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Define Define What’s important
Define - Voice of Customer Voice of the Customer Operation Requirement CTQ Characteristics 1st Priority CTQ Flow Down Factory  Management Material  Planning Team External  Customer Reduce inbound & outbound process cycle time Shorten process cycle time, improve on-time delivery No inventory discrepancy between E-book and  SAP system No tariff penalty occurred  due to inventory discrepancy Transaction DPO (Targeted to reduce DPO by 70% over existing performance level) Process cycle time Targeted to reduce CT to 3 days  (Benchmark) China  Customs No discrepancy between 2 systems’ transaction
Define - Project Scheduling Project Planning in Define Phase Project Planning Updated in Design Phase Project Milestone:    “Define”   Start to Completion: Sep.22, 2008 – Sep.26, 2008   “Measurement”   Start to Completion: Sep.29, 2008 – Oct.24, 2008   “Analysis”   Start to Completion: Oct.27 , 2008 – Oct.31, 2008   “Design”   Start to Completion:  Nov.3, 2008 – Dec.28, 2008   “Verify”   Start to Completion: Dec.1, 2008 – Jan.9, 2009
Define - IPO Process E-book Data  Accuracy SAP inventory change & transaction records   Manpower:   OP team, Warehouse team, Planner, Vendor Machine:   SAP transaction setup, E-book dataflow setup Material:   Invoice, Purchase order, Production order,  Picking list, Accessories list, Trading mode Method:   SOP, E-book instruction Mather Nature:  Customs Regulation for Free Trade Zone Transaction discrepancies E-book inventory change & transaction records   Process cycle time   Inventory discrepancies ,[object Object],[object Object],[object Object],Project Background: Input Output
Measurement Measure how you're doing
Measurement - Process Map System setup in 2 systems are quite different, that's a potential factor resulting in discrepancy.
Measurement  - Process Capability Analysis   Defect per Unit (DPU) ,[object Object],[object Object],[object Object]
Measurement – Baseline Measuring System Analysis Process Cycle Time Tariff Loss Estimate
Analyze Analyze what’s wrong
Analysis – Pareto Chart We deeply investigate discrepancy root causes for two periods, week24 to week33, week34 to week43.
Analysis – Pareto Chart Tabulated statistics: Period, Type  Using frequencies in Defect No Rows: Period    Columns: Type  1  2  3  4  5  6  All 1  55  34  5  27  6  2  129 42.64  26.36  3.88  20.93  4.65  1.55  100.00 53.40  53.13  23.81  62.79  66.67  66.67  53.09 22.634  13.992  2.058  11.111  2.469  0.823  53.086 2  48  30  16  16  3  1  114 42.11  26.32  14.04  14.04  2.63  0.88  100.00 46.60  46.88  76.19  37.21  33.33  33.33  46.91 19.753  12.346  6.584  6.584  1.235  0.412  46.914 All  103  64  21  43  9  3  243 42.39  26.34  8.64  17.70  3.70  1.23  100.00 100.00  100.00  100.00  100.00  100.00  100.00  100.00 42.387  26.337  8.642  17.695  3.704  1.235  100.000 Pearson Chi-Square = 9.746, DF = 5, P-Value = 0.083 Likelihood Ratio Chi-Square = 10.064, DF = 5, P-Value = 0.073 NOTE * 4 cells with expected counts less than 5 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Χ ² calc  = 9.746 < 11.070  We can’t reject H0. On the other hand,  P-Value = 0.083 > .05,  we could say there is no association between likelihood of defect type and sampling period.
Analysis – Failure Mode & Effect Analysis   1. Establish FMEA Rating Scales Severity of  The Effect Probability of  Occurrence Dectection of  Difficulity R isk P riority N umber X X = Calculate Risk:
Analysis – Failure Mode & Effect Analysis Identify failure mode occurred in which process Root causes from Pareto Chart
Analysis – Fail Mode & Effect Analysis Shipment Tracking Function Request for Database Function   Transaction Comparison Functions Discrepancy Tracking Function ,[object Object],1st Priority The Most Difficult Requested Actions for SAP Setup Change & Trading Mode Change   Need evaluate later.
Design Design what we can do to improve
Design – Shipment Tracking Function (database) This date is to record our process cycle time This date is to measure broker’s performance This is to measure forwarder ’s performance ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],‘ X’:   Operation Time  Difference (42.64% tariff loss)
Design – DOE The rest   ‘X’ (10% tariff loss)   DOE for Transaction Comparison Functions The designed solution is to resort database to automatically compare 2 systems’ transactions, and identify the discrepancies due to the rest ‘X’, even the old pending ‘X’ or a newly generated ‘X’. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Receive Raw  Material Move Raw  Material  to WIP Produce Machine Deliver  Machine Deliver  Accessories Same time Receive  Parts Deliver  Parts SAP E-book
Design – DOE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SAP E-book Lotus Database Receive Raw  Material Move Raw  Material  to WIP Produce Machine Deliver  Machine Deliver  Accessories Receive  Parts Deliver  Parts SAP E-book
Design – Database Functions From this database function, we can either see discrepancy result, or transaction details. Transaction Comparison Functions Discrepancy Tracking Function and Resolution Matrix
Verify Ensure Performance
Verify – SOP Supplement
Verify – SOP Supplement
Verify – SOP Supplement
Verify – Updated Process Map New New New New New New
Verify – Achievement Our conclusion based on the normal distribution, 95% CI for difference: (0.1004, 0.1512), exclude (0, 0); P-Value <.05; So reject Ho (Ho:  μ 1 =  μ 2). In other words, there has been a decrease in DPU from the before and after data. 1. DPU Reduction DPU: - 12.58% Two-Sample T-Test and CI: DPU(W19-43), DPU(W44-53)   Two-sample T for DPU(W19-43) vs DPU(W44-53)   N  Mean  StDev  SE Mean DPU(W19-43)  25  0.1632  0.0197  0.0039 DPU(W44-53)  10  0.0374  0.0343  0.011 Difference = mu (DPU(W19-43)) - mu (DPU(W44-53)) Estimate for difference:  0.1258 95% CI for difference:  (0.1004, 0.1512) T-Test of difference = 0 (vs not =): T-Value = 10.90 P-Value = 0.000  DF = 11 Outlier: before data Normal distribution
Verify – Achievement 2. Tariff Loss Reduction Reduced to 3 days Reduced by 89.36%,  total US$ 47,252.46 ,[object Object],[object Object],[object Object],3. Cycle Time Reduction Improvement Scorecard ,[object Object]
Verify – Control Plan Control Plan   End
 

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Six Sigma Dfss Application In Data Accarucy

  • 2. Define - Voice of Customer Voice of the Customer Operation Requirement CTQ Characteristics 1st Priority CTQ Flow Down Factory Management Material Planning Team External Customer Reduce inbound & outbound process cycle time Shorten process cycle time, improve on-time delivery No inventory discrepancy between E-book and SAP system No tariff penalty occurred due to inventory discrepancy Transaction DPO (Targeted to reduce DPO by 70% over existing performance level) Process cycle time Targeted to reduce CT to 3 days (Benchmark) China Customs No discrepancy between 2 systems’ transaction
  • 3. Define - Project Scheduling Project Planning in Define Phase Project Planning Updated in Design Phase Project Milestone: “Define” Start to Completion: Sep.22, 2008 – Sep.26, 2008 “Measurement” Start to Completion: Sep.29, 2008 – Oct.24, 2008 “Analysis” Start to Completion: Oct.27 , 2008 – Oct.31, 2008 “Design” Start to Completion: Nov.3, 2008 – Dec.28, 2008 “Verify” Start to Completion: Dec.1, 2008 – Jan.9, 2009
  • 4.
  • 5. Measurement Measure how you're doing
  • 6. Measurement - Process Map System setup in 2 systems are quite different, that's a potential factor resulting in discrepancy.
  • 7.
  • 8. Measurement – Baseline Measuring System Analysis Process Cycle Time Tariff Loss Estimate
  • 10. Analysis – Pareto Chart We deeply investigate discrepancy root causes for two periods, week24 to week33, week34 to week43.
  • 11.
  • 12. Analysis – Failure Mode & Effect Analysis   1. Establish FMEA Rating Scales Severity of The Effect Probability of Occurrence Dectection of Difficulity R isk P riority N umber X X = Calculate Risk:
  • 13. Analysis – Failure Mode & Effect Analysis Identify failure mode occurred in which process Root causes from Pareto Chart
  • 14.
  • 15. Design Design what we can do to improve
  • 16.
  • 17.
  • 18.
  • 19. Design – Database Functions From this database function, we can either see discrepancy result, or transaction details. Transaction Comparison Functions Discrepancy Tracking Function and Resolution Matrix
  • 21. Verify – SOP Supplement
  • 22. Verify – SOP Supplement
  • 23. Verify – SOP Supplement
  • 24. Verify – Updated Process Map New New New New New New
  • 25. Verify – Achievement Our conclusion based on the normal distribution, 95% CI for difference: (0.1004, 0.1512), exclude (0, 0); P-Value <.05; So reject Ho (Ho: μ 1 = μ 2). In other words, there has been a decrease in DPU from the before and after data. 1. DPU Reduction DPU: - 12.58% Two-Sample T-Test and CI: DPU(W19-43), DPU(W44-53) Two-sample T for DPU(W19-43) vs DPU(W44-53) N Mean StDev SE Mean DPU(W19-43) 25 0.1632 0.0197 0.0039 DPU(W44-53) 10 0.0374 0.0343 0.011 Difference = mu (DPU(W19-43)) - mu (DPU(W44-53)) Estimate for difference: 0.1258 95% CI for difference: (0.1004, 0.1512) T-Test of difference = 0 (vs not =): T-Value = 10.90 P-Value = 0.000 DF = 11 Outlier: before data Normal distribution
  • 26.
  • 27. Verify – Control Plan Control Plan End
  • 28.  

Hinweis der Redaktion

  1. Today‘s competitive world: Technology alone doesn‘t sell In addition to our technological strength: Solutions and customer closeness: Because we want to grow profitably: This means: We want to win new customers and make more business with existing customers