Ppt for Prod. n Material Mgt

8/9/2019 Ppt for Prod. n Material Mgt

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STATISTICAL

QUALITY

CONTROL

Created and presented by

SYBMS (A)


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WHAT IS STATISTICAL

QUALITY CONTROL?Quality control is a technique to monitor a

procedure with the goal of making it moreefficient

Statistical quality control (SQC) is theterm used to describe the set of statisticaltools used by quality professionals


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HOWMUCH AND HOW

OFTEN TO INSPECT?A. Consider Product Cost and

Product Volume

B. Consider Process Stability

C. Consider Lot Size


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WHERE TO INSPECT?

A.Prior to Costly Processing

B. Inbound Materials

C. Finished Products


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CATEGORIES OF SQC

They are helpful in measuring and evaluating thequality of products or services.

1. Descriptive statistics

-U

sed to describe distributions of data2. Statistical process control (SPC)

Used to determine whether a process is

performing as expected

3. Acceptance sampling Used to accept or reject entire batches by

only inspecting a few items


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CAUSES OF VARIATIONWhat prevents perfection? Process variation...

Natural Causes

1. Inherent to process

2. Random3. Cannot be controlled

4. Cannot be prevented

5. Examples

weather, accuracy ofmeasurements, capability ofmachine

Assignable Causes

1. Exogenous to process

2. Not random3. Controllable

4. Preventable

5. Examples

tool wear, Monday effect,poor maintenance


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SQC TOOLS

Check Sheet Cause-and-Effect FlowChart

Sheet


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SQC TOOLSScatter Histogram ControCharts

Diagram


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CONTROL CHARTS

Use statistical limits to identify when a sample of datafalls within a normal range of variation.Basically, a control chart is a run chart (describedearlier) that includes statistically generated upper andlower control limits.


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USES OF CONTROL CHARTS

1) To analyze data.

2) To detect any unwanted changes.

3)T

o recognize andunderstand variability.4) To determine the capability of the process.

5) To objectively identify and eliminate theroot causes .

6) To assist in the diagnosis of processproblems.

7) To determine the improvement .


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TYPES OF CONTROL CHARTS

Variable level data:

Can be measured using a continuous scale

Examples:

length, weight, time, & temperature

Attribute level data:

Can only be described by discrete characteristics

Example: defective & not defective


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BENEFITS uVariable level data:

Quick summarisation Time-saving

Easy to understand.

Persuasive evidence of

quality problems.

Attribute level data:

Sensitive leading indicators


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CONTROL CHARTS FOR

VARIABLE DATA

Mean (x-bar) charts:

Tracks the central tendency (the average

value observed) over time

Range (R) charts:

Tracks the spread of the distribution overtime (estimates the observed variation)


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CONTROL CHARTS FOR

ATTRIBUTES P-Charts:

Track the proportion defective in a sample

C-Charts:

Track the average number of defects per

unit of output


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XBAR CHART An x bar chart is used to monitor the average

value, or mean, of a process over time.

For each subgroup, the x bar value is plotted.

The upper and lower control limits define therange of inherent variation in the subgroupmeans when the process is in control.

For each of the k samples, we compute thesample mean, the sample variance, and therange .


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CASE STUDYA quality control inspector at the Cocoa Fizzsoft drink company has taken twenty-fivesamples with four observations each of thevolume of bottles filled. The data and thecomputed means are shown in the table. If

the standard deviation of the bottlingoperation is 0.14 ounces, use this informationto develop control limits of three standarddeviations for the bottling operation.


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R - CHARTS An R Chart is a control chart that is used

to monitor process variation when thevariable of interest is a quantitative

measure. These charts will allow us to see any

deviations from desired limits within thequality process and, in effect, allow thefirm to make necessary adjustments toimprove quality.


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R-CHARTSThe quality control inspector at Cocoa Fizz would liketo develop a range (R) chart in order to monitorvolume dispersion in the bottling process.


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P-CHARTS In this chart, we plot the percent of

defectives (per batch, per day, permachine, etc.)

However, the control limits in this chartare not based on the distribution of rareevents but rather on the binomialdistribution .

This chart is most applicable to situationswhere the occurrence of defectives is notrare


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P-CHARTSA production manager at a tyre manufacturing planthas inspected the number of defective tyres intwenty random samples with twenty observationseach.


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CONSTRUCTION OF P-

CHARTS


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USE OF P-CHARTS When observations can be placed into three

categories.1. Good or bad

2. Pass or fail

3. Operate or dont operate

When the data consists of multiple samples

of several observations each


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C - CHARTS In this chart, we plot the number of

defectives (per batch, per day, permachine, per 100 feet of pipe, etc.).

This chart assumes that defects of thequality attribute are rare, and the controllimits in this chart are computed based onthe Poisson distribution .


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CONSTRUCTION OF C-

CHARTS


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USE OF C-CHARTSUse only when the number of occurrences

per unit of measure can be counted; non-occurrences cannot be counted.

Scratches, chips, dents, or errors per item Cracks or faults per unit of distance

Breaks or Tears per unit of area

Bacteria or pollutants per

unit of vol

ume Calls, complaints, failures per unit of time


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CONCLUSIONA. Most technically sophisticated tool of statistical

quality control.

B. Improve the economic effectiveness .

C. Serve to illustrate the current operational conditionby providing a visual display.

D. Statistical quality control focuses on the process ofmanufacturing.


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PRESENTED BY:

Steffi Arem 04

Desai Pooja 12

Pratyusha Kapilavai 32Neha Manjrekar 41

Lisha Mathews 44

Divya Mulloli


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CONTENT :

DESAI POOJA

LISHA MATHEWS

NEHA MANJREKAR

EDITING :

PRATYUSHA KAPILAVAI

STEFFI AREMDIVYA MULLOLI

CREATION OF PPT :

PRATYUSHA KAPILAVAIDIVYA MULLOLI

DESIGNING AND FORMATING :

DESAI POOJA

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