This integration also shows you how to build the foundation for a new kind of organization – a learning organization, where front-line people work in self-managed groups, managers develop their research skills and take on the role of theory-builders, and leaders become more like philosophers who inspire the human spirit. At the core of any learning organization lie learning systems and processes firmly rooted in the two disciplines of TQC and systems thinking. Read this volume in our “Innovations in Management Series” to see how – together – these disciplines provide a powerful method for change.

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This volume explores these questions and introduces the principles and practice of a quietly growing field: systems thinking. With roots in disciplines as varied as biology, cybernetics, and ecology, systems thinking provides a way of looking at how the world works that differs markedly from the traditional reductionistic, analytic view. Why is a systemic perspective an important complement to analytic thinking? One reason is that understanding how systems work – and how we play a role in them – lets us function more effectively and proactively within them. The more we understand systemic behavior, the more we can anticipate that behavior and work with systems (rather than being controlled by them) to shape the quality of our lives.

It’s been said that systems thinking is one of the key management competencies for the 21st century. As our world becomes ever more tightly interwoven globally and as the pace of change continues to increase, we will all need to become increasingly “system-wise.” This volume gives you the language and tools you need to start applying systems thinking principles and practices in your own organization.

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

Economy vs. Ecology

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

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

Sustainabillty

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

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

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

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

A Conceptual Framework

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

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

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

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

Integrating Sustainability Strategies and Organizational Learning

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

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

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

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

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

The Sustainability Challenge

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

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

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

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

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But the importance of service quality is not limited to service industries like banking, insurance, or package delivery. As the global environment grows fiercer every day, manufacturing companies are realizing the importance of focusing on the quality of their services, not just their products.

“You can’t touch or feel a service, nor can you inspect it after the service is completed. At the point of ‘delivery,’ everything that is needed to provide the service must converge in order to provide the customer with high quality.”

“Moments of Truth”

Improving service quality promises a myriad of benefits. It costs far less to keep a customer than to win a new one, for example, and perceived high-quality service firms can often charge up to 10% more for their products than competitors. The question is how to do it.

In the rush to reap the benefits of improving service quality, companies may be too quick to borrow from past experience in manufacturing. But there are important differences that make improving service quality much more elusive.

One obvious difference between products and services is that one is tangible while the other is not. You can’t touch or feel a service, nor can you inspect it after the service is completed. At the point of “delivery,” everything that is needed to provide the service must converge in order to provide the customer with high quality. Each of those interactions are the “moments of truth” which determine whether you are seen as a high-quality service operation or not, according to Jan Carlzon in his highly-acclaimed book, Moments of Truth.

Quality, as seen by the customer, is determined by each moment-of-truth encounter with frontline personnel. The net benefit of millions of dollars worth of capital equipment, buildings, salaries, etc. that a company has assembled will be judged, in large measure, by the quality of those interactions. Those moments of truth are numerous, ephemeral and difficult, if not impossible, to measure. And yet, the long-term reputation and success of a service company is largely riding on them. This suggests that many companies are investing far too little in their front-line personnel. How many moments of truth are actually “moments of despair” for their customers?

Just-in-Time vs. Just-in-Case

For some companies, providing high quality service means creating a just-in-time (JIT) operation, where all the necessary ingredients converge at the point of delivery exactly when it is needed. In a JIT production system, inventories are kept to a minimum throughout the factory by making sure there is just enough inventory at each step of the process to supply the next batch. But it is dangerous to carry the JIT philosophy too far in the service arena.

In manufacturing, the product has already been designed; all that remains is to run the production line as smoothly as possible. Variances in the production line can then be control-charted and maintained. In a service setting, however, front-line personnel have to be ready to produce a service whose design is not fully complete until they interact with the customer. Unlike the manufacturing setting, customers often introduce variances that cannot be controlled in advance. Having adequate capacity online is critical to providing high quality service. In a JIT production system, if the production line goes down, the down-time does not affect the quality of the next product off the line. If there are enough buffer stocks of finished goods, the customer won’t even experience any difference in delivery. If you are under capacity in a service setting, however, there is no way to make it up in real time with “buffered” service time. In addition, it is virtually impossible to “recall” a poorly delivered service. A flight that arrives two hours late and causes people to miss a meeting cannot be changed. A package that is delivered too late for a speech is simply too late. The capacity has to be online and available precisely when the customer requests it. This suggests that, unlike JIT, one may need to plan in terms of Just-in-Case service capacity—that is, service capacity should be weighted more towards peak volume than average volume.

Complexity Line Model

Complexity Line Model

Work can be divided into simple and complex tasks. If the intrinsic needs of the customer is a 50-50 mix, there is a quality gap if any of the quality indicators differs from that mix. For example, Quality Goal assumes a 6040 mix, so a Gap 1 exists.

The Complexity Line Model offers a way of looking at service quality in terms of four different capacity requirements: voice of the customer, the quality goal, working quality standard, and actual quality. In the Complexity Line Model, all service work is viewed either as simple (processing) work or complex (technical) work. In reality, there are many gradations, but for ease of use we will work with the two categories. Simple work means things that can be handled by an entry-level person. Complex work, on the other hand, requires a lot more experience and skill. In general, complex work also requires more time.

Suppose we are managing a customer service call center. Ideally, we should be staffed to match the exact needs of the customer as shown on the Voice of the Customer line (see “Complexity Line Model” diagram). Suppose that the intrinsic needs of the customer calls coming into our center are split 50-50 between simple and complex. That is, on any given day, 50% of the calls are routine. The other 50% are complex and require more understanding about the business.

Gap 1: Understanding the Voice of the Customer. In reality, we never know exactly what the customer needs. The customers themselves may not fully know what they need. The quality goal line represents our current understanding of the customer’s needs. For example, we may be staffed and prepared to handle a call volume that we believe is 60% simple and 40% complex. Gap 1 represents the difference between what the customer actually needs and what we think the customer needs. In this case, 10% of the customers will not receive proper service. Reducing this gap requires an investment in understanding what the voice of the customer is.

Gap 2: Understanding the Voice of the Process. The Working Quality Standard line may be different from the quality goal if the service capacity in place is not sufficient to provide the stated quality goal. In this case, say the people doing the work are capable of handling a call volume where 70% of the work is simple, and 30% is complex. Gap 2 represents our lack of understanding of what our current system is capable of (voice of the process) relative to what we are asking it to do (quality goal). In this case, we fall short of our own goal by another 10%.

Gap 3: Managing Customer-Generated Variance. The Actual Quality line represents the day-to-day moments of truth in which the customer actually experiences our service quality. If we are staffed to meet a 70-30 complexity mix, and the volume of calls stays relatively constant, customers will experience quality at the working quality standard level. Suppose, however, that incoming call volume suddenly jumps by 20%. What will the pressure in the system do to actual quality? The only way to serve a larger number of customers with the same number of people and skill mix is to reassign complex work as simple. The work can then be given to less experienced staff (“I know Joe’s only 2 weeks on the job, but I think he can handle this one”) or we can treat it as simple and spend less time on it (“I don’t think they need to know about all the other options…”).

So now work is handled as if it is 80% simple, 20% complex, which represents the actual quality. Gap 3 represents the daily adjustments that have to be made when customer volume and special requests exceed the capacity established by the working quality standard. Although our working quality standard has not changed, actual quality has grown worse. If this becomes a frequent occurrence, the quality standard can be pulled downward toward actual quality. As the quality standard adjusts to a lower level, actual quality can get pulled down still further the next time the call volume exceeds the already lowered capacity (see “Quality Erosion over Time” diagram).

Drifting Goals Structure

The dynamics of service quality can be captured in a “Drifting Goals” archetype (B1 and B2 in “Managing All the Quality Gaps”). A gap between the quality goal and working quality standard can be closed in one of two ways—lowering the goal (B1) or raising the standard (B2). Lowering the goal is easy and quick; raising the standard takes time and investment (see “Drifting Goals: The Boiled Frog Syndrome,” Toolbox, October 1990).

Focusing on the needs of the customer can help balance the pressure to reduce the quality goal. The voice of the customer increases Gap 1 which increases the pressure to raise the quality goal (B3). In this model, we see that the art of setting quality goals requires balancing the voice of the customer with the voice of the process. In terms of TQM, this means continually trying to identify the intrinsic needs of the customer (voice of the customer) and understand the systems and processes enough (voice of the process) to design them to be in line with those needs.

Managing All the Quality Gaps

Achieving service quality excellence means managing all three gaps to set quality goals that are sensitive to the voice of the customer and the capabilities of the current system.

Maintaining the working quality standard without losing ground requires managing the gap between actual quality and working quality standard. A high-quality operation should have adequate capacity to handle the majority of the variance it encounters and should keep actual quality within a narrow band around the working quality standard. An operation that is out of control would have a wildly-fluctuating and persistent gap.

Managing All the Quality Gaps

Achieving service quality excellence means managing all three gaps simultaneously. Focusing exclusively on the customer and making your quality goals aligned with their needs will reduce Gap 1, but it will only make Gap 2 worse. If you raise the quality goal without investing in the requisite training, personnel and systems, employees will see it as nothing more than banner-waving and go on with business as usual.

If you focus exclusively on reducing Gap 2, however, you may encounter tremendous pressure in the system to close the gap by lowering the goal. The history of the quality standard can often provide compelling evidence that the quality goal is out of line with the “real” system, and lead to “Drifting Goals.” Focusing exclusively on eliminating Gap 3 will create an identical tendency for the standard to float with actual quality.

The customer’s experience of quality is determined by the sum of all three gaps. The challenge for service companies (as well as manufacturing firms with service operations) is to develop the ability to identify and eliminate all three gaps even as the voice of the customer continually changes. It requires investing in service capacity ahead of the current requirements in order to be able to treat each moment of truth with the quality that the customer intrinsically needs or wants. The Complexity Line Model is based on the work of Bob Bergin and Gerri Prusko at Hanover Insurance Co. (Worcester, MA). The author has developed and used a Service Quality Management computer simulator to provide practice fields for managers to understand the complexity line concepts. The software runs only on Macintosh computers. If you wish to acquire a copy, please write to Daniel II. Kim, MIT Organizational Learning Center, MO-294, 1 Amherst St., Cambridge, MA 02139.

Further reading: “Now Quality Means Service Too,” Fortune. Apri122, 1991; Jan Carlzon, Moments of Truth (New York: Harper & Row, 1987).

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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).

The post Managing in the Knowledge Era appeared first on The Systems Thinker.

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

—Colleen P. Lannon

Peter Senge—Creating Transformational Knowledge

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

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

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

Fragmentation

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

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

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

AutoCo: Learning Community in Action

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

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

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

Core Activities

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

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

Creating Learning Communities

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

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

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

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

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

—Edited by Joy Sobeck

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

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

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

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

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

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

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

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

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

Organizational Structures

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

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

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

Moving Toward Resolution

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

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

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

—Edited by Joy Sobeck

Margaret Wheatley—Understanding Organizations as Living Systems

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

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

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

Organizational Identity

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

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

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

The Autonomy of Living Systems

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

The post Learning Organizations: The Promise and the Possibilities appeared first on The Systems Thinker.

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

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

France’s Truffle Crisis

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

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

Creating a Causal Theory

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

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

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

Exploring Solutions

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

Technological Solutions

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

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

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

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

Using Articles for Practice

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

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

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

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

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

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

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

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

Editorial support for this article was provided by Colleen Lannon.

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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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