LSSGB Lesson6 Control

Copyright 2014, Simplilearn, All rights reserved.


Lesson 6—Control

Lean Six Sigma Green Belt


● Describe Statistical Process Control (SPC)

● Explain control charts

● Discuss control plan strategies

● Develop a control plan

● Discuss visual factory

● Describe control methods for 5S

After completing this lesson, you will be able to:

Objectives

2


Control

Topic 1—Statistical Process Control


Statistical Process Control (SPC) was developed by Walter A. Shewhart in 1924. SPC aids in the visual

monitoring of a process and controls its parameters by placing statistical measures around the

process outputs or input variables.

Following are the benefits of SPC:

● Separates the special and common causes of variability

● Recognizes the unexpected changes in the process output quickly

● Helps to identify the stable zone for variables where specification limits are unknown

● Provides useful external information for the continuous improvement of the process

● Helps in monitoring a process online

Introduction to Statistical Process Control

4


Common Cause Variation

The common cause variation is the variation that can be usually seen in the process.

Example: Minute variations in the raw materials

Features of Common Cause

VariationPart of the process and the organization is aware of its presence.

Frequency of its occurrence is high, hence can be easily predicted.

Causes for this variation are repetitive.

Identifying and removing these causes requires huge investment and is time consuming.

This variation will be within the tolerance or specification limits.

5


Special Cause Variation

The special cause variation is the variation that cannot be normally seen in the process.

Example: Machine or system crash

Not part of the process, affects the regular process, and produces defects.

Frequency of occurrence is less and cannot be predicted.

Causes are non-repetitive.Identifying and removing these causes require less investment.

Features of Special Cause

Variation

Variation affects the flow of the process due to which the defects appear.

6


Common Cause Variation vs. Special Cause Variation

Common Cause Variation

● Frequency of occurrence is high

● Predictable

● Part of the process

● High investment for removal

● Repetitive

● Elimination is difficult

Special Cause Variation

● Frequency of occurrence is less

● Unpredictable

● Is not part of the process

● Relatively less investment for removal

● Not repetitive

● Elimination is comparatively easy

Common cause variation and special cause variation can be differentiated as follows:

7


Rational Subgrouping

Approach I to construct rational subgroups

Sample consists of units produced at the same

time—consecutive units.

● Primary purpose is to detect process shifts

Approach II to construct rational subgroups

Sample consists of units that are representative of all

units produced since the last sample—random

sample.

● Often used to make decisions about acceptance

of product

● Effective at detecting shifts to out-of-control state

and back into in-control state between samples

Rational subgrouping refers to the selection of subgroups or samples in a way that if assignable

causes are present, chance for differences between subgroups will be maximized and chance for

differences due to assignable causes within a subgroup will be minimized.

8


Data Collection for SPC

The data collection process for SPC is as follows:

List the project goalsIdentify measurable items to understand process

Determine if other measures are appropriate

Document purpose of data collection

Write operational definitions for each measure

Check for availability of historic data

Document the name of the person collecting the data

Find the duration for data validation

Implement end-to-end process and ensure it is followed regularly

Finalize method of collecting, aggregating/summarizing, and displaying data

Confirm who will review and validate the data and how often

9


Data Collection for SPC—Techniques

Some of the data collection techniques for SPC are as follows:

Census Sampling Experiment Observational

Collects and uses the

complete data from the

population.

Collects data from a

subset of a population

and estimates the

population attributes.

A controlled study

performed to

understand the cause-

and-effect relationships

between multiple

variables, inputs and

outputs, etc.

Attempts to

understand cause-and-

effect relationships,

however specific

treatment is not given

to the groups.

10


Control Chart Anatomy

Control chart, developed by Walter Shewhart in the 1920s, plots and processes the data (input X data

and output Y data) over a period and connects by lines, in order to detect trends or unusual events.

Characteristics of control charts are as follows:

● They are similar to Run Charts, with an addition of control limit lines and an average/center line.

● Control charts can be used with discrete or continuous data.

● Control limits are typically set at approximately three standard deviations (3s) from the center

line.

● Specification limits (USL and LSL) do not appear on them.

11


Control Chart Anatomy—Sample

A sample control chart is shown on the screen.

12


Control Charts and Analysis

Control charts are useful for tracking process statistics over time and detecting the presence of

special causes. A process is in control when:

● most of the points fall within the bounds of the control limits; and

● the points do not display any nonrandom patterns.

The data is depicted visually in a control chart. Hence it is easy to find the differences between common cause and special cause.!

13


A standard control chart uses control limits at three standard deviations of the mean (σmean) from the

data’s grand average ( X, average of the sample averages, or μ).

The probability of an out-of-control point when the process has not changed is only 0.27%.

If the control limits are set at:

● 2 standard deviations—increases the chance of type I or alpha error.

● 4 standard deviations—increases the chance of a type II or beta error.

Setting the Control Limits

Walter Shewhart had set 3σ limits on control charts with the belief that when the process goes beyond these limits, it needs correction.!

14


An Out-Of-Control (OOC) condition is indicated if one of the following is true:

Common Rules for Control Chart Analysis

1 point is outside the Control Limits (either above UCL or below LCL).

p(f) = 0.27%

8 consecutive points are above the Center Line (CL) or consecutively below the CL.

p(f) = (0.5)8 = 0.39%

6 to 8 points are consecutively increasing or decreasing. p(f) = (0.5)6 or (0.5)8 = 1.6% to 0.39%

2 out of 3 points are within 1 σmean of either the UCL or the LCL.

p(f) = 3!

(2!1!)(0.023)2(0.477)= 0.08% for one side

15


There are two ways in which sampling of continuous data can be done.

Choosing an Appropriate Control Chart—Continuous Data

Continuous Data

Individual Data Points

(Pulling one sample at fixed frequency)

ImR Chart

(Depicts the variability of individual characteristics over time)

Subgroups

(Taking periodic group data)

X and R Chart

(If n is between 2 and 9)

X and s Chart

(When standard deviation is calculated and n≥10)

16


Discrete data can be sub-divided into two parts.

Choosing an Appropriate Control Chart—Discrete Data

Discrete Data

Defectives

Constant Subgroup Size

np Chart

(Number of Units Rejected)

Varying Subgroup Size

p Chart

(Percentage of Units Rejected)

Defects

Constant Subgroup Size

c Chart

(Number of Defects)

Varying Subgroup Size

u Chart

(Average Number of Defects per Opportunity)

17


X refers to average and the subgroup average data will be plotted on the X chart.

Some of the principles of X and R and X and s charts are as follows:

● X and R and X and s charts are two separate charts of the same subgroup data.

● X chart is a plot of the means of subgroup data and shows inter-subgroup or between-subgroup

variation.

● R chart is a plot of the subgroup ranges (or if s, plot of subgroup standard deviation) and shows

intra-subgroup variation.

● In X control charts, the control limits are calculated based on mean of means, range, or standard

deviation, and other factors.

● X and R and X and s charts can be plotted with any type of data.

X Chart Principles

18


X is the grand average and R is the average of the range.

Defining UCL and LCL in X and R Chart

UCL X= X + A2

R

LCL X= X - A2

R

UCLR = D4 R

LCLR = D3 R

A2, D3, and D4 are values from the control chart table.!19


Standard deviation of each subgroup data is represented as s. The data is divided into subgroups and

standard deviation is calculated for each subgroup.

Defining UCL and LCL in X and s Chart

Values for A3, B3, and B4 are constant and are taken from the control chart table. X and s charts are used to track process variation where the subgroup sample size ≥ 9.!

UCL = X + A3 S

LCL = X - A3 S

UCL = B4 S

LCL = B3 S

20


X and R and Subgroup Data—Example

Establish 1 σ process limits for the data set shown. Use the table of control chart constants for values of A2, D3, and D4.

n A2 D3 D4

2 1.88 0 3.27

3 1.02 0 2.57

4 0.73 0 2.28

5 0.58 0 2.11

6 0.48 0 2.00

Table for control chart constants

X Chart

In Minitab, STAT -> CONTROL CHARTS -> VARIABLE CHART FOR SUBGROUPS -> X-R!

Q

21


● In X and R chart, point SG 6 is the point of change in the process from below the center line to above the center.

● No points are outside control limits in the given process; however, examine points 6 and 7 on X chart, and points 10 and 11 on the R chart for rule #4 (If 2 out of 3 points are within 1 σmean of either the UCL or the LCL).

X and R and Subgroup Data—Constructing Chart

A

22


X and s and Subgroup Data—Example

The data in subgroups with 10 samples in each subgroup is given here along with the X chart. Using this data,

find out if the process is in control.

n A3 B3 B4

2 2.659 0 3.267

3 1.954 0 2.568

4 1.628 0 2.266

5 1.427 0 2.089

6 1.287 0.030 1.970

7 1.182 0.118 1.882

8 1.099 0.185 1.815

9 1.032 0.239 1.761

10 0.975 0.284 1.716

Table for control chart constants

X Chart

In Minitab, STAT -> CONTROL CHARTS -> VARIABLE CHART FOR SUBGROUPS -> X-S!

Q

23


● The X chart point SG 10 is the variation of the point from the mean.

● Also, points 4, 10, and 23 have more variation from the center. These points can be analyzed further.

● The points are within the limits, and hence the process is in control.

X and s—Constructing Chart

A

24


ImR charts are two separate charts of the same data. Some of the principles of the ImR chart are as

follows:

● The I-chart is a plot of the individual data points.

● The MR-chart is a plot of the moving range of the previous individuals.

● ImR charts are sensitive to trends, cycles, patterns, and normality.

● ImR charts are used:

o when subgroup variation is zero or no subgroups exist; and

o with data points from destructive testing or batch processing, or summary data from a time

period.

● Control limits of the ImR chart are calculated using the same method as the X and R chart.

ImR Chart Principles

25


In Minitab, STAT -> CONTROL CHARTS -> VARIABLE CHART FOR INDIVIDUALS -> I-MR!

The QC department at Nutri Worldwide Inc. measures the strength of its milk cartons once in

every hour. Is the process in control?

● Since the data is individual data, the ImR chart will be used here.

● This is an example of a destructive test.

ImR and Individual Data—Example

Q

26


● Moving range is the absolute value of difference between the last two data points.

● In I-chart, point 16 is close to the upper limit (analysis required).

● No points are out of control in the process.

ImR and Individual Data—Constructing Chart

A

27


Given is the data used to study the number of calls handled per hour in call center operations. This

data was studied using ImR charts to check if the process is in control. The data, ImR chart, and

analysis are as follows:

ImR Chart—IT/ITES Example

Data: ImR Chart:

Analysis:● In I-chart, all points are closer to the

mean value. The process is well within control.

● In MR chart, there are a few points closer to LCL. The process variation can be investigated further. However, no point is outside the control limits.

28


Based on sample size and data type (defects or defectives), the following types of control charts can

be selected:

Control Charts for Attribute Data

If the sample size is consistent and the data type is defectives np chart should be used

If the sample size is consistent and the data type available changes from defectives to defects

c chart should be used

If the sample size is inconsistent and the data type is defectives p chart should be used

If the sample size is inconsistent and the data type is defects u chart should be used

Control limits may be constant, such as X and R charts (for np and c charts), or vary depending on sample size (for p and u charts.)!

29


The np chart is used to measure the non-conforming proportions or number of defectives within a

standardized group size. Some of the principles of np chart are as follows:

● The expectation is that the same proportion exists in each group.

● The np chart follows binomial distribution.

● Large subgroups are required (50 minimum) for this chart.

● Subgroup size must be constant, hence there is no need to calculate p and then np to plot data

points on a control chart.

● Control limits will be constant.

np Chart Principles

30


Important formulae of np chart are as follows:

np Chart—Formulae

● Proportion of p = D

n

● np = n ∗D

n

● Control Limits = np ± 3√np (1−𝑝 ̅)

where, D = Defectives

31


The sourcing department at Nutri Worldwide Inc. measures 125 purchase orders daily

and records the number of entry errors in them. The tabulated data is given here. Is

the order entry process in control?

● Since the data has a constant subgroup size (orders processed) of defectives, an np

chart will be used.

● Assumption is that there is only one error per order possible.

np Charts and Uniform Subgroup Size—Example

Q

32


● In np chart, point 12 is beyond the control limit of three standard deviations. Analysis must be done to find

the reason and take corrective action if necessary.

● Hence, point 12 is out of control in the process.

np Charts and Uniform Subgroup Size—Constructing Chart

A

33


The p chart is used to measure the non-conforming proportion or defectives. Principles of np

chart and p chart are quite similar. Some of the principles of the p chart are as follows:

● The expectation is that the same proportion exists in each group.

● The p chart follows binomial distribution.

● The subgroup size should at least be 50, and it does not have to be constant.

● Control limits may vary from subgroup to subgroup based on the subgroup size.

p Chart Principles

Control Limits = 𝑝 ± 3 𝑝 1− 𝑝

𝑛

Note: When n changes, control limit also changes.

34


The sourcing department in Nutri Worldwide Inc. measures the number of entry

errors on a daily basis. The tabulated data is presented here. Is the order entry

process in control?

● Since the data has varying subgroup sizes (orders processed) of defectives, a p

chart will be used.

p Charts and Varying Subgroup Size—Example

Q

35


● In a p chart, point 12 has gone beyond the limit of 3 sigma level. Analysis must be done to find the

reason and take corrective action if necessary.

● Hence, point 12 is out of control in this process.

p Charts and Varying Subgroup Size—Constructing Chart

A

36


To form a c chart, measure the number of occurrences of non-conforming defects. Some of the

principles of the c chart are as follows:

● It follows a Poisson distribution.

● It is used when the sample size is fixed or the area of opportunity is constant.

● It is also used to identify attribute data for the sample.

● Each count is a subgroup of samples and the control limits will be constant.

● The subgroup size should at least be 20.

c Chart Principles

Control Limits = 𝑐 ± 3√ 𝑐

37


Final inspection grades the tinted glass on the number of white specs. The

product is priced by grade. White specs are defects, not defectives, and are

measured over a constant sample area; so c chart will be used. Is the process in

control?

● Since the data is for defects, c chart will be used.

c Chart—Example

Q

38


● Points 2, 3, 4, 12, 13, 16, and 17 are out of control in this process; additionally, points 7, 9, 18, and 19 break

rule #4.

● In this c chart, the process is not stable and many points go beyond 3 sigma control levels. Analysis must be

done to find the reason and take corrective action.

● The process is not in control.

c Chart—Constructing Chart

A

39


The u chart is also used to measure the number of non-conforming defects. Principles of c chart and

u chart are quite similar. Some of the principles of u chart are as follows:

● It follows a Poisson distribution.

● It is used to identify attribute data for the sample.

● It is used to measure defects when the sample size is not fixed.

● Control limits of the process may vary.

● The subgroup size should at least be 20.

u Chart Principles

Control limits = 𝑢 ± 3√( 𝑢

𝑎)

Where, a = area of opportunity

40


The plastics operation counts defects after a “run” which is undetermined in length (once

started, it continues until all material is used). Is the process in control?

● Since the count of defects has a varying area of opportunity and the length of runs is not

constant, u chart will be used.

u Chart—Example

Q

41


● In this u chart, point 18 has gone beyond the 3 sigma level. Analysis must be done to find the reason

and corrective action must be taken if necessary.

● Point 18 is out of control in this process.

u Chart—Constructing Chart

A

42


Control

Topic 2—Control Plan


Control plan is a written summary description of the system for controlling a process. A control plan:

● describes actions required to maintain the desired state of the process and minimize process and

product variation;

● evolves and changes with the process and product requirements;

● is also considered a knowledge-transfer document;

● can be created for a process, a step in the process, or even a piece of equipment used in the

process;

● provides a single point of reference for understanding process characteristics, specifications, and

Standard Operation Procedures for the process; and

● enables assignment of responsibility for each activity within the process.

Control Plan and its Uses

44


Following are the strategies of a control plan:

● Minimize process tampering

● State the reaction plan to out-of-control conditions

● Signal when Kaizen activities are needed

● Describe training needs for standard operating procedures

● Describe maintenance schedule requirements in case of equipment control plan

Control Plan Strategy

A good control plan should clearly describe what actions are to be taken, when to take them, and whoshould take them, thereby reducing the “fire fighting” syndrome.!

45


The elements of control plan are as follows:

● Project purpose and objectives;

● Risk management plan;

● Resource requirements documents;

● Process ownership identification;

● Communication plan recommendation;

● Process stewardship; and

● Financial analysis and results.

Elements of Control Plan

46


The elements of response plan are as follows:

Elements of Response Plan

Unstable conditions

Responsibility and ownership

Action plan

Define what

‘unstable’ means for

each Vital x by

performing FMEA.

Describe responsibility

and ownership to take

required actions

whenever any

unstable condition

occurs.

List down the action

steps to mitigate the

unstable conditions.

In the control plan, provide a contact list of technical experts who can be consulted to resolve any issues that may occur.!

47


Some of the actions that can be taken to fix and prevent the issues are:

Corrective and Preventive Actions

Containment

Fix the problem until the root cause is

identified

Correction

Fix the problem after identifying the root

cause

Prevention

Make the process mistake-proof by

eliminating the recurring issues

Corrective actions

Preventive action

48


Cost-Benefit Analysis

Start-up fees, training, tools, processes, etc.

Time, lost production, potential risk, etc.

Direct profits, increased production capabilities, etc.

Decreased production time, increased reliability and durability, etc.

Cost-Benefit Analysis

Understand Costs Identify Benefits

Monetary Cost Non-Monetary Cost Monetary Benefit Non-Monetary

Benefit

Cost-benefit analysis is used to evaluate the total anticipated cost of a project compared to the total

expected benefits, to determine whether the proposed implementation is worthwhile for a company

or project team.

To perform cost-benefit analysis, all the identified costs are subtracted from the expected benefits, to determine whether the positive benefits outweigh the negative costs.!

49


What to Control

Key Performance Input Variable (KPIV)

● The x factors are called KPIV.

● A control plan controls the KPIV.

● A control plan controls the inputs.

Key Performance Output Variable (KPOV)

● The output Y is called KPOV.

● A control plan ensures the desired state for the

KPOV.

● A control plan monitors the output.

It is important to define what needs to be controlled to define a strong control plan.

Process = f (x1, x2, x3…) = Y

Monitoring the output alone is not an effective way to control a process and will not result in in an efficient process. Both the input and output variables need to be monitored and controlled closely.!

50


The KPIVs or the inputs to the process can be identified using various sources, such as:

● Failure Mode and Effects Analysis (FMEA)

● Cause-and-Effect Matrix or Diagram and Cause Verification Matrix

● Multi-Vari Studies

● Regression Analysis

● Design of Experiments (DOE)

Identifying KPIVs

51


Developing and executing control plans require the use of the following tools:

Control Plan Tools

Control Charts

● Useful for tracking process statistics over time and detecting the presence of special causes

MSA

● A technique that identifies measurement error (variation) and sources of that error in order to reduce the variation

Error Proofing

● Also known as Poka-Yoke

● Refers to implementation of fail-safe mechanisms within a process to prevent it from creating defects

SOP

● Also known as Standard Operating Procedures

● Is a written document or instruction that details all the steps and activities of a process or procedure

● Also known as Preventive Maintenance

● Inclusion of Preventive Maintenance as part of the documented scheduled process or equipment maintenance

PM

52


After understanding the process, a multi-functional team must be formed that will be responsible for

controlling the process. Multiple tools can be used such as:

● FMEA

● Special characteristics (critical and significant)

● Control plans or lessons learned from similar parts or processes

● Technical documentation

● Validation plan results

● Optimization methods

● Team knowledge of the process

Developing a Control Plan

53


The questions that need to be asked to define the control plan are as follows:

Developing a Control Plan (contd.)

● What do you want to control?

● How often do you need to measure the process?

● Do you have an effective measurement system?

● What is the cost of sampling?

● How much shift can you tolerate?

● Who needs to see the data?

● What type of tool or chart is necessary?

● Who will generate the data?

● Who will control the process?

● What are the system requirements for auditing and maintenance?

54


It is important to identify the level of control that should be built into the process.

Choosing the Right Level of Control

55


A sample transactional control plan is shown.

Transactional Control Plan—Example

TRANSACTIONAL CONTROL PLAN

Prepared By : Business Unit : Page :Approved By : Location : Document No. :Process Owner : Department : Revision Date :

Supersedes :

Process Step

Characteristic/Parameter

CTQ/CL Specification/Requirement

Measurement Method

Sample Size

Frequency Who Measures

Where Recorded

Decision Rules/Corrective Action

Reference Number

Purchase Order

Time of entry CTD Customer order entry to PO less than 3 days

Access database server Time stamp

All entered

Weekly Admin. Access database

1. Review reason for length (Ex: Weekend error) and determine need to solve problem

Nutri Worldwide Inc.

Prototype/Purchase

1 of 1

4

06-18-2014

56


The Process Step column highlights the name of the process and distinguishes a process from a

process step or a piece of equipment.

Process Step

Process

Step

Characteristic/

Parameter

CTQ/CL

Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Reference

Number

Purchase

Order

Time of entry CTD Customer

Order entry to PO less

than 3 days

Access

database

server Time

Stamp

All

entered

Weekly Admin. Access

database


57


The Characteristic or Parameter column identifies the KPIV or KPOV to be measured to ensure the

process is in control. These parameters are often a part of the CTQ parameters of the process, as

identified in the Define phase.

Characteristic or Parameter and CTQ

Process

Step

Characteristic/

Parameter

CTQ/CL

Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Reference

Number

Purchase

Order



than 3 days

Access

database

server Time

Stamp

All

entered


database


58


The Specification or Requirement column defines the process, including the target goal of the process.

The goal for the process should be determined through team discussions, understanding the

technology and the history of the process.

Specification or Requirement

Process

Step

Characteristic/

Parameter

CTQ/CL

Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Reference

Number

Purchase

Order



than 3 days

Access

database

server Time

Stamp

All

entered


database


59


The Measurement Method column defines the tool or gauge that will be used for measurement of

the metric. Consider the following factors—availability of the equipment for the process, calibration

and MSA needs of the equipment, training needs on the tool or method, supporting Manufacturing

Performance Index (MPI), and operational blueprint requirements.

Measurement Method

Process

Step

Characteristic/

Parameter

CTQ/CL

Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Reference

Number

Purchase

Order



than 3 days

Access

database

server Time

Stamp

All

entered


database


60


Sample size refers to the number of data entries that will be used to calculate the metric. The

Frequency column defines the frequency at which the metric will be captured and analyzed. The next

column defines who will measure the metric based on the frequency defined earlier.

Sample Size, Frequency, and Who Measures

Process

Step

Characteristic/

Parameter

CTQ/CL

Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Reference

Number

Purchase

Order



than 3 days

Access

database

server Time

Stamp

All

entered


database


61


The Where Recorded section is used to indicate where the metric will be recorded. This can be done

through control sheets like charts, plots, logs, or check sheets.

Where Recorded

Process

Step

Characteristic/

Parameter

CTQ/CL

Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Reference

Number

Purchase

Order



than 3 days

Access

database

server Time

Stamp

All

entered


database


62


The Decision Rules or Corrective Action section identifies the actions to be taken for the out-of-

control specification situation(s).

Decision Rules or Corrective Action

Process

Step

Characteristic/

Parameter

CTQ/CL

Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Reference

Number

Purchase

Order



than 3 days

Access

database

server Time

Stamp

All

entered


database


63


The Reference Number section is used to facilitate access to documented or corrected procedures

against each corrective measure identified in the previous section.

Reference Number

Process

Step

Characteristic/

Parameter

CTQ/CL

Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Reference

Number

Purchase

Order



than 3 days

Access

database

server Time

Stamp

All

entered


database


64


A sample manufacturing control plan is shown.

Manufacturing Control Plan—Sample

Part Name/Family: Prepared by: Page:

Part No.: Approved by: Document No.:

Plant/Area: Revision Date:

Process

Step

Characteristic/

Parameter

CTS Specification/

Requirement

Measurement

Method

Sample

Size

Frequency Who

Measures

Where

Recorded

Decision Rules/

Corrective

Action

Injection Molding (Machine #16)

Y : Part Dimension CTQ 3.250 + 0.005 in Cpk=2 Gage # 042 5 Each hour Operator X and RChart

If out-of-control condition appears, 100% inspect all parts since last check. If Xout-of control, adjust injection pressure. If R out-of –control, adjust coolant flows.

“ X : Cavity Pressure CTQ 1200 + 15 psiCpk = 2

Pressure transducer in cavity

5 (automatic,continuous reading)

X and R Chart If out-of-control condition appears, check: Injection pressure settings; Temperature controller.

“ X : Coolant Flow CTQ 5 gal / minute Flow meter on machine

1 Each hour Operator Check Sheet by Machine

If flow is in yellow, adjust to green. If flow is in red, 100% inspect all parts since last check, and adjust flow to green.

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A sample control plan for Code Review Process in IT/ITES is shown.

IT/ITES Control Plan—Sample

Process Step What's Controlled?

Input or Output? Spec. Limits/ Requirement

Measurement Method

Control Method Sample Size Freq. Who/What Measures?

Where Recorded?

Decision Rule/ Corrective Action

Plan review for critical code

Critical code details in project plan

Input 100% Critical Code

Project Plan Weekly project mgmt reviews

100% Weekly Project Manager/ Automated workflow for project management

Project database Escalation to the Account Manager and update project plan

Conduct review for Critical Code

Critical code Input 100% Critical Code

Project Plan Weekly project mgmt reviews

100% Weekly Project Lead/ Automated workflow for project management

Project database First level escalation to project manager and second level escalation to account manager

Conduct review for Critical Code

Critical code Output 100% Critical Code

Project Plan and Code review reports

Project mgr signs off code review reports

100% As per project plan

Project Lead / Code control database

Project database/ source code database

First level escalation to project manager and second level escalation to account manager

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CuSum Chart

The Cumulative Sum Control or CuSum chart incorporates all the information by plotting the

cumulative sums of the deviations of the sample values from the target value.

The CuSum chart is used for detecting small shifts, monitoring process mean, defects, and variance.

i

j

ji xC1

0 )(

When,0 = the target for the process mean

= the average of the jth sample

The cumulative sum control chart is formed by plotting the quantity as:

jx

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A sample CuSum chart is shown here.

CuSum Chart—Sample

The data goes out of the limit for the 30th

sub group number.

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Exponentially Weighted Moving Average charts are used for:

● Plotting the data to detect small shifts over a small period of time, and

● Monitoring process mean or variance.

‘Exponentially’ in EWMA refers to more weight on the more recent observations and less weight on

the old observations.

This chart is frequently used in Stock Modeling Software Packages by the analysts who predict the

next day performance based on the previous week’s or month’s performance.

EWMA Chart

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The design parameters of the EWMA chart are L and .

● In general, 0.05 < < 0.25

● L = 3 when, has larger value

● 2.6 < L < 2.8 when, 0.1

EWMA is superior to the CuSum for larger shifts, particularly if > 0.1

EWMA Chart—Design Parameters

UCL = z + Lz

CL = z

LCL = z − Lz

Control Limits is computed as:

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EWMA Chart—Sample

The sample EWMA control chart is shown here.

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The EWMA chart:

● Stores relatively smaller data;

● Remembers only the current estimate of the variance rate and the most recent observation on

the market variable; and

● Tracks well the volatility changes.

EWMA Chart—Highlights

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Control

Topic 3—Lean Tools for Process Control


Visual controls are used in visual factory to manage the factory by vision. Following are the types of

visual controls:

Visual Controls

Helps people to read the complete process at a

glance and analyze how the process is working.

SOP

Set of rules and regulations that has to be

mandatorily followed in a particular process.

Control Chart

Provides information on process performance, helps

to understand if the process is in control and

sustains the improvements made.

Control Plan

Plans that are displayed to know the performance

of the process on a timely basis.

Control Board

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The 5S in the Lean Six sigma control methods are:

Control Methods for 5S

Seiri (Sort)) Seiton (Stabilize))

Seiketsu (Standardize)

Shitsuke (Sustain)

Seiso (Shine))

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Control Methods for 5S—Seiri

Seiri helps in:

● sorting necessary and unnecessary items;

● keeping workplace clean;

● preventing accumulation of items by assigning them tags;

● simplifying tasks;

● effectively using the workplace; and

● careful purchase of items.

Seiri(Sort)

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Control Methods for 5S—Seiton

Seiton helps in:

● improving efficiency;

● preventing loss and wastage of time;

● making workflow smooth and efficient;

● organizing storage for all items; and

● identifying frequently and non-frequently used items.

Seiton(Stabilize)

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Control Methods for 5S—Seiso

Seiso helps in:

● keeping workplace shiny clean;

● identifying malfunction in equipment;

● mess prevention; and

● finding the root cause of contamination. Seiso(Shine)

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Control Methods for 5S—Seiketsu

Seiketsu helps in:

● standardizing best practices across work place;

● performing tasks in a standard manner; and

● ensuring personal and environmental cleanliness.

Tools used:

● Job cycle charts, visual cues, scheduling of “five minute” 5S

periods, and checklists.

Seiketsu(Standardize)

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Control Methods for 5S—Seiketsu (contd.)

Steps for implementing Seiketsu are:

Assigning 3S (sort, set in order and shine) job responsibilities1

Integrating 3S duties into regular work duties 2

Checking the maintenance of 3S3

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Control Methods for 5S—Shitsuke

Shitsuke helps in:

● maintaining discipline and commitment;

● maintaining orderliness; and

● defining a new status quo and standard of work place

organization.

Tools used for sustaining 5S are:

● Signs and posters, newsletters, check sheets, pocket

manuals, team and management check-ins, performance

reviews, and department tours.

Shitsuke(Sustain)

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Knowledge CheckQuiz


a.

b.

c.

d.

QUIZFor which of the following is the control chart mainly used?

Measure the process capability

Determine causes of process variation

Detect non-random variation in processes

Determine if defective parts are being produced

1

83


Copyright 2012-2014,Simplilearn,All rights reserved

a.

b.

c.

d.

QUIZFor which of the following is the control chart mainly used?

Answer: d.

Explanation: A control chart is used to distinguish between random variation and variation due to out-of-control condition.

Measure the process capability

Determine causes of process variation

Detect non-random variation in processes

Determine if defective parts are being produced

1

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a.

b.

c.

d.

QUIZWhich of the following are calculated in X and s charts?

2

Average and Range

Mean and Variance

Variance and Standard Deviation

Average and Standard Deviation

85



a.

b.

c.

d.

QUIZWhich of the following are calculated in X and s charts?

Answer: a.

Explanation: X is average and s is standard deviation. Hence, average and standard deviation are calculated in X and s charts. It is used whenever the sample size of a subgroup is more than 9.

2

Average and Range

Mean and Variance

Variance and Standard Deviation

Average and Standard Deviation

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a.

b.

c.

d.

QUIZWhich of the following charts is used for continuous data?

3

p

u

X and R

np

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a.

b.

c.

d.

QUIZWhich of the following charts is used for continuous data?

Answer: d.

Explanation: I-MR, X and R, and X and s charts are used for continuous data.

3

p

u

X and R

np

88


a.

b.

c.

d.

QUIZWhat is a controlling process summary known as?

4

Control plan

Control process

Control program

Control chart

89



a.

b.

c.

d.

QUIZWhat is a controlling process summary known as?

Answer: b.

Explanation: A control plan is a written summary description of the system for controlling a process.

4

Control plan

Control process

Control program

Control chart

90


a.

b.

c.

d.

QUIZ Which of the following sections of a control plan identifies the KPIV or KPOV to be measured to ensure a process is in control?5

Process Step


Measurement Method

Characteristic or Parameter

91



a.

b.

c.

d.

QUIZ Which of the following sections of a control plan identifies the KPIV or KPOV to be measured to ensure a process is in control?

Answer: a.

Explanation: The Characteristic or Parameter section of a control plan identifies the KPIV or KPOV to be measured to ensure the process is in control.

5

Process Step


Measurement Method


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a.

b.

c.

d.

QUIZ Which of the following sections of a control plan defines the tool or gauge that will be used for measurement of the metric?6


Measurement Method


Process Step

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a.

b.

c.

d.

QUIZ Which of the following sections of a control plan defines the tool or gauge that will be used for measurement of the metric?

Answer: c.

Explanation: The Measurement Method section of a control plan defines the tool or gauge that will be used for measurement of the metric.

6


Measurement Method


Process Step

94


a.

b.

c.

d.

QUIZ Which of the following helps people to read the complete process at a glance and analyze how the process is working?7

Control board

Control chart

Control plan

SOP

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a.

b.

c.

d.

QUIZ Which of the following helps people to read the complete process at a glance and analyze how the process is working?

Answer: b.

Explanation: Control board helps people to read the complete process at a glance and analyze how the process is working.

7

Control board

Control chart

Control plan

SOP

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a.

b.

c.

d.

QUIZWhich of the following helps in mess prevention?

8

Seiri

Seiso

Seiketsu

Seiton

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a.

b.

c.

d.

QUIZWhich of the following helps in mess prevention?

Answer: c.

Explanation: Seiso is the stage of the 5S in Lean Six Sigma that helps in mess prevention.

8

Seiri

Seiso

Seiketsu

Seiton

98


a.

b.

c.

d.

QUIZWhich of the following stands for standardize?

9

Seiri

Seiso

Seiketsu

Seiton

99



a.

b.

c.

d.

QUIZWhich of the following stands for standardize?

Answer: d.

Explanation: Seiketsu stands for standardize.

9

Seiri

Seiso

Seiketsu

Seiton

100


● Statistical process control aids in visual monitoring of the process and controlling its

parameters by placing statistical measures around the process outputs or input variables.

● Control charts are useful for tracking process statistics over time and detecting the

presence of special causes.

● A good control plan should clearly describe what actions are to be taken, when to take

them, and who should take them, thereby reduce the fire fighting syndrome.

● After understanding the process, a multi-functional team must be formed who will be

responsible for controlling the process. Multiple tools and techniques can be used.

● Visual factory is a term used to describe a Lean production environment where charts

and signs are used to display information.

● The 5S in Lean Six sigma stands for Seiri/Sort, Seiton/Simplify, Seiso/Sweep,

Seiketsu/Standardize, and Shitsuke/Sustain.

Summary

Here is a quick recap of what we have learned in this lesson:

101



THANK YOU

LSSGB Lesson6 Control

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