• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Final six sigma report

Final six sigma report






Total Views
Views on SlideShare
Embed Views



1 Embed 34

http://www.rijuldhruv.web44.net 34


Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    Final six sigma report Final six sigma report Document Transcript

    • Team 3 is an international catapult manufacturing company, supplying IVYLeague Universities for their annual Catapult Fair Party. Recently a batch of 100,000pieces supplied to these universities did not perform according to specification, creatingdissatisfaction and reduction in sales. A Six Sigma Process, DMAIC, was employed torectify this situation. The define phase required us to construct a problem statement as well as aproblem objective. The problem statement focused the team on a deficiency andconveyed the significance of the problem. The objective addressed the problemstatement and quantified the desired improvement. We then created a SIPOC; this toolallowed the team to define the starting and ending points of the process as well asdistinguished the data collection opportunities.The ultimate goal of the SIPOC was to allow for a step by step examination of theprocess. With this information the team established the primary (shooting range) andsecondary (height) metrics. These metrics helped to vary the shooting range keepingthe height as a constant.
    • This phase facilitated the identification and summation of the meaningful data, enablingthe 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 differentoperators and utilizing ten parts with three trials per part. The data from this collectionwas input into MiniTab ™ from where the Gage R&R, Normality Test and CapabilityAnalysis were extracted.The Gage R&R displayed the repeatability of the machine and reproducibility of theoperator. Repeatability refers to the accuracy of the machine output while thereproducibility refers to the accuracy of the measurement process. The Total Gage R&Rof any project should be ideally less than 10 for its acceptability. Our catapult systemwas 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 isdesirable as it signifies the consistency of the catapult and measuring process..The team then analyzed the data further utilizing a Gage R&R Anova measure. Theobjective 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, theend result of the process would create 1 million defects for every 1 million parts, anobviously undesirable conclusion. The team then entered the Analyze phase. The purpose of this phase is toinvestigate 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 BoneDiagram, Cause and Effect matrix and Failure Mode and Effects Analysis. Detailedprocess map gives an overview of the entire process and informs the Fish BoneDiagram. The team decided the factors which most affect the process and thencategorized them as either machine, environment, material, methods or people. After acareful examination the factors were further dived into control, noise or standardoperating procedure allowing for the identification of the three most substantialcontrollable process factors. The team then moved to the Cause and Effect matrix, thegoal 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 todiscover the variations effect on the final process, where the potential failure can ariseand how to mitigate the error effects. The improve phase in which the design of the experiment is created andexamined 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 eachpossible combination of variables was ensured. After inputting the results of theexperiment back into Minitab ™ the team ran a Pareto and Normality plot of the effectsof the variables. These two charts conveyed the active and significant effects to theteam, allowing the team to discover which factors produce the greatest impact onto thespecifications. Both the Pareto and Normality chart showed that the active effects werethe location of the pin on the fixed arm and the base. The Pareto chart showed thatthose two factors were beyond the .05 alpha level, the Normality plot also displayedthese 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, utilizinga specification of 75 inches, displayed an optimal configuration of 0 for the pin locationof the fixed arm, 0 for the pin location of the movable arm, and 5 for the pin location onthe 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 theslope. The factors possessing the greatest slope, fixed and base, denoted the activeeffects. The interaction plot portrayed the interaction between the factors. While none ofthe factors were shown to have a significant interaction, the pin location of the movablearm and the base appeared to display the most interaction. The final DOErecommendation obtained from the optimization chart revealed that for a specificationgoal of 58 +/-4, the location of the pins for the fixed arm, movable arm, and base shouldbe 0, 0 and 5. This led the team to the Control phase of the Six Sigma Process. This phase isutilized to ensure that any deviation from the specification is detected and rectified priorto 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 Barcharts along with the normality and capability analysis.
    • This analysis displayed that the method created by the team resulted in theconsistent meeting of customer specification as understood through Six Sigma. TheIMR measures process variation over time as a means to examine the steadiness of theprocess. The teams chart exhibited a ‘steady’ range with the process results all withincontrol limits. The R Chart exhibited that the variation within the subgroups wasconsistent while the X Bar Chart revealed variation between the subgroups displayingthat the process is currently in control. The capability analysis further supported theimprovement 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 SixSigma Level Process. In conclusion, the utilization of the Six Sigma Process, by Team 3, directlyresulted in the reduction of variation allowing the catapult to be within customerspecifications, allowing the Catapult Fair Parties to continue.