USING GENERATIVE AI TO REVOLUTIONIZE CONTINUOUS IMPROVEMENT: Part 2 Process Maps & Value Stream Maps

CONTINUOUS IMPROVEMENT WITH TONY

NEWSLETTER, VOLUME 22, January 16, 2025

Anthony Tarantino, PhD

In this series of newsletters, I will attempt to demonstrate how artificial intelligence, particularly Generative AI, will dramatically improve the effectiveness and ease of use of tried and proven Lean Six Sigma continuous improvement tools. This applies to both large and smaller organizations.

Value Stream Mapping (VSM) is a lean-management method used to visualize the flow of materials and information as a product or service moves through the value stream. The goal is to identify and eliminate waste, thereby improving the overall efficiency and value delivered to the customer. Value Stream Mapping (VSM) is focused on Identifying ?and eliminating waste in the entire value stream to improve overall value to the customer. Its scope covers the entire flow of materials and information from raw materials to the final product/service delivered to the customer.

Process Mapping is different from Value Stream Mapping (VSM). Both tools are used to visualize workflows, but they have distinct differences in focus and scope. Process mapping is focused on detailing the specific steps involved in a particular process or workflow. The scope typically focuses on a single process or a series of related processes within an organization with the goal of understanding and documenting the current process, identify bottlenecks, and find areas for improvement.

In summary, while process mapping is more about understanding and documenting the steps within a process, value stream mapping takes a broader view to enhance the overall value delivered to the customer by eliminating waste and improving efficiency.

The major limitation in the traditional method of conducting both process and value stream mapping exercise is their reliance on subject matter experts (SMEs) to observe a process at specific times and locations. This can introduce several limitations compared to using AI-based continuous monitoring tools such as computer vision and IoT sensors. Here is a brief summary

Limitations of Using SMEs:

1. Limited Observation Time: SMEs can only observe the process for a limited duration, which may not capture all variations and anomalies that occur over time.

2. Human Error: Observations can be subjective and prone to human error, leading to potential inaccuracies in data collection.

3. Sampling Bias: Observations at specific times and locations may not be representative of the overall process, leading to biased conclusions.

4. Resource Intensive: Relying on SMEs requires significant time and effort, which can be costly and may not be feasible for large-scale or continuous monitoring.

5. Inconsistent Data: Different SMEs may have varying levels of expertise and observational skills, resulting in inconsistent data quality.

Advantages of Using Computer Vision and IoT Sensors:

1. Continuous Monitoring: Sensors and computer vision can monitor processes 24/7, capturing data continuously and identifying issues in real-time.

2. Objective Data: Automated systems provide objective and consistent data, reducing the risk of human error and bias.

3. Comprehensive Coverage: Sensors can be placed at multiple locations, providing a more complete view of the process and identifying issues that may be missed during limited observations.

4. Historical Data Analysis: Continuous monitoring allows for the collection of historical data, enabling trend analysis and long-term improvements.

5. Scalability: Automated systems can be scaled to monitor multiple processes and locations simultaneously, making them more efficient for large-scale operations.

In summary, while SMEs provide valuable insights, relying on them alone can lead to limitations in data accuracy and coverage. Using computer vision and IoT sensors for continuous monitoring offers a more comprehensive and objective approach to process

Prediction:

Applying Smart cameras and other IoT devices to value stream and process mapping will vastly improve their effectiveness while reducing much of the labor. The main challenge will be educating continuous improvement practitioner accustomed to the traditional clip board and stopwatch approaches that have changed little since Frank and Lillian Gilbreth invented them over 100 years ago. (Lillian Gilbreth is recognized as the Mother of Industrial Engineering and one of the first women to receive a PhD in Engineering.)

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?Anthony Tarantino, PhD

Six Sigma Master Black Belt, CPM (ISM), CPIM (APICS)

Adjunct Professor, Santa Clara University – Smart Mfg. & Industry 4.0

Author of Wiley's Smart Manufacturing, the Lean Six Sigma Way Amazon Links

(562) 818-3275?? ?[email protected]?

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