# 7 QC TOOLS

Updated: Sep 11, 2020

1-Stratify data

2-Pareto diagram

3-Cause & effect diagram

4-Histogram

5-Scatter Diagram

6-Check Sheets

7-Control Charts

Will be explained through the creation of a QC Story

**QC Story**

A procedure for problem-solving

‘Story’ means ‘Logical Flow’

**QC Story Steps**

**1. Define**

-Define the problem clearly

Demonstrate the importance of the problem

Show the background of the problem

Explain with data the pain and impact of pain

Define the theme with target

Form a team and appoint a leader

Present and estimated budget

Prepare a schedule

**2. Observation**

Investigate data

Go to Gemba

Observe both conforming and non-conforming products/situations

How to Collect Data

1.Have Clear Defined Objectives

•Controlling and monitoring the production process

•Analysis of non-conformance

•Inspection

2. What Is Your Purpose

•Collecting as per strata

•Collecting in Pairs (correlation)

3. Are Measurements Reliable

4. Find Right Ways to Record Data

•Arrangement

•Data sheet

**A-Check Sheet**

**What **

An easy to understand form used to answer the question “How often are certain events happening?”

**Why**

Starts the process of translating “opinion” into “fact

**When**

Gathering data in order to detect patterns. Good point to start most problem-solving cycles.

Purpose of Check Sheets

Simplification of the data collection process

Reduction of recording errors

Ease of analysis

**How **

•Team agrees as to exactly what event is being observed.

•Decide on the time period during which data will be collected. This could range from hours to weeks.

•Design a form that is clear and easy to use making sure that all columns are clearly labeled and that there is enough space to enter the data.

•Collect the data making sure that observations/ samples are as representative as possible.

**B-Pareto Principle**

VilfredoPareto (1848-1923) Italian economist

20% of the population has 80% of the wealth

Juranused the term “vital few, trivial many”. He noted that 20% of the quality problems caused 80% of the dollar loss.

**Pareto Analysis**

**What**

A bar chart that helps to prioritize actions by arranging elements in descending order of occurrence. Sorts out the “vital few” from the “trivial many”.

**Why**

•To prioritize actions needed to solve complex problems.

•To separate important from non-important causes contributing to a problem.

**When**

•Many factors are impacting a problem.

•Attention needs to be directed only to the few factors that account for most of the problems.

**How**

•Define a problem and collect data on the factors that contribute to it.

•Historical records generally provide sufficient information.

•Classify the data by type, cost, percent, number of occurrences, or whatever is appropriate for the situation.

•Arrange the data in descending order.

**How contd.**

•Draw bar graph showing constituent ratio on the vertical axis.

•Connect the cumulative percentage of each bar graph to obtain the Pareto curve.

**Stratification**

**What**

Stratification is a statistical technique of breaking down values and numbers into meaningful categories or classification.

**Why**

To focus on corrective action or identify true causes.

**When**

To identify the cause of the problem if they come from a particular source.

•To analyze root cause in conjunction with other techniques like Pareto diagram histogram and graphs.

**How**

Regroup original data as per the source of data(e.g.. Machine wise, shift-wise, model-wise, supplier-wise)

•If required collect data a fresh after making the source from which they come.

•Recreate histogram, Pareto charts, and graphs on classified data

**3. Analysis**

Find out what the main causes are

Set up Hypothesis

Eliminate irrelevant causes

Brainstorming

Cause & Effect Diagram

Mark those with a higher probability

Cause Effect Matrix

Rating and Ranking

FMEA

Test the Hypothesis

Collect data or conduct experiments to the validated hypothesis

List validated causes

Histograms

Control Charts

Scatter Diagrams

**A-Cause & Effect Diagram**

**What**

•A graphic tool used to represent the relationship between an effect and the cause that influence it.

**Why**

•Identifies various causes affecting a process.

•Helps groups in reaching a common understanding of a problem.

•Helps reduce the incidence of subjective decision making.

**When**

•Looking for all potential causes of the problem

**How**

•Define the problem or effect clearly.

•Generate the potential cause of the problem through brainstorming.

**B-Histogram**

**What**

A bar chart displays the variation within the process. Also called a frequency distribution because the frequency of occurrence of any given value is represented by the height of the bars.

**Why**

•Allows one to quickly visualize what’s going on within a large amount of data.

•Provides clues to causes of problems.

•Maybe be used to show the relationship between the engineering tolerance and the capabilities of the process

**When**

•Capabilities studies are being performed.

•Analyzing the quality of incoming material.

**How**

•Collect measurements(variable data)from a process or key characteristic.

•Thirty or more measurements are preferred.

•Construct check sheet to record the data.

•Find the range by subtracting the smallest measurements from the largest.

Taking the class interval on the horizontal axis, draw the height of the bar corresponding to frequencies in the interval on the vertical axis.

**C-Scatter Diagram**

**What**

A tool used to study the possible relationship between two variables.

**Why**

To test for possible causes and effect relationships. Though it cannot prove that one variable causes the other, the diagram does make it clear whether a relationship exists and shows the strength of that relationship.

When:

There is a need to display what happens to one variable when another one changes in order to test that the two variables are related.

**How **

•Collect 50 to 100 paired samples of data believed to be related.

•Construct a data sheet.

•Draw the horizontal and vertical axis of the diagram.

•Label the axes.

•“Cause” is usually plotted on the horizontal axis and the “effect” variable on the vertical axis.

•Plot the data on the diagram. If values repeat, circle that point.

**D-Control Chart**

**What**

A control chart is a line graph used to display variation on time-ordered fashion. A centerline and control limits are placed on the graph to help analyze the pattern of the data. **Why**

•To separate common causes from special causes of variation.

•To help assign causes of variation

**When**

Measuring control characteristics.

**Where**

At the earliest possible point in the manufacturing process.

**How**

•Define process parameter to be measured.

•Define wherein the process the control characteristics will be measured.

•Select where the control chart is to be used.

•Determine sample size and frequency.

•Take measurements.

•Plot measurements on the graph.

•Connect dots.

•After 20 plot points calculate center-line and control limits.

•Analyse pattern for special cause of variation.

**x –R Chart:**

**How to read Control Charts**

1.Out of Control Limits: Points outside the limits

2.Run: Continuously on one side of centerline

•Seven-Point length of the run is abnormal

•10 Out of 11 consecutive points on one side

•12 Out of 14 consecutive points on one side

•16 Out of 20 consecutive points on one side

3. Trend: Continuous upward or downward curve

**P AND Np CHART**

These charts are utilized for ‘Defective’ :

P Chart: when the sample is constant /Np Chart: When the sample size is varying

**C And U -CHART**

These Charts are utilized for controlling ‘Defects’C: Charts are used for products of constant size .U: Charts are used for products of Varying size

**4. Action**

Take action to eliminate the main cause

Focus on causes and not symptoms for action

Check for any unintended/side effect

List all possible solutions and priorities based on cost-benefit analysis

Implement actions on a small scale to test the effect before horizontally deploying

**5. Check**

Compare ‘BEFORE’ vs. ‘AFTER’

Use same tool as used in analysis

Use same scale to show differences clearly

Convert results into Rs.

List any other tangible and intangible effects (good or bad)

**6. Standardizations**

To convert a statistical solution to an actual solution and hold the gains

Two types

One-time change

Time-monitored change

**7. Conclusion**

Review the problem solving process and plan for future work

Sum up remaining problems

Plan what is to be done and solve these problems

Document learning from project

What went wrong

What went right