- Dr, Shewhart of Bell Labs develops control charts
- Japanese Telecommunication industry starts to use QC methods
- Deming helps Japan with Quality Control
- Dr. Juran visit to Japan
- QC Circles were born
- Deming was “discovered” in America
- TQM started in some industries
- Malcom Baldrige National Improvement Act of 1987
- ISO 9000 Quality System Standards (1987)

# Archive | December 2013

# Statistical Process Control (SPC)

**Introduction :**1. Statistical Process Control (SPC) is a technique pioneered by Dr Walter Shewhart in the 1920’s. Dr Shewhart found that in any variation, there are two distinct sources

– Common or random cause

– Assignable or non random cause.

2.SPC control charts are useful in providing information to Process owner to advise them when and when not to adjust a process.

3.SPC was not fully utilised until after the 2nd World War when Dr Deming introduced it to the Japanese Industry.

4.One of the key factors that have enabled Japanese companies to produced extremely high quality is the use of SPC.

5.It was not until the late 70’s that the American multinationals started to use SPC widely

**Introduction:Some definition about SPC:
**a)A statistical technique that is widely used to ensure that the process is meeting standards.

b)A technique using charts and graphs to check a production process to see if any way not functioning properly which could lead to poor quality.

c)An application of scientific method to control and improve manufacturing process.

d)A term used to describe the concept of using statistical tool to assist in controlling the quality of a process.

**Benefits of SPC**

- Lower manufacturing costs
- Correct standards
- Stable processes
- Realistic Specification
- Less inspection
- Decreased problem – to – solution lead time
- Better customer relations
- Reliable measures of capability
- Improved forecasting
- Improved product quality
- Decreased cycle time

# How to Set-up a Control Chart?

1.Select appropriate type of control chart to be used

2.Gather data to establish the control chart.

- A minimum of 30 subgroups is required over a time frame as determined by the sampling plan.

3.Plot the data in time order on a Trend Chart.

4.Compute the control limits & plot them on the trend chart

5.Outliers identification & exclusion

- Exclude the Out-of Control (OOC) points or outliers for which there are verified/confirmed special causes from the chart
- Re-compute the control limits, excluding the OOC points
- If there are fewer than 30 points remaining at any time, collect more data. It’s very important that the control limits are calculated using at least 30 subgroups.

**Note:**Refer to Appendix A for Control Charts for Limited Production, i.e. < 30 subgroups.

6.Validate the computed control limits against data collected by re-plotting the control chart with data & new control limits

- Do the limits detect known problems?
- Are the limits too sensitive? Would they flag problems you do not know how to react to?

7.Use the control limits established to monitor the critical parameter identified

8.For each parameter, every machine should have a separate control chart with separately computed control limits

# What’s capability index ?

**What’s capability index ?**

“Capability index” is a quantitative value to let us know information about a process performance ! It advises us of：

- How stable a process is !
- How capable of meeting specification a process is !

**Why do we need capability index**

- To have a quick understanding of process performance
- To be able to effectively predict yield, quality level and cost
- To measure the effects of change in the system with greater speed and reliability
- To alter specification limits, it will have the data to back up its decision

**Capability index application in SPC**

# GR&R

**The purpose & scope **

1.To assist in determining the following major problems of all gauging systems used throughout the manufacturing process

- Precision
- Variation
- Acceptability

2.To furnish a comparison of the accurracy of one measuring device against another

3.Why is the accuracy of the gauge wrong?

- Measurement errors
- Incorrect usage
- Equipment variation

4.The factors of the gauge system error

- Accuracy
- Stability
- Repeatability
- Reproducibility

**Accuracy**

- The difference between the observed average of measurements and the true average of the same measurements.
- Using the highest precision measuring device to get the true average.

**Repeatability**

- The amount of variation in the gauge when the same parts and part characteristics are measured several times by the same person.

**Reproducibility**

- The amount of variation in the average of the measurements when different people use the same gauge on the same parts and part characteristics.

**Stability**

- The periodic variations that occur due to enviromental changes,power fluctuations, wear or deterioration of the gauge .

# X-MR Charts

Applications where sample size for process monitoring is n=1

- 100% automated inspection and measurement
- production rate is very slow
- repeatability of measurement is negligible
- variation within unit (e.g. roll of paper) is negligible

The X-MR Chart (or I-MR Chart) is a useful control chart if the characteristic is independently and normally distributed.

If Xi is the measurement obtained during sampling i, then the Moving Range MRi is given by

MRi = Abs{Xi – Xi-1} = | Xi – Xi-1 |

e.g. MR1 = |X1 – X0|

MR2 = |X2 – X1|

MR3 = |X3 – X2|

X0 may be set at some historical estimate of the process mean.If X0 is omitted, then MR1 is not calculated.

**The Center Line and Control Limits of a X Chart are**

**The Center Line and Control Limits of a MR Chart are**

**Example**

The viscosity of an aircraft primer is an important quality characteristic. Production is in batches, with a very slow production rate. Hence, only 1 sampling is performed per batch.The viscosity over 15 batches of primer is reviewed to determine if the process is in-statistical-control.

**Smart SPC Analyst’s—Statistic—I-MR Chart**

The moving ranges are correlated, i.e. they are dependent on the current and previous data points (X0 and Xi-1).

This correlation may induce a pattern of runs or cycles on the MR Chart.

Avoid or ignore secondary indicators of instability.

# Quality Management Process

Quality Management (QM) includes “all activities of the overall management function that determine the quality policy, objectives and responsibilities, and implements them by means such as quality planning, quality assurance, quality control and quality improvement, within the quality system”.

**The quality management process involves three main elements (PMI, 2000):**

- Quality planning – Identifying which quality standards are relevant to the project and determining how to satisfy them.
- Quality assurance – Evaluating overall project performance on a regular basis to provide confidence that the project will satisfy the relevant quality standards.
- Quality control – Monitoring specific project results to determine if they comply with relevant quality standards and identifying ways to eliminate causes of unsatisfactory performance.

# Process Capability

1.Process capability is the ability of a process to meet specifications. A process must be stable before its capability can be computed.

- Not Capable

- Capable

2.A capability index is a statistic that quantifies & describes the capability of a process

# Types of Control Charts

The quality of a product or process may be assessed by means of

- Variables : actual values measured on a continuous scale e.g. length, weight, strength, resistance, etc

- Attributes : discrete data that come from classifying units (accept/reject) or from counting the number of defects on a unit

**If the quality characteristic is measurable**

- monitor its mean value and variability (range or standard deviation)

**If the quality characteristic is not measurable**

- monitor the fraction (or number) of defectives
- monitor the number of defects

**Defectives vs Defects**

Defective or Nonconforming Unit

- a unit of product that does not satisfy one or more of the specifications for the product

–e.g. a scratched media, a cracked casing, a failed PCBA

Defect or Nonconformity

- a specific point at which a specification is not satisfied

–e.g. a scratch, a crack, a defective IC

**Shewhart Control Charts – Overview**

**Shewhart Control Charts for Variables**

# Inherent or Natural Variation

**Inherent or Natural Variation**

Due to the cumulative effect of many small unavoidable causes

A process operating with only chance causes of variation present is said to be “in statistical control”

**Special or Assignable Variation**

- Due to

a) improperly adjusted machine

b) operator error

c) defective raw material

- A process operating in the presence of assignable causes of variation is said to be “out-of-control”

**Sources of Variation**

- within unit(positional variation)

- between units(unit-unit variation)

- between lots(lot-lot variation)

- between lines(line-line variation)

- across time(time-time variation)

- measurement error(repeatability & reproducibility)