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A Systems View of Business Analytics, Part 2

Recurring Patterns in Systems

Published: September 9, 2008

In this second article of a three-part series, Dave Wells describes system archetypes – the recurring patterns that are found in systems – and illustrates how they relate to the patterns found in time-series analysis.

In the first article of this series, I described how systems thinking provides a key to real analytic value – the cause-and-effect connection that is essential to understand why things happen and what to do about them. This second article describes system archetypes – the recurring patterns that are found in systems – and illustrates how they relate to the patterns found in time-series analysis. Virtually every business analytics system analyzes data over time and presents the analysis as time series graphs. Creating the graphs is relatively easy. Finding meaning in them is frequently more challenging. This is where system archetypes are valuable.

Nine system archetypes are widely recognized in systems theory. Each archetype describes a generic structure that can be generalized across many different settings. The underlying relationships are fundamentally the same regardless of the system or setting in which the archetype is found. Each of the archetypes is described below using causal loop diagramming. See the first article in this series for an introduction to the diagramming technique.

Accidental Adversaries

Localization with system-wide sub-optimization typifies the accidental-adversaries archetype. Figure 1 illustrates the archetype as a causal loop model. It is characterized by:

• Two distinct local reinforcing loops exist, represented by localities X and Y.
  
  
• Each locality behaves locally to contribute its own success.
  
  
• X locality behaves cooperatively to contribute to the other’s success.
  
  
• The two cooperative links create a system global reinforcing loop.
  
  
• X’s local actions to contribute to X’s success have unintended consequences that inhibit Y’s success.
  
  
• Y’s local actions to contribute to X’s success have unintended consequences that inhibit X’s success.
  
  
• Overall system potential is limited by the effects of unintended consequences of local optimization without global system awareness. The value of the global reinforcing loop is diminished.

As a socio-cultural example, consider the conflicting goals and activities of national security vs. workforce economics as they relate to U.S. immigration policies. In a more business-oriented scenario, consider an example where you and I are managing separate but related software development projects. Cooperatively we have agreed to develop shared and reusable software components where practical. Yet each of us, when faced with schedule pressures or conflicting needs, chooses to build local custom components.

In behavior-over-time analysis, accidental adversaries graphs as two activities – X and Y – which both experience accelerating growth early in the time scale. As local optimization limits success potential of both activities, they each decline in the later stages of the time scale. Figure 2 shows the graphical pattern of accidental adversaries.

 

Drifting Goals

Lowering the bar describes the common effect of the drifting goals archetype. Figure 3 illustrates the archetype as a causal loop model. The characteristics are:

• Two separate balancing loops exist.
  
  
• The two loops intersect at a common gap.
  
  
• One loop contributes the desired state, and another the current state.
  
  
• The gap simultaneously influences action and causes pressure to adjust the desire – in essence, to change the goal.
  
  
• As the desired state is distorted, the influence on action mutates.
  
  
• Ultimately the balance that is achieved has little relationship to the initial desired state.

As a socio-cultural example, consider space exploration and the story of NASA. In a more business-oriented scenario, consider this example: I once worked for a company where sales revenue budgets were negotiated annually. In one year, actual revenue would significantly exceed the budget, creating pressure to increase budget in the following year. The higher budget in the second year caused actual revenue to fall short of budget, creating pressure to reduce the revenue budget in year three. What are the implications of this see-saw budgeting pattern continuing over several years?

In behavior-over-time analysis, drifting goals graphs as mildly oscillating patterns of both the current state and the desired state. Current state increases slightly over time, as desired state experiences a slight decrease. Eventually equilibrium is reached and both states flatten at a level that is less than the original desired state. Figure 4 shows the graphical pattern of drifting goals.

 

Escalation

Competing for dominance best describes the nature of the escalation archetype. Figure 5 illustrates the archetype as a causal loop model. The characteristics are:

• Two separate balancing loops exist, identified here as X and Y.
  
  
• The two loops intersect at a common gap which is defined as relative results.
  
  
• The results of action in each loop influence the desired state of the other.
  
  
• The results of action in each loop influence the drive for action in the other.
  
  
• The cycle repeats with no apparent end.

The cold war is an obvious socio-cultural example of escalation. Competitive pricing is a common business-oriented example. It is common for retailers to advertise that they will match any competitor’s price. What would be the eventual outcome if two retailers each established a policy of beating the other’s best price by five percent?

In behavior-over-time analysis, escalation graphs as two activities – X and Y – that each grow in a “stair step” pattern, with each as the driving force for the next growth step of the other. Figure 6 shows the graphical pattern of escalation.

 

Fixes that Fail

The high cost of the quick fix describes the consequences of fixes that fail. Figure 7 illustrates the archetype as a causal loop model that is characterized by:

• A balancing loop that is applied to produce immediate positive results.
  
  
• The action of the balancing loop produces side-effects in the form of undesirable and unintended consequences.
  
  
• A time delay exists between taking action and realizing the side-effects.
  
  
• The side-effects impede the current state from migrating toward the desired state.

 

Free phone promotions in the wireless telecom industry are a socio-cultural example of fixes that fail. As a business-focused example, consider a company that is losing customers due to long wait times at the customer service call center. To improve customer retention the company decides to outsource call center operations. The early result is a visible reduction in wait times and a corresponding reduction of customer attrition due to call center waits. After several months, however, the customer retention rate flattens and begins to trend again toward attrition. What may be the cause and what fundamental solution may resolve it?

In behavior-over-time analysis, fixes that fail exhibits an oscillating pattern of increase followed by decrease. Each of the increases coincides with the introduction of a symptomatic solution. Each decrease that follows is the result of unintended consequences of the fix that become visible only after some delay. It is common that the time intervals between cycles decrease over time and that the amplitude of each wave also shrinks. Figure 8 shows the graphical pattern of fixes that fail.

 

Limits to Success

A growth plateau describes the effect of the limits to success archetype. Figure 9 illustrates the archetype as a causal loop model in which:

• A reinforcing loop drives growth of a current state.
  
  
• As the current state increases, it interacts with some limiting state in a way that produces a slowing action.
  
  
• The slowing action interacts with the current state in a balancing loop that inhibits current state growth and limits the growth effects of the reinforcing loop.
  
  
• Rapid growth decelerates, flattens and may ultimately decline.

One-hour photo developing is an example of limits to success where the limiting factor is the emergence of digital photography. Limiting factors may appear in many different forms including capacity constraints, market saturation, aging product lines, emerging technologies, resource limits, etc.

In behavior-over-time analysis, limits to success exhibits a growth curve that exhibits early acceleration, followed with deceleration and eventual flattening over time. Figure 10 shows the graphical pattern of limits to success.

 

Growth and Underinvestment

A common variation of limits-to-success is called growth and underinvestment. In this archetype, the limiting state is created by failure to invest, often due to short-term pressures such as limited capital. As growth stalls due to lack of resources, incentive to add capacity declines, which causes growth to slow even more. Figure 11 illustrates growth and underinvestment as a causal loop diagram. Note that a complete limits-to-success model is present. Growth and underinvestment has the same behavior over time patterns as limits to success.

 

Shifting the Burden

The enduring band-aid describes the effects of shifting the burden. Figure 12 illustrates the archetype as a causal loop model that is characterized by:

• A short-term solution is implemented that successfully resolves an ongoing problem.
  
  
• The short-term solution is implemented as a balancing loop within the system.
  
  
• As the short-term solution is used repeatedly, it diminishes the drive to implement a more fundamental solution.
  
  
• Over time, the ability to implement a fundamental solution decreases and reliance on the short-term, symptomatic solution increases.
  
  
• Ultimately, the short-term solution may produce other side-effects that emerge as new problems.

 

A common example here is overuse of temporary labor to balance workforce capacity with workload demands. Temporary labor satisfies the immediate need to increase capacity. But, when used repeatedly, the percentage of the workforce that is classified as temporary grows and many “temporary” workers become a permanent part of the workforce. Ultimately issues of Fair Labor Standards Act (FLSA) compliance, benefits eligibility and such emerge – sometimes resulting in legal action and financial penalties.

In behavior-over-time analysis, shifting the burden shows an oscillating pattern of erratic growth of a symptomatic solution. A corresponding (but not always graphed) pattern of oscillating decline in the viability of a fundamental solution occurs simultaneously. Figure 13 shows the graphical pattern of shifting the burden.

Success to the Successful

Winners and losers describes the effects of success to the successful, which makes win-win systems difficult to achieve. Figure 14 illustrates the archetype as a causal loop model that is characterized by:

• Two activities in a system (represented here as X and Y) compete for the same limited set of resources.
  
  
• Both activities are represented by reinforcing loops where resources influence success, which, in turn, influences resources.
  
  
• The early success of activity X creates incentive to allocate more resources to X.
  
  
• Allocation of resources to X instead of Y increases X’s ability to succeed.
  
  
• Allocation of resources to X instead of Y decreases Y’s ability for success.
  
  
• Continuation of the cycle reinforces positive results of X and negative results of Y.
  
  
• The combined effect of two reinforcing loops moving in opposite directions is a single reinforcing loop that enhances success of X and inhibits success of Y.
  
  
• Ultimately X is sustained while Y fails.

Consider the example of two departments in a company that are competing for priority of IT projects. The marketing department needs data and technology to get a 360-degree view of customers and the marketplace, and to manage effective marketing campaigns. The research department needs modeling and simulation technology to drive innovation of new products. Both projects are initiated at similar times. In a span of a few months, the marketing department illustrates success with campaign effectiveness metrics. In the same short time span, the research director has only anecdotal justification for the simulation project. The demonstrable success of marketing is reasoned to justify assigning more IT resources to marketing projects, which takes them away from research projects.

Success to the successful graphs as two activities – X and Y – with divergent patterns. The activity to first demonstrate success (illustrated here as X) shows a growth curve, while the other shows a corresponding decline. Over time, the gap. Figure 15 shows the graphical pattern of success to the successful.

Tragedy of the Commons

Shared resource overload is the nature of tragedy of the commons. Figure 16 illustrates the archetype as a causal loop model where:

• Two activities in a system (represented here as X and Y) depend on a shared resource of limited capacity.
  
  
• Both X and Y grow through activity that produces individual gain, as illustrated by two reinforcing loops.
  
  
• With growth over time, the total activity first approaches and then exceeds the limited capacity of the resource.
  
  
• Growth opportunities for both X and Y disappear when the capacity of the resource is exceeded.
  
  
• Ultimately (especially for consumables), the resource is depleted, which stalls (or even reverses) growth of both X and Y.

Consider the example of a company that depends extensively on the subject and domain knowledge of one person. Initially that person provides valuable knowledge that fuels growth of programs, products, marketing, sales and quality. As each area grows, demands on the expert increase to the point where he can’t keep pace with demand. Ultimately, the demands become burdensome, the job becomes unrewarding and the expert resigns.

In behavior-over-time analysis, tragedy of the commons illustrates three variables. Two activities – X and Y – exhibit early and steady incline followed by late and rapid decline. The common resource of limited capacity exhibits rapid growth of demand (coincidental with the growth peak of the two activities) followed by very rapid decline.

Putting the Archetypes to Work

The archetypes described in this article are an effective way to gain insight from analytics. It is valuable to understand the relationships between archetypes shown as causal loop diagrams and the patterns found in time-series analysis. Whether you’re looking at a behavior-over-time graph and asking “Why?” or at a system model and asking “What should I expect?” the archetypes offer a view into the dynamics of systems. Understanding cause-and-effect relationships really is the key to gaining insight through analytics.

And More to Come

Causal loop diagrams illustrate influences, and system archetypes extend them to understand the recurring patterns of why things happen. But there is more to analytics than understanding “why.” Sometimes we want to simulate and to forecast “how much.” A systems modeling technique called stock-and-flow provides the means to quantify influences. Quantification enables simulation, and simulation is at the heart of “what if” analysis and predictive analytics. I will explore stock-and-flow modeling as the third and final article in this series.