Team 3 is an international catapult manufacturing company, supplying IVY

League Universities for their annual Catapult Fair Party. Recently a batch of 100,000

pieces supplied to these universities did not perform according to specification, creating

dissatisfaction and reduction in sales. A Six Sigma Process, DMAIC, was employed to

rectify this situation.

The define phase required us to construct a problem statement as well as a

problem objective. The problem statement focused the team on a deficiency and

conveyed the significance of the problem. The objective addressed the problem

statement and quantified the desired improvement. We then created a SIPOC; this tool

allowed the team to define the starting and ending points of the process as well as

distinguished the data collection opportunities.

The ultimate goal of the SIPOC was to allow for a step by step examination of the

process. With this information the team established the primary (shooting range) and

secondary (height) metrics. These metrics helped to vary the shooting range keeping

the height as a constant.

This phase facilitated the identification and summation of the meaningful data, enabling

the group to transition into the measure phase.

The measure phase allowed for the collection and ultimately analysis of the data.

The team ‘ran’ and collected 90 different catapult shot readings using three different

operators and utilizing ten parts with three trials per part. The data from this collection

was input into MiniTab ™ from where the Gage R&R, Normality Test and Capability

Analysis were extracted.

The Gage R&R displayed the repeatability of the machine and reproducibility of the

operator. Repeatability refers to the accuracy of the machine output while the

reproducibility refers to the accuracy of the measurement process. The Total Gage R&R

of any project should be ideally less than 10 for its acceptability. Our catapult system

was below 10, 9.93 specifically, signifying acceptance. The part to part variation

represents the variation between the items tested. A 90% Variation between the parts is

desirable as it signifies the consistency of the catapult and measuring process.

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The team then analyzed the data further utilizing a Gage R&R Anova measure. The

objective of which is to determine the cause of the variation; operator, part or machine.

The results of the capability test exposed to the team, that as currently formulated, the

end result of the process would create 1 million defects for every 1 million parts, an

obviously undesirable conclusion.

The team then entered the Analyze phase. The purpose of this phase is to

investigate the data so as to determine the major factors which cause significant

variation in the results. This is done through a Detailed Process Map, Fish Bone

Diagram, Cause and Effect matrix and Failure Mode and Effects Analysis. Detailed

process map gives an overview of the entire process and informs the Fish Bone

Diagram. The team decided the factors which most affect the process and then

categorized them as either machine, environment, material, methods or people. After a

careful examination the factors were further dived into control, noise or standard

operating procedure allowing for the identification of the three most substantial

controllable process factors. The team then moved to the Cause and Effect matrix, the

goal of which was to organize the possible sources of variation using the process steps,

inputs and outputs. The FMEA tool was utilized to analyze each process input to

discover the variations effect on the final process, where the potential failure can arise

and how to mitigate the error effects.

The improve phase in which the design of the experiment is created and

examined exposed the active effects within the process. The team using Minitab ™

created a DOE where a random order of 50 test runs was created in which each

possible combination of variables was ensured. After inputting the results of the

experiment back into Minitab ™ the team ran a Pareto and Normality plot of the effects

of the variables. These two charts conveyed the active and significant effects to the

team, allowing the team to discover which factors produce the greatest impact onto the

specifications. Both the Pareto and Normality chart showed that the active effects were

the location of the pin on the fixed arm and the base. The Pareto chart showed that

those two factors were beyond the .05 alpha level, the Normality plot also displayed

these two factors as active through their extreme distance from the ‘normality’ line. A

cube plot was also utilized to determine the optimal configuration, at the extreme points,

of the factors in order to achieve the desired specification. The teams cube plot, utilizing

a specification of 75 inches, displayed an optimal configuration of 0 for the pin location

of the fixed arm, 0 for the pin location of the movable arm, and 5 for the pin location on

the base. The pin locations were classified from 0-5. Another chart generated in Minitab

™, the main effect plot, also confirmed the previous results through the degree of the

slope. The factors possessing the greatest slope, fixed and base, denoted the active

effects. The interaction plot portrayed the interaction between the factors. While none of

the factors were shown to have a significant interaction, the pin location of the movable

arm and the base appeared to display the most interaction. The final DOE

recommendation obtained from the optimization chart revealed that for a specification

goal of 58 +/-4, the location of the pins for the fixed arm, movable arm, and base should

be 0, 0 and 5.

This led the team to the Control phase of the Six Sigma Process. This phase is

utilized to ensure that any deviation from the specification is detected and rectified prior

to a defect. In order to achieve this goal the team re-ran the experiment 50 times,

ultimately re-analyzing the data with the Individual Moving Range, X Bar and R Bar

charts along with the normality and capability analysis.

This analysis displayed that the method created by the team resulted in the

consistent meeting of customer specification as understood through Six Sigma. The

IMR measures process variation over time as a means to examine the steadiness of the

process. The teams chart exhibited a ‘steady’ range with the process results all within

control limits. The R Chart exhibited that the variation within the subgroups was

consistent while the X Bar Chart revealed variation between the subgroups displaying

that the process is currently in control. The capability analysis further supported the

improvement of the process by exposing that our product now meets customer

specifications and possess a 5.73 sigma level, which is approximately equal to a Six

Sigma Level Process.

In conclusion, the utilization of the Six Sigma Process, by Team 3, directly

resulted in the reduction of variation allowing the catapult to be within customer

specifications, allowing the Catapult Fair Parties to continue.