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 식스 시그마 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 새로운 프로세스를 위한 것입니다. 목표는 데이터 수집 및 보고의 수동적이고 시간 소모적인 부분을 없애고, 실무자들이 결과를 해석하고, 의사 결정을 내리고, 낭비를 줄이고 프로세스 변동을 최소화하기 위한 개선 사항을 구현하는 데 즉시 집중할 수 있도록 하는 것입니다.
이러한 과제들은 현대 제조 및 운영 우수성의 핵심 기능들을 포괄합니다. 즉, 가치 흐름 맵(Value Stream Map) 및 고장 모드 및 영향 분석(Failure Mode and Effects Analysis)을 생성하여 심층적인 프로세스 분석 및 최적화를 수행하고, 데이터 분석에 대해서도 심도 있게 다룹니다. Statistical Process Control (SPC) to interpret control charts and recommend Designs of Experiments. For financial oversight, prompts are tailored for 비용 절감 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.
이러한 질문들에 더하여, 순수 미술에도 관심이 있으실 가능성이 높습니다. 제조 AI 도구:
프로세스 분석 및 최적화
[prompt_formatter title=”Automated Value Stream Mapping (VSM) Generator” description=”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.” temperature=”0.3″ thinking=”high”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (검은 띠) 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 time⸻4. 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) * 100⸻6. 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⸻[/prompt_formatter]
[prompt_formatter title=”Dynamic Process Simulation for Bottleneck Analysis” description=”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.” temperature=”0.7″ thinking=”high”]**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: $bottleneck3⸻⸻Ensure clarity and precision in the output to facilitate decision-making in manufacturing process improvements.[/prompt_formatter]
[prompt_formatter title=”Root Cause Analysis with Causal AI” description=”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.” temperature=”0.7″ thinking=”high”]**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 방법 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_explanation⸻⸻Repeat 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.[/prompt_formatter]
[prompt_formatter title=”AI-Powered Failure Mode and Effects Analysis (FMEA) Generator” description=”Generates a preliminary FMEA table based on a process description and historical failure data. It identifies potential failure modes, their effects, and suggests initial severity, occurrence, and detection ratings.” temperature=”0.7″ thinking=”medium”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in manufacturing. Your task is to generate a preliminary Failure Mode and Effects Analysis (FMEA) table.⸻**INPUTS**⸻1. Process Description: {process_description}⸻2. Historical Failure Data (CSV format): {historical_failure_data}⸻⸻**INSTRUCTIONS**⸻1. Analyze the provided {process_description} to understand the key steps and components involved.⸻2. Review the {historical_failure_data} to identify common failure modes, their causes, and effects.⸻3. For each identified failure mode, perform the following:⸻⸻ a. List the potential failure mode.⸻ b. Describe the potential effects of the failure mode on the process.⸻ c. Suggest initial severity, occurrence, and detection ratings based on historical data and industry standards.⸻⸻4. Compile the information into a structured FMEA table with the following columns:⸻ – Failure Mode⸻ – Potential Effects⸻ – Severity Rating⸻ – Occurrence Rating⸻ – Detection Rating⸻⸻**OUTPUT FORMAT**⸻Provide the FMEA table in CSV format. Each row should represent a unique failure mode with its corresponding details.⸻[/prompt_formatter]
[prompt_formatter title=”Kaizen Event Idea Generator and Prioritizer” description=”Takes a problem statement and process data as input to brainstorm a list of potential 카이젠 event ideas. It then prioritizes these ideas based on estimated impact, effort, and alignment with business objectives.” temperature=”0.7″ thinking=”medium”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in manufacturing or production improvement. Your task is to generate and prioritize Kaizen event ideas based on a given problem statement and process data.⸻⸻**INPUTS**⸻1. Problem Statement: “{problem_statement}”⸻2. Process Data (CSV format): “{process_data_csv}”⸻3. Business Objectives (CSV format): “{business_objectives_csv}”⸻⸻**TASKS**⸻1. Analyze the provided problem statement and process data to identify key areas for improvement.⸻2. Generate a list of potential Kaizen event ideas that address the identified areas.⸻3. For each idea, estimate the potential impact, required effort, and alignment with the provided business objectives.⸻4. Prioritize the ideas based on a weighted scoring system considering impact, effort, and alignment.⸻⸻**OUTPUT FORMAT**⸻Provide the output in a markdown table with the following columns:⸻- Kaizen Event Idea⸻- Estimated Impact (High/Medium/Low)⸻- Required Effort (High/Medium/Low)⸻- Alignment with Business Objectives (High/Medium/Low)⸻- Priority Score (Numerical value)⸻⸻**INSTRUCTIONS**⸻- Use the problem statement to focus the brainstorming process.⸻- Analyze the process data to uncover inefficiencies or bottlenecks.⸻- Consider the business objectives to ensure alignment with strategic goals.⸻- Use a consistent method to estimate impact, effort, and alignment.⸻- Calculate the priority score using a formula: Priority Score = (Impact Weight * Impact) + (Effort Weight * (1 – Effort)) + (Alignment Weight * Alignment), where weights are predefined as needed.⸻- Present the ideas in descending order of priority score.[/prompt_formatter]
데이터 분석 및 통계적 공정 관리(SPC)
[prompt_formatter title=”Intelligent Control Chart Pattern Recognition” description=”Analyzes time-series data from a process and automatically identifies non-random patterns in control charts (e.g., shifts, trends, cycles). It provides a statistical interpretation of these patterns, suggesting potential special causes of variation.” temperature=”0.7″ thinking=”high”]**CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer working on manufacturing or production improvement. Your task is to analyze time-series data from a process to identify non-random patterns in control charts and provide statistical interpretations and potential special causes of variation.⸻⸻**INPUTS**⸻1. Time-series data in CSV format: “{time_series_data}”⸻2. Control chart type (e.g., X-bar, R, S): “{control_chart_type}”⸻⸻**INSTRUCTIONS**⸻1. Load the provided time-series data “{time_series_data}” and identify the control chart type “{control_chart_type}”.⸻2. Analyze the data to detect non-random patterns such as shifts, trends, and cycles.⸻3. For each identified pattern, provide a statistical interpretation and suggest potential special causes of variation.⸻4. Summarize the findings in a structured format.⸻⸻**OUTPUT FORMAT**⸻Provide the analysis in the following format:⸻⸻- **Detected Patterns**:⸻ – Pattern Type: $pattern_type⸻ – Description: $pattern_description⸻ – Statistical Interpretation: $statistical_interpretation⸻ – Suggested Special Causes: $special_causes⸻⸻Repeat the above structure for each identified pattern.⸻⸻**ADDITIONAL NOTES**⸻Ensure the analysis is concise and focused on providing actionable insights for process improvement.[/prompt_formatter]
[prompt_formatter title=”Predictive Quality Control Modeler” description=”Develops a predictive model based on historical process data to forecast product quality in real-time.” temperature=”0.7″ thinking=”high”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in manufacturing. Your task is to develop a predictive model using historical process data to forecast product quality in real-time. This model will enable proactive adjustments to process parameters to prevent defects.⸻⸻**INPUTS**⸻1. Historical process data in CSV format: “{historical_process_data_csv}”⸻2. List of key process parameters to monitor: “{key_process_parameters}”⸻3. Desired prediction time frame (e.g., real-time, hourly, daily): “{prediction_time_frame}”⸻⸻**INSTRUCTIONS**⸻1. **Data Preprocessing**:⸻- Load the historical process data from “{historical_process_data_csv}”.⸻- Clean the data by handling missing values and outliers.⸻- Normalize or standardize the data if necessary.⸻⸻2. **Feature Selection**:⸻- Identify and extract features related to “{key_process_parameters}”.⸻- Determine the most significant features impacting product quality using statistical methods or machine learning techniques.⸻⸻3. **Model Development**:⸻- Select appropriate machine learning algorithms suitable for time-series prediction.⸻- Train the model using the preprocessed data and selected features.⸻- Validate the model using cross-validation techniques to ensure accuracy and reliability.⸻⸻4. **Real-Time Prediction Setup**:⸻- Configure the model to make predictions based on the “{prediction_time_frame}”.⸻- Implement a system to update the model with new data continuously.⸻- Set up alerts or notifications for significant deviations in predicted quality.⸻⸻5. **Output**:⸻- Provide a summary of the model’s performance metrics (e.g., accuracy, precision, recall).⸻- Generate a real-time prediction dashboard in HTML format to visualize product quality forecasts and process parameter trends.⸻- Include recommendations for proactive process adjustments based on prediction outcomes.⸻⸻**OUTPUT FORMAT**⸻- Summary of performance metrics: $performance_summary⸻- Real-time prediction dashboard (HTML): $prediction_dashboard_html⸻- Recommendations for process adjustments: $process_adjustments_recommendations[/prompt_formatter]
[prompt_formatter title=”Design of Experiments (DOE) Recommender” description=”Recommends an appropriate Design of Experiments strategy based on the user’s project goal, number of factors, and constraints, and generates the experimental run sheet in CSV format.” temperature=”0.7″ thinking=”medium”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in selecting an appropriate Design of Experiments (DOE) strategy for manufacturing or production improvement.⸻The goal is to recommend a DOE strategy and generate an experimental run sheet based on the user’s project goal, number of factors, and constraints.⸻⸻**INPUTS**⸻1. Project Goal: “{project_goal}”⸻2. Number of Factors: “{number_of_factors}”⸻3. Constraints (e.g., budget, time, resources): “{constraints}”⸻⸻**TASKS**⸻1. Analyze the project goal to determine the type of response variable and the desired outcome.⸻2. Evaluate the number of factors and constraints to identify feasible DOE strategies.⸻3. Recommend the most suitable DOE strategy (e.g., full factorial, fractional factorial, response surface) based on the analysis.⸻4. Generate an experimental run sheet in CSV format for the recommended DOE strategy.⸻⸻**OUTPUT FORMAT**⸻1. Recommended DOE Strategy: $doe_strategy⸻2. Justification for the recommended strategy: $justification⸻3. Experimental Run Sheet (CSV format):⸻$csv_runsheet⸻⸻**INSTRUCTIONS FOR AI**⸻- Use the project goal to understand the nature of the experiment and the critical response variables.⸻- Consider the number of factors and constraints to determine the complexity and feasibility of different DOE strategies.⸻- Provide a clear recommendation with justification for the chosen DOE strategy.⸻- Create a detailed experimental run sheet in CSV format that aligns with the recommended DOE strategy.⸻- Ensure the output is structured and formatted as specified.[/prompt_formatter]
[prompt_formatter title=”Measurement System Analysis (MSA) Interpretation Assistant” description=”Analyzes Gage R&R study data provided in a CSV format and generates a comprehensive report. The report interprets the results, highlights potential issues with the measurement system, and suggests corrective actions.” temperature=”0.3″ thinking=”medium”]**CONTEXT**⸻You are an AI assistant tasked with analyzing a Gage R&R study to evaluate the measurement system’s reliability and accuracy. The user will provide Gage R&R study data in CSV format. Your goal is to interpret this data, identify any issues, and suggest corrective actions.⸻⸻**INPUT**⸻1. CSV data of Gage R&R study: “{gage_rr_csv_data}”⸻⸻**TASKS**⸻1. Parse the provided CSV data to extract relevant information such as parts, operators, trials, and measurements.⸻2. Calculate the following metrics:⸻ – Total Gage R&R as a percentage of total variation⸻ – Repeatability and Reproducibility components⸻ – Part-to-Part variation⸻3. Interpret the calculated metrics to assess the measurement system’s adequacy.⸻ – Determine if the Total Gage R&R percentage is acceptable (typically less than 10% is considered excellent, 10%-30% may be acceptable depending on the application, and above 30% is generally unacceptable).⸻ – Analyze Repeatability and Reproducibility to identify dominant sources of variation.⸻4. Highlight potential issues with the measurement system based on the analysis.⸻5. Suggest corrective actions to improve the measurement system, if necessary.⸻⸻**OUTPUT FORMAT**⸻Provide a comprehensive report in markdown format including:⸻- Summary of the Gage R&R study results⸻- Interpretation of the results with specific focus on Total Gage R&R, Repeatability, Reproducibility, and Part-to-Part variation⸻- Identification of potential issues⸻- Suggested corrective actions⸻- Any additional insights or recommendations⸻⸻**NOTE**⸻Ensure clarity and precision in the report to aid expert-level Lean 6 Sigma engineers in decision-making. Use markdown elements like headings, bullet points, and tables for better readability.[/prompt_formatter]
[prompt_formatter title=”Process Capability Analysis and Improvement Suggester” description=”Calculates 프로세스 능력 indices (Cp, Cpk) from production data and compares them against targets. Based on the analysis, it suggests specific areas for process improvement to enhance capability.” temperature=”0.5″ thinking=”medium”]**CONTEXT:**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in manufacturing. Your task is to analyze production data to calculate process capability indices (Cp, Cpk) and suggest improvements.⸻⸻**INPUTS:**⸻1. Production data in CSV format: “{production_data_csv}”⸻2. Target Cp and Cpk values: “{target_cp}” and “{target_cpk}”⸻⸻**INSTRUCTIONS:**⸻1. Parse the provided production data from the CSV string “{production_data_csv}”.⸻2. Calculate the process capability indices Cp and Cpk using the parsed data.⸻3. Compare the calculated Cp and Cpk values against the target values “{target_cp}” and “{target_cpk}”.⸻4. Identify specific areas where the process capability is below target.⸻5. Suggest targeted process improvements to enhance capability in the identified areas.⸻⸻**OUTPUT FORMAT:**⸻Provide the following in markdown format:⸻- **Calculated Indices:** Display the calculated Cp and Cpk values.⸻- **Comparison Summary:** Summarize how the calculated values compare to the targets.⸻- **Improvement Suggestions:** List specific areas for improvement and suggest actionable steps to enhance process capability.⸻⸻**EXAMPLE OUTPUT:**⸻“`markdown⸻**Calculated Indices:**⸻- Cp: $calculated_cp⸻- Cpk: $calculated_cpk⸻⸻**Comparison Summary:**⸻- The calculated Cp is $comparison_cp compared to the target of {target_cp}.⸻- The calculated Cpk is $comparison_cpk compared to the target of {target_cpk}.⸻⸻**Improvement Suggestions:**⸻1. Area: $area1⸻ – Suggestion: $suggestion1⸻2. Area: $area2⸻ – Suggestion: $suggestion2⸻“`[/prompt_formatter]
Cost Reduction and Financial Impact
[prompt_formatter title=”Cost of Poor Quality (COPQ) Calculator and Simulator” description=”Calculates the Cost of Poor Quality by analyzing data on internal failures (scrap, rework) and external failures (warranty claims, returns). It also allows the user to simulate the financial impact of reducing specific defect types.” temperature=”0.3″ thinking=”medium”]**CONTEXT**⸻You are an AI assisting Lean 6 Sigma Black Belt engineers in calculating and simulating the Cost of Poor Quality (COPQ) in a manufacturing environment. The focus is on internal failures (scrap, rework) and external failures (warranty claims, returns).⸻⸻**INPUTS**⸻1. Internal Failure Data: “{internal_failure_data}” (CSV format: “Type, Cost”)⸻2. External Failure Data: “{external_failure_data}” (CSV format: “Type, Cost”)⸻3. Defect Reduction Simulation: “{defect_reduction_simulation}” (CSV format: “Defect Type, Reduction Percentage”)⸻⸻**TASKS**⸻1. Parse the internal and external failure data to calculate the total COPQ.⸻2. For each defect type in the simulation input, calculate the potential cost savings by applying the specified reduction percentage.⸻3. Provide a summary of the total COPQ and the potential savings for each defect type.⸻⸻**OUTPUT FORMAT**⸻1. Total COPQ: $total_copq⸻2. Savings Simulation:⸻ – Defect Type: $defect_type⸻ – Original Cost: $original_cost⸻ – Reduction Percentage: $reduction_percentage⸻ – Potential Savings: $potential_savings⸻⸻**INSTRUCTIONS**⸻- Use the provided CSV data to perform calculations.⸻- Ensure all monetary values are formatted to two decimal places.⸻- Present the output in a clear and concise manner using markdown for readability.⸻- Do not include any additional commentary or explanation beyond the requested output.⸻[/prompt_formatter]
[prompt_formatter title=”Lean Cost Reduction Opportunity Identifier” description=”Analyzes operational data to identify and quantify cost reduction opportunities based on 린(Lean) 원칙.” temperature=”0.7″ thinking=”medium”]**CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with identifying cost reduction opportunities in a manufacturing setting. You will provide operational data for analysis.⸻⸻**INPUTS**⸻1. Operational Data CSV: “{operational_data_csv}” (Include columns such as cycle times, inventory levels, energy consumption)⸻2. Target Areas for Improvement: “{target_areas}” (e.g., waste reduction, inventory optimization, energy efficiency)⸻⸻**INSTRUCTIONS**⸻1. Parse the provided operational data CSV to extract relevant metrics for analysis.⸻2. For each target area specified, perform a detailed analysis to identify inefficiencies and potential cost reduction opportunities.⸻3. Quantify the potential savings for each identified opportunity using Lean principles.⸻4. Categorize the opportunities into the specified target areas.⸻5. Provide a summary of findings, including potential savings and recommendations for implementation.⸻⸻**OUTPUT FORMAT**⸻- Summary of Findings: A detailed markdown report with sections for each target area.⸻- Each section should include:⸻ – Identified Opportunities⸻ – Quantified Potential Savings⸻ – Recommendations for Implementation⸻- Use bullet points and tables where applicable for clarity.[/prompt_formatter]
[prompt_formatter title=”Total Productive Maintenance (TPM) Cost-Benefit Analyzer” description=”Evaluates the potential financial return of implementing a TPM program by analyzing current maintenance costs, equipment downtime, and production losses. It projects the expected 투자수익률(ROI) based on user-provided data on anticipated improvements.” temperature=”0.3″ thinking=”medium”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in evaluating the financial return of implementing a 전체 생산 유지보수 (TPM) program. Your task is to analyze current maintenance costs, equipment downtime, and production losses, and project the expected ROI based on anticipated improvements provided by the user.⸻⸻**INPUTS**⸻1. Current maintenance costs in CSV format: “{current_maintenance_costs}”⸻2. Current equipment downtime in CSV format: “{current_equipment_downtime}”⸻3. Current production losses in CSV format: “{current_production_losses}”⸻4. Anticipated improvements in percentage for maintenance costs, downtime, and production losses in CSV format: “{anticipated_improvements}”⸻⸻**INSTRUCTIONS**⸻1. Parse the user inputs to extract current maintenance costs, equipment downtime, production losses, and anticipated improvements.⸻2. Calculate the total current costs by summing maintenance costs, downtime costs, and production losses.⸻3. Apply the anticipated improvements to each category to estimate the reduced costs.⸻4. Calculate the total reduced costs after improvements.⸻5. Determine the cost savings by subtracting the total reduced costs from the total current costs.⸻6. Calculate the ROI using the formula: ROI = (Cost Savings / Total Current Costs) * 100.⸻7. Provide a summary of the analysis including the total current costs, total reduced costs, cost savings, and projected ROI.⸻⸻**OUTPUT FORMAT**⸻Provide the output in the following format:⸻- Total Current Costs: $total_current_costs⸻- Total Reduced Costs: $total_reduced_costs⸻- Cost Savings: $cost_savings⸻- Projected ROI: $projected_roi%⸻- Summary: $summary[/prompt_formatter]
[prompt_formatter title=”Value-Added vs. Non-Value-Added Cost Analysis” description=”Breaks down process costs from a provided dataset into value-added and non-value-added categories. This prompt provides a visual representation of cost distribution, helping to target waste elimination efforts with the greatest financial impact.” temperature=”0.7″ thinking=”medium”]**CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with analyzing process costs to identify value-added and non-value-added activities. You aim to visualize cost distribution to prioritize waste elimination efforts effectively.⸻⸻**INPUTS**⸻1. Provide a CSV string of your process cost data, formatted as “Activity,Cost”. Example: “{activity_cost_data}”.⸻2. Provide a list of activities considered value-added. Example: “{value_added_activities}”.⸻⸻**INSTRUCTIONS**⸻1. Parse the provided CSV string to extract activities and their associated costs.⸻2. Categorize each activity as value-added or non-value-added based on the provided list of value-added activities.⸻3. Calculate the total cost for value-added and non-value-added activities.⸻4. Generate a visual representation (e.g., pie chart) of the cost distribution between value-added and non-value-added categories.⸻5. Provide a summary of the financial impact, highlighting areas with the greatest potential for cost reduction.⸻⸻**OUTPUT FORMAT**⸻1. A summary of total costs for value-added and non-value-added activities.⸻2. A pie chart in “Mermaid” format illustrating the cost distribution.⸻3. A textual analysis of the financial impact and recommendations for waste elimination.⸻⸻**EXAMPLE OUTPUT**⸻$summary: Total Value-Added Cost: $X, Total Non-Value-Added Cost: $Y⸻$chart:“`mermaid⸻pie⸻title Cost Distribution⸻”Value-Added”: X⸻”Non-Value-Added”: Y“`⸻$analysis: The analysis indicates that non-value-added activities account for Z% of total costs. Prioritize reducing costs in these areas for maximum financial impact.[/prompt_formatter]
[prompt_formatter title=”Supply Chain Cost Optimization Modeler” description=”Analyzes supplier performance data, transportation costs, and inventory holding costs to recommend optimal ordering quantities and supplier selection strategies. It aims to minimize total supply chain costs while maintaining desired service levels.” temperature=”0.7″ thinking=”high”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in optimizing supply chain costs. You will analyze supplier performance data, transportation costs, and inventory holding costs to recommend optimal ordering quantities and supplier selection strategies.⸻**INPUTS**⸻1. Supplier Performance Data (CSV): “{supplier_performance_data}”⸻2. Transportation Costs (CSV): “{transportation_costs}”⸻3. Inventory Holding Costs (CSV): “{inventory_holding_costs}”⸻**TASKS**⸻1. Parse the supplier performance data, transportation costs, and inventory holding costs from the provided CSV inputs.⸻2. Calculate the total cost for each supplier, considering performance metrics, transportation, and inventory holding costs.⸻3. Identify potential cost-saving opportunities by analyzing the cost breakdown and supplier performance.⸻4. Recommend optimal ordering quantities for each supplier to minimize total supply chain costs while maintaining desired service levels.⸻5. Suggest supplier selection strategies based on the analysis to further optimize costs.⸻**OUTPUT FORMAT**⸻Provide a detailed report in markdown format, including:⸻- A summary of the total costs for each supplier and potential cost-saving opportunities.⸻- Recommended ordering quantities for each supplier.⸻- Suggested supplier selection strategies.⸻- Visualizations of cost breakdowns and supplier performance using “Mermaid” format for diagrams.⸻- Any assumptions made during the analysis.⸻[/prompt_formatter]
Project Management and Reporting
[prompt_formatter title=”DMAIC Project Charter and A3 Report Generator” description=”Creates a comprehensive DMAIC project charter and an A3 report in markdown format based on user inputs for the problem statement, goals, scope, and team members.” temperature=”0.7″ thinking=”medium”]**CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with initiating a DMAIC project in a manufacturing setting. Your goal is to create a standardized project charter and an A3 report to ensure a thorough project kickoff.⸻⸻**INPUTS**⸻1. Problem Statement: {problem_statement}⸻2. Project Goals: {project_goals}⸻3. Project Scope: {project_scope}⸻4. Team Members: {team_members}⸻⸻**TASKS**⸻1. Analyze the provided inputs to understand the project’s context and objectives.⸻2. Generate a DMAIC Project Charter in markdown format, including sections for the problem statement, goals, scope, and team members.⸻3. Create an A3 Report in markdown format, incorporating the project charter details and adding sections for background, current state, root cause analysis, countermeasures, implementation plan, and follow-up actions.⸻⸻**OUTPUT FORMAT**⸻Provide the output in markdown format with the following structure:⸻⸻# DMAIC Project Charter⸻## Problem Statement⸻{problem_statement}⸻## Project Goals⸻{project_goals}⸻## Project Scope⸻{project_scope}⸻## Team Members⸻{team_members}⸻⸻# A3 Report⸻## Background⸻$background⸻## Current State⸻$current_state⸻## Root Cause Analysis⸻$root_cause_analysis⸻## Countermeasures⸻$countermeasures⸻## Implementation Plan⸻$implementation_plan⸻## Follow-up Actions⸻$follow_up_actions⸻⸻Ensure clarity and conciseness in each section to facilitate effective 의사소통 and project alignment.[/prompt_formatter]
[prompt_formatter title=”Stakeholder Communication Plan Creator” description=”Develops a detailed communication plan for a Lean Six Sigma project based on a list of stakeholders and their roles. The plan outlines the frequency, channel, and key messages for each stakeholder group to ensure alignment and buy-in.” temperature=”0.7″ thinking=”medium”]**TASK CONTEXT**⸻You are an AI assisting expert-level Lean Six Sigma (Black Belt) engineers in creating a comprehensive communication plan for a manufacturing or production improvement project. The goal is to ensure all stakeholders are aligned and supportive of the project objectives.⸻⸻**INPUTS**⸻1. List of stakeholders and their roles in CSV format: “{stakeholder_list}”⸻2. Project objectives and key messages in text format: “{project_objectives}”⸻3. Preferred communication channels and frequency guidelines in text format: “{communication_guidelines}”⸻⸻**INSTRUCTIONS**⸻1. Parse the input CSV string “{stakeholder_list}” to identify each stakeholder and their role.⸻2. Analyze the project objectives and key messages from “{project_objectives}”.⸻3. Review the preferred communication channels and frequency guidelines from “{communication_guidelines}”.⸻4. For each stakeholder group identified, determine the most suitable communication channel and frequency based on their role and the provided guidelines.⸻5. Develop key messages tailored to each stakeholder group, ensuring alignment with the project objectives.⸻6. Compile a detailed communication plan outlining the stakeholder group, role, communication channel, frequency, and key messages.⸻⸻**OUTPUT FORMAT**⸻Provide the communication plan in a structured markdown table with the following columns: Stakeholder Group, Role, Communication Channel, Frequency, Key Messages.⸻Ensure clarity and conciseness in the presentation of the plan.[/prompt_formatter]
[prompt_formatter title=”Control Plan and SOP Drafter” description=”Generates a draft control plan and SOP document based on the improved process steps and key process input variables (KPIVs) identified in a project.” temperature=”0.5″ thinking=”medium”]**TASK CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in drafting a Control Plan and Standard Operating Procedure (SOP) for a manufacturing process. The goal is to standardize and sustain improvements by documenting the improved process steps and key process input variables (KPIVs).⸻⸻**INPUTS**⸻1. Improved Process Steps: A list of sequential steps representing the improved process, formatted as a CSV string: “{improved_process_steps}”.⸻2. Key Process Input Variables (KPIVs): A list of KPIVs associated with the process, formatted as a CSV string: “{kpivs}”.⸻3. Process Name: A brief name of the process for which the Control Plan and SOP are being drafted: “{process_name}”.⸻⸻**INSTRUCTIONS**⸻1. Analyze the provided improved process steps and KPIVs to understand the process flow and critical inputs.⸻2. Draft a Control Plan that includes:⸻ – Process Name⸻ – List of Improved Process Steps⸻ – Associated KPIVs for each step⸻ – Control Methods for monitoring each KPIV⸻ – Reaction Plan for deviations in KPIVs⸻3. Draft a Standard Operating Procedure (SOP) that includes:⸻ – Process Name⸻ – Purpose and Scope of the SOP⸻ – Detailed description of each Improved Process Step⸻ – Safety and Quality Considerations⸻ – Roles and Responsibilities⸻ – Required Tools and Equipment⸻4. Format the output as a structured document using markdown, with clear headings and subheadings for each section.⸻⸻**OUTPUT FORMAT**⸻Provide the output in markdown format with the following structure:⸻⸻# Control Plan for {process_name}⸻## Process Steps and KPIVs⸻- Step 1: {step_1_description}⸻ – KPIVs: {kpiv_list_for_step_1}⸻ – Control Method: {control_method_for_step_1}⸻ – Reaction Plan: {reaction_plan_for_step_1}⸻- Step 2: {step_2_description}⸻ – KPIVs: {kpiv_list_for_step_2}⸻ – Control Method: {control_method_for_step_2}⸻ – Reaction Plan: {reaction_plan_for_step_2}⸻…⸻⸻# Standard Operating Procedure (SOP) for {process_name}⸻## Purpose and Scope⸻{purpose_and_scope_description}⸻## Process Steps⸻1. {step_1_description}⸻ – Safety Considerations: {safety_considerations_for_step_1}⸻ – Quality Considerations: {quality_considerations_for_step_1}⸻ – Roles and Responsibilities: {roles_for_step_1}⸻ – Tools and Equipment: {tools_for_step_1}⸻2. {step_2_description}⸻ – Safety Considerations: {safety_considerations_for_step_2}⸻ – Quality Considerations: {quality_considerations_for_step_2}⸻ – Roles and Responsibilities: {roles_for_step_2}⸻ – Tools and Equipment: {tools_for_step_2}⸻…⸻[/prompt_formatter]
[prompt_formatter title=”Gemba Walk Checklist and Findings Consolidator” description=”Creates a tailored 겐바 워크 checklist based on the specific process area and focus of the walk. It then consolidates observations from multiple inputs into a categorized summary of findings and opportunities.” temperature=”0.7″ thinking=”medium”]**TASK CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with conducting a 겐바 walk in a manufacturing environment. Your goal is to create a tailored checklist for the walk and consolidate observations into a summary of findings and opportunities.⸻⸻**INPUTS**⸻1. Process Area: “{process_area}”⸻2. Focus of the Walk: “{focus_of_walk}”⸻3. Observations CSV: “{observations_csv}”⸻⸻**INSTRUCTIONS**⸻1. Analyze the provided “{process_area}” and “{focus_of_walk}” to generate a tailored Gemba walk checklist.⸻2. Use the checklist to guide the Gemba walk and collect observations.⸻3. Consolidate the observations from “{observations_csv}” into a categorized summary.⸻4. Identify key findings and opportunities for improvement based on the consolidated observations.⸻⸻**OUTPUT FORMAT**⸻1. **Checklist**: Provide a detailed checklist tailored to the specified process area and focus.⸻2. **Consolidated Summary**: Present a categorized summary of findings and opportunities based on the observations.⸻3. **Recommendations**: List actionable recommendations for process improvement.⸻⸻**OUTPUT EXAMPLE**⸻**Checklist**⸻- Item 1⸻- Item 2⸻⸻**Consolidated Summary**⸻- Category 1: Finding 1, Finding 2⸻- Category 2: Finding 3⸻⸻**Recommendations**⸻- Recommendation 1⸻- Recommendation 2[/prompt_formatter]
[prompt_formatter title=”Lean Six Sigma Project Portfolio Dashboard Generator” description=”Takes a CSV file of multiple project statuses, metrics, and timelines to generate an HTML-based dashboard. This provides a high-level overview of the entire continuous improvement program’s health and progress.” temperature=”0.7″ thinking=”medium”]**TASK**⸻Generate an HTML-based dashboard from a CSV file containing Lean Six Sigma project data to provide a high-level overview of the program’s health and progress.⸻⸻**INPUTS**⸻1. CSV file content with project data: “{csv_project_data}”⸻⸻**INSTRUCTIONS**⸻1. Parse the CSV data provided in “{csv_project_data}”. Extract key information such as project names, statuses, metrics, timelines, and any other relevant data.⸻2. Analyze the extracted data to identify overall trends, project completion rates, and any bottlenecks or areas needing attention.⸻3. Design an HTML structure that includes sections for:⸻ – A summary of total projects, completed projects, ongoing projects, and stalled projects.⸻ – Visual representations (e.g., charts or graphs) of project metrics and timelines.⸻ – Highlighted insights or alerts for projects that require immediate attention.⸻4. Convert the analyzed data into an HTML format, ensuring it is well-organized and visually appealing. Use tables, charts, and 색상 코드 to enhance readability and comprehension.⸻5. Output the final HTML code as a string, ready to be used in a web environment.⸻⸻**OUTPUT FORMAT**⸻Provide the final HTML code as a single string. Ensure the code is clean, well-commented, and ready for deployment.⸻⸻**ADDITIONAL NOTES**⸻- Ensure that the dashboard is responsive and can be viewed on different devices.⸻- Use CSS for styling to maintain a professional appearance.⸻- Consider accessibility standards in the design.⸻[/prompt_formatter]
Advanced Analytics and Predictive Solutions
[prompt_formatter title=”Predictive Maintenance Schedule Optimizer” description=”Analyzes equipment sensor data and maintenance logs to predict the optimal time for maintenance activities. This prompt helps to minimize downtime and prevent unexpected equipment failures.” temperature=”0.7″ thinking=”high”]**CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer focused on optimizing maintenance schedules in a manufacturing environment. Your goal is to minimize equipment downtime and prevent unexpected failures by analyzing sensor data and maintenance logs.⸻⸻**INPUTS**⸻1. Equipment sensor data in CSV format: “{sensor_data_csv}”⸻2. Maintenance logs in CSV format: “{maintenance_logs_csv}”⸻3. Desired prediction time frame (e.g., “weekly”, “monthly”): “{prediction_time_frame}”⸻⸻**TASKS**⸻1. Parse the provided equipment sensor data and maintenance logs.⸻2. Identify patterns and correlations between sensor readings and past maintenance activities.⸻3. Use statistical models or machine learning algorithms to predict the optimal maintenance schedule within the specified time frame.⸻4. Generate a maintenance schedule that minimizes downtime and prevents unexpected failures.⸻⸻**OUTPUT FORMAT**⸻Provide the following outputs:⸻- A summary of key patterns and correlations identified ($summary).⸻- A predictive maintenance schedule in CSV format, detailing the recommended maintenance activities and their optimal timing ($schedule_csv).⸻- Visualizations of sensor data trends and maintenance predictions using “Mermaid” format for diagrams ($visualizations).⸻⸻**INSTRUCTIONS**⸻1. Use the provided CSV data to perform data analysis and predictions.⸻2. Ensure the output is clear, actionable, and tailored to the specified prediction time frame.⸻3. Present the results in a structured and professional manner suitable for expert-level review.[/prompt_formatter]
[prompt_formatter title=”Production Scheduling and Sequencing Optimizer” description=”Generates an optimized production schedule based on customer orders, processing times, and resource availability to maximize throughput and minimize changeover times. It provides the schedule in a CSV format for easy integration.” temperature=”0.7″ thinking=”high”]**TASK CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with optimizing production scheduling and sequencing. Your goal is to maximize throughput and minimize changeover times based on customer orders, processing times, and resource availability.⸻⸻**INPUTS**⸻1. Customer Orders: Provide a CSV string with columns for Order ID, Product ID, Quantity, and Due Date. Example: “{customer_orders_csv}”⸻2. Processing Times: Provide a CSV string with columns for Product ID and Processing Time per Unit. Example: “{processing_times_csv}”⸻3. Resource Availability: Provide a CSV string with columns for Resource ID, Available Start Time, and Available End Time. Example: “{resource_availability_csv}”⸻⸻**INSTRUCTIONS**⸻1. Parse the provided CSV strings to extract customer orders, processing times, and resource availability.⸻2. Analyze the data to identify constraints and opportunities for optimizing the production schedule.⸻3. Use advanced scheduling algorithms to generate an optimized production schedule that maximizes throughput and minimizes changeover times.⸻4. Ensure the schedule respects all constraints, including due dates and resource availability.⸻5. Format the optimized schedule as a CSV string with columns for Order ID, Product ID, Scheduled Start Time, Scheduled End Time, and Assigned Resource ID.⸻⸻**OUTPUT FORMAT**⸻Provide the optimized production schedule in the following CSV format:⸻Order ID, Product ID, Scheduled Start Time, Scheduled End Time, Assigned Resource ID⸻⸻**ADDITIONAL NOTES**⸻- Consider using heuristic or metaheuristic methods if necessary to handle complex scheduling scenarios.⸻- Prioritize minimizing changeover times between different product types.⸻- Ensure the output is ready for easy integration into existing systems.[/prompt_formatter]
[prompt_formatter title=”Inventory Optimization and Demand Forecasting Engine” description=”Analyzes historical sales data to forecast future demand and recommend optimal inventory levels for various SKUs. This helps to reduce holding costs and prevent stockouts.” temperature=”0.7″ thinking=”medium”]**TASK CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with optimizing inventory levels and forecasting demand for various SKUs in a manufacturing environment. Your goal is to reduce holding costs and prevent stockouts by analyzing historical sales data.⸻⸻**INPUTS**⸻1. Historical sales data for SKUs in CSV format: “{historical_sales_data}”⸻2. Desired forecast period in months: “{forecast_period}”⸻3. Current inventory levels for SKUs in CSV format: “{current_inventory_levels}”⸻⸻**INSTRUCTIONS**⸻1. Parse the provided historical sales data and current inventory levels.⸻2. Use time series analysis to forecast future demand for each SKU over the specified forecast period.⸻3. Calculate optimal inventory levels for each SKU to minimize holding costs while preventing stockouts.⸻4. Provide a summary of the forecasted demand and recommended inventory levels for each SKU.⸻5. Highlight any SKUs with potential stockout risks or excessive inventory levels.⸻⸻**OUTPUT FORMAT**⸻Provide the results in a CSV format with the following columns: SKU, Forecasted Demand, Recommended Inventory Level, Stockout Risk, Excess Inventory Alert.⸻⸻**ADDITIONAL NOTES**⸻Ensure that the analysis accounts for seasonal trends and any anomalies in the historical data. Use appropriate statistical methods to enhance the accuracy of the forecasts.[/prompt_formatter]
[prompt_formatter title=”Supplier Quality Performance Predictor” description=”Uses historical supplier data to predict future supplier performance for proactive risk management.” temperature=”0.7″ thinking=”medium”]**TASK CONTEXT**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer focused on improving supplier quality performance in manufacturing. Your objective is to predict future supplier performance using historical data to manage risks proactively.⸻⸻**INPUTS**⸻1. Historical Supplier Data: Provide a CSV string containing historical data with columns such as “Supplier ID”, “On-Time Delivery Rate”, “Defect Rate”, “Audit Score”, and “Number of Deliveries”. Example: “{historical_supplier_data}”.⸻2. Prediction Timeframe: Specify the timeframe for which you want to predict supplier performance, e.g., “6 months”. Example: “{prediction_timeframe}”.⸻⸻**INSTRUCTIONS**⸻1. Parse the provided historical supplier data to identify key performance indicators (KPIs) such as average on-time delivery rate, average defect rate, and average audit score for each supplier.⸻2. Use statistical analysis or machine learning techniques to model the relationship between these KPIs and future performance. Consider using regression analysis or time series forecasting.⸻3. Predict the future performance of each supplier for the specified timeframe, focusing on on-time delivery rate and defect rate.⸻4. Identify suppliers with potential risks based on predicted performance, highlighting those with significant deviations from historical averages.⸻5. Provide a summary of the predicted performance for each supplier, including any identified risks, in a structured format.⸻⸻**OUTPUT FORMAT**⸻Provide the output in a structured text format with the following sections:⸻- Summary of Historical Performance: A brief overview of the historical performance metrics for each supplier.⸻- Predicted Performance: A detailed prediction of future performance for each supplier, including on-time delivery rate and defect rate.⸻- 위험 평가: Highlight suppliers with potential risks based on predicted performance, including any recommendations for risk mitigation.⸻- Use markdown for headings and bullet points for clarity.⸻⸻**ADDITIONAL NOTES**⸻Ensure that the analysis considers any seasonal trends or external factors that might influence supplier performance.⸻Use appropriate statistical or machine learning libraries if needed to enhance prediction accuracy.[/prompt_formatter]
[prompt_formatter title=”Workforce Allocation and Skill-Based Routing Simulator” description=”Simulates different workforce allocation scenarios based on employee skill matrices and production demand. It recommends the optimal allocation to maximize productivity and quality.” temperature=”0.7″ thinking=”medium”]**CONTEXT:**⸻You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with optimizing workforce allocation in a manufacturing environment. You have access to an employee skill matrix and current production demand data. Your goal is to simulate various workforce allocation scenarios to identify the optimal configuration that maximizes productivity and quality.⸻⸻**USER INPUTS:**⸻1. Employee Skill Matrix in CSV format: “{employee_skill_matrix_csv}”⸻2. Production Demand Data in CSV format: “{production_demand_csv}”⸻⸻**TASKS:**⸻1. Parse the provided employee skill matrix and production demand data.⸻2. Identify key skills required for each production demand.⸻3. Simulate different workforce allocation scenarios by matching employee skills to production demands.⸻4. Evaluate each scenario based on productivity and quality metrics.⸻5. Recommend the optimal workforce allocation scenario.⸻⸻**OUTPUT FORMAT:**⸻Provide a detailed report in markdown format that includes:⸻- A summary of the parsed employee skill matrix and production demand data.⸻- A description of each simulated workforce allocation scenario, including the allocation strategy and expected outcomes.⸻- An evaluation of each scenario with productivity and quality metrics.⸻- A final recommendation for the optimal workforce allocation scenario, with a rationale for the choice.⸻⸻**ADDITIONAL INSTRUCTIONS:**⸻Use structured markdown for clarity, including tables for data representation and bullet points for scenario descriptions. Ensure the report is concise and focused on actionable insights.[/prompt_formatter]
Continuous Improvement and Innovation
[prompt_formatter title=”Poka-Yoke (Error-Proofing) Idea Generator” description=”Brainstorms potential 포카요케 solutions for a given process step where errors frequently occur. It categorizes the ideas into control and warning methods based on the user’s description of the error.” temperature=”0.7″ thinking=”medium”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in generating Poka-Yoke solutions for error-proofing in manufacturing processes. Your task is to brainstorm potential solutions for a specific process step where errors frequently occur.⸻⸻**INPUTS**⸻1. Description of the process step where errors occur: “{process_step_description}”⸻2. Description of the error(s) occurring: “{error_description}”⸻⸻**INSTRUCTIONS**⸻1. Analyze the provided process step and error description to understand the context and nature of the errors.⸻2. Generate a list of potential Poka-Yoke solutions, categorizing them into two types:⸻ a. Control Methods: Solutions that prevent the error from occurring.⸻ b. Warning Methods: Solutions that alert operators to the presence of an error.⸻3. For each solution, provide a brief explanation of how it addresses the error.⸻⸻**OUTPUT FORMAT**⸻Provide the output in the following markdown format:⸻⸻### Poka-Yoke Solutions for {process_step_description}⸻⸻#### Control Methods⸻1. **Solution Name**: $control_solution_name1⸻ – **Description**: $control_solution_description1⸻2. **Solution Name**: $control_solution_name2⸻ – **Description**: $control_solution_description2⸻⸻#### Warning Methods⸻1. **Solution Name**: $warning_solution_name1⸻ – **Description**: $warning_solution_description1⸻2. **Solution Name**: $warning_solution_name2⸻ – **Description**: $warning_solution_description2⸻⸻Ensure the solutions are practical and relevant to the given process step and error description.[/prompt_formatter]
[prompt_formatter title=”5S Audit and Improvement Recommendation Generator” description=”Creates a customized 5S audit checklist for a specific work area and then, based on audit scores, generates a prioritized list of improvement recommendations.” temperature=”0.7″ thinking=”medium”]**CONTEXT**⸻You are an AI assisting expert-level Lean 6 Sigma (Black Belt) engineers in executing a 5S audit and generating improvement recommendations for a specific work area in manufacturing. The goal is to standardize and sustain workplace organization.⸻⸻**INPUTS**⸻1. Work Area Description: “{work_area_description}”⸻2. Audit Scores (CSV format): “{audit_scores_csv}”⸻⸻**INSTRUCTIONS**⸻1. Analyze the provided work area description to understand the specific context and requirements.⸻2. Use the work area description to create a customized 5S audit checklist. The checklist should include items related to Sort, Set in order, Shine, Standardize, and Sustain.⸻3. Parse the audit scores CSV to extract scores for each 5S category.⸻4. Calculate the overall 5S score and identify areas with the lowest scores.⸻5. Generate a prioritized list of improvement recommendations based on the audit scores, focusing on areas with the lowest scores first.⸻6. Provide the output in a structured format with sections for the customized checklist, audit scores analysis, and improvement recommendations.⸻⸻**OUTPUT FORMAT**⸻1. **Customized 5S Audit Checklist**⸻- List of items under each 5S category⸻2. **Audit Scores Analysis**⸻- Overall 5S Score: $overall_score⸻- Scores by Category: $scores_by_category⸻3. **Improvement Recommendations**⸻- Prioritized list with specific actions for improvement based on low-scoring areas[/prompt_formatter]
[prompt_formatter title=”Benchmarking and Best Practice Finder” description=”Scans and synthesizes information to identify industry-specific benchmarks and best practices for a given process.” temperature=”0.7″ thinking=”medium”]**TASK**⸻Identify industry-specific benchmarks and best practices for a given manufacturing process to set performance targets and identify improvement opportunities.⸻⸻**INPUTS**⸻1. Industry: {industry_name}⸻2. Process: {process_name}⸻3. 주요 성과 지표 (KPI) or Metric: {kpi_metric}⸻⸻**INSTRUCTIONS**⸻1. Analyze the provided industry and process to understand the context and specific requirements.⸻2. Search for industry reports, case studies, and publications relevant to {industry_name} and {process_name}.⸻3. Extract data on benchmarks and best practices related to {kpi_metric}.⸻4. Summarize the findings, highlighting key benchmarks and best practices.⸻5. Provide a comparison of these benchmarks against typical performance metrics in the industry.⸻6. Suggest potential improvement opportunities based on the identified best practices.⸻⸻**OUTPUT FORMAT**⸻- Summary of industry benchmarks and best practices for {process_name} in {industry_name}.⸻- Comparison table of identified benchmarks vs. typical industry metrics.⸻- List of suggested improvement opportunities.⸻⸻**NOTE**⸻Ensure the information is up-to-date and relevant to the specified industry and process.[/prompt_formatter]
[prompt_formatter title=”Hoshin Kanri X-Matrix Cascading Goals Decomposer” description=”Takes high-level strategic objectives and cascades them down into specific, measurable, achievable, relevant, and time-bound (SMART) goals for different departments and 팀. It generates a visual representation of the X-Matrix in Mermaid format.” temperature=”0.7″ thinking=”high”]**CONTEXT:** You are an expert-level Lean 6 Sigma (Black Belt) engineer tasked with decomposing high-level strategic objectives into SMART goals for various departments and teams. The output will be a visual representation of the X-Matrix in Mermaid format.⸻⸻**INPUTS:**⸻1. High-level strategic objectives: “{strategic_objectives}”⸻2. List of departments or teams: “{departments_teams}”⸻3. Time frame for achieving goals: “{time_frame}”⸻⸻**INSTRUCTIONS:**⸻1. **Parse Inputs:** Identify and list the high-level strategic objectives from {strategic_objectives}.⸻2. **Decompose Objectives:** For each strategic objective, decompose it into specific, measurable, achievable, relevant, and time-bound (SMART) goals for each department or team listed in {departments_teams}.⸻3. **Assign Time Frames:** Ensure each SMART goal aligns with the provided {time_frame}.⸻4. **Generate X-Matrix:** Create a visual representation of the cascading goals in the form of an X-Matrix using Mermaid format.⸻5. **Output Format:** Provide the X-Matrix in Mermaid format, ensuring clarity and logical flow of information.⸻⸻**OUTPUT:**⸻- A Mermaid format X-Matrix diagram representing the cascading goals.⸻- Ensure the diagram includes strategic objectives, SMART goals for each department or team, and the time frame alignment.⸻- Use the following Mermaid syntax structure for the X-Matrix:⸻“`mermaid⸻graph TD⸻$strategicObjective1 –> $SMARTGoal1⸻$strategicObjective2 –> $SMARTGoal2⸻…⸻“`⸻Replace $strategicObjective1, $SMARTGoal1, etc., with actual parsed and decomposed values.[/prompt_formatter]
[prompt_formatter title=”Innovation and Creativity Technique Facilitator (트리즈)” description=”Applies principles from the Theory of Inventive Problem Solving (TRIZ) to a user-defined technical problem or contradiction in a process. It suggests inventive solutions by analyzing the underlying contradictions and applying TRIZ principles.” temperature=”0.7″ thinking=”high”]**CONTEXT**⸻You are an AI assistant specialized in facilitating the application of TRIZ principles for expert-level Lean 6 Sigma (Black Belt) engineers. Your task is to analyze a user-defined technical problem or contradiction in a manufacturing process and suggest inventive solutions by applying TRIZ principles.⸻⸻**INPUTS**⸻1. Technical Problem or Contradiction: “{technical_problem}”⸻2. Key Parameters Involved: “{key_parameters}”⸻⸻**INSTRUCTIONS**⸻1. Analyze the provided technical problem or contradiction “{technical_problem}” to identify the underlying contradictions.⸻2. List the key parameters involved in the problem “{key_parameters}”.⸻3. Apply TRIZ principles to the identified contradictions to suggest inventive solutions.⸻4. Provide a summary of the suggested solutions, including the TRIZ principles applied.⸻⸻**OUTPUT FORMAT**⸻- **Identified Contradictions**: $contradictions⸻- **Suggested Solutions**:⸻ – Solution 1: $solution1 (TRIZ Principle: $principle1)⸻ – Solution 2: $solution2 (TRIZ Principle: $principle2)⸻ – Solution 3: $solution3 (TRIZ Principle: $principle3)⸻- **Summary**: $summary[/prompt_formatter]
