Six Sigma Workshop Fallstudie och praktisk övning

Transcription

1 Six Sigma Workshop Fallstudie och praktisk övning 0-fels LEAN-konferens, Folkets Hus 05-NOV-2013 Lars Öst

2 GE business segments Technology Infrastructure Energy Infrastructure GE Capital NBC Universal Healthcare Energy Commercial Finance Aviation Water GE Money Transportation Oil & Gas Corporate Treasury Enterprise Solutions 2

3 Broad solutions for healthcare Broad-based Technologies Diagnostic imaging & surgery technologies Clinical products Medical diagnostics Information Technology Integrated admin. & clinical Electronic medical records Picture Archiving System (PACS) Life Sciences Discovery tools Protein & cell sciences Clinical tissue biomarkers 3

4 Umeå site main products Protein detection and analysis Systems for detection and analysis of proteins for academic research and drug discovery. Biacore: systems for comprehensive characterization of molecular binding events. Electrophoresis: gel filtration systems for protein detection. Scanners: 2D electrophoresis for protein analysis. Biacore X100 Scanner Typhoon Lab chromatography Lab scale protein purification systems for academic research and drug discovery. ÄKTA platform: wide range of chromatography systems, from simple to advanced, from micro flows and up -the industry standard. New generation of ÄKTA platform launched in 2009 with ÄKTAavant. BioProcess Upstream and downstream process systems for manufacturing of biopharmaceuticals. WAVE: bioreactor systems for cell culture. ÄKTApilot, ÄKTAcrossflow and ÄKTAready: chromatography media filtration systems for protein purification. Manufacturing scale up and small scale manufacturing. ÄKTAprocess: large scale manufacturing. ÄKTA prime WAVE system 500/1000 ÄKTA avant 4 ÄKTA process

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6 Lean eller Six Sigma varför välja? 6

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11 Six Sigma Approach Practical Problem Statistical Problem Traditional Approach Statistical Approach to Problem Solving Systematic Provides a Structure Data Driven Focused on Statistically Significant Root Causes & Solutions Statistical Solution Practical Solution 11

12 Six Sigma Focus Y = f(x) To get results, should we focus our behavior on the Y or X? Y 1...Y n Dependent Output Signal Effect Symptom Monitor X 1... X n Independent Input-Process Cause Problem Control Control X to Control Y Dr. Mikel J. Harry V3.0

13 Statistical Objective of Six Sigma Process Off Target Target Excessive Variation Target USL Upper Specification limit LSL Lower Specification Limit Defects LSL USL LSL USL Center Process Target Reduce Spread LSL USL Reduce Variation & Center the Process Customers feel the variation more than the mean. 13

14 Statistical Objective of Six Sigma Target... 6 standard deviations are contained between the target (or mean) and each specification limit. 1 Standard Deviation (σ) LSL USL 14

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16 Six Sigma DMAIC Process 16

17 DEFINE Phase Steps DMAIC Define Measure Analyze Improve Control A. Identify Customer, & Project CTQs B. Develop Team Charter C. Define Process Map 17

18 MEASURE Phase Steps DMAIC Define Measure Analyze Improve Control 1. Select CTQ Characteristics 2. Define Performance Standards 3. Measurements Systems Analysis 18

19 ANALYZE Phase Steps Define Measure Analyze Improve Control DMAIC 4. Establish Process Capability 5. Define Performance Objectives 6. Identify Variation Sources 19

20 IMPROVE Phase Steps DMAIC Define Measure Analyze Improve Control 7. Screen Potential Causes 8. Discover Variable Relationships 9. Establish Operating Tolerance 20

21 CONTROL Phase Steps DMAI Define Measure Analyze Improve Control 10. Validate Measurement System for X 11. Determine Process Capability 12. Implement Process Control 21

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23 ÄKTA Avant Pump Project Team: Göran Jonsson Kenneth Eklund

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25 Gage R&R Chocolate Exercise 4 groups Min 3 operators and 1 scriber per group One measurement system per group 10 chocolate tabs per group Measure (non-destructively) tab s height Specs are: 76,5-77,5 mm How important this spec to the customer? 25

26 Hur tolkar jag graferna? Xbar Chart by Instrument The control limits indicate measurement system variation. We want the measured points to fall outside of these limits, ie we want the sample to sample variation to exceed the measurement system variation. Instrument*Sample Interaction This graph shows the average sample measurements for each sample from each of the instrumens/operators. If for instance there is a bias between instruments depending on which part is measured this graph will help in quantifying that. R Chart by Instrument Visualizes the range (max-min) for each of the groups of replicates made by each Instrument (operator). Indicator of measurement system stability. Components of Variation Shows what percentage of the total is due to gage variation and part-to part variation. Gage variation is split into repeatability and reproducibility. Both gage and part to part variation is listed in % of total, % of study (stdev ratios) and % of tolerance. Ideally, part-to-part captures most of the variation. By Instrument This graph aims to visualize whether one Instrument or operator consistently measures higher or lower than the other(s). If so, the red line through the group averages will not be straight. Also, in the Instrument*Sample plot above there will be an offset between the lines. By Sample Shows the variability in measurements for each sample across all instruments/operators. Ideally there should be little variation within each sample compared to the variation between samples this will enable the gage to see more distinct categories when sampling the process. 26

27 Hur tolkar jag siffrorna? Analysis of Variance (ANOVA) Two ANOVA tests are performed, one testing for significance in Sample/Part, Instrument/Operator together with the interation Sample*Instrument. The 2nd ANOVA table only tests the significance of the main effects, ie Sample/Part and Instrument/Operator. P values and their meaning The p values are the statistical likelihood that a factor has had No Effect. We normally want at least 95% probability for an effect to consider it significant, i.e. If p<0.05 we consider an effect significant at a confidence level of 95%. Övriga tumregler gäller egentligen bara när Part-to-Part likställs med hela Processen, dvs när man har massor av mätpunkter (som beskriver processen). Total variation = Gage R&R + Part-to-Part These tables decompose the total observed variation in the Gage R&R investigation into: Total Gage R&R overall gage variation Repeatability overall repeatability variation across all samples within each instrument/operator Reproducibility variation in results across all instruments /operators within each sample. Reproducibility is further split into instrument/operator and instrument*sample interaction. (Recall the p-values from the previous slide for the statistical significance of these variation sources.) Part-to-Part variation is the variation due to the actual samples, taking away the Gage variation. Ideally most of the variation will be found here. Rules of thumb Max 8% of total variance due to gage Max 30% of study variation due to gage 5.15*Gage stdev max 30% of tolerance Gage should be able to split part-to-part variation into at least 4 distinct categories 27

28 Rosa grupp - Måttband 28

29 Rosa grupp - Måttband % GRR = 5.15 σ Tolerance Measure 100% = 271,43 < 30 NO BAD 29

30 Orange grupp - Talmeter 30

31 Orange grupp - Talmeter % GRR = 5.15 σ Tolerance Measure 100% = 110,90 < 30 NO BAD 31

32 Grön grupp - Skjutmått 32

33 Grön grupp - Skjutmått % GRR = 5.15 σ Tolerance Measure 100% = 28,43 < 30 YES GOOD!!! 33

34 Blå grupp - Järnlinjal 34

35 Blå grupp - Järnlinjal % GRR = 5.15 σ Tolerance Measure 100% = 256,49 < 30 NO BAD 35

36 And the winner is Skjutmått 2. Talmeter 3. Järnlinjal 4. Måttband 36

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38 Läsetips Understanding Variation The key to Managing Chaos (Donald J Wheeler) Målet (Eliyahu M. Goldratt) Så lyckas man med förbättringsprojekt (sid 14-15) 38

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