Section 2 Variation Fundamentals

Lesson S02-07

Text: Section 2 pages 41 – 58

Duration: 30 minutes

The Central Limit Theorem suggests:

• Averages from a stable process tend to follow a normal distribution, even if individuals are not normal.
• The tendency for the averages to form a normal distribution improves as the sample size increases.

Next, we build on our conceptual understanding of Process Control and consider how we may make best use of the data collected.

There is usually concern pertaining to the amount of data that is required for SPC purposes. People responsible for understanding the process may propose to measure every unit produced (for the important characteristics).

The objective of SPC is to determine process stability or instability. SPC is not an indicator of process capability.

Process capability refers to whether parts are in or out of the specification limits. We will learn procedures for estimating process capability, but realize that SPC should not be used for capability assessment.

Central Limit Theorem Introduction

Central Limit Theorem Application

A Conceptual Understanding of Process Control

Section 2 Variation Fundamentals

Lesson S02-06

Text: Section 2 pages 33 – 40

Duration: 10 minutes

Are lists of numbers an informative manner for viewing data? Since interesting statistics such as the mean, median, standard deviation and range cannot easily be observed, other methods for displaying information may provide better insights.

Dot plots are a popular way of viewing data.

Another typical tool for looking at data is the histogram. A histogram can provide a sense of the distribution if the process is in control.

Unfortunately, histograms can be somewhat misleading.

Histograms

Trending Process

Nonnormal Distributions

Section 2 Variation Fundamentals

Lesson S02-05

Text: Section 2 pages 20 – 32

Duration: 22 minutes

A discussion of control limits and specification limits naturally leads into the issue of quality. Careful consideration should be given to defining quality, because most manufacturers are extremely concerned about quality.
So, what is quality? Have many businesses clearly and unambiguously defined quality? Is it a vague notion? Should it be subjective?

Inspection vs. Process Understanding. Statistical process control is all about control. People naturally go through control cycles in their daily lives.

What is Quality?

The Large Tires Example

Closeness to Target

Inspection vs Process Understanding

Summary

Section 2 Variation Fundamentals

Lesson S02-04

Text: Section 2 pages 16 – 19

Duration: 22 minutes

All processes have some degree of common cause variation, so the exact same data is not anticipated.

When the same distribution is seen over and over again in time, the process is said to be “in control.” In other words, the values being monitored should cluster around the same location and the variation in the values should be constant over time.

When the distribution does not remain consistent over time, the process is said to be “out of control.”

In Control Process

Out of Control Process

Misconceptions

Section 2 Variation Fundamentals

Lesson S02-03

Text: Section 2 pages 10 – 15

Duration: 20 minutes

All of the discussion regarding variation has been leading up to a fundamental concept for controlling variation. It is the notion of control limits.

Control limits represent the range over which the data is expected to fall. That is, control limits are limits of expected variation. More specifically, it is expected that 99.73% of the statistics we monitor will be within the control limits.

A special cause source of variation is any source that results in unpredictable behavior, i.e. anything unexpected. Often, the term “assignable cause” is used instead of “special cause.”

Specification limits often appear on a blueprint in industrial applications. In manufacturing, specification limits are generally dictated by a customer or product design engineer.

Conceptually: The Idea of a Control Chart

The Difference Between Specification Limits and Control Limits

Review of Specifications vs. Control Limits

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FMEA

Failure Mode and Effects Analysis (FMEA) is a method designed to:• Identify and fully understand potential failure modes and their causes, and the effects of failure on the system or end users, for a given product or process.

• Identify and fully understand potential failure modes and their causes, and the effects of failure on the system or end users, for a given product or process.• Assess the risk associated with the identified failure modes,
• Assess the risk associated with the identified failure modes, effects and causes, and prioritize issues for corrective action.
• Identify and carry out corrective actions to address the most serious concerns.

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