Industrial Engineering - QUALITY AND RELATED CONCEPTS

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QUALITY AND RELATED CONCEPTS

INTRODUCTION
THE RAPIDLY INCREASING GLOBAL COMPETITION OVER THE PAST DECADE HAS LED TO THE EMERGENCE OF NEW SCENARIOS FOR MOST OF THE INDUSTRIAL SECTORS. THE INDUSTRIES ARE NOW ASSOCIATED WITH RAPID TECHNOLOGICAL CHANGES AND PRODUCT VARIETY PROLIFERATION IN ORDER TO REMAIN COMPETITIVE. THE COMPETITIVENESS OF A COMPANY IS MOSTLY DEPENDENT ON ITS ABILITY TO PERFORM WELL IN DIMENSIONS SUCH AS COST, QUALITY, DELIVERY, DEPENDABILITY AND SPEED, INNOVATION AND FLEXIBILITY TO ADAPT ITSELF TO VARIATIONS IN DEMAND.
AIMING AT IMPROVING ORGANIZATIONAL PERFORMANCE THROUGH THE EFFECTIVE USE OF PRODUCTION CAPABILITY AND TECHNOLOGY, OPERATIONS STRATEGY SUCH AS TOTAL QUALITY MANAGEMENT (TQM), QUALITY FUNCTION DEPLOYMENT (QFD), SIX SIGMA, BUSINESS PROCESS RE-ENGINEERING (BPR), JUST IN TIME (JIT), BENCHMARKING, PERFORMANCE MEASUREMENT AND MANY OTHERS ARE COMMONLY USED. THE CONCEPT OF QUALITY HAS EVOLVED FROM MERE SPECIFICATIONS, CONTROLS, INSPECTIONS, SYSTEMS, AND METHODS FOR REGULATORY COMPLIANCE TO A HARMONIZED RELATIONSHIP WITH BUSINESS STRATEGIES AIMED AT SATISFYING BOTH THE INTERNAL AND EXTERNAL CUSTOMER. TODAY, QUALITY AND VALUE ARE, FIRST AND ABOVE ALL, GIVENS, AND THE CUSTOMER EXPECTS THEM. QUALITY IN THE SUCCESSFUL ORGANIZATION IS FULLY INTEGRATED INTO ALL OF THE BUSINESS PROCESSES AND IS AN EXTENSION OF EVERYTHING ELSE THAT HAS TO HAPPEN ALONG THE PATH TO SUCCESS, BOTH FOR THE COMPANY AND FOR THE PEOPLE INVOLVED.

QUALITY DEFINITION(S)
AS SPECIFIED BY JOSEPH JURAN, QUALITY IS THE FITNESS OF USE I.E. IT IS THE VALUE OF THE GOODS AND SERVICES AS PERCEIVED BY THE SUPPLIER, PRODUCER AND CUSTOMER. THE MEASURE ALSO PERTAINS TO THE DEGREE TO WHICH PRODUCTS AND SERVICES CONFORM TO SPECIFICATIONS, REQUIREMENTS AND STANDARDS AT AN ACCEPTABLE PRICE. SOME OF THE DEFINITIONS OF THE TERM ‘QUALITY', PROVIDED BY QUALITY GURUS ARE AS FOLLOWS:
  • QUALITY IS FITNESS FOR USE (JURAN)
  • QUALITY IS CONFORMANCE TO REQUIREMENTS (CROSBY)  
  • THE EFFICIENT PRODUCTION OF THE QUALITY THAT THE MARKET EXPECTS (DEMING)
  • QUALITY IS WHAT THE CUSTOMER SAYS, IT IS (FEIGENBAUM)
  • QUALITY IS THE LOSS THAT A PRODUCT COSTS TO THE SOCIETY AFTER BEING SHIPPED TO THE CUSTOMER (TAGUCHI)
  • THE TOTALITY OF FEATURES AND CHARACTERISTICS OF A PRODUCT OR SERVICES THAT BEAR ON ITS ABILITY TO SATISFY STATED OR IMPLIED NEEDS OF THE CUSTOMERS (ASQC)
  • A QUALITY SYSTEM IS THE AGREED ON COMPANY WIDE AND PLANT WIDE OPERATING WORK STRUCTURE, DOCUMENTED IN EFFECTIVE, INTEGRATED,  TECHNICAL AND MANAGERIAL PROCEDURES FOR GUIDING THE CO-COORDINATED ACTIONS OF PEOPLE, THE MACHINES, OR THE INFORMATION OF COMPANY IN THE BEST AND MOST PRACTICAL WAYS TO ASSUME CUSTOMER QUALITY SATISFACTION AND ECONOMICAL COSTS OF QUALITY. (FEIGENBAUM)
DIMENSIONS OF PRODUCT QUALITY
AS PRESCRIBED BY GARVIN, THE EIGHT DIMENSIONS OF QUALITY ARE:
  •      PERFORMANCE          (WILL THE PRODUCT DO THE INTENDED JOB?)
  •      RELIABILITY                (HOW OFTEN THE PRODUCT FAILS?)
  •      DURABILITY                 (HOW LONG THE PRODUCT LASTS?)
  •      SERVICEABILITY          (HOW EASY IS TO REPAIR THE PRODUCT?)
  •      AESTHETICS              (WHAT DOES THE PRODUCT LOOK LIKE?)
  •      FEATURES                 (WHAT DOES THE PRODUCT DO?)
  •      PERCEIVED QUALITY   (WHAT IS THE REPUTATION OF A COMPANY OR ITS PRODUCTS?)
DIMENSIONS OF SERVICE QUALITY
  •      RELIABILITY
  •      RESPONSIVENESS
  •      COMPETENCE
  •      COURTESY
  •      COMMUNICATION
  •      CREDIBILITY
  •      SECURITY
THREE ASPECTS OF QUALITY (FIGURE)
THE THREE ASPECTS OF QUALITY AND THEIR LINKAGES WITH EACH OTHER HAVE BEEN DEPICTED IN THE FIGURE BELOW:


EVOLUTION OF QUALITY 

DURING THE EARLY DAYS OF MANUFACTURING, AN OPERATIVE'S WORK WAS INSPECTED AND A DECISION MADE WHETHER TO ACCEPT OR REJECT IT. THE FOCUS WAS JUST TO ACCEPT OR REJECT THE PRODUCTS BASED ON THE SPECIFICATION. NO EFFORT WAS MADE ON DEFECT PREVENTION.

IN THE 1920'S STATISTICAL THEORY BEGAN TO BE APPLIED EFFECTIVELY TO QUALITY CONTROL, AND IN 1924 SHEWHART MADE THE FIRST ATTEMPT OF A MODERN CONTROL CHART. HIS WORK WAS LATER DEVELOPED BY DEMING AND THE EARLY WORK OF SHEWHART, DEMING, DODGE AND ROMIG CONSTITUTES MUCH OF WHAT TODAY COMPRISES THE THEORY OF STATISTICAL PROCESS CONTROL (SPC). HOWEVER, THERE WAS LITTLE USE OF THESE TECHNIQUES IN MANUFACTURING COMPANIES UNTIL THE LATE 1940'S.
IN THE EARLY 1950'S, QUALITY MANAGEMENT PRACTICES DEVELOPED RAPIDLY IN JAPANESE PLANTS, AND BECOME A MAJOR THEME IN JAPANESE MANAGEMENT PHILOSOPHY, SUCH THAT, BY 1960, QUALITY CONTROL AND MANAGEMENT HAD BECOME A NATIONAL PREOCCUPATION. 

IN 1969, FEIGENBAUM PRESENTED A PAPER IN A CONFERENCE AND THE TERM “TOTAL QUALITY” WAS USED FOR THE FIRST TIME, AND REFERRED TO WIDER ISSUES SUCH AS PLANNING, ORGANIZATION AND MANAGEMENT RESPONSIBILITY. ISHIKAWA PRESENTED A PAPER EXPLAINING HOW “TOTAL QUALITY CONTROL” IN JAPAN WAS DIFFERENT, IT MEANING “COMPANY WIDE QUALITY CONTROL”, AND DESCRIBING HOW ALL EMPLOYEES, FROM TOP MANAGEMENT TO THE WORKERS, MUST STUDY AND PARTICIPATE IN QUALITY CONTROL. COMPANY WIDE QUALITY MANAGEMENT WAS COMMON IN JAPANESE COMPANIES BY THE LATE 1970'S.

TOTAL QUALITY MANAGEMENT (TQM) CAME INTO EXISTENCE IN 1980 BY THE WESTERN WORLD. TQM IS NOW PART OF A MUCH WIDER CONCEPT THAT ADDRESSES OVERALL ORGANIZATIONAL PERFORMANCE AND RECOGNIZES THE IMPORTANCE OF PROCESSES.

AS WE MOVE INTO THE 21ST CENTURY, TQM HAS DEVELOPED IN MANY COUNTRIES INTO HOLISTIC FRAMEWORKS, AIMED AT HELPING ORGANIZATIONS ACHIEVE EXCELLENT PERFORMANCE, PARTICULARLY IN CUSTOMER AND BUSINESS RESULTS. 

HISTORICAL ASPECTS OF QUALITY
     EDWARD DEMING
    • POSTULATED STATISTICAL QUALITY CONTROL PRINCIPLES
    • 14 POINTS OF QUALITY MANAGEMENT
    • THESE PRINCIPLES SUCCESSFULLY ADAPTED BY JAPANESE MANUFACTURES 
     WILLIAM CROSBY
    • EMPHASIZED HUMANISTIC BEHAVIORAL ASPECTS OF QUALITY IMPROVEMENT
    • BECOMING MORE IMPORTANT NOW
     JOSEPH JURAN'S QUALITY TRILOGY
A. QUALITY PLANNING
    • SET OF QUALITY GOALS
    • SET PLANS FOR OPERATIONS BASED ON THESE GOALS 
B. QUALITY CONTROL
    • RESPONSIBLE FOR MEETING QUALITY GOALS
    • PREVENT ADVERSE CHANGES
    • SET AND OBSERVE
      •        PERFORMANCE MEASURES
      •        COMPARE WITH INDUSTRY STANDARDS
      •        BENCHMARKING  
C. QUALITY IMPROVEMENT
    • MOVING FROM CURRENT LEVEL TO THE NEXT HIGHER LEVEL
ORGANIZE TEAMS, TRAIN OPERATORS TO IDENTIFY AND CORRECT QUALITY PROBLEMS.

QUALITY CONTROL
INSPECTION, ANALYSIS AND ACTION APPLIED TO A PORTION OF THE PRODUCT IN A MANUFACTURING OPERATION TO ESTIMATE OVERALL QUALITY OF THE PRODUCT AND DETERMINE WHAT, IF ANY, CHANGES MUST BE MADE TO ACHIEVE OR MAINTAIN THE REQUIRED LEVEL OF QUALITY.
QUALITY CONTROL OF A PRODUCT CAN BE VIEWED AS A SYSTEM WHICH ENSURES
  •      PROPER PLANNING
  •      RIGHT DESIGN
  •      PROPER EQUIPMENT
  •      PROPER INSPECTION
  •      CORRECTIVE ACTION 
TRADITIONAL CONCEPT: QUALITY CONTROL HAS BEEN CONCERNED WITH DETECTING POOR QUALITY IN MANUFACTURING PRODUCTS AND TAKING CORRECTIVE ACTION TO ELIMINATE IT.

MODERN CONCEPT: QUALITY CONTROL ENCOMPASSES A BROADER SCOPE OF ACTIVITIES INCLUDING:
  •      ROBUST DESIGN
  •      STATISTICAL PROECESS CONTROL
TWO ASPECTS OF QUALITY CONTROL
  •      OFF-LINE QUALITY CONTROL
  •      ON-LINE QUALITY CONTROL



OFF-LINE QUALITY CONTROL ENCOMPASSES ALL THOSE ACTIVITIES THAT ARE PERFORMED BEFORE THE ACTUAL MANUFACTURING OF THE PRODUCT OR SERVICE RENDERED

ON-LINE QUALITY CONTROL ACTIVITIES START FROM THE MANUFACTURING OF A PRODUCT TILL IT GOES IN THE FIELD AND ALSO AFTER SALE SERVICE. THE QUALITY TOOLS USED IN THE PHASE ARE STATISTICAL PROCESS CONTROL AND ACCEPTANCE SAMPLING 

IMPORTANCE OF QUALITY CONTROL
  • QUALITY IS VITAL IN ALL AREAS OF BUSINESS, INCLUDING THE PRODUCT DEVELOPMENT AND PRODUCTION FUNCTIONS
  • COST OF QUALITY IS ULTIMATELY REDUCED BY INVESTING MONEY UP FRONT IN QUALITY DESIGN AND DEVELOPMENT
  • TYPICAL COSTS OF POOR QUALITY INCLUDE DOWNTIME, REPAIR COSTS, REWORK, AND EMPLOYEE TURNOVER
BENEFITS OF QUALITY CONTROL
A WELL-ESTABLISHED, COMMITTED QUALITY SYSTEM IN AN ORGANIZATION WILL RENDER THE FOLLOWING BENEFITS
  •      IMPROVEMENT IN THE QUALITY OF PRODUCT
  •      HIGHER PRODUCTIVITY
  •      COST REDUCTION
  •      CONTINUOUS IMPROVEMENT IN QUALITY OF PRODUCT
QUALITY COSTS
QUALITY COSTS COMPONENTS ARE
  •      PREVENTION COSTS
  •      APPRAISAL COSTS
  •      INTERNAL FAILURE COSTS
  •      EXTERNAL FAILURE COSTS 
PREVENTION COSTS
THESE COSTS ARE INCURRED IN THE PROCESS OF TRYING TO PREVENT DEFECTS AND ERRORS FROM OCCURRING. THE COSTS INVOLVED ARE FOR
  •      PLANNING THE QUALITY CONTROL PROCESS
  •      TRAINING AND EDUCATING
  •      DESIGNING THE PRODUCT FOR QUALITY
  •      DESIGNING THE PRODUCTION SYSTEM FOR QUALITY
  •      PREVENTIVE MAINTENANCE 
APPRAISAL COSTS (DETECTION COSTS)
THESE ARE THE COSTS OF DETERMINING THE CURRENT QUALITY OF THE PRODUCTION SYSTEM OR INSPECTION AND TESTING THROUGH SAMPLING. THE COSTS INVOLVED ARE FOR
  •      MEASURING AND TESTING PARTS AND MATERIALS
  •      CONDUCTING STATISTICAL PROCESS CONTROL
  •      RECEIVING INSPECTION
  •      REPORTING ON QUALITY
INTERNAL FAILURE COSTS
THESE COSTS ARE INCURRED WHEN DEFECTS AND ERRORS ARE FOUND BEFORE SHIPMENT OR DELIVERY TO THE CUSTOMER. THE COSTS INVOLVED ARE FOR
  •      LABOR AND MATERIALS THAT ARE SCRAPPED
  •      REWORKING AND RETESTING TO CORRECT DEFECTS
  •      LOST PROFITS

EXTERNAL FAILURE COSTS
THESE ARE THE COSTS OF TRYING CORRECT DEFECTS AND ERRORS AFTER RECEIPT BY THE CUSTOMER.
THE COSTS INVOLVED ARE FOR
  •      QUICK RESPONSE TO COMPLAINTS
  •      ADJUSTMENTS TO CORRECT THE PROBLEM
  •      LOST GOODWILL
  •      WARRANTIES AND INSURANCE
  •      SETTLEMENTS OF LAWSUITS
  •      PRODUCT RECALL
COQ = PREVENTION COST + APPRAISAL COST + INTERNAL FAILURE COST + EXTERNAL FAILURE COST

SEVEN BASIC QUALITY CONTROL TOOLS
  •      HISTOGRAMS
  •      RUN CHARTS
  •      PARETO CHARTS
  •      FLOW CHARTS
  •      SCATTER DIAGRAMS
  •      CAUSE AND EFFECT DIAGRAMS
  •      CONTROL CHARTS
HISTOGRAMS
A HISTOGRAM IS A BAR GRAPH USED TO PRESENT FREQUENCY DATA. HISTOGRAMS PROVIDE AN EASY WAY TO EVALUATE THE DISTRIBUTION OF DATA OVER DIFFERENT CATEGORIES

STEPS IN MAKING HISTOGRAM

  • DEFINE CATEGORIES FOR DATA
  • COLLECT DATA, SORT THEM INTO THE CATEGORIES
  • COUNT THE DATA FOR EACH CATEGORY
  • DRAW THE DIAGRAM. EACH CATEGORY FINDS ITS PLACE ON THE X-AXIS.
  • THE BARS WILL BE AS HIGH AS THE VALUE FOR THE CATEGORY. THE HISTOGRAM REVEALS THE FOLLOWING ABOUT THE PROCESS
    • CENTERING OF THE PROCESS DATA: THE CENTERING OF THE DATA PROVIDES INFORMATION ON THE PROCESS ABOUT SOME MEAN.
    • SPREAD OF THE DATA: HISTOGRAM WIDTH DEFINES THE VARIABILITY OF THE PROCESS ABOUT THE MEAN
    • SHAPE OF THE HISTOGRAM: BELL OR NORMAL SHAPED HISTOGRAM IS EXPECTED. OTHER THAN NORMAL OR BELL SHAPE MEANS SOMETHING WRONG WITH THE PROCESS RESPONSIBLE FOR POOR QUALITY.

    LIMITATIONS OF THE HISTOGRAMS 
    • THE RANDOMNESS IN THE DATA IN DEVELOPING HISTOGRAM LOSSES THE VITAL INFORMATION
    • AS DATA ARE NOT REPRESENTED IN ORDER, THE TIME-DEPENDENT OR TIME-RELATED TRENDS IN THE PROCESS MAY NOT BE REVEALED

    RUN CHART
    RUN CHARTS ARE BETTER OPTION OVER HISTOGRAMS AS THEY OVERCOME THE LIMITATIONS OF THE HISTOGRAMS. A RUN CHART REPRESENTS CHANGE IN MEASUREMENT OVER A SEQUENCE OR TIME. RUN CHARTS ARE USED TO DETERMINE CYCLIC EVENTS AND THEIR AVERAGE VALUES. 

    STEPS IN MAKING RUN CHARTS
    1. ARRANGE DATA WITH TIME SEQUENCE
    2. PLOT THE DATA IN ORDER
    3. INTERPRETING DATA, RUN CHART REVEALS THE FOLLOWING ABOUT THE PROCESS
    • RUN CHARTS DISPLAY PROCESS PERFORMANCE OVER TIME TRENDS, CYCLES, AND LARGE
    • VARIATIONS ARE CLEARLY VISIBLE
    • AN AVERAGE LINE MAY BE ADDED TO A RUN CHART TO CLARIFY MOVEMENT OF THE DATA AWAY FROM THE PROCESS AVERAGE 

TWO TYPES OF MISTAKE NORMALLY PEOPLE COMMIT WHILE INTERPRETING THE RUN CHART
  1. CYCLE OR TREND EXIST BUT ACTUALLY IT IS NOT
  2. CYCLE OR TREND DOES NOT EXIST BUT ACTUALLY THEY EXIST
TO OVERCOME THIS PROBLEM A THUMB RULE IS TO LOOK AT THE DATA FOR A LONG PERIOD OF TIME

PARETO CHART
VILFREDO PARETO AN ITALIAN ECONOMIST PROVIDED A GOLDEN RULE WHICH FITS INTO MANY MANAGERIAL SITUATIONS. THE GOLDEN RULE HE NOTICED IS “WEALTH IS CONCENTRATED IN A FEW PEOPLE”. PARETO PRINCIPLE : “THE MAJORITY OF WEALTH IS HELD BY A DISPROPORTIONATELY SMALL SEGMENT OF THE POPULATION”. THIS PRINCIPLE IS ALSO KNOWN AS 80 / 20 PRINCIPLE. 80% OF THE PROBLEMS ARE CAUSED BY 20% OF THE CAUSES.

JURAN HAS NOTICED THAT THIS PRINCIPLE APPLIES TO QUALITY IMPROVEMENT AS WELL. ACCORDING TO JURAN THE PROBLEMS THAT OCCUR A FEW ARE VERY FREQUENT WHILE OTHER IMPORTANT PROBLEMS OCCUR SELDOM. HE GIVEN THE PHRASE AS “VITAL FEW AND THE TRIVIAL MANY”.

PARETO CHARTS ARE USED TO APPLY THE 80/20 RULE OF JOSEPH JURAN WHICH STATES THAT 80% OF THE PROBLEMS ARE THE RESULT OF 20% OF THE PROBLEMS. A PARETO CHART CAN BE USED TO IDENTIFY THAT 20% ROOT CAUSES OF PROBLEM.

STEPS IN MAKING PARETO CHARTS
THE PARETO CHART OF THE FOLLOWING PROBLEM IS GIVEN BELOW:

FLOW CHART
A FLOW CHART IS WAY OF REPRESENTING A PROCEDURE USING SIMPLE SYMBOLS AND ARROWS. A FLOW CHART SHOWS THE ACTIVITIES IN A PROCESS AND THE RELATIONSHIPS BETWEEN THEM. A FLOW CHART LETS A PROCESS BE UNDERSTOOD EASILY. IT ALSO DEMONSTRATE THE RELATIONSHIPS BETWEEN THE ELEMENTS OF THE PROCESS.

STEPS IN MAKING FLOW CHARTS
  • DETERMINE THE PROCESS NEED TO BE REPRESENTED BY FLOW CHART
  • LIST DOWN THE SEQUENCE OF OPERATION AND OTHER DETAILS
  • START AT A CERTAIN POINT AND GO THEN STEP BY STEP
  • USING FLOW CHART SYMBOLS
  • WRITE THE TITLES TO EACH ELEMENT 
SCATTER DIAGRAM
SCATTER DIAGRAM IS A STATISTICAL CHART WHICH SHOWS A TREND IN A SERIES OF DATA. IT DEMONSTRATES CORRELATIONS BETWEEN VALUES.

STEPS IN MAKING SCATTER DIAGRAM
  • PLOT THE DATA POINTS
  • DRAW TREND LINE BY FITTING A STRAIGHT LINE
  • UPWARD LINE SHOWS THE POSITIVE TREND(X INCREASES AND Y INCREASES)
DOWNWARD LINE SHOWS THE NEGATIVE TREND  (X INCREASES AND Y DECREASES)


CAUSE AND EFFECT DIAGRAMS (STEPS)
A CAUSE AND EFFECT DIAGRAM SHOWS THE RELATIONSHIP BETWEEN EFFECT AND THE CATEGORIES OF THEIR CAUSES. THE DIAGRAM LOOK LIKE A FISHBONE IT IS THEREFORE ALSO CALLED FISH-BONE DIAGRAM. CAUSE AND EFFECT DIAGRAM ENABLES A TEAM TO FOCUS ON THE CONTENT OF A PROBLEM. IT HELPS TO PROVIDE A COMPREHENSIVE PICTURE OF THE PROBLEM AND THE ROOT CAUSES OF THE SAME.
STEPS IN MAKING CAUSE AND EFFECT DIAGRAM
  • DETERMINE THE EFFECT OR PROBLEM
  • CATEGORIZE THE POSSIBLE CAUSES
  • DESCRIBE THE POSSIBLE CAUSES
DRAW AN ARROW HORIZONTALLY POINTING TO AN EFFECT

  1. DRAW FOUR OR MORE BRANCHES OFF THE LARGE ARROW TO REPRESENT MAIN CATEGORIES OF POTENTIAL CAUSES. TYPICAL CATEGORIES ARE MAN, MACHINERY, METHODS, AND MATERIALS.
  2. SECONDARY CAUSES CAN BE LISTED ON BRANCHES OFF THE CATEGORY BRANCHES
  3. ADDITIONAL CAUSES CAN BE BRANCHED OFF THE SECONDARY CAUSES.
  4. ADDITIONAL CAUSES, IF ANY, MAY FURTHER BE BRANCHED OFF THE TERTIARY CAUSES. THE PROCESS GOES ON TILL ALL THE POSSIBLE CAUSES HAVE BEEN EXPLORED. 


CONTROL CHART
CONTROL CHARTS ARE STATISTICAL TOOL, SHOWING WHETHER A PROCESS IS IN CONTROL OR NOT.  IT IS A GRAPHICAL TOOL FOR MONITORING THE ACTIVITIES OF AN ONGOING PROCESS ALSO REFERRED AS SHEWHART CONTROL CHARTS.

STEPS IN MAKING CONTROL CHART
  • DEFINE UPPER LIMIT, LOWER LIMIT AND CENTER LINE
  • DRAW CHART
  • PLOT THE DATA POINTS INTO CHART
  • INTERPRET THE CONTROL CHART 

CONTROL CHARTS
CONTROL CHARTS ARE STATISTICAL TOOL, SHOWING WHETHER A PROCESS IS IN CONTROL OR NOT.  IT IS A GRAPHICAL TOOL FOR MONITORING THE ACTIVITIES OF AN ONGOING PROCESS ALSO REFERRED AS SHEWHART CONTROL CHARTS. CONTROL CHARTS ARE USED FOR PROCESS MONITORING AND VARIABILITY REDUCTION.

BEFORE DISCUSSING AND CALCULATING THE LIMITS ETC. OF CONTROL CHARTS, IT IS NECESSARY TO UNDERSTAND CAUSES OF VARIATIONS PRESENT IN THE SYSTEM. VARIABILITY IS AN INHERENT FEATURE OF EVERY PROCESS.  PRODUCTION DATA ALWAYS HAVE SOME VARIABILITY.  

CAUSES OF VARIATIONS
TWO TYPES OF CAUSES ARE PRESENT IN THE PRODUCTION SYSTEM
  • SPECIAL CAUSES: VARIATION DUE TO IDENTIFIABLE FACTORS IN THE PRODUCTION PROCESS. EXAMPLES OF SPECIAL CAUSES INCLUDE: WRONG TOOL, WRONG PRODUCTION METHOD, IMPROPER RAW MATERIAL, OPERATOR'S SKILL, WRONG DIE ETC. CONTROL OF PROCESS IS ACHIEVED THROUGH THE ELIMINATION OF SPECIAL CAUSES. ACCORDING TO DEMING, ONLY 15% OF THE PROBLEMS ARE DUE TO THE SPECIAL CAUSES. SPECIAL CAUSES OR ALSO SOMETIMES REFERRED AS ASSIGNABLE CAUSES
  •  COMMON CAUSES: VARIATION INHERENT IN THE PROCESS. IMPROVEMENT OF PROCESS IS ACCOMPLISHED THROUGH THE REDUCTION OF COMMON CAUSES AND IMPROVING THE SYSTEM. ACCORDING TO DEMING, 85% OF THE PROBLEMS ARE DUE TO THE COMMON CAUSES.
ASSIGNABLE CAUSES ARE CONTROLLED BY THE USE OF STATISTICAL PROCESS CHARTS.

STEPS IN CONSTRUCTING A CONTROL CHART
  •      DCIDE WHAT TO MEASURE OR COUNT
  •      COLLECT THE SAMPLE DATA
  •      PLOT THE SAMPLES ON A CONTROL CHART
  •      CALCULATE AND PLOT THE CONTROL LIMITS ON THE CONTROL CHART
  •      DETERMINE IF THE DATA IS IN CONTROL
  •      IF NON-RANDOM VARIATION IS PRESENT, DISCARD THE DATA (FIX THE PROBLEM) AND RECALCULATE THE CONTROL LIMITS
  •      WHEN DATA ARE WITH IN THE CONTROL LIMITS WE LEAVE THE PROCESS ASSUMING THERE ARE ONLY CHANCE CAUSES PRESENT
A PROCESS IS IN CONTROL IF
  •      NO SAMPLE POINTS OUTSIDE CONTROL LIMITS
  •      MOST POINTS NEAR PROCESS AVERAGE OR CENTER LINE
  •      ABOUT EQUAL NUMBER OF POINTS ABOVE AND BELOW THE CENTER LINE
  •      SAMPLE POINT ARE DISTRIBUTED RANDOMLY
TYPES OF PROCESS DATA
TWO TYPES OF PROCESS DATA:
  1. VARIABLE: CONTINUOUS DATA. THINGS WE CAN MEASURE. EXAMPLE INCLUDES LENGTH, WEIGHT, TIME, TEMPERATURE, DIAMETER, ETC.
  2. ATTRIBUTE: DISCRETE DATA. THINGS WE COUNT. EXAMPLES INCLUDE NUMBER OR PERCENT DEFECTIVE ITEMS IN A LOT, NUMBER OF DEFECTS PER ITEM ETC.
TYPES OF CONTROL CHARTS: THE CLASSIFICATION OF CONTROL CHARTS DEPEND UPON THE TYPE OF DATA.
  1. VARIABLE CHARTS: ARE MEANT FOR VARIABLE TYPE OF DATA. X BAR AND R CHART, X BAR AND SIGMA CHART, CHART FOR THE INDIVIDUAL UNITS
  2. ATTRIBUTE CHATS  : ARE MEANT FOR ATTRIBUTE TYPE OF DATA. P CHART, NP CHART, C CHART, U CHART, U CHART 
CONTROL CHARTS FOR THE VARIABLE TYPE OF DATA (X BAR AND R CHARTS)
IN THE X BAR CHART THE SAMPLE MEANS ARE PLOTTED IN ORDER TO CONTROL THE MEAN VALUE OF A VARIABLE. IN R CHART, THE SAMPLE RANGES ARE PLOTTED IN ORDER TO CONTROL THE VARIABILITY OF A VARIABLE
CENTRE LINE, UPPER CONTROL LIMIT, LOWER CONTROL LIMIT FOR X BAR AND R CHARTS ARE CALCULATED. THE FORMULAE USED ARE AS FOLLOWING: 

R CHART
ALL THE DATA ARE WITHIN THE LCL AND UCL IN R CHART. HENCE VARIABILITY OF THE PROCESS DATA IS NOT AN ISSUE TO WORRY.

CONTROL CHARTS FOR ATTRIBUTE TYPE DATA (P, C, U CHARTS)
P-CHARTS CALCULATES THE PERCENT DEFECTIVE IN SAMPLE. P-CHARTS ARE USED WHEN OBSERVATIONS CAN BE PLACED IN TWO CATEGORIES SUCH AS YES OR NO, GOOD OR BAD, PASS OR FAIL ETC.

C-CHARTS COUNTS THE NUMBER OF DEFECTS IN AN ITEM. C-CHARTS ARE USED ONLY WHEN THE NUMBER OF OCCURRENCE PER UNIT OF MEASURE CAN BE COUNTED SUCH AS NUMBER OF SCRATCHES, CRACKS ETC.

U-CHART COUNTS THE NUMBER OF DEFECT PER SAMPLE. THE U CHART IS USED WHEN IT IS NOT POSSIBLE TO HAVE A SAMPLE SIZE OF A FIXED SIZE.

FOR ATTRIBUTE CONTROL CHARTS, THE ESTIMATE OF THE VARIABILITY OF THE PROCESS IS A FUNCTION OF THE PROCESS AVERAGE.

CENTRE LINE, UPPER CONTROL LIMIT, LOWER CONTROL LIMIT FOR C, P, AND U CHARTS ARE CALCULATED. THE FORMULAE USED ARE AS FOLLOWING:


SAMPLE DATA AT S.N 16 , 18, AND 20  ARE ABOVE THE UCL. EFFORTS MUST BE MADE TO FIND THE SPECIAL CAUSES AND REVISED LIMITS ARE ADVISED TO CALCULATE AFTER DELETING THESE DATA. THERE IS IMPORTANT OBSERVATION THAT IS CLEARLY VISIBLE FROM THE DATA POINTS THAT THERE IS AN INCREASING TREND IN THE AVERAGE PROPORTION DEFECTIVES BEYOND SAMPLE NUMBER15 ALSO, DATA SHOW CYCLIC PATTERN. PROCESS APPEARS TO BE OUT OF CONTROL AND ALSO THERE IS A STRONG EVIDENCE THAT DATA ARE NOT FROM INDEPENDENT SOURCE.



PROCESS CAPABILITY


PROCESS STABILITY
A PROCESS OUTPUT IS CONSIDERED STABLE WHEN IT CONSISTS OF ONLY COMMON-CAUSE VARIATION AND HAS THE REPRODUCIBILITY OVER A LONG PERIOD OF TIME. COMMON-CAUSE VARIATION ORIGINATES FROM THE BASIC ELEMENTS OF A MANUFACTURING PROCESS. WHICH ARE 5 MS:
  •      MACHINE,
  •      MAN (OPERATOR),
  •      MATERIAL,
  •      METHOD OF WORK, AND
  •      MEASUREMENT SYSTEM
THE PROCESS PARAMETERS CAN NOT BE CORRECTLY ESTIMATED FOR AN UNSTABLE PROCESS BECAUSE OF THE FOLLOWING REASONS.
  •      NO WELL DEFINED OUTPUT DISTRIBUTION
  •      MISLEADING DECISIONS
  •      NO USEFUL ESTIMATION OF PROCESS CAPABILITY
  •      NO USEFUL PURPOSE FOR PROCESS IMPROVEMENT
PROCESS CAPABILITY
PREREQUISITES FOR PROCESS CAPABILITY IS TO ESTIMATION OF PROCESS AVERAGE AND PROCESS STANDARD DEVIATION.
PROCESS CAPABILITY FOR BILATERAL SPECIFICATION

A PROCESS PRODUCING A CHARACTERISTIC WITH A BILATERAL SPECIFICATION MEETS THE MINIMUM REQUIREMENT OF CAPABILITY WHEN IT IS STABLE, AND HAS NO MORE THAN 0.135 PERCENT OF ITS OUTPUT FOR THIS CHARACTERISTIC OUTSIDE EITHER SPECIFICATION LIMIT.
PROCESS CAPABILITY FOR UNILATERAL SPECIFICATION

A  PROCESS  PRODUCING A CHARACTERISTIC WITH A UNILATERAL SPECIFICATION  MEETS THE MINIMUM REQUIREMENT OF  CAPABILITY  WHEN IT IS STABLE, AND  HAS NO MORE  THAN 0.135 PERCENT OF ITS OUTPUT FOR THIS CHARACTERISTIC OUTSIDE THE SINGLE SPECIFICATION LIMIT.


WHY PROCESSES FAIL? 
  •       PROCESS VARIATION (SPREAD) IS TOO LARGE
  •       PROCESS AVERAGE IS NOT PROPERLY CENTERED
  •       PROCESS AVERAGE IS NOT PROPERLY CENTERED AND PROCESS VARIATION IS TOO LARGE

1. CAPABILITY INDEX, CP FOR BILATERAL SPECIFICATION 
     WHEN PROCESS AVERAGE IS EQUAL TO NOMINAL VALUE:
             CP = (USL – LSL) / 6S 
     WHEN PROCESS AVERAGE IS NOT EQUAL TO NOMINAL VALUE:
            CP = MINIMUM (M - LSL/ 3S   , USL- M / 3S )
2. CAPABILITY INDEX, CP FOR UNILATERAL SPECIFICATION 
     IN CASE OF USL :
CP = MAXIMUM (USL- M / 3S   , USL-X / 3S ) 
     IN CASE OF LSL :
CP = MAXIMUM (M-LSL / 3S   , X - USL / 3S )

FLOW CHART FOR CONDUCTING A PROCESS CAPABILITY  



References : -  www.nptel.iitm.ac.in/



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