company redoubles its marketing efforts and sales rise even further. But after a point, pushing harder on the marketing has less and less effect on sales — the company has hit some limit, such as market saturation or production capacity. To continue its upward path, the company may need to invest in new production capacity or explore new markets.

Diets and Weight Loss

Examples abound where rapid success is followed by a slowdown or decline in results. Dieters usually find that losing the first ten pounds is a lot easier than losing the last two, and losing weight the first time around is a lot easier than losing it the next time.

On a diet, eating less leads to weight loss, which encourages the person to continue to eat less (loop R2 in the “Dieting Bind” diagram). But, over time, the body adjusts to the lower intake of food by lowering the rate at which it burns the calories. Eventually the weight loss slows or even stops. The limit here is the body’s metabolic rate — how fast it will burn the food. To continue losing weight, the person needs to increase the metabolic rate by combining exercise with dieting.

But pushing equally hard on exercising isn’t the full answer either, since intense exercise burns simple sugars and not the stored fat that is the real target for weight loss. Intense exercise is counterproductive towards the dieter’s goal because it increases appetite while only temporarily raising the metabolism. The real leverage is to engage in steady exercise such as long, brisk walks that will increase the metabolic rate to a permanently higher level.

Service Capacity Limit

People Express airlines is one of the best-known casualties of the “Limits to Success” archetype (see “‘Flying’ People Express Again,” November, 1990). Its tremendous growth was fueled by a rapid expansion of fleet and mutes along with unheard of low airfares. As the fleet capacity grew, People Express was able to carry more passengers and boost revenues, allowing it to expand fleet capacity even more (loop R3). The quality of its service was initially very good, so the positive experience of many fliers increased word-of-mouth advertising and the number of passengers.

The “engine of growth” at People Express was seen as physical capacity — expanding fleet size, employees and routes. But its “limit to success” was service capacity — the ability to invest time and money in training its employees — which became more difficult to sustain as the company grew (loop R4).

The number of passengers flown eventually outstripped the airline’s capacity to provide good service. As a result, quality suffered and it began losing passengers (loop B3). When competitors began matching low rates on selected routes, People Express’ market competitiveness suffered even more. Focusing only on the reinforcing side of the structure turned rapid growth into a tailspin, contributing to the airline’s demise.

Simply hiring more employees was not the answer to People Express’ service capacity problems. Similar to the dieter’s reliance on intense exercise, it only masked the real need for the steady long-term commitment to hire and train the necessary people to bring service quality up to a high and sustainable level.

Using the Archetype

The “Limits to Success” archetype should not be seen as a tool to be applied only when something “stalls out.” It is most helpful when it is used in advance to see how the cumulative effects of continued success might lead to future problems. When the times are good and everything is growing rapidly, we tend to operate with an “if it ain’t broke, don’t fix it” attitude. By the time something breaks, however, it may be too late to apply a fix.

Using the “Limits to Success” template can help highlight potential problems by raising questions such as “what kind of pressures are building in the organization as a result of the growth?” By tracing through their implications, you can then plan for ways to release those pressures before an organizational gasket blows.

“Limits to Success” and other templates have been developed over the years through the efforts of many system dynamicists, including Peter Senge, John Sterman, Michael Goodman, Jennifer Kemeny, Ernst Diehl. and Christian Kampmarut. The use of the term “archetype” was first coined by Peter Senge in his book, The Fifth Discipline, Doubleday, 1990.

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“A lot of corporate deaths are infant mortality,” notes Arie de Geus, former director of planning at Shell. “Many companies fail to develop succession rules, remaining too dependent on certain individuals. But long-established companies also die or weaken to the point that they become easy prey for predators.” What causes their demise, and how can such an outcome be prevented?

De Geus believes a company’s survival depends on its ability to detect and adapt to critical changes in its environment. “Managing internal change by foresight, rather than by crisis, is only possible if the change in the environment is seen on time,” he insists. At Shell, ‘managing by foresight’ takes the form of scenario-based planning, in which manager’s chart out their responses to alternative future scenarios. This strategy served Shell well in the turbulent oil industry of the 1970s and 1980s, and de Geus believes it has widespread applicability, “since the environment in which all companies work has shown oscillations of increasing frequency and amplitude since the 1970s.”

Remembering the Future

The reasoning behind scenario planning, explains de Geus, is that by planning out alternative strategies, companies can better prepare for and adapt to changes in its environment. Simply put, “We will not perceive a signal from the outside world unless it is relevant to a future which we have already worked out.”

This philosophy goes back to the work of Swedish neurobiologist David Ingvar. According to Ingvar, the human brain is constantly generating multiple scenarios of the future and then storing these alternatives. In effect, we are continually creating and saving memories of the future. By engaging in this activity 24 hours a day, we mentally prepare for future possibilities. These “memories of the future” protect the brain from information overload by directing us toward signals that are relevant to a future that we have already “seen” in our mind’s eye.

According to Ingvar’s work, being single-minded is not a compliment — in individuals or in companies. Says de Geus, “Most companies have usually worked out only one path — the operating plan or the strategy which covers only the near future. This corporate ‘one-track mind’ means the company sees and hears very few possibilities for change.” It also increases the possibility of missing important signals that appear tangential or unrelated to the operating plan.

Signals of Change

Why don’t many companies see the signals of change? “In the euphoria of expansion, changes in the environment are often not seen or are not seen for what they are,” explains de Geus. “The newspapers are full of examples of companies which, in the face of change, continue to pursue their previously successful expansion policies, more or less adapted to what they see as a temporary aberration.”

When Shell reviewed the past scenarios generated by its planning group, they discovered that they had actually predicted many major shifts in the environment prior to their occurrence. In 1972, for example, Shell’s planners presented scenarios of possible disruptions in oil supply and demand, but the managers greeted them with skepticism.

“How can a group who is offered such insights on a platter not be able to see its value?” wondered de Geus and the other members of Group Planning. They came to the conclusion that if their scenarios did not match managers’ mental models — their “memories of the future” — the managers wouldn’t take them seriously.

This realization led to a fundamental change in the way Group Planning saw its role. As Pierre Wack, former senior planner at Shell, described it, “We no longer saw our task as producing a documented view of the future business environment five or ten years ahead. Our real target was the microcosms [mental models] of our decision makers: unless we influenced the mental image, the picture of reality held by critical decision makers, our scenarios would be like water on a stone.”

Planning as Learning

In 1985, Shell used scenario planning to help its managers prepare for another possible disruption in the industry. Group Planning brought together the top managers and presented them with the following scenario: “it’s April 1986 and the price of oil has fallen from $30 at the end of 1985 to $16 today. What do you think your government will do? What do you think your competition will do? And what, if anything, will you do?” The price of oil at the time of the meeting was $28 and rising, de Geus recalls, and “$16 was the end of the world to oil people.” But the object of the exercise was not to debate if the scenario would happen, only what Shell would do if it did.

As a result of those discussions, Shell assigned 150 engineers to work on designing a cheaper oil platform that would help the company benefit if oil prices dropped. By April of 1986, oil prices had fallen — to S10 a barrel. While other companies announced massive layoffs and slowed investments in refineries, Shell announced two new oil fields in the North Sea. And while other companies were responding to the crisis by strengthening central control, Shell gave its operating companies more room to maneuver and adapt to changing local conditions. Because the managers had already developed action plans for surviving a steep price drop, they were able to quickly adapt when that possibility became a reality.

Transitional Objects

Play is a key element of the scenario planning process at Shell. “In a corporate setting, just explaining your mental model to someone else will not have a big impact on them,” de Geus explains. “You need to provide a transitional object that the other person can play with.” The introduction of computer models and management “flight simulators” into the corporate boardrooms has been a significant advancement in this area. Using system dynamics models in the planning process, for example, has shortened the learning cycle. “Learnings in these play sessions are usually so graphic that no one needs to write the conclusions down. The result is that people find more options—more time paths into the future.”

Corporate Survivors

Sometime after its “Fortune 500” comparison, Shell planners conducted another study. This time they searched for companies that would inspire Shell — companies that were older (Shell itself was 100), were relatively as important in their industries, had experienced some fundamental environmental changes (like the oil shocks of the 70’s), and had survived with their corporate identity intact. They came up with a handful — companies like DuPont, the Suez Canal Company, and the Hudson Bay Company. Their ages varied from 200 to 700 years, many times the average life expectancy of a person.

“Unless we influenced the mental image, the picture of reality held by critical decision makers, our scenarios would be like water on a stone.”

“Some of the older companies like the Swedish firm Stora have had their ups and downs as a result of changes in the world over its 700 years of existence,” notes de Geus, “but most of the time they had picked up the signals of change and had acted upon them before it developed into a crisis. They show that it is possible to see the signals of change earlier than most companies do — and that, when it comes to corporate life expectancies, there is considerable room for improvement.”

Further reading: Arie de Geus, “Planning as Learning,” Harvard Business Review, March/April 1988 and “Stockton Lecture,” London Business School, May 3, 1990. Pierre Wack, “Scenarios: Shooting the Rapids,” Harvard Business Review, November/December 1985 and “Scenarios: Uncharted Waters Ahead,” Harvard Business Review, September/October 1985.

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Over one-third of all the papers discussed some aspect of learning — from the use of computers in the classroom (“Systems, Science, and Schools”) to the design of learner-directed learning environments (“Designing Learning Environments”).

The central issue — and challenge — facing all those involved in systems thinking is how to enhance the learning process and accelerate the rate at which people internalize the principles and concepts of systems thinking. Barry Richmond of High Performance Systems, Inc. (Lyme, NH) presented the challenge eloquently in his paper “Systems Thinking: A critical set of Critical Thinking Skills for the 90’s and Beyond,” in which he offered a framework for dis-cussing the task ahead.

If systems thinking is a powerful aid for managing in a world of ever-increasing complexity, then the challenge that must be addressed, according to Richmond, is “How can the framework, process and technologies of systems thinking he transferred to the rest of the world in an amount of time that is considerably less than what it currently takes to get a Master’s or Ph.D. degree in (system dynamics]?”

The first step in that process is clarifying what skills and concepts are integral to systems thinking. Richmond identified five critical thinking skills (see diagram) which systems thinkers must be engaged in simultaneously. He argued that presenting systems thinking in its entirety leads to “cognitive overload” which frustrates and discourages would-be learners. Working with each of the five pieces separately, he suggested, will allow people to assimilate it piece by piece.

Progress is already being made on breaking down systems thinking into more manageable pans. For example, Daniel Kim outlined ten distinct “bitesize” systems thinking tools in his paper “Total Quality and System Dynamics: Complementary Approaches to Organizational Learning” (see this issue’s Toolbox). Combined with the tools and methodology of Total Quality (TQ), these tools can help managers learn more effectively at both the conceptual and operational level. TQ’s emphasis on experimentation as part of a continual improvement process will greatly enhance the transfer of systems thinking skills. And systems thinking will extend the reach of TQ beyond its current arenas of success by providing a framework for dealing with interdependencies that span great gulfs of space and time.

In their paper “Systems Thinking and Organizational Learning: Acting Locally and Thinking Globally in the Organization of the Future,” Peter Senge and John Sterman described the process of engaging managers in the design of “virtual” worlds — learning environments where they can surface and test operating assumptions, formulate hypotheses, run experiments, and reflect on the consequences of their actions. These virtual worlds can accelerate learning by minimizing the confounding factors and delays that contribute to ambiguity and distortion in the real world (see diagram). Significant learning begins as managers interweave their explorations of the virtual world with the real world in a seamless process of clarifying their “mental models” and adjusting their strategies, structures, and decisions accordingly.

Other new developments include computer technologies that aid learning in one or more of the five critical thinking areas. In his paper “Causal Tracing: One Technical Solution to the Modeling Dilemma,” Robert Eberlein presented a computer tool that facilitates the development of dynamic and structural thinking. His software shortens and simplifies the process of tracing through causal chains and plotting the time-behavior of the variables.

Other software such as HyperCardnd (Apple Computer, Cupertino, CA), STELLAStackTm (High Performance Systems, Lyme, NH), and MicroWorlds Creatorrm (MicroWorlds, Cambridge, MA) enable the creation of engaging, user-friendly simulation gaming interfaces with relative ease, as Ken Simons described in his paper “New Technologies in Simulation Games.”

Computer simulation games — alone or as part of a virtual world — can be particularly useful in developing dynamic thinking skills by linking behavior with past actions. Users can also practice their scientific thinking skills by running many quick experiments via repeated simulations.

The rate and quality of learning depends heavily on the skills we bring to a new problem or situation. Hence, the identification and development of ‘critical thinking skills is vital. Systems thinking encompasses at least five critical thinking skills that must operate simultaneously, but be mastered one at a time. Much has been done to address the need for transferring those skills to a broader audience; much remains to be done. The 1991 International System Dynamics Conference will undoubtedly deliver many more responses to the challenge.

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Contrary to conventional wisdom, Americans are proving not only able but willing to separate their garbage. In fact we’re becoming such ferocious recyclers that old newspapers are piling up by the ton with no place to go.

As the market for used newsprint crashes, some recyclers are getting discouraged, and some purveyors of conventional wisdom are saying, see there? Recycling just doesn’t work. There’s no market for it.

It would be more accurate to say we don’t know if recycling works; we haven’t yet tried it. When we do, gluts and scarcities will be signals not that there’s no market, but that the market is working the way it always works — in fits and starts.

What we are doing so far is separating, not recycling. We’re beginning to reclaim materials before they get to the dump. We have barely begun to close the loop — to reuse major materials in the same products: newspapers back to newspapers; plastic soda bottles back to soda bottles.

Product-to-same-product recycling is the only kind that can work in the long run.

Product-to-same-product recycling is the only kind that can work in the long run. Turning newspapers into cattle bedding will be helpful for a while, but eventually it will clog, either because we use newspapers faster than cattle bedding, or vice versa. Similarly the plastics industry is congratulating itself too soon for turning soda bottles into plastic flowerpots. Given the nation’s consumption rate of soda versus flowerpots one can easily predict a market collapse due to flowerpot glut.

What works is illustrated by the nation’s one smooth-running and economical recycling system—aluminum cans back into aluminum cans.

Even when newspapers are printed on recycled newsprint and the plastics industry makes new bottles out of old, there will be glitches, scarcities and overflows. These are inevitable in the evolution of any production system, especially one that is guided by the market. The only way the market can sense a large potential supply of something new is to let that something accumulate somewhere. The only way the market can stimulate a demand is to bring the price down low enough, and be sufficiently assuring about future supply, to stimulate new users.

In short, don’t let a temporary newspaper glut discourage you. We’re just at the beginning of a major industrial transformation. We’re working out a material-supply system consistent with a finite planet. It will be totally different from the wasteful, polluting system we have now — and it will take a while to get it right.

It’s worth keeping part of our attention cast ahead of the immediate economics to the place where we’re ultimately headed. A sustainable, economic, ecologically supportable materials system will re-use everything it can. It will add virgin materials only as necessary to sustain product quality. It won’t waste materials on unproduc-tive purposes such as packaging—it will use uniform and minimal packaging, standard bottles or boxes of standard sizes, interchangeable among products, for easy re-use. Only the label will distinguish the product.

” ..gluts and scarcities will be signals that the market is working the way it always works—in fits and starts.”

Marketers will have to attract consumers with a reason to buy their product that’s more important than glitzy packaging.

For easy recycling the use of mixed materials in manufacture (like the infamous squeezable ketchup bottle with seven different laminated plastics) will be discouraged. Products will be designed to last longer and be easily repairable. There will be thriving businesses that refurbish or recapture the components of large appliances. All this can be brought about simply by adding a tax to each product equivalent to the real cost of its disposal. That’s not distorting to the market, it’s correcting the market by adding a cost signal that should have been there all along.

When materials are finally so well used that they must be thrown out, they will be more carefully separated than they are today. First and most important, toxics — heavy metals, organic chemicals, radioactive materials — will not be allowed at all in municipal waste streams or sewage treatment systems. Toxics will go into entirely separate collection and disposal systems.

With the hazardous wastes out of the way, all organic wastes will be composted, along with sewage sludge. That single step will reduce garbage volume by 30 percent and provide tons of safe, free fertilizer. If glass, paper and metals are recycled, that would bring the garbage flow down to just 10 percent of what it is now. And that doesn’t include the possibility of recycling plastic.

No part of this dream system is impossible. Every piece of it is operating somewhere. Denmark has a model system for processing toxic wastes. The Netherlands has a massive composting system, as do many American cities. Technologies exist to reclaim paper, glass, metal and some plastics. Put them all together and we’d have the same material quality of life but with less mining, less air and water pollution, less traffic, less taxes, and 90 percent less stuff to haul to the curb every week.

Along the way toward a sustainable materials system — or any large social goal — there will be fits and starts, market failures, and disappointments. It’s worth hanging in there. We’re on the right track.

Donella Meadows is an adjunct professor of environmental and policy studies at Dartmouth College. She writes a weekly column for the Plainfield. NH Valley News.

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There are at least ten distinct types of systems thinking tools (a full-page summary diagram appears on the next page) which fall under four broad categories: I. Brainstorming Tools, II. Dynamic Thinking Tools, III. Structural Thinking Tools, and IV. Computer-Based Tools. Although each of the tools are designed to stand alone, they also build upon one another and can be used in combination to achieve deeper insights into dynamic behavior.

Brainstorming Tools

The Double-Q (QQ) Diagram is based on what is commonly known as a fishbone or cause-and-effect diagram. The Q’s stand for qualitative and quantitative, and the technique is designed to help participants begin to see the whole system. During a structured brainstorming session with the QQ diagram, both sides of an issue remain equally visible and properly balanced, avoiding a “top heavy” perspective. The diagram also provides a visual map of the key factors involved. Once those factors are pinpointed, Behavior Over Time Diagrams and/or Causal Loop Diagrams can be used to explore how they interact.

A QQ diagram begins with a heavy horizontal arrow that points to the issue being addressed. Major “hard” (quantitative) factors branch off along the top and “soft” (qualitative) factors run along the bottom. Arrows leading off of the major factors represent sub-factors. These sub-factors can in turn have sub sub-factors leading off of them. However, many layers of nesting may be a sign that one of the sub-factors should be turned into a major factor.

Although QQ diagramming may sound like a very rigid process, it can help give form and structure to “fuzzy” problems. It can be likened to the free flowing visualization process that an artist uses which encourages creativity while still adhering to artistic “rules.”

Dynamic Thinking Tools

Behavior Over Time (BOT) Diagram. BOT diagrams are more than simple line projections—they require an understanding of the dynamic relationships among the variables being drawn. For example, say we were trying to project the relationship between sales, inventory, and production. If sales jump 20%, production cannot jump instantaneously to the new sales number. In addition, inventory must drop below its previous level while production catches up with sales. By sketching out the behavior of different variables on the same graph, we can gain a more explicit understanding of how these variables interrelate.

Causal Loop Diagram (CLD). The Causal Loop Diagram provides a useful way to represent dynamic interrelationships. CLD’s make explicit one’s understanding of a system structure, provide a visual representation with which to communicate that understanding, and capture complex dynamics in a succinct form. CLD’s can be combined with BOT’s to form structure-behavior pairs which provide a rich framework for describing complex dynamic phenomena (see Toolbox, Vol. 1, No. 1, “Reinforcing and Balancing Loops: Building Blocks of Dynamic Systems” and Vol. 1, No. 2, “Balancing Loops with Delays: Teeter Tottering on Seesaws”). The CLD’s are the systems thinker’s equivalent of the artist’s primary colors.

System Archetypes is the name given to certain common dynamics that seem to recur in many different situations. These archetypes, consisting of various combinations of balancing and reinforcing loops, are the systems thinker’s “paint-by-numbers” set — users can take real-world examples and fit them into the appropriate archetype. Specific archetypes include: Drifting Goals, Shifting the Burden, Limits to Success, Success to the Successful, Fixes that Backfire, Tragedy of the Commons, Escalation.

Structural Thinking Tools

Graphical Function Diagrams, Structure-Behavior Pairs and Policy Structure Diagram can be viewed as the building blocks for computer models. Graphical Functions are useful for clarifying nonlinear relationships between variables. Structure-Behavior Pairs link a specific structure with its corresponding behavior. Policy Structure Diagrams represent the decision-making processes that drive policies. In a sense, when we use these tools we are moving from painting on canvas to sculpting with hammer and chisel.

Computer-Based Tools

This class of tools, including computer models, management flight simulators, and learning laboratories, demands the highest level of technical proficiency to create. On the other hand, very little advance training is required to use them once they are developed. We will discuss these tools in future issues from the perspective of predesigned packages — art appreciation versus a creation.

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