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A Systems View of Business Analytics, Part 3

Making Cause and Effect Measurable

Published: October 21, 2008

This third and final article of Dave Wells’ systems-thinking series examines stock-and-flow models – the systems thinking tool specifically designed to answer “how much.”

The first article of this series introduced systems thinking as a tool that helps you to understand cause-and-effect relationships. Cause-and-effect is the key to finding root causes and really understanding why things happen. The second article described repeating patterns of system behavior that are known as system archetypes. The combination of causal modeling and system archetypes provides insight into the “what” and the “why” of system behaviors – a good beginning of business analysis; but “what” and “why” only scratch the surface of analytic purpose. Depth of analysis must extend to include “how much.” This third and final article of the systems-thinking series examines stock-and-flow models – the systems thinking tool specifically designed to answer “how much.”

Stocks and Flows

The causal loop techniques described in previous articles help to understand influences among the things in a system. But they make no distinction between the transient things and those things that accumulate. Yet things that accumulate are often the most important to examine when analyzing a system. They are quantifiable and provide the means by which influence can be measured. Understanding the dynamics of things that accumulate in a system is central to modeling and simulating system behaviors. Stock-and-flow models are designed to meet this need.

The things that accumulate in a system are called stocks. A stock is an accumulation of something in a system – either concrete and tangible things (i.e., dollars or widgets) or abstract and intangible things (i.e., knowledge or morale). Tangible stocks are accumulations of consumable resources. Intangible stocks are accumulations of catalytic resources.

A stock changes through the influences of flows. A flow is an action that influences a stock by increasing or decreasing the quantity of the stock. Flows are of two kinds: inflow that increases the accumulated quantity and outflow that decreases the quantity.

The relationships of stocks and flows are graphically represented using stock-and-flow diagrams. Figure 1 shows a simple stock-and-flow diagram for the accumulation of workforce capacity.

 

The diagram notation is as follows:

• The stock is illustrated as a rectangle – in this example workforce capacity.
  
  
• The arrows indicate two flows: hiring and workload assignment. Inflow and outflow are designated by the directions of the arrows.
  
  
• The containers on the arrows illustrate how each flow is quantified – hiring becomes hiring rate and workload assignment becomes workload assignment rate.
  
  
• The “clouds” at each end of the diagram mark the boundaries of the problem space. They indicate that the flow hiring rate arrives from someplace beyond the scope of the diagram and that the flow workload assignment rate travels to a place beyond the scope of the diagram.

Throughout this article, I’ll continue to build upon the simple example shown in workforce capacity model. It is important to mention, however, that stock-and-flow models are not always as simple as one stock with two flows. Stock-and-flow sequences may involve more complex and interrelated stocks and flows as shown in the materials to shipment sequence illustrated by Figure 2.

Measurement of Stocks and Flows

Measurement is a key concept of stock-and-flow modeling. Stocks are always measured as units – dollars, items, etc. In this example, the measurement unit for workforce capacity is employee full time equivalents (FTEs). Flows are measured as rate of flow, which is expressed as units per time period. Flow measures for this example might be FTEs hired per week for hiring rate and FTEs assigned per week for workload assignment rate. Consistency of measurement within a stock-and-flow sequence is important. Measuring workforce capacity as FTEs and quantifying flows as headcounts would make little sense. It would be similarly nonsensical to measure the inflow on a weekly basis and the outflow as a monthly amount.

External influences often affect the rate of a flow. In stock-and-flow modeling these influences are known as converters. (The term “converter” may seem odd for this concept right now. Bear with me. It is standard stock-and-flow terminology, and it will make sense before we’re through.) Connectors link converters to flows as shown in Figure 3.

• Labor budget is a converter that affects hiring rate.
  
  
• Outstanding orders is a converter that affects workload assignment rate.

 

From Causal Loop to Stock and Flow

Causal loop diagrams (CLDs) will likely be the initial method to analyze system dynamics, with stock and flow modeling used where quantification is needed. A stock-and-flow diagram typically examines a portion of a causal loop model to distinguish stocks from flows and to determine how each is measured. Figure 3 uses a causal loop model from Part 1 of this series to illustrate how causal loop extends to become stock-and-flow diagrams.

A systematic process of working from a CLD to create stock-and-flow diagrams uses a sequence of steps described below:

• Identify critical behaviors of the system – those that are problematic, under study or analysis, or central to the goals and strategies of the organization.
  
  
• Identify the stocks that participate in critical behaviors of the system – those things that are accumulated in the system upon which critical behaviors are dependent.
  
  
• Name each stock with a term that is quantitative but not comparative. The example in Figure 3 adds “(FTE count)” to the name “workforce capacity” to make it quantitative. But it does not say “more workforce capacity,” which is comparative language.
  
  
• Examine every link to each stock to determine if it becomes a flow. If the influence is one that changes the accumulated quantity of the stock, then it is a flow.
  
  
• Add each flow to the diagram expressing the influence as units-over-time or rate of flow. The example translates “hiring” CLD to “hiring rate” and “workload” to “workload assignment rate.”
  
  
• Examine each flow in context of the system-wide CLD to identify links that are converters – influences that regulate or otherwise affect the rate of flow. Labor budget and outstanding orders are converters in the example.
  
  
• Mark the boundaries – start and end – of the model.

The act of creating stock-and-flow models from causal loop diagrams also serves to test the causal models and make them more complete. It is common, for example, to discover influences previously not modeled when analyzing rate of flow and identifying converters that affect the rate.

Multiple Stocks and Flows

It is possible – even probable – to derive many stock-and-flow sequences from a single causal loop model. When this occurs, valuable insight can be derived by identifying the interconnections among stock-and-flow sequences. Interconnections occur when a flow in one sequence acts as a converter in another sequence. Figure 5 illustrates an example of interconnected stock-and-flow sequences.

Labor budget is a stock whose in-flow is labor cost allocation rate. The cost allocation rate is a converter that affects hiring rate which is an inflow to workforce capacity. Similarly, outstanding orders is a stock with the inflow of order received rate. In both instances the converter link is a flow-to-flow connection. The stock itself is never used as a converter. The result of this analysis is greater insight that may add understanding and detail to the CLD.

Here we begin to make sense of the term “converter.” The units of measure vary among the three stock-and-flow sequences. Labor budget is measured in dollars. Workforce capacity is measured in FTEs. Outstanding orders is measured as days to complete. A conversion formula is needed to describe the influence of labor cost allocation dollars upon hiring rate FTEs – in other words: How do dollars convert to FTEs? Similarly, we need to know how days to complete is converted to FTEs for the influence of outstanding orders upon workforce capacity.

The Business Intelligence Connection

So what does all of this have to do with business analytics? The most obvious connection is use of stock-and-flow models as the basis for predictive modeling and computer-based simulation. The quantitative nature of these models – stocks measured as units and flows as units per time period – make it practical to define simulation models and apply simulation and predictive analytics tools effectively.

But I believe the business intelligence (BI) connection is much deeper than simulation and prediction. The power of business intelligence is in the ability to deliver insight, gain understanding, enable reasoning, support planning and drive innovation.

Insight is a clear and deep perception of a complex situation or condition – the ability to “see inside” the situation. Insightful analytics are those that create the ability to look inside deeply enough to understand the causes of a situation or condition. Stock-and-flow modeling provides a tool for greater insight through analysis of system behaviors.

Understanding is the ability to perceive, discern and distinguish. Distinguishing stocks from flows, units from rates and causes from effects certainly enhances understanding of how a system behaves.

Reasoning is the ability to identify root causes, to understand cause and effect and to logically develop conclusions based upon that understanding. Quantifying influences and thinking through questions such as how dollars convert to FTEs undoubtedly advances the capacity to reason about system behaviors.

Planning is the ability to determine a course of action based upon understanding and reasoning. The value of insight is limited unless analytics can help to determine what to do next. By extending understanding and reasoning, stock-and-flow modeling enhances planning capabilities.

Innovation is the ability to create something new and different – a device or a process – through study and experimentation. Innovation often occurs by combining or connecting existing things in different ways. Stock-and-flow modeling provides a means to study a system in new and different ways. And simulation certainly has a role in experimentation.

The example shown in Figure 6 is identical to that of Figure 5 with only one exception. The model in Figure 5 does not link order received rate to labor cost allocation rate – the converter that is shown as a dotted line.

 

This example illustrates:

• Insight to see that labor cost budgeting is not currently influenced by the rate at which new orders are received,
  
  
• Understanding to recognize that this situation isolates labor budgets from the realities of workload dynamics, which is a fundamental cause of the gap between workload and workforce capacity,
  
  
• Reasoning to conclude that the gap will improve if order received rate becomes an influence to determine labor cost allocations,
  
  
• Planning is required to define a course of action through which labor cost allocations are influenced by orders received,
  
  
• Innovation opportunity to establish a new process through which order received rate influences labor cost allocations, which in turn influences hiring rate and narrows the gap between workload and workforce capacity.

Final Thoughts

This series of three articles provides an introduction to systems thinking. There is much more to be learned than what I have written in this series of articles. I have presented the core concepts that I believe have real affinity with business analytics.

Systems thinking is a mature discipline with roots dating back to 1961 (Industrial Dynamics, Forrester, MIT Press, 1961). It is widely known and practiced in many areas where understanding of cause and effect really matters – areas such as manufacturing, process control, industrial systems and organizational dynamics. Business intelligence has the same critical need to understand cause-and-effect relationships. It doesn’t make sense for us to reinvent the wheel. The business intelligence community needs to learn and adopt the practices of systems thinking. The time has come for systems thinking to become a central discipline of business analysis.

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