25+ AI Prompts for Lean 6 Sigma

A3 Thinking, Cost of Poor Quality (COPQ), Design of Experiments (DOE), Failure Mode and Effects Analysis (FMEA), Hoshin Kanri, Lean 6 Sigma, Poka-yoke, Statistical Process Control (SPC), Value Stream Mapping

Lean six sigma — Practical tools for lean and six sigma to enhance project execution and decision-making on the factory floor.

The following 30 prompt are designed as practical tools for Yellow, Green, and Black Belts to accelerate project execution directly on the factory floor. They provide structured inputs to execute specific, high-value Lean and Six Sigma tasks, such as generating a complete Value Stream Map from raw production data, performing a root cause analysis on a complex defect dataset, or drafting a detailed FMEA for a new process. The objective is to bypass the manual and time-consuming aspects of data compilation and reporting, allowing practitioners to focus immediately on interpreting results, making decisions, and implementing improvements to cut waste and reduce process variation.

The scope of these prompts spans the critical functions of modern manufacturing and operational excellence: in-depth Process Analysis and Optimization by generating Value Stream Maps and Failure Mode and Effects Analyses, and delve into Data Analysis and Statistical Process Control (SPC) to interpret control charts and recommend Designs of Experiments. For financial oversight, prompts are tailored for Cost Reduction and Financial Impact, such as calculating the Cost of Poor Quality. To streamline initiatives, a suite of prompts aids in Project Management and Reporting by creating project charters and A3 reports, while Advanced Analytics and Predictive Solutions offer capabilities like maintenance schedule optimization and demand forecasting. Finally, prompts focused on Continuous Improvement and Innovation facilitate everything from generating Poka-Yoke ideas to structuring Hoshin Kanri strategic plans.

Complementary to these prompts, you likely also be interested in pure manufacturing AI tools:

Process Analysis and Optimization

Automated Value Stream Mapping (VSM) Generator

Analyzes process data in CSV format to generate a VSM in Mermaid format, identifying bottlenecks and areas for improvement. This prompt highlights non-value-added activities and calculates process efficiency and lead time based on the provided data.

Recommended temperature: 0.3 Recommended thinking complexity: high

User's inputs: {process_data_csv}, {key_metrics}

**CONTEXT**You are an AI assisting expert-level Lean 6 Sigma (<a  data-ilj-link-preview="true"  data-featured-image="https://innovation.world/wp-content/uploads/2025/10/six-sigma-belts-300x210.jpg"  data-excerpt="Six Sigma implementation involves a hierarchy of certified professionals, designated by colored "belts" similar to martial arts. Key roles include Master Black Belts, who mentor and train; Black Belts, who lead complex improvement projects; Green Belts, who lead smaller projects as part of their regular duties; and Yellow Belts, who have basic training and participate..." href="https://innovation.world/invention/six-sigma-belts/">Black Belt</a>) engineers in manufacturing. Your task is to analyze process data provided in CSV format to generate a Value Stream Map (VSM) in Mermaid format. The VSM should identify bottlenecks, highlight non-value-added activities, and calculate process efficiency and lead time.**INPUTS**1. Process data in CSV format: “{process_data_csv}”2. Key performance metrics to focus on (e.g., cycle time, wait time): “{key_metrics}”**INSTRUCTIONS**1. Parse the provided CSV data to extract relevant process steps and metrics.2. Identify and list all process steps, noting the sequence and dependencies.3. For each process step, calculate the following: – Cycle time – Wait time – Value-added time – Non-value-added time4. Identify bottlenecks by comparing cycle times and wait times across process steps.5. Highlight non-value-added activities and calculate the overall process efficiency using the formula: – Process Efficiency = (Total Value-Added Time / Total Lead Time) * 1006. Generate a Value Stream Map in Mermaid format, including: – Process steps with calculated times – Bottlenecks and non-value-added activities – Process efficiency and lead time**OUTPUT FORMAT**Provide the output in the following format:“`mermaid$mermaid_diagram“`Include a summary of identified bottlenecks and areas for improvement:“`$summary“`

Dynamic Process Simulation for Bottleneck Analysis

Creates a discrete-event simulation model of a manufacturing process based on user-defined steps, resources, and processing times. It then runs multiple iterations to predict throughput, resource utilization, and potential bottlenecks under various conditions.

Recommended temperature: 0.7 Recommended thinking complexity: high

User's inputs: None detected

**TASK OVERVIEW**Create a discrete-event simulation model to analyze a manufacturing process for bottlenecks. Use user-defined steps, resources, and processing times to predict throughput, resource utilization, and potential bottlenecks.**INPUTS**1. Process Steps: Provide a CSV string of process steps in sequence, e.g., “{step1,step2,step3}”.2. Resources: Provide a CSV string of resources available for each step, e.g., “{resource1,resource2,resource3}”.3. Processing Times: Provide a CSV string of processing times for each step in minutes, e.g., “{time1,time2,time3}”.**INSTRUCTIONS**1. Parse the user inputs to create a structured list of process steps, resources, and processing times.2. Develop a discrete-event simulation model using the provided inputs.3. Run multiple iterations of the simulation to evaluate different scenarios.4. Calculate and output the following: a. Throughput: Determine the average number of units processed per time unit. b. Resource Utilization: Calculate the percentage of time each resource is actively used. c. Bottleneck Identification: Identify steps where delays occur most frequently and resources are over-utilized.**OUTPUT FORMAT**Provide the results in the following format:- **Throughput:** $throughput_value units/time- **Resource Utilization:** – Resource 1: $utilization1% – Resource 2: $utilization2% – Resource 3: $utilization3%- **Bottleneck Analysis:** – Step 1: $bottleneck1 – Step 2: $bottleneck2 – Step 3: $bottleneck3Ensure clarity and precision in the output to facilitate decision-making in manufacturing process improvements.

Root Cause Analysis with Causal AI

Analyzes a dataset of process parameters and quality outcomes to identify the most likely root causes of defects. This prompt goes beyond correlation to suggest causal relationships, helping to focus improvement efforts.

Recommended temperature: 0.7 Recommended thinking complexity: high

User's inputs: {process_data_csv}, {defect_list}

**CONTEXT**You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with identifying the root causes of defects in a manufacturing process. You have a dataset containing process parameters and quality outcomes. Your goal is to determine causal relationships, not just correlations, to effectively focus improvement efforts.**INPUTS**1. Dataset of process parameters and quality outcomes in CSV format: “{process_data_csv}”2. List of known defects to focus on: “{defect_list}”**INSTRUCTIONS**1. Load the dataset from “{process_data_csv}”.2. Parse the dataset to identify all process parameters and quality outcomes.3. For each defect in “{defect_list}”, perform the following: a. Identify potential causal relationships between process parameters and the specific defect using causal inference techniques. b. Rank these relationships based on their likelihood of causality. c. Provide a brief explanation of the method used to determine causality for each relationship.4. Summarize the findings in a structured format, highlighting the most likely root causes for each defect.**OUTPUT FORMAT**Provide the results in the following format:- Defect: $defect_name – Most Likely Root Causes: 1. Process Parameter: $parameter_name – Causality Likelihood: $likelihood_score – Explanation: $causality_explanation 2. Process Parameter: $parameter_name – Causality Likelihood: $likelihood_score – Explanation: $causality_explanationRepeat for each defect in “{defect_list}”.**NOTE**Ensure that the analysis goes beyond mere correlation and focuses on identifying causal relationships to guide effective process improvements.

AI-Powered Failure Mode and Effects Analysis (FMEA) Generator

Generates a preliminary FMEA table based on a process description and historical failure data. It identifies potential...

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Topics covered: AI Prompts, Lean Sigma, Process Improvement, Waste Reduction, Six Sigma, Continuous Improvement, Quality Management, Data Analysis, Efficiency, Statistical Tools, Root Cause Analysis, Value Stream Mapping, ISO 9001, ISO 14001, ISO/IEC 27001, and AS9100..