DESIGN OF EXPERIMENTS (DOE) CHECKLIST

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--- PROBLEM DEFINITION & OBJECTIVE SETTING ---
[ ] Describe the Manufacturing Problem
[ ] What are the initial observations and symptoms of the problem?
[ ] What is the current process capability (e.g., Cp, Cpk)?
[ ] What is the primary objective of the DOE? (Choose One) (Reduce Variation, Improve Mean, Reduce Cost, Improve Quality, Other (Specify))
[ ] Define specific, measurable, achievable, relevant, and time-bound (SMART) objectives for the DOE.
[ ] Target improvement percentage (e.g., reduce defect rate by 10%)
[ ] What are the key constraints limiting the improvement? (Cost, Time, Equipment, Material, Other (Specify))
[ ] Describe the current process control measures (if any).

--- FACTOR & RESPONSE SELECTION ---
[ ] Describe the manufacturing process being studied.
[ ] What is the desired output metric (e.g., throughput, yield, defect rate)?
[ ] Select the units of measurement for the response variable (e.g., pieces/hour, %, parts per million). (Pieces/Hour, Percentage (%), Parts Per Million (PPM), Millimeters (mm), Seconds, Other (Specify in LONG_TEXT))
[ ] List potential factors that could influence the response.
[ ] For each potential factor, briefly describe how it impacts the response (positive or negative).
[ ] Estimate the current average value of the response variable.
[ ] Which factors are considered the most critical to investigate (based on prior knowledge or experience)?
[ ] Describe any constraints on the factor ranges (e.g., equipment limitations, safety regulations).

--- EXPERIMENTAL DESIGN SELECTION ---
[ ] Primary Design Type (Full Factorial, Fractional Factorial, Response Surface Methodology (RSM), Mixture Design, Taguchi Design)
[ ] Number of Factors to be Studied
[ ] Number of Levels per Factor
[ ] Central Composite Design (If using RSM) (Face-Centered, Circle, Incomplete Block)
[ ] Justification for Chosen Design
[ ] Randomization Method (Latin Square, Random Order, Cyclical)
[ ] Number of Replicates
[ ] Considerations for Interactions (if applicable)

--- EXPERIMENTAL SETUP & VALIDATION ---
[ ] Equipment Calibration Date
[ ] Standard Operating Procedure (SOP) Verified? (Yes, No)
[ ] Describe Equipment Setup and Configuration
[ ] Measurement System Analysis (MSA) Score (e.g., % agreement)
[ ] Environmental Conditions Controlled? (Yes, No)
[ ] Document any deviations from planned setup
[ ] Attach Photos/Videos of Setup (Optional)
[ ] Date of Setup Verification

--- DATA COLLECTION & ANALYSIS ---
[ ] Number of Replicates per Run
[ ] Measurement Resolution (e.g., decimal places)
[ ] Calibration and Measurement System Analysis (MSA) Documentation Review
[ ] Statistical Software Used (e.g., Minitab, R, JMP) (Minitab, R, JMP, Other)
[ ] Sample Size for Each Factor Level
[ ] Description of Data Validation Procedures
[ ] Analysis Method Used (e.g., ANOVA, Regression) (ANOVA, Regression, Other)
[ ] Raw Data File (CSV, Excel)

--- RESULTS INTERPRETATION & CONCLUSION ---
[ ] Summarize the key findings of the DOE.
[ ] What is the R-squared value for the model? (Indicates model fit)
[ ] Which factors were found to be statistically significant (p < 0.05)? (Factor A, Factor B, Factor C, No significant factors found)
[ ] Describe the interaction effects observed (if any).
[ ] What is the predicted optimal setting for the factors?
[ ] Does the model adequately explain the variability in the response? (Based on R-squared & Residual Analysis) (Yes, the model is a good fit., The model needs improvement., The model is not appropriate.)
[ ] What conclusions can be drawn from the DOE results regarding the original manufacturing problem?
[ ] Which of the following recommendations are made based on the DOE? (Adjust factor settings, Modify process parameters, Investigate further, No action required)

--- IMPLEMENTATION & VERIFICATION ---
[ ] Describe the proposed changes to the manufacturing process based on DOE findings.
[ ] Target improvement percentage for the response variable (e.g., yield, defect rate).
[ ] Planned start date for implementing the changes.
[ ] Planned completion date for implementation.
[ ] Number of production runs to monitor after implementation.
[ ] Method for initial verification (e.g., pilot run, gradual rollout). (Pilot Run, Gradual Rollout, Full Production Implementation)
[ ] Describe the verification plan, including data collection methods and acceptance criteria.
[ ] Which key performance indicators (KPIs) will be monitored during verification? (Yield, Defect Rate, Cycle Time, Material Waste, Cost per Unit)
[ ] Verification Result: Pass/Fail (Pass, Fail, Needs Further Investigation)

--- DOCUMENTATION & REPORTING ---
[ ] Project Objective Summary
[ ] Detailed Experimental Procedure
[ ] Raw Data Files (CSV, Excel)
[ ] Statistical Analysis Output (e.g., Minitab, JMP)
[ ] Number of Replicates Run
[ ] List of Assumptions Made During Analysis
[ ] Potential Limitations of the Study
[ ] Report Distribution List (Engineering Team, Quality Control, Management, Other)
[ ] Report Completion Date
[ ] Engineer Signature

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