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Introduction to Statistical Process Control: Enhancing Quality through Process Optimization

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minya
32 diapositives · 481 Views · Jul 17, 2014

This guide covers the fundamentals of Statistical Process Control (SPC), emphasizing its significance as a continuous quality improvement method. It provides a structured approach for analyzing consumer food product manufacturing processes, from raw materials procurement to packaging. Readers will learn to develop flow charts, identify key process and control variables, and evaluate their effects on product quality and process efficiency. Techniques such as Pareto analysis, Ishikawa diagrams, and control charts will be explored to facilitate data-driven decision-making for optimizing quality in production.

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  1. CHE 185 – PROCESS CONTROL AND DYNAMICS AN INTRODUCTION TO STATISTICAL PROCESS CONTROL(SPC)

  2. STATISTICAL PROCESS CONTROL • A METHOD FOR CONTINUOUS QUALITY IMPROVEMENT • BASED ON PROCESS OPTIMIZATION AND PRODUCT QUALITY

  3. OPTIMIZATION VARIABLES • WHAT SHOULD BE IMPROVED? • WHAT PROCESS VARIABLES CAN BE USED TO ACHIEVE THE IMPROVEMENT?

  4. CONSIDER A CONSUMER FOOD PRODUCT • DEFINE THE STEPS IN PRODUCT MANUFACTURING • RAW MATERIALS PROCUREMENT • RAW MATERIALS STORAGE AND PREPARATION • MIXING AND/OR REACTION • COOKING,CURING OR COOLING • PRODUCT FINISHING • PRODUCT PACKAGING

  5. FLOW CHART ANALYSIS • DEVELOP A FLOW CHART FOR EACH AREA OF THE PROCESS • DETERMINE THE PROCEDURES USED IN EACH AREA • WHAT ARE THE VARIABLES USED FOR CONTROL? • WHAT IS THEIR INFLUENCE ON THE PRODUCT QUALITY? • WHAT IS THEIR INFLUENCE ON THE PROCESS EFFICIENCY?

  6. TYPICAL FLOW CHART 1 RAW MATERIALS PROCUREMENT, STORAGE AND PREPARATION RAW MATERIAL DELIVERY BY TRUCK, RAIL OR PIPELINE R. M. #1 STORAGE R. M. #2 STORAGE R. M. #4 STORAGE R. M. #3 STORAGE TO MIXING OPERATION

  7. TYPICAL FLOW CHART 2 MIXING AND PREREACTION FROM RAW MATERIALS STORAGE BLENDING LIQUID MIXING SOLIDS MIXING TO COOKING (REACTION)

  8. TYPICAL FLOW CHART 3 FROM REACTION PREPARATION COOKING COOLING TO SORTING AND PACKAGING

  9. TYPICAL FLOW CHART 4 FROM REACTION SAMPLING OFF-SPEC REJECT PRODUCT PACKAGING

  10. TYPICAL FLOW CHART 5 FROM PRODUCT FINISHING SMALL PACKS FINAL SORTING AND PACKING OPERATIONS MEDIUM PACKS BULK PACKS WAREHOUSING AND SHIPPING TO CUSTOMERS

  11. CONSIDER THE PRODUCT FROM THE CUSTOMER’S PERSPECTIVE WHAT DOES THE CUSTOMER VALUE IN THE PRODUCT? • COLOR • COST • AVAILABILITY • UNIFORMITY • TASTE • FLAVOR • TEXTURE • SHAPE

  12. SELECTION OF CONTROL VARIABLES • DETERMINE WHICH PROCESS VARIABLES EFFECT THE QUALITIES VALUED BY THE CUSTOMER • DETERMINE WHICH PROCESS VARIAIBLES EFFECT THE OPERATING EFFICIENCY OF THE PROCESS • DETERMINE WHICH CAN BE MEASURED TO A LEVEL TO ALLOW IMPROVEMENT

  13. DEFINING THE PRIMARY CONTROL VARIABLES • PARETO’S LAW • 80% OF THE DEFECTS ARISE FROM 20% OF THE CAUSES • 80% OF THE COMPLAINTS ORIGINATE FROM 20% OF THE CUSTOMERS • DEVELOP THE 3-DIMENSIONAL MATRIX CAUSE/PRIORITY/CAPABILITY • DETERMINE IF VARIABLES ARE MEASURED TO DEVELOP A DATA BASE FOR DEFECTS

  14. COLLECTION OF DATA TYPICAL CHART LISTS FREQUENCY OF DEFECTS BY CATEGORY

  15. PRESENTATION OF DATA -PARETO CHART 1.00 0.80 0.60 PERCENT 0.40 CUMULATIVE 32% 19% 0.20 16% 13% 11% 6% 1% 0% 0 COST TASTE SHAPE COLOR FLAVOR TEXTURE UNIFORMITY AVAILABILITY

  16. PRESENTATION OF CAUSES ISHIKAWA DIAGRAM, ALSO CALLED FISHBONE OR CAUSE & EFFECT DIAGRAM WAREHOUSE OPNS. PACKAGING OPNS. SHIPPING/LOADING EFFECT (DEFECT) COOKING PROCESS MIXING/BLENDING RAW MATERIALS

  17. DETAILS OF CAUSES EACH OF THE FISHBONES CAN BE EXAMINED IN MORE DETAIL FOR SOURCES BREAKAGE SCANNERS PNEUMATIC CONVEYORS PACKAGING OPERATIONS STACKING DEVICES PACKAGE SEALING

  18. VARIABLE RELATIONSHIPS 1 SCATTER DIAGRAMS ATTEMPT TO DEFINE PRIMARY VARIABLES 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0 10 10.10 10.20 10.30 10.40 10.50

  19. VARIABLE RELATIONSHIPS 2 SCATTER DIAGRAMS ATTEMPT TO DEFINE PRIMARY VARIABLES 0.10 0.08 0.06 0.04 0.02 0 10 10.10 10.20 10.30 10.40 10.50

  20. CONTROL CHARTS THESE ARE RECORDS OF DEVIATIONS FROM MEAN VALUES OF A VARIABLE 40 30 f ¾ ¾ ® 20 F ( int ) 10 0 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 int m 4 × s m + 4 × s - Histogram Normal fit

  21. NORMAL DISTRIBUTION PARAMETERS • MEAN • VARIANCE • STANDARD DEVIATION • 68.3% LIE WITHIN + 1σ • 95.4% LIE WITHIN +2σ • 99.7% LIE WITHIN +3σ • 99.99% LIE WITHIN +4σ

  22. LEVELS OF MEASUREMENT ERROR • THE ACTUAL MEASUREMENT IS A COMBINATION OF • THE ACTUAL PROCESS VARIATION • THE VARIATION IN MEASUREMENT • THESE TWO ARE SUMMED TO PROVIDE THE ACTUAL SIGNAL σ2OUTPUT = σ2PROCESS + σ2MEASUREMENT

  23. PROCESS CONTROL CHART 1

  24. PROCESS CONTROL CHART 2

  25. PROCESS CONTROL CHART 3 SOURCES OF VARIATION DURING SAMPLING • CATEGORIES FOR ANALYSIS • MEASUREMENT METHODS - CALIBRATION STANDARDS AND REPRODUCIBILITY OF TECHNIQUES • MATERIALS - DESTRUCTIVE vs. NON- DESTRUCTIVE TESTING • EQUIPMENT - SENSITIVITY OF DEVICE COMPARED WITH MEASUREMENT TOLERANCE • PEOPLE - DIFFERENT SKILLS LEVELS • ENVIRONMENT - PROCESS CONDITIONS

  26. VARIABLE CONTROL CHART 1 • VARIABLE DATA CAN BE ANALYZED INDEPENDENTLY

  27. VARIABLE CONTROL CHART 2 • DATA CAN BE ANALYZED BASED ON RANGES

  28. VARIABLE CONTROL CHART 3 • DATA CAN BE ANALYZED BASED ON DEVIATION FROM MEAN

  29. PROCESS CAPABILITY RANGES FOR PROCESS CAPABILITY CAN BE ESTABLISHED BASED ON DATA COLLECTED • UPPER AND LOWER TOLERANCE LIMITS ARE TYPICALLY SET AT + 3F VALUES • USING RANGE VARIABLES, THERE IS A LOWER LIMIT AT ZERO

  30. ANALYZING CONTROL CHART DATA CONTROL CHART DATA CAN PROVIDE INSIGHT INTO PROCESS OPERATION • SINGLE POINTS OUTSIDE THE LIMITS • MAY RESULT FROM SPECIAL CONDITIONS • MAY RESULT FROM MISMEASUREMENT • A SERIES OF POINTS ABOVE OR BELOW THE MEAN MAY INDICATE A PROCESS DRIFT • CYCLING - MAY BE RELATED TO SPECIFIC OPERATION METHODS OR TESTING PROCEDURES

  31. CONSIDERATIONS FOR CONTINUOUS SYSTEMS SPC WAS DEVELOPED FOR DISCRETE PRODUCTION • CONTINUOUS PRODUCTION DIFFERENCES • DATA POINTS - ARE ABUNDANT AND DATA COMPRESSION IS NORMALLY REQUIRED • AUTOCORRELATION - DEVIATIONS ARE DEPENDENT UPON THE LAST DEVIATION. TRENDS ARE NOT AS OBVIOUS • NON-GAUSSIAN DISTRIBUTIONS - BI-MODAL AND MULTI-MODAL MAY BE THE RULE

  32. METHODS USED FOR CONTINUOUS SYSTEM SPC • SINCE DATA IS READILY AVAILABLE AND AUTOMATIC CONTROL SYSTEMS ARE USED, FILTERING THE SIGNAL FROM NOISE REQUIRES SPECIAL TECHNIQUES • EXPONENTIALLY WEIGHTED MOVING AVERAGES -CAN BE USED TO SEPARATE THE NOISE FROM THE SIGNAL • EXPONENTIALLY WEIGHT MOVING STANDARD DEVIATION -CAN BE USED TO DEFINE THE MAGNITUDE OF THE NOISE • DATA CAN BE AUTOMATICALLY ANALYZED TO OPTIMIZE TUNING PARAMETERS