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Catapult DOE Case Study

The document describes a case study involving optimizing a catapult to hit targets within a specified range. A team is tasked with developing a process to reliably hit targets from 5-12 feet away within 6 inches of accuracy. The team conducts experiments to identify key factors (stop pin position, draw back angle, front tension pin) affecting the distance and variation. A full factorial design of experiments is used to determine the relationship between factors and the distance response. The analysis results in an equation to predict distance based on factor settings. Based on minimizing variation, the recommended settings are a stop pin of 2, front tension pin of 2, and a draw back angle that satisfies the equation to hit the 60 inch target distance.

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Catapult Case Study 6σ Green Belt Training 1 2 34 5 1 2 3 4 5 1 2 3 4
Catapult Exercise • You have been pre-selected (based on your skills and past performance records) for the openings posted for Her Majesty’s Catapult Squad. • Problem Statement: Catapult launching is not capable of meeting Her Majesty’s requirements over the target range of 5-12 feet +/- 6 inches. • Goal: Your mission is to optimize the inputs to be able to hit a target repeatedly within this range so that Her Majesty can conquer the evil Empire. • A successful catapult squad can place a payload to the target every time. (Only 3.4 misses per million!) • The distance from the target varies due to several factors; consequently, you don’t know the distance until you’re in position. • May the FORCE be with You!! Heads rolled on the last crew which is why we have postings for the current positions!
Project DefinitionProject Definition Problem Statement: Catapult launching is not capable of meeting Her Majesty’s requirements over the target range of 5-12 feet +/- 6 inches. CTS’s: Target distance (5 to 12 feet) Consistency (+/- 6 inches) Speed (rapid set up and launch capability) Defect Definition: Payloads outside of the target specification Metrics: Distance (inches) Standard deviation (inches) Project Objective: To develop a standard process and y = f(x) equation so that the catapult can be shot to meet the customer requirements (distance) and minimize variation (< 6 inch radius). Current/Goal/Stretch Goal Current DPMO/Zst - TBD Goal DPMO/Zst - TBD Benefits: • improved accuracy • reduced variation • customer satisfaction • we live! Progress to Date: • Team members selected
Catapult Nomenclature Rubber band attachment point Rubber band attachment point Arm stop positionArm stop position Front arm tension point Front arm tension point 1 2 34 5 1 2 3 4 5 1 2 3 4 Draw-back angleDraw-back angle Ball typeBall type Cup locationCup location Number of rubber bands Number of rubber bands
Update RecommendedUpdate Recommended Actions To DateActions To Date Description of potential Root cause or potential vital X What leads me to believe this is a potential X (Data, local knowledge, Oberservation, Tool?) What change is being proposed to the X to possibly generate a process improvement To be tested (team agreement) Rubber band C&E Matrix, FMEA Constant time between shots No Operator C&E Matrix, FMEA ANOVA Equal Variances Evaluate using previous data Standard Operating Procedures There currently is none An SOP was implemented to stabilize the launches Create prior to Hypothesis Testing and/or DOE Ball type C&E Matrix, FMEA Test the ball type in a 2 sample t-test Equal Variance Test Yes
Step 7: Screen PotentialStep 7: Screen Potential CausesCauses Narrow it Down  Screening is done using Graphical Tools, Experiments, and Hypothesis Tests to identify and prove which are the vital X’s  This is the middle of the funnel for most projects (multiple X’s or with variable relationships between X’s) – for some simpler projects with a single X, this is the bottom of the funnel, the final vital X Important X’s for Y = f(X1, X2, …, Xn) – we still need to determine “f”
Hypothesis Testing ForHypothesis Testing For Sources of VariationSources of Variation  What potential sources of variation can be explored using Hypothesis Testing ?  What are sources of data that can be analyzed – Passive: existing data – Active: sampling the process  Ball Type – Whiffle versus Ping Pong?  Mean or variation?  Catapult Setup – Floor type – Table top or floor? Tile or carpet?  Mean or variation?
Update RecommendedUpdate Recommended Actions To DateActions To Date Description of potential Root cause or potential vital X What leads me to believe this is a potential X (Data, local knowledge, Oberservation, Tool?) What change is being proposed to the X to possibly generate a process improvement To be tested (team agreement) Ball Type 2 sample t-test Equal Variance DOE – test 2 different types Completed Draw Back Angle C&E Matrix, and FMEA DOE – test 2 different settings Yes Front Tension Pin C&E Matrix, and FMEA DOE – test 2 different settings Yes Rubber band C&E Matrix FMEA Time between shots No Operator 2 sample t-test 1 operator for DOE No Standard Operating Procedures There currently is none An SOP was implemented to stabilize the launches Not at this time; SOP’s to be applied in DOE Pin Height C&E Matrix, FMEA DOE – test 2 different settings Yes
22KK Full Factorial Design ofFull Factorial Design of ExperimentsExperiments Catapult ExerciseCatapult Exercise
The Breakthrough StrategyImproveThe Breakthrough StrategyImprove 1. Select Output Characteristics 2. Define Performance Standards 3. Validate Measurement System 4. Establish Process Capability 5. Define Performance Objectives 6. Identify Variation Sources 7. Screen Potential Causes 8. Discover Variable Relationships 9. Establish Operating Tolerances 10.Validate Measurement System 11.Determine Process Capability 12.Implement Process Controls
Step 8: Discover VariableStep 8: Discover Variable RelationshipsRelationships How the X’s affect Y  Evaluate how my Vital X’s affect Y, either independently or in combination with other Vital X’s. This is primarily done through the use of DOE or Regression.  This is the bottom of the funnel, I know which X’s affect my Y and I know how they affect Y  The function Y = f(X1, X2,…, Xn) is called a “transfer function” – it describes how a change in one or more of the X’s transfers to a change in Y We now know what Y = f(X1, X2, …, Xn) is The variable relationships within many Green Belt projects are frequently established simply through the use of standard hypothesis tests.
Step 9: Establish OperatingStep 9: Establish Operating TolerancesTolerances How To Set My Xs  I know which X’s are important. What settings do I use to improve my project?  In the case of a variable X (e.g. PSI on an air feed), I have to provide a setting tolerance (e.g. a target amount ± an allowed amount of variation about the target)  In the case of a non-variable X (e.g. Supplier), I know which value of the variable provides the best value of Y, therefore I have specified the absolute operating tolerance Make use of what we know about Y = f(X1, X2, …, Xn) In our case study we will “set the X’s” at the settings of our improved process.
Improve PhaseImprove Phase We will conduct a 2k factorial experiment in order to identify the proper factors and levels to achieve the highest capability (Zst). – You only have enough resources to investigate three X’s at 2 levels – Determine your factors and their respective levels. – Use the knowledge you learned in DMA and as a team determine what factors and the respective levels you want to use to conduct the DOE
Philosophy of ExperimentationPhilosophy of Experimentation Catapult Process Responses Distance Variation Controllable X’s Draw Back Angle Fr Arm Tension Stop Pin Uncontrollable X’s “Noise” Adjustment X’s “SOP’s” Temp Air flowDistractions Setup Ball Type ReleaseOperator
Step 2: Factors, Level SettingsStep 2: Factors, Level Settings and Sample Sizeand Sample Size  Use the Strategy for Experimentation to complete the following:  Conduct a 3 factor, 2 level full factorial design with 6 repeats to optimize the catapult settings to hit a target within +/- 6” Factors: A: Stop Pin: 2 and 4 B: Draw back angle: 140 and 180 C: Front Tension Pin: 2 and 4
Step 4: Create The Design InStep 4: Create The Design In MinitabMinitab Stat>DOE>Factorial>Create Factorial Design • Select the number of factors (3) • Open the ‘Designs…’ window • Highlight the ‘Full Factorial’ • Maintain all other defaults • OK
Creating The Design, Cont’dCreating The Design, Cont’d • Un-check the ‘Randomize runs’ box • OK • Enter Factor Names • Enter Factor Low & High Settings • OK
Resultant Design In The WorksheetResultant Design In The Worksheet Session Window Output: Factorial Design Full Factorial Design Factors: 3 Base Design: 3, 8 Runs: 8 Replicates: 1 Blocks: none Center pts (total): 0 All terms are free from aliasing Worksheet Output:
Step 5: Conduct The ExperimentStep 5: Conduct The Experiment  Populate the worksheet with the results of your experiment and calculate the Row Averages and Row Standard Deviations: AvrgD Calculation: Calc>Row Statistics, Check ‘Mean’, Input Variables: ‘Y1 through Y6’, Store Result in: ‘AvrgD’ SD Calculation: Calc>Row Statistics, Check ‘Standard deviation’, Input Variables: ‘Y1 through Y6’, Store Result in: ‘SD’ CATAPULT Round 5 DOE Example.mtw
Graphical Analysis: FactorialGraphical Analysis: Factorial PlotsPlots  Stat>DOE>Factori al> Factorial Plots… • Complete setups for • Main Effects Plot
Graphical Analysis: FactorialGraphical Analysis: Factorial Plots Cont’dPlots Cont’d  Complete setups for – Cube plot – Interaction plot
AvrgD: Main Effects PlotAvrgD: Main Effects Plot Which factor has the greatest effect on the Average Distance? StopPin DrawAngle TensionPin 2 4 140 180 2 4 40 50 60 70 80 AvrgD Main Effects Plot (data means) for AvrgD
AvrgD: Interaction PlotAvrgD: Interaction Plot  There appears to be one potential interaction: – StopPin*DrawAngle – The effect that Stop Pin has on Distance also depends on the Draw Back Angle 140 180 2 4 20 60 100 20 60 100 StopPin DrawAngle TensionPin 2 4 140 180 Interaction Plot (data means) for AvrgD
AvrgD: Cube PlotAvrgD: Cube Plot Is a distance of 70” achievable with the Tension Pin at setting 2? Is a distance of 50” achievable with the Draw Back Angle at 140? 36.00 59.83 60.54 93.25 15.50 33.13 63.50 109.63 2 4 StopPin DrawAngle TensionPin 140 180 2 4 Cube Plot (data means) for AvrgD
Step 6: Perform TheStep 6: Perform The DOE Analysis For TheDOE Analysis For The Full ModelFull Model Stat>DOE>Factorial>Analyze Factorial Design:
Step 6: Full Model AnalysisStep 6: Full Model Analysis Cont’dCont’d
Running the Full ModelRunning the Full Model 0 10 20 30 40 AC ABC A BC AB C B Pareto Chart of the Effects (response is AvrgD, Alpha = .10) A: StopPin B: DrawAngl C: TensionP • With all terms in the model, only one appears above the significance level (red line)
Step 8: Reducing The ModelStep 8: Reducing The Model 0 5 10 A BC AB C B Pareto Chart of the Standardized Effects (response is AvrgD, Alpha = .10) A: StopPin B: DrawAngl C: TensionP 0 1 2 3 4 5 6 7 A AB C B Pareto Chart of the Standardized Effects (response is AvrgD, Alpha = .10) A: StopPin B: DrawAngl C: TensionP
AvrgD: Final Reduced ModelAvrgD: Final Reduced Model Estimated Effects and Coefficients for AvrgD (coded units) Term Effect Coef SE Coef T P Constant 58.922 3.091 19.06 0.000 StopPin 6.969 3.484 3.091 1.13 0.342 DrawAngl 45.615 22.807 3.091 7.38 0.005 TensionP 30.073 15.036 3.091 4.87 0.017 StopPin*DrawAngl -16.635 -8.318 3.091 -2.69 0.074 Analysis of Variance for AvrgD (coded units) Source DF Seq SS Adj SS Adj MS F P Main Effects 3 6067.3 6067.3 2022.42 26.47 0.012 2-Way Interactions 1 553.5 553.5 553.47 7.24 0.074 Residual Error 3 229.2 229.2 76.42 Total 7 6850.0 Estimated Coefficients for AvrgD using data in uncoded units Term Coef Constant -378.724 StopPin 70.0260 DrawAngl 2.38802 TensionP 15.0365 StopPin*DrawAngl -0.415885 Use the uncoded coefficients to create the equation
Creating the y = f(x) for AvrgDCreating the y = f(x) for AvrgD Create the equation from the un-coded coefficients: AvrgDuncoded = -378.72 + 70.03 * StopPin + 2.39 * DrawAngle + 15.04 * TensionPin - 0.42 * StopPin*DrawAngle Estimated Coefficients for AvrgD using data in uncoded units Term Coef Constant -378.724 StopPin 70.0260 DrawAngl 2.38802 TensionP 15.0365 StopPin*DrawAngl -0.415885
Setting Pin Positions To Minimize VariationSetting Pin Positions To Minimize Variation Given a target of 60”, where would you set the pin positions to minimize variation? 3.118 2.463 6.026 14.716 0.548 2.011 1.844 3.694 2 4 StopPin DrawAngle TensionPin 140 180 2 4 Cube Plot (data means) for SD 36.00 59.83 60.54 93.25 15.50 33.13 63.50 109.63 2 4 StopPin DrawAngle TensionPin 140 180 2 4 Cube Plot (data means) for AvrgD Settings at approx. 60” There are 4 possible combinations to hit the target. Which one minimizes variation?
Recommended Pin Factor Level SettingsRecommended Pin Factor Level Settings  Stop Pin: 2  Front Tension Pin: 2  Recall the equation for AvrgD: – AvrgD = -378.7 + 70.03 * StopPin + 2.39 * DrawAngle + 15.04 * TensionPin - 0.42 * StopPin*DrawAngle  Enter the fixed settings into the equation for AvrgD: – AvrgD = -378.7 + (70.03 * 2) + 2.39 * DrawAngle + (15.04 * 2) - 0.42 * (2*DrawAngle)  Reduce the equation: – AvrgD = -378.7 + 140.06 + 2.39 * DrawAngle + 30.08 - 0.84 * DrawAngle – AvrgD = -208.6 + 1.55*DrawAngle  Substitute the target (60”) for AvrgD and solve for Draw Angle – DrawAngle = 173.3 degrees
Okay… Your Turn!Okay… Your Turn! Round #5: DOERound #5: DOE  Open the DOE design in Minitab: – CATAPULT Round 5 DOE Worksheet.mtw  Conduct the experiment and record the data  Analyze the experiment – Graphically – Analytically  Obtain a prediction equation for Distance  Present your cube plots to the instructor to receive your target Obtain a target value from the instructor Establish Pin factor levels based on goal to minimize variation Conduct a validation run – Launch 10 shots using the predicted settings Calculate the mean and standard deviation Flipchart your results
Catapult Nomenclature Rubber band attachment point Rubber band attachment point Arm stop positionArm stop position Front arm tension point Front arm tension point 1 2 34 5 1 2 3 4 5 1 2 3 4 Draw-back angleDraw-back angle Ball typeBall type Cup locationCup location Number of rubber bands Number of rubber bands
Step 10: Validate MeasuringStep 10: Validate Measuring SystemSystem Can I Measure My Xs & Y?  In the case of a variable X (e.g. PSI on an air feed), I need to validate that it can be measured (a vital X MSA)  In the case of a non-variable X, I need to validate that I can tell whether the X is the right value (e.g. is this from Supplier A?)  Also, I might have improved my Y so much that I can no longer “read” my process, and may have to improve my measurement system to truly measure the improvement Can’t control Y = f(X1, X2, …, Xn) if you can’t measure it
Case Study 2: The CatapultCase Study 2: The Catapult Control
The Breakthrough StrategyThe Breakthrough Strategy ControlControl 1. Select Output Characteristics 2. Define Performance Standards 3. Validate Measurement System 4. Establish Process Capability 5. Define Performance Objectives 6. Identify Variation Sources 7. Screen Potential Causes 8. Discover Variable Relationships 9. Establish Operating Tolerances 10. Validate Measurement System 11. Determine Process Capability 12. Implement Process Controls
Case Study 2: The CatapultCase Study 2: The Catapult Final Capability
Step 11: Determine Process CapabilityStep 11: Determine Process Capability Where Am I?  This measures the capability of controlling my Xs at their optimal settings  This is also the time when we determine formal results by comparing a new capability analysis with the baseline capability analysis (step 4) and our goals (step 5)  Common tools: – Six Sigma Capability Analysis (Normal) for continuous data – Six Sigma “Product Report” for discrete data Can you consistently make X1, X2, …, Xn to produce “good” Y’s? For our case study, we will rerun the Capability Analysis in MINITAB using our new process to see before and after.
Improved CapabilityImproved Capability Method 1 – Capability Analysis (NormalMethod 1 – Capability Analysis (Normal)) Based on the Project work conducted by the team, shown below is the improved capability. 30 35 40 45 50 LSL USL Process Capability Analysis for Improved Dis USL Target LSL Mean Sample N StDev (Within) StDev (Overall) Z.Bench Z.USL Z.LSL Cpk Cpm Z.Bench Z.USL Z.LSL Ppk PPM < LSL PPM > USL PPM Total PPM < LSL PPM > USL PPM Total PPM < LSL PPM > USL PPM Total 41.0000 * 37.0000 38.9405 100 0.392507 0.416816 4.91 5.25 4.94 1.65 * 4.61 4.94 4.66 1.55 0.00 0.00 0.00 0.38 0.08 0.46 1.62 0.39 2.00 Process Data Potential (Within) Capability Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance Within Overall
Case Study ExerciseCase Study Exercise Round #6: Process CapabilityRound #6: Process Capability Objective: To determine Capability for your improved process –Catapult settings based on: Optimum conditions from the DOE Results from validation run SOP’s –Each of 5 Operators will launch 5 balls at a time –Alternate Operators until you have launched 50 balls –Enter into: Catapult Round 6 Cap Worksheet.mtw –Generate the Process Capability (Normal) using n=5 Deliverable: Using the table on the following page, record on a flip chart the following for your team – Final Average, inches – Final Standard deviation, inches – Final Short term capability (Zst) – Final DPMO Time: 30 minutes
Catapult Project MetricsCatapult Project Metrics Parameter Team 1 Team 2 Team 3 Team 4 Team 5 BASELINE Average, in. St. Dev., in. Zst DPMO Objective DPMO FINAL Average, in. St. Dev., in. Zst DPMO % Reduction in DPMO
Step 12: Implement ProcessStep 12: Implement Process ControlsControls Let’s Not Do This Again  The X’s you have determined as vital, their settings, and other actions you have taken to make the improvement must be: – nailed down – set in concrete – fully implemented (NOT just agreed to) – put into a rigorous audit schedule – Documented in a Control Plan BEFORE you can say a project is closed! How do you control X1, X2, …, Xn to always produce “good” Y’s?
Controls – Two RequirementsControls – Two Requirements 1) The actual controls  Controls must be placed on completed projects to make sure that they do not decay – Energy must be expended on processes to keep them in their optimum condition. – The degree of control is proportional to the risk of the process decaying from its final project derived settings. 2) The Control Plan  The controls must be documented carefully, answering: – What is being done? – Who is to do it (position not name of person)? – When is to be done? – What will be action if process does decay? – What will make it difficult to change the project settings OR controls? “If it isn’t written down, It doesn’t exist”
Case Study 2: The CatapultCase Study 2: The Catapult SPC, Variable Data
Why Use SPC for Variables?Why Use SPC for Variables? SPC for variable data is used to: Keep process centered Minimize variation Reduce excursions Validate improvements Focus Six Sigma® process activity
What is SPC for Variables?What is SPC for Variables? SPC for variable data is  Industry standard control language  Reliable, easy method of determining – Common cause variation – Special cause variation  Graphical communication  Set of statistical tools for analyzing variables performance data Statistical Process Control  Is application of statistical tools and methods to provide feedback  Sets limits of variation  Provides trigger for action
SPC FunctionSPC Function SPC Charts Used to monitor and control process under local responsibility Require process owners to – take measurements – Plot and interpret data – Take action Provide a history of the process
Components of a ControlComponents of a Control ChartChart 10 9 8 7 6 5 4 3 2 1 0 0 5 10 15 20 Upper Control Limit Lower Control Limit Mean Nonrandom Variation Region ‘Special Cause Variation’ Observation number Observationvalue Random Variation Region ‘Common Cause Variation’Observation 10
Statistics of a Control ChartStatistics of a Control Chart 10 9 8 7 6 5 4 3 2 1 0 0 5 10 15 20 Nonrandom Variation Region Observation number Observationvalue Random Variation Region LCL - 3σ UCL + 3σ Mean 99.73% area
Establishing Process ControlEstablishing Process Control LimitsLimits Control limits are  Are statistical limits set +/- 3 standard deviations from the mean  Set when process is in control – Fixed at baseline value – Adjusted for improvements – Never widened  Control limits are not related to specification limits Control Limits are not specification limits
Definition of ControlDefinition of Control In control is  A statistical term for process variation – Within three standard deviations of the mean – That is random without cause – That does not show run patterns – That does not show trend patterns  No assignable cause variation
Control Chart RoadmapControl Chart Roadmap Variable Data Xbar-R Chart I-MR Chart Xbar-s chart N<10 No Yes N=1 NoYes
Xbar-R Chart PrinciplesXbar-R Chart Principles Xbar-R Charts (and Xbar-s) are two separate charts of the same subgroup data  Xbar chart is a plot of the subgroup means  R chart is a plot of the subgroup ranges (or if s, plot of subgroup standard deviation)  Most sensitive charts for tracking and identifying assignable cause of variation  Based on control chart factors that assume a normal distribution within subgroups  Establish three sigma process limits
Xbar-R Chart ExerciseXbar-R Chart Exercise Open the Minitab file: Catapult Variable SPC Example.mtw This was a teams’ initial (Round 3) capability study 5 operators launched 5 shots each – Sequence was repeated 4 times – Total observations: 100
Xbar-R Minitab InstructionsXbar-R Minitab Instructions Stat>Control Charts>Xbar-R…
Example Xbar-R ChartsExample Xbar-R Charts 0Subgroup 10 20 34 35 36 37 38 39 40 41 42 SampleMean 1 1 1 1 1 1 Mean=38.65 UCL=41.15 LCL=36.15 0 5 10 15 SampleRange 1 R=4.337 UCL=9.172 LCL=0 Xbar/R Chart for Dist.
Exercise: Catapult Xbar-R ChartsExercise: Catapult Xbar-R Charts  Individually with your team, plot your Catapult capability data from Round #6  Create the standard Xbar-R chart – Is your process in control?  Sort the data by Operator  Create a Xbar-R chart with control limits by ‘Operator’ – Is there a visual difference between operators with respect to  Central tendency?  Variation? – Are they individually in control?  What happens to the control chart if the subgroup size is the total number of shots per operator?
I-MR Chart PrinciplesI-MR Chart Principles Individual and Moving Range Charts are two separate charts of the same data  I chart is a plot of the individual data  MR chart is a plot of the moving range of the previous individuals  I-MR charts are sensitive to trends, cycles and patterns  Used when subgroup variation is zero or no subgroups exist – Destructive testing – Batch processing
Example: How to Create an I-MR ChartExample: How to Create an I-MR Chart Stat>Control Charts>I-MR…
Example I-MR ChartExample I-MR Chart Compare this chart to the Xbar-R chart 0Subgroup 50 100 30 40 50 IndividualValue 1 1 1 1 1 Mean=38.65 UCL=43.97 LCL=33.33 0 5 10 MovingRange 1 1 1 1 1 R=2 UCL=6.535 LCL=0 I and MR Chart for Dist.
SPC ExerciseSPC Exercise Catapult Variable SPC Example II.mtwCatapult Variable SPC Example II.mtw  A catapult team decided that they needed to be able to control the Draw Back Angle.  An observer requested an operator to launch consecutive balls at an angle of 180 degrees.  The observer, through special visual imaging equipment, recorded the angle and the distance of each launch.  Is the operator able to control the angle?  Generate a control chart and flip chart your response.
Case Study 2: The CatapultCase Study 2: The Catapult Audit
Catapult Control Plan ExerciseCatapult Control Plan Exercise Purpose: To develop a Control Plan that sustains the gains of the work our Six Sigma Team performed that optimizes the capability of the catapult to deliver conforming product. You and your Six Sigma team have have successfully completed a project by identifying the critical X’s and their respective levels in order to achieve your project objective. It is now time to hand the project over to the Process Owner, therefore a Control Plan needs to be developed. This Control Plan needs to contain the proper information that will allow the Process Owner to sustain the gains your team has achieved. Any six sigma project must have a control plan
Catapult Control Plan FormCatapult Control Plan Form Catapult Control Plan.pptCatapult Control Plan.ppt KPOV KPIV Measurement Method Who Measures Process Step Control Tool KPIV/KPOV Requirement Specification/ Requirement USL LSL CTQ Sample Size Frequency Where Recorded SOP Reference Decision Rule/ Corrective Action
Optional Round #7: AuditOptional Round #7: Audit Train a Replacement/Audit  Prove that your experimental results can sustain the test of time over a broad inference space. – Poke-Yoke your process so that it is robust to the many operators that are likely to run the device. – Your Crack Marketing Team received a letter from Her Majesty in which she described the test as follows:  “The winning design will be the one that is able to launch ammunition into a fine goblet from various distances.”  Further, the marketing team overheard that Her Majesty herself would fire the catapult after a short tutorial from the gunsmith.  After you have completed the control plan and documented your SOP’s, I will give you a new target to hit. You will have 5 minutes and 3 launches (within those 5 minutes) to identify a new set of operating conditions. You will set up the catapult at these new conditions. I will read your Standard Operating Procedures and then launch 10 balls. The capability of these launches will be included in the final competition calculations.
Case Study 2: The CatapultCase Study 2: The Catapult Summary
Project Debriefs / SummaryProject Debriefs / Summary Parameter Team 1 Team 2 Team 3 Team 4 Team 5 Baseline Zst Final Zst Baseline DPMO Final DPMO Baseline Average, in. Final Average, in. Baseline St. Dev., in. Final St. Dev., in.
ReviewReview In preparation for closing out your project, include: – Strategy, action plan and goals for the project – Tools and techniques used during the project – Brief technical discussion of what was learned by completing the project – Brief discussion of team dynamics