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Conduct situation analysis

  1. conduct situation analysis
  2. 1.1 Form problem solving team if appropriate or required.
    1.2 Conduct first problem solving team meeting. 1.3 Review all available sources of information related to the situation.
    1.4 Define the historical state of the situation. 1.5 Define the current situation in general term. 1.6 Establish the improvement objective related to the current situation.
    1.7 Identify all driving and restraining forces. 1.8 Establish the nature of the information required to satisfy the objectives.
    1.9 Determine what type of performance measure meets the
    information needs. 1.10 Select an apropriate index of variability for the given performance measures.

  3. Conduct literature review
  4. 2.1 Examine experimentel objective. 2.2 Examine dependent variables. 2.3 Examine measurement methodology. 2.4 Examine experimental factors. 2.5 Examine blocking variables. 2.6 Examine covariates. 2.7 Evaluate author bias. 2.8 Evaluate clarity of contents. 2.9 Evaluate recent references and bibliography. 2.10 Evaluate assumption of reasearch methods and related activities. 2.11 Study analytical methods. 2.12 Study degree to which conclusion are supported by numerical evidence.
    2.13 Study sampling methodology. 2.14 Assess reference status. 2.15 Assess background of author. 2.16 Consider text books. 2.17 Consider journal articles. 2.18 Consider perodicals. 2.19 Consider white papers. 2.20 Consider video tapes. 2.21 Consider audio tape. 2.22 Consider seminar,materials.

  5. Conduct training activities.
  6. 3.1 Establish training and education needs. 3.2 Identify target population. 3.3 Identify knowledge requirements. 3.4 Identify skills requirements. 3.5 Device training and education action plan. 3.6 prepare initial cost estimate. 3.7 Obtain mamagement approval for further development. 3.8 Develop training materials. 3.9 Conduct necessary training.

  7. Synthesize and focus.
  8. 4.1 Revise problem statement as necessary. 4.2 Write technical report if appropriate or required. 4.3 write management report if appropriate or required. 4.4 Disseminate report's and imformation to all concerned. 4.5 Prepare technical level presentation if appropriate or required. 4.6 Prepare management level presentation if appropriate or required. 4.7 Make presentation if appropriate or required. 4.8 Obtain management approval for continual investigation. 4.9 Prepare a preliminary agenda for next team meeting. 4.10 Schedule next problem solving team meeting. 4.11 Disseminate the preliminary agenda to team member and resource personnel.
    4.12 Conduct problem solving team meeting.

  9. Define dependent variables.
  10. 5.1 Review carefully the phenomenon under investigation. 5.2 Identify potential response variables to be studied. 5.3 Define the unique property of each response variables. 5.4 Deserible the constraining properties of each variables. 5.5 Deserible the desire properties of each variables. 5.6 Select the most variable response variable for each phenomenon.

  11. Define independent variables.
  12. 6.1 construct a cause and effect matrix(c&e) for use during brainstroming.
    6.2 Integrate literature search result into the (c&e) matrix. 6.3 Identify all individuals whom could make a contribution
    to brain stroming. 6.4 Prepare a working copy of the (c&e) matrix for each individual. 6.5 Distribute copy of the c&e matrix for each individuals. 6.6 Record respondent (c&e) inputs as they are gathered. 6.7 Shedule first group lavel brainstorming sessions and notify all participants.
    6.8 Conduct group lavel brainstroming session.

  13. Define control variables.
  14. 7.1 Identify the key independent variables to be controlled. 7.2 Define and control objectives for each variables. 7.3 Determine technical feasibility for controlling with spc methods. 7.4 Prepare initial cost estimates. 7.5 Obtain management approval and support.

  15. Identify measurement system.
  16. 8.1 Brainstorming potential measurement scales for each response variables.
    8.2 Establish a methodology for each measurement scale. 8.3 Identify all practical cost/time/resources/constraints related to each scale.
    8.4 Study data analysis implication of the various measurement scale. 8.5 Establish selection criteria. 8.6 Select the top releted mesurement system based on criteria.

  17. Identify data vehicle
  18. 9.1 Define the primary objective of data vehicle. 9.2 Identify all characteristics which the data vehicle must passess. 9.3 Review all data requirements in relation to the data vehicle characteristics.
    9.4 Define all process performance specification in relation to the data vehicle characteristics.
    9.5 Define final test of characteristics requirements and specifications. 9.6 Establish degree of compatibility between criteria and existing vehicles. 9.7 Make engg. decision related to feasibility of using existing vehicles.

  19. design measurment validation study
  20. 10.1 define a test to study measurement sensitivity in relation to data vehicles.
    10.2 Prepare initial cost estimate for the study. 10.3 Obtain management approval and funding for the study. 10.4 Develop measurement sensitivity test. 10.5 Study measurement sensitivity, validation and reliability. 10.6 Modify the measurement methodology as required. 10.7 Document all pertinent information related to the methodology.

  21. Design hierarchical pareto study
  22. 11.1 Establish objective related to study. 11.2 Review the product quality parameters subject to study. 11.3 Define defect/ failure coding system if necessary. 11.4 Select the most appropriate type of formate in which to display data.

  23. Design parameter capability study
  24. 12.1 Review the variable to be included in the study. 12.2 Identify the specification related to study. 12.3 Define operating limits of the specification. 12.4 Define model distribution parameter if appropriate or required. 12.5 Establish criteria capability indices if appropriate or required. 12.6 Identify all pertinent condition to which the study is restricted. 12.7 Select representative personnel for participation in the study. 12.8 Make necessary administrative and logical arrangements.

  25. Design multi variable study
  26. 13.1 Establish objective for the study. 13.2 Review the response characteristics to be investicated. 13.3 Define all major categories for variation worthy of investigation. 13.4 Design multi variable chart format based on define categories of variation. 13.5 Circulate formate among colleagues for critical review. 13.6 Review miv chart format as necessary.

  27. Design experimental study
  28. 14.1 define experimental objectives. 14.2 Establish the number of factors to be studied. 14.3 Make initial selection of factors based on the objectives and constraints. 14.4 Determine the degree of experimental confounding consider to be tolerance.
    14.5 Define level for the experimental factors to be used. 14.6 Revise system components. 14.7 Designate primary control variables. 14.8 Establish testing condition for each primary control variables. 14.9 Revise system components. 14.10 Identify blocking variables. 14.11 Define how each blocking variables must be handled. 14.12 Revise system components. 14.13 Identify background variables which could contaminate experimental results.
    14.14 Define how the effect of uncontrolled variables must be handled. 14.15 Revise system components. 14.16 Fanalized the list of experimental factors and related variables. 14.17 Construct an appropriate statistical model if appropriate. 14.18 Develop statistical hypotheses based on the model objectives and constraints.
    14.19 Define criteria for selecting an appropriate experimental design. 14.20 Select an appropriate experimental design. 14.21 Evaluate rationality of all underlying assumptions related to the design.
    14.22 Revise system components. 14.23 Prepare initial cost estimate for the experiments. 14.24 Conduct a dry run of the experiments. 14.25 Document all related aspects for the experimental design. 14.26 Obtain mngt. approval and support for continued investigation. 14.27 Revise system components.

  29. Design parameter control study.
  30. 15.1 Review the system variables under consideration. 15.2 Select the statistical parameter to be controlled relative to each variables.
    15.3 Establish the degree of sensitivity each variable parameter must display.
    15.4 Selects appropriate types of charts based on parameters & sensitivity. 15.5 Define an appropriate control chart formate for each variable parameter.
    15.6 Define type of centerline to be used for each parameter. 15.7 Select the statistical control limits to be used on each chart. 15.8 Establish bias for calculating control limit for each parameter. 15.9 Evaluate all pertinent assumptions underlying the selected charts.

  31. Develop data analysis system.
  32. 16.1 Define data analysis objectives. 16.2 Establish the level of analytical precision required. 16.3 Identify output formates which will satisfy the objectives. 16.4 Identify specific methods which will drive the output formates. 16.5 Select specific data analysis methods to be used. 16.6 Study all assumption underlying the selected methods. 16.7 Device plan to ensure compliance with all relevent assumptions. 16.8 Establish required test sensitivity and confidense/risk level. 16.9 Identify experimental error source if appropriate or required. 16.10 Derive sample size based on appropriate equation or tables. 16.11 Identify practical constraints surrounding the sample size. 16.12 Adjust risk and sensitivity parameters based on constraints. 16.13 Revise sample size as necessary. 16.14 Obtain management approval and support for continued investigation. 16.15 Revise system components. 16.16 Identify the most efficient computational strategy. 16.17 Define hardware requirements. 16.18 Define software requirements. 16.19 Estimate cost related to the data analysis system. 16.20 Obtain mngt. funding. 16.21 Conduct a complete dry run of the analytical system. 16.22 Revise system components. 16.23 Prepare all necessary written instruction.

  33. Develop data tracking system
  34. 17.1 Define tracking requirements. 17.2 Establish data gates within the process. 17.3 Devise sample coding /lebeling system. 17.4 Design tracking system. 17.5 Estimate cost related to tracking system. 17.6 Obtain mngt. approval and funding. 17.7 conduct dry run of the tracking system. 17.8 Revise system components. 17.9 Document all related aspects of the tracking system.

  35. Develop data collection system
  36. 18.1 Establish a data formate consistent with data analysis requirements. 18.2 Identify all pertinent information which must be attached to the data. 18.3 Devise method for establising data accuracy and validity. 18.4 Establish data collection points /gates within the process. 18.5 Devise data collection forms. 18.6 Provide independent evaluation of data collections forms. 18.7 Conduct dry run of the data collection device & aids. 18.8 Evaluate result of the dry run. 18.9 Revice system components. 18.10 Document all related aspects of the data collection system. 18.11 Prepare cost estimates. 18.12 Obtain mngt. approval for continued development. 18.13 Write detail sample preparation instructions. 18.14 Write detail data collection instructions. 18.15 Review instructions by appropriate person. 18.16 Revise data collection instruction as required. 18.17 Document all related aspects of the data collection system.

  37. Conduct data collection
  38. 19.1 Insure that all required test and measurements apparatus are calibrated.
    19.2 Prepare sample in accordance to instructions. 19.3 Subject samples to the process. 19.4 Record response data on data collection forms. 19.5 Verify accuracy of data recording.

  39. Conduct data analysis.
  40. 20.1 Review data analysis plan. 20.2 Construct data summary tables. 20.3 Display data in accordance to the data analysis plan. 20.4 Study data display for potential distortion as a function of scale. 20.5 Verify accuracy of the information. 20.6 Interpret graphical outcomes. 20.7 Compute define summary indices. 20.8 Compute define statistics. 20.9 Consider all pertinent underlying assumptions and test if necessary. 20.10 Interpret statistical outcomes. 20.11 Rationalize statistical outcomes against data display. 20.12 Constructs statistical summary tables if appropriate or required. 20.13 Conduct secondary explorating data analysis activities. 20.14 Verify computational accuracy. 20.15 Draw conclusion strictly based on the data. 20.16 Establish implication based on the data and interpretation. 20.17 Document conclusion & implications. 20.18 Translate all analytical outcomes into cost & percentage. 20.19 Construct technical level summary tables,charts and graphs.

  41. Establish sample size.
  42. 21.1 Establish sampling objectives. 21.2 Preliminary consideration. 21.3 Classify the response measurment scale. 21.4 Establish required test sensitivity. 21.5 Establish type 1 error probability(alpha risk-producer's risk). 21.6 Establish type 2 error probability(beta risk-consumer's risk). 21.7 Identify experimental error source(residual). 21.8 Define no of required runs. 21.9 Establish the degree of replication necessary at each run. 21.10 Identify practical constraints surrounding the sample sizes. 21.11 Adjust risk & sensitivity parameter based on constraints. 21.12 Revise system components. 21.13 Document all related aspects of the sample size determination. 21.14 Obtain mngt. approval & support for continued investigation.

  43. Establish sampling methodology .
  44. 22.1 Random 22.2 Sequential 22.3 Systematic(based on specified clacification variables). 22.4 Time based. 22.5 Stratified sequential. 22.6 Stratified random. 22.7 Stratified systematic. 22.8 Establish sampling interval. 22.9 Rational subgrouping.

  45. Design test vehicle.
  46. 23.1 Establish design criteria. 23.2 Conduct necessary design activity. 23.3 Prepare initial cost estimates. 23.4 Obtain mngt. approval for further development. 23.5 Construct prototype test vehicle. 23.6 Verify test vehicle design as required. 23.7 Document all pertinent aspects of the test vehicle.
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