A simple balancing loop can be thought of as a basic control loop. In this type of feedback loop, any discrepancies between desired and actual states are immediately closed.

In a typical production setting, for example, a backlog represents a gap between new orders (desired) and shipments (actual). If there are no significant delays in the system, a production increase would lead to higher shipments, thereby reducing the back-log (B1). A graph of this system over time shows one blip in production to adjust to the higher level of new orders.

Delays in the Loop

If, however, the factory is already working at maximum capacity, there will be a delay before production capacity can be increased. In the meantime, the backlog will continue to grow. When the new capacity comes on line, production can be ramped up to decrease the backlog. As production moves along, however, the backlog may be reduced to levels that are too low for effective scheduling, so production has to be throttled back. As the adjustments are made, the backlog may grow again, leading to another production adjustment.

This oscillating behavior is common in most balancing loops with significant delays: the delay in response often means we overshoot our target and oscillate around the goal.

Coupled Loops

Of course, a loop rarely exists in isolation. In this case, the dynamics of the delay inherent in loop 51 may affect the quality of customer order fulfillment. The size of the backlog and the length of the delay in bringing more production on line may lead to a decrease in service quality, which, after yet another delay, may affect future business and the current order backlog (B2).

While the company is trying to work through the backlog, many customers may take their business elsewhere until the factory can be more prompt in shipping their orders. With fewer new orders coming in, production is able CO work off the backlog even faster, and after a while, the backlog becomes so small that the factory is able to ship faster than its competitors. This leads to a revival of new orders, which leads to higher backlogs, thus repeating the cycle as shown.

Complex Dynamics

Balancing loops coupled with delays can create complex behavior because there are so many different sources and sizes of delays. When you suspect that this structure is at work, it is helpful to identify and quantify the delays involved. If you can shorten the internal delays, you can achieve greater stability in your system. If you can’t shorten them, being aware of them will at least better prepare you to deal with the consequences of the delays.

TIP: A “boom and bust” phenomenon often signals the existence of balancing loops with delays. If you suspect such a dynamic is at work, by to identify and quantify the key delays in the system.

Drawing balancing loops with delays can help you begin to identify explicitly the delays that may be affecting other processes. It is important to remember, however, that causal loop diagrams are not meant to show the kind or lengths of delays; for that you would need to map out accumulator and flow structures and translate them into computer simulation models. Identifying where delays exist, however, is an important first step in gaining insight into your system’s behavior. Further reading: “Delays: Accumulators in Disguise,” Volume 2, Number 5 (June/114 1991).

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“The stakes are high. Worldwide, the FAO estimates, overfishing and poor management lower the optimum revenues from fishing by at least $15 billion a year. Since 1989, too many boats chasing too few fish have kept the world fleet operating at a loss, despite $54 billion in annual subsidies.” (“Not ‘So Many Fish in the Sea,’ “Business Week, July 4, 1994).

Fishing “Tragedy”

In the fishing industry, the efforts of each individual pay off in tens of larger catches (RI anti R2). However, if the catch rate exceeds the regeneration rate, fish catches will decrease even as the fishermen increase their efforts (83 and B4).

• • •

At one point in the recent movie “Forrest Gump,” Forrest, a novice shrimp boat captain, goes out for his first catch and brings back only five shrimp. “Catch one more, and you can have yourself a shrimp cocktail,” quips a more experienced captain. The audience is left to puzzle over Forrest’s ill fortune — is it the quality of his nets, his lack of experience, or his choice of fishing grounds! The answer comes in the form of a hurricane that sweeps through the area and destroys every boat except Forrest’s. Suddenly his nets are over-flowing, and he’s on his way to becoming a millionaire.

Forrest’s experience sums up the current dilemma facing the fishing industry: there is a limited number of fish (or shrimp) in the ocean, and, therefore, each individual’s success is determined to a large extent by the total number of fishermen. This storyline of multiple groups competing for a limited resource is the foundation of the “Tragedy of the Commons” archetype.

At the heart of the “Tragedy of the Commons” structure is a set of reinforcing loops representing the actions of individual players. These actions make perfect sense to each person — for example, each fisherman’s individual efforts (number of trips, investment in better nets or more boats, etc.) pay off in terms of larger catches (loops RI and R2 in “Fishing ‘Tragedy “). However, if the amount of activity grows too large for the system to support, everyone experiences diminishing benefits, even as they try harder (B3 and B4).

Managing the Commons

Managing a “Tragedy of the Commons” structure involves balancing the resource’s renewal or regeneration rate with how fast it is being depleted. In order to succeed in this balancing act, we need a better understanding of the dynamics surrounding the resource limit: how much of the resource is actually left; how fast is it being depleted; and how fast is it being regenerated? An accumulator and flow diagram can make these structural factors more explicit.

In the case of the fishing industry, the number of fish available can be described using a simple fish lifecycle: the fish hatch, and (if they do not meet an untimely demise) they mature, spawn, and eventually die. The number of new fish being hatched is determined by the number of spawning fish that are available, which is dependent on how many fish grow to maturity (R5 in “Fish Lifecycle”).

Ideally, fishing catches consist mostly of full-grown fish. However, the desired catch for fisherman is determined by overall poundage, and if there are not enough full-grown fish to make up the desired poundage (R6), the number of small- and medium-sized fish being caught will increase (R7 and R8 in “Fish Regeneration Dynamics”). This not only reduces the total fish population, but more importantly, it also depletes the stock of spawning fish. Over time, the effects of this depletion will show up in fewer hatches, which means fewer fish to mature and spawn, leading to a downward spiral in the fish population.

As the population continues to decline, fishermen are forced to go after smaller and smaller fish in order to net the total poundage desired. As Business Week explains, “Since 1960, the total catch of ocean fish and shellfish has risen 66%, to 82 million metric tons in 1992. To reach that level, fleets fish for more species and catch younger fish, which cuts the number of adults of spawning age.” If the population drops below a certain threshold level, there will not be a sufficient number of breeders available and the fish population may face total collapse.

The Dilemma of Delays

Because of the potential threat to the economies of fishing communities around the world, fisheries and governments are scrambling to salvage the nearly depleted stocks of fish. Some of the proposals, according to Business Week, include quotas for certain species, a moratorium on boat licenses, and cutbacks in the number of days that boats can spend at sea each year. To ease the financial pain these measures would cause, aid in the form of boat buy-out programs and low-interest loans to restructure debt could be implemented.

The problem with these proposals, according to some critics, is that they don’t address the underlying problem: fish stocks are being depleted faster than they are being replenished. The difficulty of managing this process lies in the long time delays inherent in the system. The number of full-grown and medium-grown fish can decline for many years before the effects show up in terms of fewer hatches and a smaller population. There is usually a critical point at which the regeneration rates drop dramatically; but by the time the total population reaches this level, there may be too few spawning fish to restock the population. The industry or government is then forced to take drastic measures, such as imposing bans on fishing.

To prevent stock depletion, the fishing industry can develop early-detection systems that will identify an impending problem before the population reaches a critical level. Lowering caps on how many pounds of fish can be caught and tightening weight restrictions might help guarantee that enough spawning fish are preserved to keep population replenishment consistent. If the population is managed more efficiently, not only will the fish population and ocean ecosystems be protected, but the financial success of the industry as a whole will be optimized. Instead of having to face oscillating stocks of fish and the financial uncertainty that comes with such a dynamic, fishermen could expect more stability over time.

Organizational “Commons”

This type of crisis is not unique to the fishing industry. Companies with a centralized sales force, engineering department, or maintenance function can experience similar dynamics. As each division requests more efforts on its behalf, the central resource’s ability to service all of its customers goes down. In this case, usually an implicit or explicit limit is keeping the resource constrained at a specific level.

Although the setting may be different, the lesson is the same: when managing a common resource, it is important to consider the whole system. There will always be a struggle to maintain the right balance between acquisitions and depletions, and early detection signals are necessary to catch potential problems before individual actions over-whelm the system. In a “Tragedy of the Commons” scenario, it is critical to remember that policies aimed at specific parties or behaviors will not be sufficient to counter the reinforcing actions that are played out over time. Just as a hurricane can hardly be expected to wipe out the other players currently using our shared resource, it won’t be long before Forrest Gump sees other boats on the horizon. Other resources: The Fishbanks Game, do Karen Burnett-Kurie, University of New Hampshire, (603) 862-2186.

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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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Perceptions and reality often differ, and it is usually important to capture these differences in your causal diagrams. The true state of affairs can be very different from the perception of that state by the actors in the system.

WHAT IS THE QUALITY OF OUR PRODUCTS?

Distinguishing between actual and perceived conditions adds important information to a model for the management of product quality.

People’s perceptions can be influenced by reporting delays, measurement error, bias, or other distortions, causing people to make different decisions than they would if they had more accurate information.

For example, do the top managers of a firm know the true quality of their products? How do they respond if there is a quality shortfall? In drawing a CLD depicting this situation, separating perceived and actual conditions helps prompt questions such as, How long does it take to measure quality? To change management’s opinion about quality even after data are available? To implement a quality improvement program? To realize results? You might discover that there are significant delays in assessing product quality and in changing management’s opinion about quality (see “What Is the Quality of Our Products?”).

In addition, bias in the reporting system may cause reported quality to differ from quality as experienced by the customer. Customers don’t file warranty claims for all problems or report all defects to their sales representatives. Sales and repair personnel may not report all customer complaints to the home office. Sub ordinates, wishing to avoid delivering bad news, may filter the quality assessment in formation that reaches senior management. The diagram shows these biases, helping to explain how management might come to hold a grossly exaggerated view of product quality. Such a model can serve as the basis for conversation about ways to shorten the delays, overcome the biases, and avoid quality erosion.

Don’t Forget Delays

Delays are critical in creating dynamics. Delays give systems inertia, can cause oscillations, and are often responsible for trade-offs between the short- and long-run effects of our policies. Your CLDs should include delays that are important to the dynamics you are trying to represent or are significant relative to the time horizon relevant to your issue.

For example, when the price of a product rises, supply will tend to increase, but only after significant delays while new capacity is ordered and built and while new businesses enter the market. It’s therefore important to include these delays (by writing the word “delay” or drawing hash marks over the relevant link) because they will affect the system’s behavior over time. Remember that there are both physical, or material, delays, such as the delay between ordering and receiving materials, and information, or perceptual, delays, such as the time required to report sales data, revise forecasts, and make decisions. Both types of delays should be represented in your causal maps.

It’s also useful to remember that delays always involve stock and flow structures. This is why it is doubly important to include delays in your diagrams—they will remind you that there is a stock and flow structure embedded in that particular relationship. You can then explore whether it is useful to make that structure explicit in your diagram. Make stock and flow structures explicit if doing so is important in communicating the basis for the dynamics your map seeks to explain.

Don’t Put All the Loops into One Large Diagram

Our short-term memory can hold from five to nine chunks of information at once. This limits the effective size and complexity of a causal map. Presenting a complex CLD all at once makes it hard to see the loops, understand which are important, or determine how they generate certain behaviors. Resist the temptation to put all the loops you or your client have identified into a single comprehensive diagram.

How then do you communicate the rich feedback structure of a system without oversimplifying? Build up your model in stages, with a series of smaller causal loop diagrams. Each diagram should correspond to one part of the story being told. These diagrams can have enough detail to show how the process actually operates. Then combine simpler versions of the diagrams into a high-level overview to show how they interact with one another. (For more details about ensuring the clarity of your diagrams, see “Tips for CLD Layout.”)

Check to make sure your audience is following the logic of the causal links. If they are not able to follow the thinking without assistance, you may need to include more detail or make some of the intermediate variables more explicit. “Making Links Explicit” shows an example.

MAKING LINKS EXPLICIT

Make intermediate links explicit to clarify a causal relationship.

Once you’ve clarified the logic to the satisfaction of all, you often can “chunk” the more detailed representation into a simpler, more aggregate form. The simpler diagram then serves as shorthand for the richer, underlying causal structure.

Name Your Loops

Whether you use CLDs to elicit clients’ mental models or to communicate the important feedback processes that you believe are responsible for a problem, you will often find yourself trying to keep track of more loops than you can handle. Your diagrams can easily overwhelm the people you are trying to reach. To help your audience navigate the network of loops, give each a number and a name. Numbering the loops R1, R2, B1, B2, and so on helps your audience find each loop as you discuss it. Naming the loops helps your audience understand the function of each loop and provides useful shorthand for discussion.

When working with a client group, it’s often possible to get them to name their own loops. Many times, they will suggest a whimsical phrase or some organization-specific jargon for each loop. For instance, if you have a loop that depicts how working too much overtime might eventually undermine productivity, the group might label it “Burnout.” They might call a loop that shows how schedule pressure can lead to increased errors “Haste Makes Waste.” Loop names make it easy to refer to complex chunks of feedback structure, leading to productive conversations and, ultimately, changes in deeply ingrained behavior.

John D. Sterman is the J. Spencer Standish Professor of Management at the Sloan School of Management of the Massachusetts Institute of Technology and director of MIT’s System Dynamics Group.

This article is part of a 2-part series. Click here to view the first part.

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