In business process management, it’s crucial to understand and document your workflows visually. Two common methods for doing this are creating process models and process maps. At first glance, these terms seem very similar – they both involve diagramming processes, right?
But when you dig deeper, there are some important distinctions between process modeling and process mapping. In this article, I’ll explain what each method entails and when you should use one versus the other.
What is a Process Model?
A process model provides a detailed, graphical representation of a workflow using standardized notation. The model visualizes the end-to-end flow through a process, including
- The sequence of activities
- Decisions and branches
- Roles involved
- Systems used
- Inputs and outputs
- Metrics like cycle times
Process models are created using a notation called BPMN (Business Process Model and Notation) BPMN provides common shapes and symbols so that anyone can understand the model.
Some key icons in BPMN process models include:
- Rounded rectangles for tasks
- Diamonds for decisions
- Arrows for flow
- Swimlanes to show roles
By following this standardized notation, process models allow stakeholders to analyze, simulate, and optimize workflows in a quantitative way.
What is Process Mapping?
Process mapping refers to documenting a workflow visually through a diagram, without necessarily using a strict notation like BPMN. The goal is to provide a high-level overview of the steps and flow.
Process maps are often created in workshops and sessions with process participants. The mapping may cover elements such as:
- Major tasks
- Handoffs
- Decisions
- Departmental responsibilities
But process maps don’t require getting into the detailed mechanics of the process like a process model. There also isn’t a standard for shapes and symbols. Many process maps are simple flowcharts.
The Key Differences
While both methods document processes visually some notable ways process modeling and process mapping differ include
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Purpose: Process modeling supports analysis and optimization. Process mapping provides a basic overview.
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Notation: Process models follow BPMN standards. Process maps can be basic flowcharts.
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Quantitative vs. Qualitative: Process models use quantitative data like metrics. Process maps are more qualitative.
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Level of Detail: Process models are more granular. Process maps show high-level steps.
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Creation: Process models use system data. Process maps use workshops.
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Fluidity: Process models are meant to be static representations. Process maps are more fluid.
Let’s explore each of these differences in more depth:
Purpose: Analysis vs. Overview
Process models support process improvement activities like:
- Identifying inefficiencies
- Simulating new workflows
- Automation planning
- Benchmarking as-is vs. to-be processes
Their analytical nature makes them core to process optimization initiatives.
Process maps simply provide a basic visualization for reference of the current workflow. They don’t necessarily facilitate deep analysis on their own.
Notation: BPMN vs. Ad hoc
Process models strictly follow BPMN standards. This notation is essential for the model being interpretable by different stakeholders.
Process maps can use any symbols or visual flow the creator wants. There are no rules. Simple flowchart shapes are common.
Quantitative vs. Qualitative
Process models incorporate quantitative data such as:
- Timing information
- Cycle times
- Decision probabilities
- Resource usage
This makes them data-driven views of the process.
Process maps tend to be more qualitative, focused on the sequence of steps and departmental hand-offs. They don’t include detailed metrics.
Level of Detail
Process models are meant to capture the full mechanics of a workflow in granular detail. This includes subtle branches and exceptions.
Process maps only focus on the major steps and flow at a high level. They don’t document the nitty gritty of the process.
Creation Method
Process models are generated from system data through process mining algorithms. This data-driven approach surfaces the real process versus assumptions.
Process maps are created in collaborative workshops with process participants. This can introduce subjectivity and incorrect information.
Fluidity
Process models represent a static snapshot of the workflow at a point in time based on the underlying system data.
Process maps can be updated and modified more fluidly over time through new workshops. But this also means they can become outdated.
When Should You Use Each Method?
Now that you understand the core differences, when should you use process modeling versus process mapping?
When Process Modeling Works Best
Process modeling is most useful when:
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You want an objective, data-driven view of the workflow.
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You need to analyze and improve processes quantitatively.
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Standardized notation is required for broad understanding.
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Fluidity and frequent changes are not needed.
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You aim to discover processes versus just document them.
When Process Mapping is Better Suited
Consider process mapping when:
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You just need a simple, high-level workflow overview.
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Getting input from process participants is important.
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You want a more qualitative vs. quantitative view.
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Your processes change too rapidly for static process models.
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You want a lightweight approach without complex BPMN models.
For optimal results, leading companies often combine both methods. Process discovery creates detailed process models from system data. Then workshops validate and augment the models with process mapping techniques.
Best Practices for Process Modeling and Mapping
No matter which approach you use, here are some best practices to follow:
For Process Modeling
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Keep models simple and readable at high zoom levels. Too much detail creates clutter.
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Use sub-processes and hierarchies to organize complex workflows.
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Incorporate swimlanes to show roles, systems, departments, etc.
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Update models regularly to reflect process changes.
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Annotate models with optimization ideas for analysis.
For Process Mapping
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Have an experienced facilitator guide workshops.
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Include participants from all roles and departments involved.
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Encourage open conversation to capture details.
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Build consensus and validate the final map with participants.
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Treat initial maps as drafts open to revision after review.
Wrapping Up
Process modeling and process mapping serve complementary purposes in understanding and improving workflows. Process modeling provides detailed, data-driven analysis while process mapping delivers a qualitative overview.
Combining both techniques can give your organization tremendous insight into your processes. With this knowledge, you’re equipped to optimize, streamline, and automate business operations for the future. So don’t be afraid to mix and match process models and process maps to build process excellence!
Process mining, process modeling and process mapping are distinct, but related, methods of visualizing and analyzing business processes.
Every business is, ultimately, a collection ofbusiness processes. Processes power the creation of new products, facilitate the delivery of services, enforce company policies, maintain compliance and ensure the organization is, at all times, moving toward its overarching goals.
Each business process is a complex set of interconnected and interdependent activities that work in tandem to drive a particular business outcome. Employee performance reviews, marketing activities, content creation, software development and sales are all common types of business processes. To ensure these processes are working as intended, businesses need a way to easily define, analyze, adjust and oversee each workflow.
Organizations have developed methods to transform these abstract workflows into concrete, comprehensive pictures that illustrate the inner workings of each process. These methods include process mining, process modeling and process mapping. While each technique helps an enterprise manage, optimize, and automate processes, they do so in slightly different ways.
Pros and cons of process mining, process modeling and process mapping
Process mining makes a science out of business process management — but certain prerequisites must be met before it can be applied:
- Pro: Process mining extracts the data from IT system event logs and renders it practical and usable for enterprise teams.
- Pro: Process-mining algorithms can make existing process models more accurate and generate hypothetical models of what would happen if a process were changed.
- Pro: Process mining provides a data-driven view of actually existing workflows and their outcomes, supplying the enterprise with more objective business intelligence to guide resource allocation, automation initiatives, workflow optimization and other key business decisions.
- Con: Organizations need to use specialized tools to deploy process mining because the method relies on advanced data-mining algorithms. That said, employees don’t necessarily need data science backgrounds to conduct process mining, as most process-mining tools automate the application of algorithms and the generation of models.
Process modeling is useful for furnishing enterprises with more objective views of the workflows that power their operations. However, there are some types of data these models cannot capture:
- Pro: Process models offer objectively accurate representations of processes, removing human error and digging beyond assumptions to uncover what workflows look like in practice.
- Pro: Process models visually depict quantitative process data like time, success rates, error rates and objectively measurable outcomes, allowing for a more informed analysis of business processes and business logic. Without a process model, teams are limited to discussing workflows in qualitative terms that do not necessarily reflect reality.
- Pro: Process models make disseminating and discussing processes easier by transforming abstract workflows into concrete s.
- Con: Process models cannot capture qualitative data about how employees experience workflows in the real world; they can only reflect data recorded in an event log.
Process mapping is a fast and flexible way to generate broad overviews of processes, but maps can sometimes be inaccurate because they rely on qualitative reports from employees:
- Pro: Process maps can capture qualitative data about how workflows manifest in real-world employee activities and interactions.
- Pro: Process maps don’t require much in the way of specialized tools, and they can be produced relatively quickly and easily.
- Con: Because process maps are based on employee workshops and interviews, they are less objective than process models and may contain flawed, incomplete or inaccurate information.
Business Process Map and Process Model
What is the difference between process modeling and process mapping?
Ultimately, process modeling is a more dynamic, agile approach that puts the processes in the context of the entire organization, which supports process lifecycles and continuous improvement. Process modeling is more focused on analysis and optimization of processes, whereas process mapping is about understanding the current state.
What is the difference between process modeling and process modeling?
It focuses more on diagramming existing processes as a point of reference, whereas process modeling is used as part of process simulation and process optimization. What is process modeling? Process modeling is the graphical representation of business processes or workflows in detail and in the context of business operations.
What is process mining & process mapping?
Process mining, process modeling and process mapping are distinct, but related, methods of visualizing and analyzing business processes. Every business is, ultimately, a collection of business processes.
What is process mapping?
Process mapping is the process of drawing out all the steps and tasks involved in a business process, to create a process map, also known as a workflow diagram, which visually describes what happens, step-by-step.
