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  • 1Planificacin Agregada

    Topic 6Quality Control

    Operations Management

    Economy and Business Organization Department

    Quality Control 2

    Index

    Quality control Seven Ishikawas tools

    Pareto chart / Cause-and-effect diagram / Check sheets / Histogram / Scatter diagram / Control chart / Stratification

    Statistical Process Control (SPC) Analysis of the process capacity Acceptance sample Sampling by attributes Sampling plan. Characteristic curve. Bibliography

  • 2Quality Control 3

    Quality control

    Evaluates the results of the process by comparing to the ideal results. If there is any difference between them, then the objective is to minimise it.

    Apart from separating correct products (the ones that comply specifications) from defective products that must be redone, includes the prevention concept (actions to guarantee the expected results).

    All the efforts dedicated to obtain products or services that comply design specifications at minimum cost.

    Quality Control 4

    Pareto chart Cause-and-effect diagrams Check sheets Histograms Scatter diagram Stratification Control chart

    Ishikawas basic tools

  • 3Quality Control 5

    Pareto chart

    Based on the idea that, in general, most defects in an article can be attributed to a reduced number of causes (Pareto Law 20-80)

    Classifies few vital causes from the rest of trivial causes.

    Pareto diagrams identify the causes of a quality problem rapidly and easily.

    Quality Control 6

    Pareto chart. Example

    A company produces an article which presents several manufacturing defects. The objective is to remove them. Management wishes to know which are the causes of most defective items.

    Defect type

    Quantity of defective

    articles

    Accumulated quantities

    % defective products

    %

    accumulated

    Grated surface 198 198 66,00 66,00 Arm rupture 53 251 17,67 83,67 Spots 28 279 9,33 93,00 Adjustment 11 290 3,67 96,67 Tension 2 292 0,67 97,33 Others 8 300 2,67 100,00 Total 300 100,00

  • 4Quality Control 7

    Pareto chart. Example

    0

    50

    100

    150

    200

    250

    300

    Ral

    lado

    supe

    rfici

    al

    Rup

    tura

    sbr

    azo

    Man

    chas

    Ajus

    te

    Tens

    in

    Otro

    s

    Causes

    Qua

    ntity

    of d

    efec

    tive

    prod

    .

    0,00

    20,00

    40,00

    60,00

    80,00

    100,00

    % a

    ccum

    ulat

    ed

    If the two main causes of the problem are removed (grated surface and arm rupture), 84% of defective articles are avoided.

    Quality Control 8

    Cause-and-effect diagram

    Cause-and-effect diagram, also known as Ishikawa diagramor fishbone, is used to classify and clear the causes that originate an effect.

    It is necessary to identify and face the causes (and NOT the effects) to solve a problem.

    The basic structure of these diagrams is a central arrow and thestudied effect is placed on the right. Consequently, firstly thequality problem must be defined and the effect that measures it.Then the causes are classified.

  • 5Quality Control 9

    Cause-and-effect diagram

    The causes are placed tidily in the main branches:

    Effect

    Machines Personnel

    Methods Materials

    Inside these main branches, causes are placed in little branches. A brainstorming session can be performed previously to identify

    the causes.

    Quality Control 10

    Variable dimension

    Machines Personnel

    Methods Materials

    Stability

    Operation

    Inspection

    Tools

    Abrasion

    Deformation

    Motivation

    Concentration

    Abilities

    Training

    Experience Tiredness

    Health

    Illness

    Orden

    PositionAdjustm.

    AngleWork

    Variety

    Procedure

    Materials quality

    Raw material

    Storage

    ShapeDiameter

    Components

    Final comment: the identified and classified causes are potential causes. This diagram is the starting point to verify and confirm the real causes.

    Cause-and-effect diagram. Example

  • 6Quality Control 11

    Check sheets

    Check sheets are printed sheets that allow data collection in a simple and precise way so that collection tasks are easier for the operators.

    The fundamental objectives are: To ease data collection and organize data for further analysis.

    There are different type of templates according to its application.

    Quality Control 12

    Check sheets for defective articles

    Theyre used to detect the type of defects and their frequency percentages in defective products in order to reduce them.

    Cdigo del Producto: 25312-A

    Proceso: inspeccin final

    Plantilla de inspeccin

    Defectos: rallado, incompleto, deformado

    Fecha: 12-marzo-97Operario:Lote:

    I I I I

    Tipo

    Rallado

    Fisuras

    Deformado

    Incompleto

    Otros

    Total

    I I I I I I I I

    I I I II I I I

    I I I II I I I

    I I I I

    I I I I

    I I I I

    I I I II I I I

    I I I

    I I I I

    I I I

    I I I I

    I I I I I I I I

    I I 37

    23

    5

    8

    14

    Total 87Observaciones:

  • 7Quality Control 13

    Sheets for defects location

    Sketches of the manufactured piece where defects are located. They allow to detect if defects are always placed in the same place.

    Quality Control 14

    Check sheets to control the distribution of the production process

    Theyre used to collect data of continuous variables such as weight, diameter, volume, Then, it is possible to draw an histogram to study the distribution of the production process, and calculate the average and dispersion.

    Cdigo del Producto: Proceso: Lote:

    Plantilla de inspeccinFecha:Medidor:Observaciones:

    I I I I

    Dimensin

    310-319320-329330-339340-349350-359

    Total

    I I I II I I II I I II I I I I I I I

    I I I II I I II I I I

    I I I I

    I I I I

    I I I I

    I

    I I I I I I I

    I I I I

    I I I I

    I I I I

    I I 2

    211723

    7

    I I I I360-369370-379380-389390-399400-410

    I I I I I I I I

    I I I II I I I

    I I I II I I I

    I I I II I I I

    I I I II I I II I I I

    I I I

    I I I I

    I I I I I

    I I I I I I I I

    I I

    I I I I

    I I I I

    I I I

    I I

    I I I I

    I I

    I

    38

    31178

    34

    420-429430-439

    I I I II I I I

    I I I 31

    Frecuencias10 30 35 405 15 20 25 45

    300-309

  • 8Quality Control 15

    Histogram

    An histogram is a graphical representation of the distribution of data. Data is organised to study frequency of ocurrence.

    Example: Consider 100 measurements of the diameter of a cylindrical

    piece. The number of measurements n should be (at least) between 50 and 100 to study a certain characteristic.

    Firstly, data is divided in 10 groups of 10 measurements. For each set of data, we determine the maximum and minimum values.

    Quality Control 16

    Histogram. Example

    Data Max Min

    7,38 7,39 7,41 7,19 7,26 7,52 7,39 7,20 7,41 7,40 7,52 7,19 7,31 7,42 7,43 7,39 7,28 7,33 7,32 7,37 7,36 7,26 7,43 7,26 7,35 7,33 7,23 7,58 7,39 7,45 7,35 7,29 7,42 7,35 7,58 7,23 7,40 7,36 7,36 7,38 7,48 7,39 7,44 7,36 7,42 7,28 7,48 7,28 7,35 7,35 7,38 7,46 7,36 7,39 7,19 7,28 7,41 7,38 7,46 7,19 7,29 7,29 7,42 7,53 7,38 7,35 7,39 7,39 7,28 7,41 7,53 7,28 7,53 7,26 7,36 7,42 7,39 7,34 7,34 7,27 7,39 7,20 7,53 7,2 7,38 7,34 7,42 7,45 7,35 7,38 7,38 7,44 7,29 7,38 7,45 7,29 7,51 7,52 7,45 7,36 7,38 7,37 7,39 7,46 7,42 7,30 7,52 7,3 7,33 7,44 7,34 7,34 7,33 7,33 7,37 7,36 7,37 7,41 7,44 7,33

    Secondly, the amplitude of all data is determined: Maximum value Minimum value = 7,58 7,19 = 0,39 This value is divided by k = 10 to obtain the number of classes (number of

    groups or bars) of the graphic:

    0,050,040,03910

    0,39

    kMinMaxh

  • 9Quality Control 17

    Histogram. Example.

    The interval h is the unit to adjust the horizontal axis (bar width). In this case we consider 0,05.

    The number of classes k depends on data collection:

    The value that limits the first bar is fixed taking into account the extreme of the amplitude + half of the accuracy of collected data. In this case, the minimum value is 7,19 accuracy of real data is 0,01. Consequently, the limits of first bar is : 7,19 - 0,01/2 = 7,185

    Rest of limits of bars will be: 7,185 - 7,235; 7,235 7,285; 7,285 7,335; ...

    Data collection n Number of classes k < 50 5 7

    50 - 100 6 10 100 - 250 7 12

    > 250 10 20

    Quality Control 18

    Histogram. Example

    Then, the frequencies table must be calculated accounting data that belongs to each interval:

    Frequency table Class Limits Aver.

    n Classes value

    Frequencies Frequency 1 7,185 7,235 7,21 I I I I 4 2 7,235 7,285 7,26 I I I I 5 3 7,285 7,335 7,31 I I I I I I I I I 11 4 7,335 7,385 7,36 I I I I I I I I I I I I I I I I I I I I I I I I 29 5 7,385 7,435 7,41 I I I I I I I I I I I I I I I I I I I I I I I I I I I I 35 6 7,435 7,485 7,46 I I I I I I I I 9 7 7,485 7,535 7,51 I I I I 4 8 7,535 7,585 7,56 I I 2 9 7,585 7,635 7,61 I 1

    N = 100 These data allows to draw the histogram. In Cartesian axis, horizontal axis

    represents a quality characteristic and the vertical axis represents the frequency (number of data inside one bar).

  • 10