By Steven H. Jones
 

360 Degree Process Mapping with Causal Loop Diagrams

Historically the Lean tool of Process Mapping relied on a linear approach to illustrate the sub process flow of activity.  On paper this makes sense, but in real world production environments every process step can effect and be effected by multiple inputs and outputs.  With this observation traditional process mapping is found lacking and insufficient.  An alternative and more effective tool to linear process mapping uses Causal Loop Diagrams to generate a 360 Degree process map. 

The primary objective of a Causal Loop Diagram is to “tell the whole story”. Causal Loop Diagrams account for multiple effects on each process step or variable by illustrating and tracking the positive or negative correlating effect.  Using this methodology on static processes will provide a greater perspective on the interaction and variability of process steps.  This is incredibly important when developing current or futures state high level process maps.  

Building a Causal Loop Process Map

Causal Loops have three main components.  They are process variables, value directions and directional flow arrows. The names of each Process Variables are defined as non qualitative events (steps) in the process and should always be expressed in the noun form.  Value directions illustrate the effect one variable has on another variable that it is connected to.  The directional flow arrows illustrate the direction of the process between the variables.  Value directions should be located at the end point of each arrow as an “s” for “same” or “o” for “opposite”.  They will direct the reader to see how each variable increases or decreases from the effect of the preceding variable.           

All Causal Loop Diagrams are built on two basic types of loops, they are Reinforcing and Balancing loops. 

Reinforcing Loops illustrate the production of an opposing interaction between variables.  These loops are comprised of variables whose interaction causes an increase or decrease the value of variables they are connected to.  Charting these actions reveal upward or downward trends resulting from the interaction of the variables.  These occurrences are sometimes referred to as virtuous or vicious cycles as they produce a nearly perpetual positive (virtuous) or negative (vicious) results.  In either case the value will eventually stabilize either at zero or the point of diminished returns.

Balancing Loops illustrate the opposite behavior.  Here the interaction of the variables produces an offsetting value.  The opposing or negative interaction may cancel or limit the progression of the process.  As such the output of the interaction will remain relatively flat and constant over time.  This interaction produces an outcome that could be tracked in a flat graphical chart.

One easy way to identify a loop's type as a balancing or reinforcing is by counting the quantity of opposite interactions or “o's” in the loop. Whenever you have an odd number of “o's” you can determine that the loop is a balancing.  Conversely any loop with an even number of “o's” automatically indicates your loop to be reinforcing.

You should always review your diagram before labeling it as a Balancing (B) or Reinforcing (R) loop to ensure the story and data agree with the label.

Basic Examples

Let's use variables that represent quantities that can vary over time. A term like “Gross Profit” will have variation. 

For our example of a Balancing Loop, let's look at the interaction of Production Errors and Gross Profit.  In our hypothetical organization the data tells us that as Production Errors increase, Gross Profit decreases and as Production Errors decrease Gross profit increases.  In the life of this organization we learn that as the GP decreases management attention increases.  This temporary attention produces the Hawthorn Effect and we see a decrease in Production Errors and GP begins to increase.  But as GP increases the Hawthorne Effect expires and Production Errors increase again.  These two variables interact over time to produce a flat GP and Production error rate.  (“Management Attention” is not listed as a variable as it is not a constant step or measurement in our process)

Now let's look at an example of a Reinforcing Loop.  In this example the data shows that as the Gross Profit increases the R&D Budget increases.  As the R&D budget increases new products are built, more automation in the production line is implemented and GP increases.  As the GP increases the budget for R&D increases and the organization produces better products and reduces production costs.  This interaction increases Gross Profit.  Here we can graph a reinforcing loop producing a Virtuous Cycle.  Theoretically this cycle will go on continuously, however eventually the Law of Diminishing Returns comes into play.

Now that we have laid the foundation of causal loops let's illustrate a real life process with a traditional process map and then illustrate the same process in a causal loop diagram.

Scenario:

An IT network monitoring firm is tasked to grow its business by improving its accuracy remotely monitoring servers. It is believed by the head of the organization that if the remote server monitoring accuracy grows, new business can be won and the overall business will grow.  As such, Business Growth is the KPOV (Key Process Output Variable).  A linear high level process diagram would be illustrated as follows:

This linear approach is accurate but fails to examine and identify the interrelationships between and across all process steps and effectively drive the KPOV.  What is missing is the correlation of the end of the linear process map to its starting point.  By using a simple Causal Loop Diagram to map this process we can see the process from a 360 degree perspective.  This alternative view can help identify potential root causes and potential solutions via process interventions.

The 360 Degree View By converting the process steps into process variables we can begin to view this process from a non -linear perspective and immediately identify a process constraint that limits the effective growth of the organization. In the Causal Loop we can immediately see the opposing relationship between the New Server Installs and Accurate Server Configuration.  This opposition balances the overall process cycle.  The data actually shows that as more servers are installed the accuracy of the installations decrease.  Now the process constraint limiting efficiency is easily identified.  Once the data has validated the observation, we can pilot a test to introduce an intervention at the point of process constraint to change the direction of the relationship between the variables. Done effectively this will produce a reinforcing effect on the loop and the process.

About the author
Steven H. Jones is a Process Engineer who received his certification as a Lean Six Sigma Black Belt by the George Group while employed and Xerox Global Services.   He started his career at the 3M Corporation, an early adopter of the Lean Six Sigma methodology in 1988 and has worked in quality improvement of Telecommunications and IT arenas since 1993.  Since then he has provided quality improvement and process engineering services domestically and internationally to clients such as BP Canada, Convergys, Intercontinental Hotels, and Microsoft.  He is currently a Senior Process Engineer with Siemens Business Services and can be reached at steven.jones@sbs.siemens.com.

Tell us what you think about this article. Send a note to the Editor.

www.manorhouseassociates.com