Spc 5g6c6y

Statistical Process Contol

(SPC) Presented By: Aditya Meena Abhishek Raj

What is SPC? SPC stands for

Statistical

Collection, analyzing and interpreting data

Process

An activity which transforms input into output by utilizing resources

Control

Measuring and monitoring performance

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Statistical Process Control (SPC) • SPC is a methodology for charting the process and quickly determining when a process is "out of control“. – (e.g., a special cause variation is present because something unusual is occurring in the process).

• The process is then investigated to determine the root cause of the "out of control" condition. • When the root cause of the problem is determined, a strategy is identified to correct it.

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Statistical Process Control (SPC) • The management responsible to reduce common cause or system variation as well as special cause variation. • This is done through process improvement techniques, investing in new technology, or reengineering the process to have fewer steps and therefore less variation. • Reduced variation makes the process more predictable with process output closer to the desired or nominal value.

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Rationale for SPC • The rationale for SPC is to improve product quality and simultaneously reduce costs, and to improve product image in order to successfully compete in world markets.

DATA and its Types ATTRIBUTE DATA Counted data or attribute data answers to the questions of “how many” or “how often.” VARIABLE DATA Measured data (variable data) answers to the questions like “how long,” “what volume,” “how much time” and “how far.” This data is generally measured with some instrument or device.

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The SPC steps Basic approach: • Awareness that a problem exists. • Determine the specific problem to be solved. • Diagnose the causes of the problem. • Determine and implement remedies. • Implement controls to hold the gains achieved by solving the problem. 7

SPC requires the use of statistics • Quality improvement efforts have their foundation in statistics. • SPC involves the

collection tabulation analysis interpretation presentation of numerical data.

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What are 7-QC Tools Graphs

Scatter Diagram

Pareto diagram Cause & Effect

Diagram

Histograms

Control Chart

Check Sheets

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SPC is comprised of 7 tools: • • • • • • •

Pareto diagram Histogram Cause and Effect Diagram Check sheet Process flow diagram Scatter diagram Control chart 10

Pareto diagram

(64)

60 Percent from each cause

A pareto diagram is a graph that ranks data classifications in descending order from left to right.

70

50

40 30 20 10

(13)

(10) (6)

(3)

(2)

(2)

0

Causes of poor quality 11

Pareto diagram • Sometimes a pareto diagram has a cumulative line. • This line represents the sum of the data as they are added together from left to right.

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Histogram

The histogram, graphically shows the process capability and, if desired, the relationship to the specifications and the nominal.

It also suggests the shape of the population and indicates if there are any gaps in the data.

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Histogram

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Histogram

Data Range

Frequency

0-10

1

10-20

3

20-30

6

30-40

4

40-50

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Cause-and-Effect Diagrams •Show the relationships between a problem and its possible causes. •Developed by Kaoru Ishikawa (1953) •Also known as … •Fishbone diagrams •Ishikawa diagrams

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Cause and Effect “Skeleton” Materials

Procedures

Quality Problem

People

Equipment

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Fishbone Diagram Measurement Faulty testing equipment

Incorrect specifications Improper methods

Inaccurate temperature control Dust and Dirt

Environment

Human

Machines Out of adjustment

Poor supervision Lack of concentration

Tooling problems Old / worn

Inadequate training

Quality Problem Defective from vendor Not to specifications Materialhandling problems

Materials

Poor process design Ineffective quality management

Deficiencies in product design

Process

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Cause-and-Effect Diagrams • Advantages – making the diagram is educational in itself – diagram demonstrates knowledge of problem solving team – diagram results in active searches for causes – diagram is a guide for data collection

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Cause-and-Effect Diagrams To construct the skeleton, : • For manufacturing - the 4 M’s man, method, machine, material • For service applications equipment, policies, procedures, people

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Check Sheets Check sheets explore what and where an event of interest is occurring. Attribute Check Sheet Order Types

7am-9am

9am-11am

11am-1pm

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19

1pm-3pm

3pm-5-pm

Emergency Nonemergency Rework Safety Stock Prototype Order

Other

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20

28

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Flowcharts – Graphical description of how work is done. – Used to describe processes that are to be improved.

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Flowcharts Activity

Decision

Yes

No

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Flowcharts

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Flow Diagrams

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Process Chart Symbols Operations Inspection

Transportation Delay Storage

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Scatter Diagram

. (a) Positive correlation

(b) No correlation

(c) Curvilinear relationship

The patterns described in (a) and (b) are easy to understand; however, those described in (c) are more difficult. 27

Process Control Charts Statistical technique for tracking a process and determining if it is going “out to control” • Establish capability of process under normal conditions • Use normal process as benchmark to statistically identify abnormal process behavior • Correct process when signs of abnormal performance first begin to appear • Control the process rather than inspect the product!

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Process Control Charts Upper Spec Limit Upper Control Limit

6 Target Spec

3 Lower Control Limit Lower Spec Limit 29

Process Control Charts In control

Out of control !

Look for special cause ! Back in control!

UCL

Target

LCL Time Samples

Natural variation 30

When to Take Action • A single point goes beyond control limits (above or below) • Two consecutive points are near the same limit (above or below) • A run of 5 points above or below the process mean • Five or more points trending toward either limit • A sharp change in level • Other erratic behavior

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Types of Control Charts • Attribute control charts – Monitors frequency (proportion) of defectives – p - charts

• Defects control charts – Monitors number (count) of defects per unit – c – charts

• Variable control charts – Monitors continuous variables – x-bar and R charts 32

1. Attribute Control Charts • p - charts • Estimate and control the frequency of defects in a population • Examples – Invoices with error s (ing) – Incorrect numbers (banking) – Mal-shaped pretzels (food processing) – Defective components (electronics) – Any product with “good/not good” distinctions 33

Using p-charts • Find long-run proportion defective (p-bar) when the process is in control. • Select a standard sample size n • Determine control limits UCL  p  z p LCL  p  z p

p 

p(1  p) n 34

2. Defect Control Charts • c-charts • Estimate & control the number of defects per unit • Examples – – – – –

Defects per square yard of fabric Crimes in a neighborhood Potholes per mile of road Bad bytes per packet Most often used with continuous process (vs. batch)

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Using c-charts • Find long-run proportion defective (c-bar) when the process is in control. • Determine control limits

UCL  c  z c LCL  c  z c

c  c

C: count the Number of defects 36

3. Control Charts for Variables • x-bar and R charts • Monitor the condition or state of continuously variable processes • Use to control continuous variables – Length, weight, hardness, acidity, electrical resistance

• Examples – – – –

Weight of a box of corn flakes (food processing) Departmental budget variances (ing Length of wait for service (retailing) Thickness of paper leaving a paper-making machine

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x-bar and R charts • Two things can go wrong – process mean goes out of control – process variability goes out of control

• Two control solutions – X-bar charts for mean – R charts for variability

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Range (R) Chart • Choose sample size n • Determine average in-control sample ranges R-bar where R=max-min • Construct R-chart with limits:

R  R/ n

UCL  D4 R

LCL  D3 R 39

Mean (x-bar) Chart • Choose sample size n (same as for R-charts) • Determine average of in-control sample means (x-double-bar) – x-bar = sample mean – k = number of observations of n samples

• Construct x-bar-chart with limits: x  x /k

UCL  x  A2 R

LCL  x  A2 R 40

Benefits of SPC Factual decision Waste reduction

Operator involvement

IMPROVEMENT

PERFORMANCE

Increased monitoring

COPQ reduction Customer satisfaction 41

benefits  Provides surveillance and for keeping processes in control  Signals when a problem with the process has occurred  Detects assignable causes of variation  Reduces need for inspection  Monitors process quality  Provides mechanism to make process changes and track effects of those changes  Once a process is stable, provides process capability analysis with comparison to the product tolerance

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SUMMARY • SPC using statistical techniques to  measure and analyze the variation in processes  to monitor product quality and  maintain processes to fixed targets.

• Statistical quality control using statistical techniques for  measuring and improving the quality of processes,  sampling plans,  experimental design,  variation reduction,  process capability analysis,  process improvement plans.

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SUMMARY • A primary tool used for SPC is  the control chart,  a graphical representation of certain descriptive statistics for specific quantitative measurements of the process.

• These descriptive statistics are displayed in the control chart in comparison to their "in-control" sampling distributions. • The comparison detects any unusual variation in the process, which could indicate a problem with the process. 44

Steps in Implementing SPC – The Preparation Phase • • • •

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The three phases in implementing SPC are preparation, planning and execution. The preparation phase has 3 steps: 1. Commit to SPC – top management must be committed. It requires spending money, utilizing human resources, changing the organization’s culture, hiring employees with new skills, or retaining consultants. 2. Form a SPC Committee – SPC can be delegated to a cross functional team that is tasked to oversee implementation and execution. A typical team will be composed of representatives from manufacturing, quality assurance, engineering, finance, and statistics. In a manufacturing plant, the manufacturing member should be the team leader. The function of the team will be to plan and organize the implementation for its unique application, to provide training for the operators, and to monitor and guide the execution phase. Forming the committee is top management’s responsibility. 3. Train the SPC Committee: The training must be done by an expert. The will then know enough to set objectives and to determine which process should be targeted first. Continued help from a statistics expert remains critical.

Steps in Implementing SPC – The Planning Phase • •

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•

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The planning phase includes the next 5 steps: 4. Set SPC Objectives: How will we measure success (balance sheet, customer , reduction in scrap, lower cost of quality). Objectives may be added, eliminated, or changed, but they must be in place and understood by all. 5. Identify Target Processes: Select a few processes for pilot implementation. With some initial successes under its belt, the organization can go with confidence to the processes that are the most critical. Start implementation at the front of a series of processes. 6. Train Appropriate Operators and Teams: The operators and teams who will be directly involved with the collection, plotting, and interpretation of SPC data, and those who will be involved in getting the targeted processes under control will require training in the use of quality tools. 7. Ensure Repeatability and Reproducibility of Gauges and Methods: All measuring instruments from simple calipers and micrometers to coordinate measuring machines must be calibrated and certified for acceptable performance. 8. Delegate Responsibility for Operators to Play a Key Role: Operators need to be delegated the responsibility for collecting and plotting the data, maintaining the SPC control charts, and taking appropriate action.

Steps in Implementing SPC – The Execution Phase • •

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• •

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The execution phase includes 9 steps: 9. Flowchart the Process: Flowcharting will reveal process features or factors that were not known to everyone. The development of the process flowcharts should be the responsibility of special teams composed of the process operators, their internal suppliers and consumers, and appropriate . 10. Eliminate the Causes of Special Variation: The cause and effect diagram is then used to list all the factors (causes) that might impact the output (effect). Then by applying other tools such as Pareto Charts, histograms, and stratification, the special causes can be identified and eliminated. Elimination of special causes should be a team effort. 11. Develop Control Charts: The statistics expert or consultant can help develop the appropriate control charts and calculate valid upper and lower limits and process averages. 12. Collect and Plot SPC Data & Monitor: The process operator takes the sample data and plots it on the control chart at regular intervals. The operator carefully observes the location of the plots, knowing they should be inside the control limits. 13. Determine Process Capability: When a process is in control and is still not capable of meeting the customer specifications, it is up to management to upgrade the process capability, which may require the purchase of new equipment.

Steps in Implementing SPC – The Execution Phase 14. Respond to Trends and Out of Limits Data: With experience, operators may be able to handle many of these situations on their own, but if they cannot, it is important they summon help immediately. The process should be stopped till the cause is identified and removed. Prevent the production of defective products that must be scrapped or reworked. 15. Track SPC Data: The SPC committee and management should see where they should concentrate resources for improvement. 16. Eliminate the Root Cause of Any New Special Cause of Variation: For example, it is possible that the material from a new vendor for raw material may cause the process to shift the process average one way or the other. Eliminating the root cause may require management approved procedure mandating the use of preferred suppliers. 17. Narrow the Limits for Continual Improvement: Narrowing the limits will result in fewer parts failing to meet the specifications. Quality will improve, and costs will decrease. The key is finding ways to improve the process.

Inhibitors of SPC • • •

• • • • •

The most common inhibitor of SPC is lack of resources. Capability in Statistics: Many organizations do not have the in house expertise in statistics that is necessary for SPC. Misdirected Responsibility for SPC: The process operators will require help from the statistician and others from time to time, but they are the appropriate owners of SPC for their processes. Failure to Understand the Target Process: A good SPC system cannot be designed for a process that is not fully understood. Failure to Have Process Under Control: Before SPC can be effective, any special cause of variation must be removed. Inadequate Training and Discipline: Everyone who will be involved in the SPC program must be trained. Measurement Repeatability and Reproducibility: Before a gauge is used for SPC it should be calibrated and its repeatability certified. Low Production Rates: Low rates of production offers an opportunity for taking a 100% sample.