Overview

Maps are visual representations of specific areas to help you navigate unfamiliar territory. For instance, a transportation system map shows how people move among locations, or a supply system map depicts how products and services travel from suppliers to customers.

Maps can be powerful tools for visualizing and understanding complex systems as they show how individual components interact and combine to make a larger whole. (To better understand what is a system, read this article.)

To create an effective system map, you need to understand the system’s key components and purpose. Here’s what’s involved.

Components of Systems Maps

All maps have some similarities. They start with collecting data through desktop research, interviews, surveys, or focus groups. For instance, input from manufacturing employees, such as machine operators, production supervisors, and quality control personnel helps a company map its entire production process.

Maps also involve different stakeholders. Many hidden system elements are revealed only by involving diverse stakeholders, exposing barriers, challenges, or delays in meeting organizational objectives.

Finally, it’s critical to define a map’s boundaries. If too large, the map becomes too complex; if too small, there won’t be interesting insights. For example, the boundaries in mapping customer service should range from initial contact to the issue’s resolution. The granularity of the map should show the customer’s journey, but not every transaction within the business.

There are five well-known approaches to systems mapping: social network analysis, causal loop, process flow, life cycle analysis, and theory of change. Here’s a brief overview of when and how to use them, as well as their shortcomings.

Social Network Analysis

As the name indicates, social network analysis involves mapping and analyzing relationships between individuals, groups, or organizations to reveal communication and collaboration patterns. Seeing how employees and teams connect helps managers to identify communication gaps and bottlenecks, as well as the organization’s key influencers and informal leaders.

In 1967, psychologist Stanley Milgram conducted a pioneering ‘small world’ experiment where participants were asked to pass a message to a specific target person through their acquaintances. On average, the message reached the target person within just six degrees of separation. This finding highlights the remarkable interconnectedness of social networks.

Social network analysis typically involves collecting data on the network’s members, such as identities, affiliations, and interactions. Data is then visualized as a map using the following:

Nodes (people, organizations, or any other relevant units) are typically depicted as shapes, such as circles or squares. They may differ in size, colour, or shape to convey additional information about the entities’ characteristics or attributes.

Edges, or the lines between nodes, represent connections or relationships within the social network. Vary the edges’ direction, thickness, or colour to show different relationship or interaction types or strengths, depending on the context of the social network being depicted. For example, in a social network map showing collaborations between employees in a large organization, nodes could represent employees, and edges their interactions – such as collaborations on projects, sharing of information, or joint participation in committees. An edge’s thickness or colour could represent a collaboration’s frequency or intensity, while a node’s size or colour could represent an employee’s seniority or expertise.

Social network analysis has some shortcomings, including:

• Inaccuracies in data used for mapping;
• Ethical concerns related to privacy and consent;
• Lack of context in capturing the nature and quality of relationships;
• The dynamic nature of networks; and,
• Interpretation challenges.

Causal Loop Maps

Causal loop maps illustrate cause-and-effect relationships between variables or factors within a system. Rather than problem –> solution, a causal loop map may show problem –> solution –> less problem –> smaller solution.

Causal loop mapping shows underlying and rarely visualized feedback loops. For example, it can reveal reasons for declining organizational performance, such as employee dissatisfaction. It can also show the effectiveness of change management initiatives, such as the importance of employee morale, communication channels, workflow, and resource allocation.

To start your system map, simply list the key variables as nouns and the interactions between them. Use causal language (“causes,” “effects,” “leads to,” etc.) to describe relationships between variables. Once links are exposed, the feedback loops can be established. By connecting several loops, you create a concise story about a particular problem or issue.

Generally, there are two types of causal loops: reinforcing and balancing. Reinforcing loops amplify change in one direction (positive feedback), like how interest on a savings account increases the balance. Balancing loops counter change in one direction with change in the opposite direction, aiming to maintain a desired state (negative feedback).

Imagine a company that is struggling with declining sales. Using causal loop diagrams, the system’s variables could include “sales revenue,” “marketing budget,” “customer satisfaction,” and “competitor activity,” among others. The links between these variables depict the causal relationships:

Causal loop maps require complete and accurate data and assumptions about the relationships among the variables. As this data is not readily available, causal loop maps may not be effective. They can also quickly become enormous, with spaghetti-like diagrams, making it difficult to determine actions, interactions, and outcomes.

Process Flow Maps

Process flow mapping, also known as process mapping or process diagramming, maps a process’s or system’s flow of activities, information, and resources over time.

A process flow map allows an organization’s members to build a shared understanding of how the process should be performed. By visually mapping process steps, organizations can identify opportunities for automation, standardization, or streamlining of activities to reduce costs and improve quality. It can also reveal shortcomings and obstacles.

Innovators need to first identify a process’s key steps or activities. Then represent steps using rectangles or other shapes and activities through arrows indicating the flow between them. Arrows should show movement of inputs or information and the process’s progress. Use diamonds to represent key decisions.

For example, in a corporate hiring process, a diamond can indicate a decision point, such as “proceed” or “reject” the candidate, and arrows show the different decision paths or outcomes.

Process flow mapping is iterative. As processes evolve, update your process flow maps to reflect the process’s current state. Regular process flow map reviews and updates can help organizations continuously improve their processes and adapt to changing business needs.

Process maps have numerous shortcomings, including:

• Oversimplification of complex processes;
• Limited context about organizational and environmental factors;
• Potential bias in the mapping process;
• Difficulty capturing dynamic and changing processes;
• Reliance on subjective interpretation;
• Challenges in maintaining and updating maps as processes evolve; and,
• May not capture a process’s emotional or human aspects, or effectively represent non-linear or iterative processes.

Recognize these limitations and use process flow mapping as one tool among others to gain insights into processes and make informed decisions.

Life Cycle Analysis

Life cycle analysis or assessment (LCA) maps out the entire life cycle of a product, from raw material extraction to end-of-life disposal or recycling. It helps organizations to assess and optimize products’ and processes’ environmental performance by showing the impacts (both direct and indirect) of a product’s life cycle.

LCA is already used in various industries, such as manufacturing, agriculture, construction, and transportation. It enables decision-makers to identify hotspots or areas with the highest environmental impacts, and prioritize efforts to reduce or mitigate those. These environmental impacts are often associated with the production of raw materials, manufacturing, transportation, use, and disposal.

Depending on the analysis’s scope and purpose, LCA may include the flow of materials, energy, and emissions at each stage of the product’s life cycle. The analysis typically involves collecting data on the inputs, outputs, and emissions.

Let’s look at an analysis of disposable and non-disposable diapers. In 2005, the U.K. Environment Agency conducted a study on disposable and non-disposable diapers while Kimberly-Clark did a similar analysis of its product. Surprisingly, the studies found no significant difference between the diaper types’ environmental impacts as each system had different life cycle stages that were the main source of impact as seen below.

Life cycle stages depict each stage as a box or shape, such as a rectangle. Vary their size or colour to show the relative importance or contribution of each stage to the product’s overall environmental impacts.

Flows represent the inputs, outputs, and emissions associated with each life cycle stage. Use arrows or lines between boxes to show the flow of the life cycle stages. Change the arrow’s direction, thickness, or colour to represent the direction, quantity, or environmental impact of the flows, depending on the context of the life cycle being depicted.

LCA has some shortcomings to keep in mind:

• Its subjective nature causes inconsistent LCAs of the same product;
• Relies on the manager’s judgment to determine the focus, and what to/how to measure;
• Subjectivity introduces biases into results, despite efforts to avoid them; 
• Not all data on indirect or upstream impacts can be captured, resulting in an incomplete environmental footprint assessment; and,
• Systems’ and technologies’ dynamic nature quickly makes life cycle analysis results outdated.

Theory of Change

Theory of change maps outline causal relationships between inputs, activities, outputs, outcomes, and impacts and can identify the assumptions and risks associated with the change process. They help organizations to clarify strategic goals and how to achieve them, as well as measure progress.

Theory of change is widely used in corporate settings, such as program evaluation, strategic planning, and social change initiatives. Non-profit organizations and governments also use it to articulate their intended impact and design strategies to achieve them.

Mapping a theory of change shows the key components of a program, initiative, or intervention and their anticipated relationships or interactions. It visually depicts how change should occur, and how inputs and activities contribute.

When mapping a theory of change, start with the desired impact and then work backwards to identify the ingredients needed. It usually includes the following:

• Inputs – Resources, such as funding, personnel, and infrastructure, invested in the program to implement activities.
• Activities – Actions, interventions, or strategies implemented to bring about change. Activities are designed to produce specific outputs.
• Outputs – The program activities’ immediate products or deliverables, which are typically measurable and represent tangible results.
• Outcomes – Expected changes/results (short, intermediate, and long term) from the program’s activities and outputs.
• Impacts – What the program aims to achieve long term at the systemic or societal level, or the ultimate changes sought.

Let’s look at a company that wants to implement a sustainability initiative:

A theory of change can also be used to evaluate an initiative’s success. When outputs do not translate to outcomes, it is unlikely you achieved the desired impact.

Theory of change mapping helps with planning and evaluation, but has limitations:

• Detecting challenges and opportunities;
• Capturing the complexity and nuances of real-world social dynamics; and,
• Adequately engaging stakeholders and external factors.

As a result, a theory of change may oversimplify complex social systems, leading to incomplete or inaccurate understanding of the challenges and opportunities at hand.

Recap: The table below summarizes when and how to use the five systems mapping approaches and limitations for each.

	Social Network Analysis	Causal Loop Maps	Process Flow Map	Life Cycle Analysis	Theory of Change
Definition	Maps and analyzes relationships between individuals, groups, or organizations.	Illustrates cause-and-effect relationships between variables or factors within a system.	Maps a process’s or system’s flow of activities, information, and resources.	Maps a product’s entire life cycle, from raw material extraction to end-of-life disposal or recycling.	Maps the causal relationships between inputs, activities, outputs, outcomes, and impacts.
Application	Helps organizations understand communication and collaboration patterns.	Helps organizations reveal reasons for problems and assess the effectiveness of change management initiatives.	Helps organizations identify opportunities for automation, standardization, or streamlining of activities to reduce costs and improve quality.	Helps organizations assess and optimize environmental performance by showing the direct and indirect impacts of a product’s life cycle.	Helps organizations clarify strategic goals and how to achieve and measure them.
Process	Map the nodes: – Units, such as people, teams, or organizations – Use points or shapes – Use different attributes, such as colour or size   Map the interactions: – Show relationships between units as lines or arrows – Use different attributes, such as thickness or colour	Map the nodes: – Units of interest, represented as nouns: Customers, marketing expense Map the interactions: – Causal connections between units – Verbs or causal language – causes, effects Show final loops and indicate if reinforcing or balancing	Map the nodes: –  Key steps or activities represented as rectangles – Include decision diamonds, where decisions need to be made   Map the interactions: – Use arrows to indicate movement of inputs or information	Map the nodes: – Life cycle stages, such as extraction, production   Map the interactions: -Use arrows showing flow of inputs, outputs, and related emissions associated with each stage	Map the nodes: – Inputs – Activities –  Outputs – Outcomes –  Impacts Map the interactions: – If no interactions exist, order sequentially (first to last)
Limitations	– Inaccuracies in data used for mapping; – Ethical concerns related to privacy and consent; – Lack of context in capturing the nature and quality of relationships; – The dynamic nature of networks; and, – Interpretation challenges.	– Require complete and accurate data and assumptions about the relationships among the variables; and, – Quickly becomes enormously complex.	– Oversimplification of complex processes; – Limited context about organizational and environmental factors; – Potential bias in the mapping process; – Difficulty capturing dynamic and changing processes; – Reliance on subjective interpretation; – Challenges in maintaining and updating maps as processes evolve; and, – May not capture a process’s emotional or human aspects, or effectively represent non-linear or iterative processes.	– Subjective nature causes inconsistent LCAs on the same product; – Relies on the manager’s judgment to determine the focus, and what to/how to measure; – Subjectivity introduces biases into results, despite efforts to avoid them;  – Not all data on indirect or upstream impacts can be captured, resulting in an incomplete environmental footprint assessment; and, – Systems and technologies’ dynamic nature can quickly make life cycle analysis results outdated.	– Detecting challenges and opportunities; – Capturing the complexity and nuances of real-world social dynamic; and, – Adequately engaging stakeholders and external factors.

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