CI-1

1. Process Analysis for Continuous Improvement

2. Benchmarking

3. Process Improvement Approaches

4. Pareto Chart

5. Cause and Effect Diagram

6. Acceptance Sampling

7. Process Control Charts

8. Control Limits vs Specification Limits

CI-2

Variability Types

Random Variability: Natural variations in the output of a process, created by countless minor errors. (not readily fixable)

Assignable Variations: A variation whose cause can be identified.• Equipment or Process cause.• Type of variability that is most commonly addressed.

-3 3-2 2 -3 3-2 2

Preferred

CI-3

Areas of Waste

1. Over production – making or doing more than required

2. Waiting – for info, materials, people, maintenance, etc

3. Transport – moving people or goods around or between

sites

4. Poor process design – too many/too few steps, non

standardization, inspection rather than prevention

5. Inventory – raw materials, WIP, finished goods, papers,

electronic files

6. Motion – inefficient layouts or poor ergonomics at workstations

or in offices.

7. Defects – errors, scrap, rework, non-conformance

8. Underutilized personnel resources and creativity – ideas

that are not listened to skills that are not utilized

CI-4

Continuous Improvement of Process Analysis

• Proactive vs Reactive• overall proactive is cheaper

• Various Methods for Process Improvement• Kaizen• Process Based Management• Taguchi

• General Steps for Process Improvement Model.1. Define the problem in the context of the process2. Identify, analyze & document the process3. Measure current performance 4. Understand why the process is performing as it is.5. Develop a strategy & implement the alternative chosen6. Evaluate the results of the new process7. Commit to continuous improvement of the process

CI-5

Benchmarking

• The search for industry “best practice” that lead to superior business performance.

• Perspective companies to bench mark are typically leaders in their industry

• Not necessarily in your industry.

Benchmarking steps:1. Identify what is to be benchmarked.2. Identify competitive companies3. Determine data collection method, collect data4. Determine current performance gap5. Project future performance levels.6. Communicate benchmarking findings; gain acceptance7. Establish process improvement goals8. Develop action plan9. Implement specific actions & monitor progress10. Recalibrate benchmarks; return to 1.

CI-6

Process Improvement Approaches

• BrainstormingGenerating ideas in an environment free of criticism & intimidation

4 basic principles1. No criticism2. No constraints3. Build on other peoples ideas4. Encourage participation

• What are some obstacles to effective Brainstorming?

• Process documentation:• Process mapping (flow charting)• Run Diagram (line chart)• Check sheets• Pareto charts• Cause and Effect diagrams

CI-7

Market Indicatorsreview to

determine MarketDemand

Development ofGrain Contractwith Customer

Determination ofhow contract is

met

Communicationof Contract

Requirements

Price

Merchandiser

AcceptableContract

Terms

Merchandiser

PriceQuality

Delivery DateLocationLogistics

Location and source(s)

of grain

LocationDP

Current Inventory vsupcoming deliveries

Existing contractdemand

Merchandiser

Requirementssent to

appropriate facilityand functions

Facility notificationGrain specifications

Grain quantityShipping instructions

Carrier informationInvoicing information

Monday, June 10, 2002

Page 1

Customer Contracting Flow

Merchandiser

Processes

Outcomes

Measures/Issues

Weather ConditionsAnticipated yields

Commodities pricing

CI-8

Development ofGrain Contractwith Supplier

Receiving andInspection ofGrain Delivery

Communicationof Grain Delivery

Servicesperformed toconvert to or

facilitate grainspecs

AcceptableContractTerms

Anyone

Classification &Acceptance

of Grain

Scale Attendant

WeightGrade

MoistureDamageQuantityFarm ID

Quality &

Acceptancecommunicated to appropriate

functions

Info to Branchmanager and

Merchandiser fordetermination if grainsatisfies customer’s

current or futurecontract demands

Accounting fortracking & payment

Outside Person

Grain satisfiescustomer

requirements

Conversions include:Blending & Drying

Facilitation includes:Segregating & facility

location

Monday, June 10, 2002

Page 1

Grain Contracting, Conversion & Shipping Flow

Outside Person

Processes

Outcomes

Measures/Issues

PriceQuality

Delivery DateServicesLocation

Supplier Acceptance

Shipping Grain toCustomer

Grain satisfiescustomer

requirements

Includes:PackagingLogistics

Carrier interfaceTracking to customer

specified location

Outside Person/Merchandiser

CI-9

Receiving andInspection of

Grain Delivery

Communicationof Grain Delivery

Servicesperformed toconvert to or

facilitate grainspecs

Classification &Acceptance

of GrainScale Ticket

Scale Attendant

WeightGrade

MoistureDamageQuantityFarm ID

Quality &

Acceptancecommunicated to appropriate

functions

Info to Branchmanager and

Merchandiser fordetermination if grainsatisfies customer’s

current or futurecontract demands

Accounting fortracking & billing

Outside Person

Grain satisfiescustomer

requirements

Conversions include:Blending & Drying

Facilitation includes:Segregating & facility

location

Monday, June 10, 2002

Page 1

Services-Only Flow

Outside Person

Processes

Outcomes

Measures/Issues

Shipping Grain toCustomer

Grain satisfiescustomer

requirements

Includes:PackagingLogistics

Carrier interfaceTracking to customer

specified location

Outside Person

CI-11

Pareto Chart

• Typically a bar chart that shows the frequency of different types of defects in a product or service.• Shows which problems may make the largest improvement if a solution is found• Separates vital problems from trivial ones.• Is tracking the number of occurrences for a specific fault the best measure for a Pareto Chart? Why or why not?

0

10

20

30

40

50

60

70

80

L Wing R Wing Cockpit Rudder

CI-12

Cause and Effect Diagram

• Also called a Fishbone Diagram• Useful for communicating the potential causes of an out-of-control quality characteristic and coordinating the choice of a corrective action.

1. List the concern or problem (effect)2. Identify the factors influencing this concern.3. List the reasons why this factor is influencing this concern.4. Investigate potential solutions to remedy the concern.

Effect

CI-13

Acceptance Sampling

• A form of inspection applicable to lots or batches.• Purpose is to decide if a batch satisfies a predetermined standard.• Most useful for the following conditions:

• Large # of items produced in short time• Cost consequences of passing defects is low• Destructive testing is required• Fatigue or boredom caused by inspecting large # of items leads to errors.

• Sampling Plan• Specify the batch size• Specify a frequency• Specify an acceptance/rejection criteria

• Methods• Single Sampling• Double Sampling• Multiple Sampling

• Cost & time required typically define the plan

CI-14

Statistical Sampling and Control

• What types of product characteristics could we sample?• What are we trying to control?

Central Limit Theorem• Assumes if you have a large enough sample size then the distribution of the sample mean will approximate a normal distribution, regardless of their true distribution.• Most say that 30-50 is a large enough sample.• Use the Student t for less • Statistical sampling uses inferential statistics

• The idea is that the sample taken describes the entire population.

Error Types• Type I (): The sample is incorrectly rejected when the batch is good. (Producer’s Risk). Rejects the process as being out of control when it is in control.• Type II(): The sample is accepted though the batch is bad. (Buyer’s Risk)

• Accepts the process as in control when it is not. • Selecting and is not trivial

CI-15

SPC CHART

2.5

2.4

2.3

2.2

2.1

2.0

1.9

1.8

1.7

1.6

1.5

UCL

LCL

_X

.

..

.. .

..

..

.

Sample1 2 3 4 5 6 7 8 9 10

CI-16

Mean Control Charts: Variable Data

Mean Control Charts • Process control charts designed to detect shifts in the mean value of a process.• If the process true mean of sample is unknown then use : =

• X +/- Z x =

• X ; Grand Mean or average of the average observations and samples• = process standard deviation

• x = / (n) 1/2 ; Standard deviation of distribution of

sample mean

• Hypothesis is that the process is in-control (Ho )

• If process true standard deviation of sample is not known then use X-bar chart.

= -• Lower Control Limit: X - A2* R = -•

Upper Control Limit: X + A2 * R

• Compares each sample mean to a Grand Mean

• A2 found in table (pg 310) and are based on number of observations per samples.

CI-17

Range Control Chart: Variable Data

Range Control Chart _If the process variance is unknown then use a R-chart. _

• Lower Control Limit: R * D3

_• Upper Control Limit: R * D4

• Compares each range of a sample to a mean of the range of other samples.

• D3 & D4 are found in a table (pg 310) and are based on the number of observations per samples.

To adequately establish the variability of a process it is recommended to use both mean and range control charts on a sample.

• Accuracy• Dispersion

CI-18

Mean Control Charts: Variable Data

Four samples of three observations each have been taken, with actual measurements (in centimeters) shown below. Construct three sigma mean and range charts and determine if corrective action is needed.

Sample 1 2 3 412.3 11.9 12.0 12.112.2 12.2 12.2 11.812.1 12.2 11.8 11.8

The standard deviation of the sample is known and is 0.6, construct three sigma mean chart. Use the above data.

CI-19

Process Control Charts: Attribute Data

What is an Attribute? Use when data is subjective and can be counted.

• P chart is used when the number of both good and bad are known.• C chart is used when the number of bad parts only are known.

P Chart• Underlying sample distribution is the binomial• Monitors the proportion of defects generated in a process• Constructed and used similar to the mean• Difference is we use the percent defects in a population

• LCL: P - 3p

• UCL: P + 3 p

• p = (( P (1-P) )/ n)1/2

CI-20

Process Control Charts: Attribute Data

C Chart

• Underlying sample distribution is the Poisson.

• large sample sizes approximate a normal

distribution)

• To track the countable number of defects when the

ample size is the same.

• The mean number of defects per unit is C and standard

deviation is (C)1/2

• If C is unknown then use C = # of defects / # of

samples.

• If less than zero, use zero.

• LCL: C - 3 (C) 1/2

• UCL: C + 3 (C) 1/2

CI-21

Attributes Problem

A town’s department of public works is concerned about adverse public reaction to a sewer project that is currently in progress. Because of this, The Commissioner of Public Works has authorized a weekly survey to be conducted of town residents. Each week, a sample of 100 residents is questioned on their feelings towards the project. The results to date are shown below. Analyze this data using an appropriate control chart with a 5% risk of Type I error. Is the community sentiment stable?Week 1 2 3 4 5 6 7 8Opposed 10 8 12 6 4 14 8

10

Construct the appropriate control chart for the observations listed below, and determine if the process is in control using two sigma limits.

Observation 1 2 3 4 5 6 7 8 9 10 11Number of 9 4 3 5 6 3 4 2 3 2 3Defects per unit

CI-22

Control Limits vs Specification Limits

The quality control limits represent the natural variation produced by a process.

• Does not guarantee the product meets the design specifications.• Must be able to define a way of telling if a process is capable to produce at a given design specification.• Cp defines the relationship between control limits of a process and the specification limits of design.• Cp = (USL - LSL)/6

• USL = upper spec limit• LSL = lower spec limit• = Standard deviation of the process

• can be approximated by R/D2

• Cp = 1 says process is barely capable• Cp > 1 says Process is capable• Cp < 1 says process is not capable• Usually strive for a Cp = 1.33 or better

Cp does not consider how well the process is centered within the limit

CI-23

Control Limits: Cpk

Cpk is used to define if the process and the spec limit are

centered.

• Compute by

• finding difference between each of the specification limits

and the mean

• identifying the smaller difference

• divide difference by three standard deviations of the

process.

• Cpk = Min [ ((Upper spec - Process mean)/3),

((Process mean - Lower Spec)/3)]

Limitations of Capability Index1. The process may not be stable.

• capability index is meaningless.2. The process output may not be normally distributed.

• Inference about fraction of output that is not acceptable will be incorrect.

3. The process is not centered• if the Cp index is used a misleading result is given.

CI-24

Control Limits Problems

The specification limit for a product is 8cm and 10 cm. A

process that produces the product has a mean of 9.5cm and a

standard deviation of 0.2cm. What is the process capability,

Cpk?

Is the process capable?

Studies on a machine that molds plastic water pipe indicates that when it is injecting 1-inch diameter pipe, the process standard deviation is 0.05 inches. The one-inch pipe has a specification of 1-inch plus or minus 0.10 inch. What is the process capability index (Cp)?Is the process capable?