Using the Archetype Family Tree

The Systems Archetype Family Tree is based on two principles: that the systems archetypes are related strategically to each other, and that many situations can be described by progressing through several archetypes as they are linked on the tree (see “The Archetype Family Tree”). The tool is intended to help you use the relationships between archetypes to figure out how to begin looking at a new situation, and to gain increasing understanding of a problem as you work through the tree.

For example, suppose you were one of the proprietors of that lawn care company. You would start at the top of the chart, thinking about the nature of your situation. Is the phenomenon you want to understand something that used to be growing, whose growth you would like to reinstate, or something that is growing too quickly and you are worried about where it might lead? Then wind your way through the statements on the reinforcing (left-hand) trunk of the tree to see if they apply to your situation.

In this particular case, growth was not an issue; they had a debt problem to consider. If you, like the lawn care company owners, are trying to fix a chronic problem that persists despite your efforts to fix it, you want to work through the balancing (right-hand) trunk. Again, follow the chain of logical relationships, continuing to identify elements of your story.

For example, the lawn care proprietors’ fix” was to borrow whenever their cash flow was low (BI in “A Diagnostic Journey through the Archetype Family Tree”). As they serviced their mounting debt, however, the fix came back to haunt them in a “Fixes that Fail” structure (R2).

A Diganostic Journey Through The Archetype Family Tree

Working through the family tree reveals that what began as a simple balancing process(borrowing to meet cash needs) becoming a “Shifting the Burden” structure, in which the real leverage is to tighten financial control.

The lawn care proprietors sensed they were trapped in a “Shifting the Burden” process because they were not addressing the more fundamental issue of weak financial controls (B3). The borrowing fix was making matters worse by reinforcing a “loose” spending mentality (R4). Having diagnosed the situation, they could take effective action by focusing more attention on the root causes of their problems — low income and high spending — by tightening their budget and investing in better financial management tools.

Experimenting

Experimentation is key to using the archetypes most effectively. It is probably most useful to look at a particular situation or problem through the lens of several different archetypes, moving through the “tree” as needed. You may find yourself combining archetypes, adding loops and links to adapt them more completely to your story. By the time you have gleaned what you can from them, the loops may be five or six generations removed from the original archetype with which you began.

Michael Goodman is vice president of Innovation Associates (Framingham, MA). Art Kleiner has a long-standing background writing about business, environment, and systemic issues. This material will appear, in a different form, in the Fifth Discipline Fieldbook (Doubleday, forthcoming Spring 1994) .

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Of course, the observations would be exactly the same. This is precisely the dilemma that occurs in a “Growth and Underinvestment” structure: how can you tell whether your customers are defecting because of actions you are taking, or simply because of the “natural” dynamics of the product lifecycle?

A Dying Product Line

A manager in a Fortune 500 consumer products company (CPC) once told a story about a product they had decided to discontinue. They felt that its projected future market potential was not worth further investment. In fact, they were convinced the product was entering its dying stages, so they decided to hasten the inevitable by shutting the plant down.

At the same time, however, they were just beginning a strategic alliance with a Japanese manufacturer who wanted to take over the product line. As a gesture of good will, CPC agreed to license the product — on the condition that they would not renew the license if the Japanese firm was unable to sell at least 5000 units per year. Much to their surprise, the Japanese firm sold over 15,000 units in the first year alone.

It was the same product, manufactured at the same plant, which was operated by the same people. How, then, was the Japanese firm able to produce results CPC could not even imagine, let alone achieve? The answer lies, in part, in understanding the dynamics of the “Growth and Underinvestment” archetype.

Growth and Underinvestment

The “Growth and Underinvestment” structure is a special case of the “Limits to Success” archetype (see “Growth and Underinvestment: Is Your Company Playing with a Wooden Racket?” Vol. 3, No. 5). The storyline of the archetype can be described as follows: A company experiences a growth in demand that begins to outstrip the firm’s capacity. When the capacity shortfall persists, the company’s performance (such as on-time delivery) suffers and demand decreases. The fall in demand, however, is then seen as a reason for not making future investments in capacity, rather than a symptom of past underinvestments. This leads to a self-fulfilling cycle of continued underinvestment and falling demand. In the end, the decision to shut down production, as in CPC’s case, may seem the only appropriate action.

How, then, can an organization avoid doing something that it cannot even see? As in the case of the earth-centered view of the universe, we need a theory that provides us with a different interpretation of the same observations. The following seven-step process can help us use the “Growth and Underinvestment” archetype to better assess investment choices.

1. Identify Interlocked Patterns of Behavior

Ancient astronomers studied the movement of the sun and related its orbiting patterns to the changing seasons. Similarly, to recognize a “Growth and Underinvestment” archetype, we need to identify relevant patterns that appear to be interconnected — such as capacity investment decisions with customer orders or performance measures (e.g. de-livery delay). If there appears to be a systematic correlation, it may be an indication that the two are causally linked.

2. Identify Perceptual Delays

A critical step in analyzing how investment decisions are made is identifying the delay between the time when performance falls (e.g. deteriorating service quality) and when additional capacity actually comes on line. A significant source of that delay is in perceiving the declining performance (see “Underinvesting in Service Capacity”). Questions such as “How fast do we believe we should respond to falling performance measures?” or “What are the internal ‘hurdles’ that a product must pass?” can help surface mental models that may be blinding the organization to the need to invest.

3. Quantify and Minimize Acquisition Delays

In order to identify acquisition delays, you need to have a clear idea of the procedures and people that will be involved in the process of deciding and acquiring the additional capacity. Quantifying those delays requires a thorough understanding of how the whole process actually operates — not the just way it is “supposed” to work.

Minimizing both the perceptual and acquisition delays is important because if the time delay in adding capacity is too long, the performance measure will continue to deteriorate until product sales falls off. When sales fall, it alleviates the pressure on the performance measure (B2) which, in turn, can send a signal that further investments are not necessary (B3). The lack of investment pushes the two balancing loops into a figure-8 cycle that becomes a vicious reinforcing spiral of deteriorating quality and lower demand. Although the decreasing product sales are a result of the company’s inaction, it looks as though the customers have made a unilateral decision not to buy the product.

4. Identify Related Capacity Shortfalls

Expanding capacity for a product often entails further investments in many areas to develop support mechanisms and infrastructures. Expanding service capacity, for example, may lead to increased sales that will outstrip capacity somewhere else. If other parts of the system are too sluggish to capitalize on the added capacity, the customers may still view the company as providing poor service. Demand will then drop, thus kicking off the figure-8 dynamic described above.

5. Check for Eroding Performance Standards

To what extent are current investment decisions based on standards derived from past performance? For example, a 50-hour work week or a 3-month backlog may be the currently accepted signals that trigger additional investments, but the signal to expand may seldom come because of the demand-dampening effect that results when we wait too long to invest. An additional danger lies in the existence of a link between current performance levels and the performance standard, because it can also create a reinforcing cycle of eroding standards that leads to underinvestment and further erosion (R4).

6. Avoid Self•Fulfilling Prophecies

As in CPC’s case, we need to ultimately question the deep set of assumptions that drive our capacity investment decisions. The BCG Growth-Share matrix is an example of a framework for making strategic investment decisions that can lead to self-fulfilling prophecies. Through rigorous analysis based on a set of assumptions, the process produces categories — question marks, stars, cash cows, and dogs — which guide investment decisions. Problems arise when the labels outlive the relevancy of the analysis and simply become self-sustaining prophecies, i.e., you believe that a product is a “dog,” therefore you underinvest in it, and it stays a dog. Avoiding that danger requires going back and challenging the basic assumptions about the product, which includes reevaluating both the product and the market.

7. Search for Diverse Inputs

Challenging basic assumptions requires having multiple viewpoints which can move the discussion beyond current understanding. When making investment decisions, try to involve people who have a new perspective on issues such as who the customers are and what they see as the benefits of the product. This may help you break out of the box of current thinking, which is particularly important if you arc contemplating abandoning a product.

Encouraging “Intrapreneurs’

The real message of the “Growth and Underinvestment” archetype is that investment decisions should be made from a fresh perspective each time. Instead of relying on past performance or past decisions, try playing “intrapreneur” and look at the process as if you are introducing a brand new product. This may provide the necessary perspective to see new life where others see only a dead product.

Note: The “Growth and Underinvestment” archetype is a special variant of the “Limits to Success” archetype. It is difficult to illustrate with general examples because it requires specific detailed information about how investment decisions are made within companies. For additional help in using this archetype, see “Using ‘Limits to Success’ as a Planning Tool,” Vol. 4, No. 2.

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If you were a little bit better, you might have been willing to invest in a new high-performance racket. But you decide that tennis is really not for you. Besides, another friend has just given you a pair of ski boots. They’re a little beat up and a bit tight at the toes, but then again you don’t know whether you’ll like skiing….

Growth and Underinvestment

The above scenario is an example of the “Growth and Underinvestment” archetype at work. At its core is a reinforcing loop that drives the growth of a performance indicator and a balancing force which opposes that growth (loops RI and B1 in “Growth and Underinvestment Archetype”). An additional loop (B2) links performance to capacity investments, and shows how deteriorating performance can justify underinvesting in capacity needed to lift the limit to growth. This propensity to underinvest in the face of growth makes “Growth and Underinvestment” a special case of the “Limits to Success” archetype (see “Limits to Success: When the ‘Best of Times’ Becomes the ‘Worst of Times,— Toolbox, December 1990).

In the tennis example, the reinforcing process is practice, which improves performance (loop RI in “Practice Makes Perfect?”). Improvement slows, however, as you reach the point at which the equipment limits your ability (loop B1). If your decision to purchase better equipment is dependent on your past performance, you may fall victim to this archetype. Without investing in better equipment, your performance will likely plateau — or even decline as you become frustrated and spend less time practicing. The result then justifies your decision not to invest in a new racket.

Legacy of the Past

Often in a “Growth and Underinvestment” situation, ghosts of past failures remain as a systemic legacy, influencing current decisions. A classic example is the story of a capital equipment manufacturer. The company’s CEO had seen an industry downturn in which the company had been saddled with too much capacity, so he was cautious about expanding. The company’s product was selling well, however, and a backlog began to pile up — three months’ worth of orders, then four, then five. The CEO continued to believe that it was just a temporary spurt. When the backlog grew to six months, he finally agreed to expand production capacity.

It took about a year and a half for the additional capacity to come online. In the meantime, demand trailed off as people found alternative sources. The company gradually worked off the backlog, and orders started to pick up again. After a couple of years they were in a similar backlog, but the CEO was even more reluctant to invest in new capacity because of what appeared to be a continual cycle of growing and falling demand.

The “Growth and Underinvestment” archetype reveals that the company’s slow response may actually have created the cyclical demand. The reinforcing action of marketing activities, coupled with the balancing action of delivery delays, trace out a “Limits to Success” archetype, in which the limit is production capacity (loops RI and B1 in “Capacity Delays and Underinvestment”). As performance declined relative to performance standards, the perceived need to invest increased, until investments were finally made (loop B2).

Because of the delay in capacity coming online, however, delivery performance continued to decline for a while, hurting new orders. In the meantime, deliveries began to increase and the company crawled out of backlog. This led the CEO once again to question the need to invest in capacity, making him even more conservative the next time they were in a backlog situation.

Downward Spiral

If this dynamic continues through many cycles, customers are not likely to keep coming back. The result may be a downward spiral of cutting back on investments: the two balancing loops lock into a figure eight dynamic in which the effects of the reinforcing loop no longer have much impact on growth, while the combined balancing loops create a counter-reinforcing process of continual cutbacks. As demand goes down, delivery performance goes back up, creating less need for capacity investments. If capacity dips below the level needed to service incoming orders, performance will go down again, reducing demand even further. Perceived need to invest will be decreased, so investments will decrease, leading to even less capacity over time (as older equipment depreciates or is taken offline). Thankfully, the reverse situation can also be true: the two balancing loops can trace out a reinforcing loop that continues to expand demand and performance.

Breaking the Cycle

To determine whether a Growth and Underinvestment” structure is at work, start by looking for patterns of oscillations in customer demand. If you overlay that with capacity investments and find that they follow the same pattern, you’re probably in a “Growth and Underinvestment” situation.

If a company waits until it receives signals from the marketplace to invest in capacity, it may be too late to prevent some fall-off in demand that will result because of the delay between investment decisions and capacity coming online. The key is to develop a way of assessing capacity needs relative to demands before the performance indicator starts to suffer.

Take some time early in the growth phase to determine what the limits may be, especially with respect to capacity. Studying the market response and characteristics of your target customers during an upswing can help you anticipate future capacity needs.

Also make sure internal systems are set up to deal with growth: if you have an aggressive growth strategy but a sluggish internal system for responding to performance shortfalls, then you might have created a structural inability to handle continued growth.

Most importantly, explore the assumptions driving your capacity investment decisions. Past performance may be a consideration, but it should not dominate your decisions. Instead, identify the marketplace factors that are driving growth. Otherwise you may end up with investment decisions that are too dependent on past experience and not on present (and future) needs.

“Growth and Underinvestment” and other archetypes can be found in The Fifth Discipline By Peter Senge (Doubleday).

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The ultimate success of a system dynamics investigation depends on a clear initial identification of an important purpose and objective. Presumably a system dynamics model will organize, clarify, and unify knowledge. The model should give people a more effective understanding about an important system that has previously exhibited puzzling or controversial behavior. In general, influential system dynamics projects are those that change the way people think about a system. Mere confirmation that current beliefs and policies are correct may be satisfying but hardly necessary, unless there are differences of opinion to be resolved. Changing and unifying viewpoints means that the relevant mental models are being altered.

Unifying Knowledge

Complex systems defy intuitive solutions. Even a third-order, linear differential equation is unsolvable by inspection. Yet, important situations in management, economics, medicine, and social behavior usually lose reality if simplified to less than fifth order nonlinear dynamic systems.

Attempts to deal with nonlinear dynamic systems using ordinary processes of description and debate lead to internal inconsistencies. Underlying assumptions may have been left unclear and contradictory, and mental models are often logically incomplete. Resulting behavior is likely to be contrary to that implied by the assumptions being made about underlying system structure and governing policies.

System dynamics modeling can be effective because it builds on the reliable part of our understanding of systems while compensating for the unreliable part. The system dynamics procedure untangles several threads that cause confusion in ordinary debate: underlying assumptions (structure, policies, and parameters), and implied behavior. By considering assumptions independently from resulting behavior, there is less inclination for people to differ on assumptions (on which they actually can agree) merely because they initially disagree with the dynamic conclusions that might follow.

If we divide knowledge of systems into three categories, we can illustrate wherein lie the strengths and weaknesses of mental models and simulation models (see “Three Categories of Information”). The top of the figure represents knowledge about structure and policies; that is, about the elementary parts of a system. This is local non-dynamic knowledge. It describes information available at each decision-making point. It identifies who controls each part of a system. It reveals how pressures and crises influence decisions. In general, information about structure and policies is far more reliable, and is more often seen in the same way by different people, than is generally assumed. It is only necessary to dig out the information by using system dynamics insights about how to organize structural information to address a particular set of dynamic issues.

THREE CATEGORIES OF INFORMATION

There are three categories of information about a system: knowledge about structure and policies; assumptions about how the system will behave based on the observed structure and policies; and the actual system behavior as it is observed in real life. The usual discrepancy is across the boundary a-a: expected behavior is not consistent with the known structure and policies in the system.

The middle of the figure represents assumptions about how the system will behave, based on the observed structure and policies in the top section. This middle body of beliefs are, in effect, the assumed intuitive solutions to the dynamic equations described by the structure and policies in the top section of the diagram. They represent the solutions, arrived at by introspection and debate and compromise, to the high-order nonlinear system described in the top part of the figure. In the middle lie the presumptions that lead managers to change policies or lead governments to change laws. Based on assumptions about how behavior is expected to change, policies and laws in the top section are altered in an effort to achieve assumed improved behavior in the middle section.

The bottom of the figure represents the actual system behavior as it is observed in real life. Very often, actual behavior differs substantially from expected behavior. In other words, discrepancies exist across the boundary b-b. The surprise that observed structure and policies do not lead to the expected behavior is usually explained by assuming that information about structure and policies must have been incorrect. Unjustifiably blaming inadequate knowledge about parts of the system has resulted in devoting uncounted millions of hours to data gathering, questionnaires, and interviews that have failed to significantly improve the understanding of systems.

A system dynamics investigation usually shows that the important discrepancy is not across the boundary b-b, but across the boundary a-a. When a model is built from the observed and agreed-upon structure and policies, the model usually exhibits the actual behavior of the real system. The existing knowledge about the parts of the system is shown to explain the actual behavior. The dissidence in the diagram arises because the intuitively expected behavior in the middle section is inconsistent with the known structure and policies in the top section.

UNDERINVESTMENT IN CAPACITY

Rising backlog dampens customer orders because of increasing delivery delay (B1). However, if management is reluctant to invest in capacity expansions until the backlog reaches a certain level (Standard “Buffer” Backlog), orders will be driven down until demand equals capacity (R3). The awaited signal to expand capacity never comes, because capacity is controlling sales rather than potential demand controlling capacity (B2). If management tries lowering price to stimulate demand (B4), the resulting lower profit margins will further justify a delay in capacity investment (R5).

These discrepancies can be found repeatedly in the corporate world. A frequently recurring example in which known corporate policies cause a loss of market share and instability of employment arises from the way delivery delay affects sales and expansion of capacity (see “Underinvestment in Capacity”). Rising backlog (and the accompanying increase in delivery delay) discourages incoming orders for a product (B1) even while management favors larger backlogs as a safety buffer against business downturns. As management waits for still higher backlogs before expanding capacity, orders are driven down by unfavorable delivery delay until orders equal capacity (R3). The awaited signal for expansion of capacity never comes because capacity is controlling sales, rather than potential demand controlling capacity (B2).

When sales fail to rise because of long delivery delays, management may then lower price in an attempt to stimulate more sales (B4). Sales increase briefly but only long enough to build up sufficient additional backlog and delivery delay to compensate for the lower prices. In addition, price reductions lower profit margins until there is no longer economic justification for expansion (R5). In such a situation, adequate information about individual relationships in the system is always available for successful modeling, but managers are not aware of how the different activities of the company are influencing one another.

Lack of capacity may exist in manufacturing, product service, skilled sales people, or even in prompt answering of telephones. For example, airlines cut fares to attract passengers. But how often, because of inadequate telephone capacity, are potential customers put on “hold” until they hang up in favor of another airline?

System dynamics models have little impact unless they change the way people perceive a situation. A model must help to organize information in a more understandable way. A model should link the past to the present by showing how present conditions arose, and extend the present into persuasive alternative futures under a variety of scenarios determined by policy alternatives. In other words, a system dynamics model, if it is to be effective, must communicate with and modify the prior mental models. Only people’s beliefs—that is, their mental models—will determine action. Computer models must relate to and improve mental models if the computer models are to fill an effective role.

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