Value Stream Mapping using Process Mining

Introduction

Value Stream Mapping (VSM) is a popular lean management tool to analyze, design and improve the flow of materials and information required to deliver a product or service to the customer.

What is the purpose of Value Stream Mapping?

1. Identify value-adding and non-value-adding steps (waste).

2. Visualize the current state of a process.

3. Design a future state that improves efficiency and flow.

What does Value Stream Mapping aim to measure?

1. Process Steps

2. Material Flows and Information Flows

3. Lead Times, Cycle Times & Waiting Times

4. Inventory, Queues and Rework Loops

Traditional Value Stream Mapping involves the pen and paper method which requires analysts and managers to keenly observe the processes and make observations post which calculation and analysis is performed on the collected data. With increasing complexity of processes, there are a number of limitations to the traditional VSM method.

Challenges with traditional Value Stream Mapping

• Static Snapshot: VSM captures a single point in time and doesn't reflect real-time or continuous process changes.

• Manual & Time-Consuming: Creating accurate VSMs requires manual observation, interviews, and workshops, which are time- and resource-intensive.

• Subjectivity & Human Bias: Data often comes from interviews and observations, leading to biased or incomplete representations.

• Limited Data Granularity: Cannot capture variability, exceptions, or alternate process paths, especially in complex environments.

• Not Suited for High-Volume Digital Processes: In digital environments (e.g., ERP workflows, service desks), traditional VSM can’t scale or capture the full process.

• Poor Integration with IT Systems: VSM isn't inherently linked to real system logs or process data, making it disconnected from actual system performance.

• No Feedback Loop: VSM lacks built-in mechanisms to update or adapt the map as processes evolve over time.

• Doesn’t Detect Root Causes Easily: While it visualizes flow, it doesn’t diagnose why delays or inefficiencies occur — requires additional analysis.

• Focuses on Flow, Not Decision-Making: It misses decision points, business rules, or compliance paths that are crucial in complex services or regulated industries.

• Limited in Service Industries: Originally built for manufacturing; doesn’t always adapt well to knowledge work, services, or digital ecosystems.

• Overlooks Automation Potential: Traditional VSM doesn’t identify opportunities for automation or digital intervention unless explicitly added.

Consultants and researchers have also noted these limitations:

1. Firstly, VSM can be seriously challenging when a product or process is complex. Today, products contain hundreds of parts and sub-assemblies which follow different paths and processes in the production. 

2. Secondly, the level of difficulty in VSM preparation increases (Dal Forno et al., 2014). The high level of effort involved in collecting data and the time spent on constructing the current state map (CSM) is frequently reported as the costliest stage. 

3. Thirdly, since VSM is a static pen and paper-based method, its accuracy level and its ability to capture dynamics is limited. 

4. Many companies fail to apply VSM continuously in the same frequency as products and processes change. To see the effects of the improvements, continuous monitoring for a several months is required. 

   
   

There is a need for improvement to handle both product and process complexity, to reduce the high-level manual effort when creating the CSM, and to capture the process dynamics and deviations.

Are there any modern methods to perform Value Stream Mapping which resolves these limitations and adds value?

What is Process Mining?

Process mining is a data-driven technique used to analyze and improve business processes by extracting knowledge from event logs readily available in today's information systems. It combines elements of data science, process modeling, and business process management (BPM) to visualize, monitor, and enhance real-life workflows. Process mining helps organizations uncover how processes actually run, as opposed to how they are supposed to run and detect bottlenecks, inefficiencies, deviations, or compliance violations. The three main types of process mining are: discovery (creating a process model from data), conformance checking (comparing a discovered model with an existing one), and enhancement (improving an existing model based on actual data).

Here’s a very simple example: imagine a pizza delivery company that uses a system to track every order. Each order goes through steps like Order Received, Pizza Prepared, Pizza Baked, Out for Delivery, and Delivered. These steps generate event logs with timestamps and case IDs. By applying process mining to these logs, the company might discover that on Fridays, a large number of orders are stuck at the Baking stage, causing delays. This insight allows them to investigate the cause (e.g., oven capacity or staff shortage) and take action to improve efficiency. With tools like ARIS, Celonis, ProM, or Disco, businesses can visualize this flow and use real data to drive operational improvements.

How can Process Mining be used for Value Stream Mapping?

Today’s Information Systems contain data about all the processes run by the company. Event logs in particular gives accurate information about a particular process. Traditionally, event logs are used to mine processes, check the processes for bottlenecks and risks, perform conformance checks with respect to regulations and the ideal modeled version of processes. 

One out of the box use case for Process Mining and Modern Business Process Management is to be able to perform accurate Value Stream Mapping of business processes. 

Process-Mining based Value Stream Mapping allows for continuous monitoring, ability to handle high product and process variability, and reduces the manual effort typically involved in collecting and analyzing data. This integration helps professionals visualize real-time bottlenecks, inefficiencies and waste – especially relevant for lean production goals like reducing inventory and lead times. This new method support dynamic, individualized value streams which makes it suitable for complex material flows (like mixed-model assembly lines). 

Here is a primary comparison between traditional VSM and Process-Mining based VSM.

Traditional VSM vs. Process-Mining based VSM

Aspect	Traditional VSM	Process Mining-Based VSM
Tooling	Pen and paper	Automated software with algorithms
Data Source	Manual observations	Event logs from information systems
Effort	High manual effort	High initial setup, but scalable
Complexity Handling	Poor; breaks down with high variability	Excellent; supports dynamic processes and variants
Dynamics	Static snapshot	Continuous, real-time updates possible
Scalability	Limited to selected parts/processes	Scales across entire systems and product variants
Waste Identification	Subjective, limited	Data-driven, detailed (e.g., delays, inventory buildup)
Use Case Example	Mapping a single assembly line manually	Automatically profiling all processes for inefficiencies

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