The Application of Six-Sigma DMAIC to a Distribution ...pasichile2013.auburn.edu/presentations/edgardo_escalante.pdfDMAIC to a Distribution System Edgardo J. Escalante, Ph.D. ... Critical-to-quality

1

Dr. Edgardo J. Escalante 1

The Application of Six-Sigma

DMAIC to a Distribution SystemEdgardo J. Escalante, Ph.D.

ITESM | México

Pan-American Advanced Studies Institute on Modeling,

Simulation and Optimization of Globalized Physical Distribution

Systems (PASI)

August 9, 2013

Santiago, Chile


C O N T E N T S

� Brief introduction to Six-Sigma

� Case development and hands-on exercises

� Conclusions

2


The Scientific Method

The quality movement and continuous

improvement can be seen as the

application of the Scientific Method

– a tool to obtain new knowledge

Box (1995)


The Meaning of Six Sigma

It is a

metric,

a working philosophy, and

a target

3


The meaning of Six Sigma

As a metrics →way of measuring the performance of a process

As a working philosophy → continuous improvement of processes

As a target → world class performance process (3.4 part-per-million opportunities)


SIX SIGMA is a BUSINESS

STRATEGY to satisfy CUSTOMER

requirements

4


DR. GENICHI TAGUCHI’S CONCEPTS

� An important product quality dimension is the total

loss to society

� In a competitive economy, quality continual

improvement and cost reductions are needed to

survive

� A quality continual improvement program includes a

constant variation reduction of a product’s

characteristics with respect to their target value

Taguchi (1987)


Lower specificationlimit (LSL)

Conforming product

Non conforming

product

Classic definition of Quality

Conformance to specifications for fitness for consumer

use

Target value

Non conforming

product

Upper specificationlimit (USL)

5


Modern definition of Quality

Uniformity around a target value

Lower specificationlimit (LSL)

Upper specificationlimit (USL)

Non conforming

product

Target value

Non conforming

product

Sullivan (1984)


Dr. Taguchi’s Definition of Quality

The loss a product causes to society if it’s not

performing at its target value (m)

LSL USL LSL USL

m m

Good BadBad Good

Taguchi (1987)

6


Two important things regarding process

control and improvement:

� Process variation

� Process mean


Definition of variation

For Shewhart (1931), sampling variation or

fluctuations are defined as differences between

things even if produced under presumably the

same conditions.

7


Importance of Variation and Centering

Service evaluation in a bank in ten occasions:

Badservice

Excellentservice

Variation

1 2 3 4 5 6


Is reducing variation enough?

Badservice

Excellentservice

1 2 3 4 5 6

Variation

8


What about centering too?

Badservice

Excellentservice

Target


The planning of statistical experiments is used to identify

the optimum values of the parameters that reduce variation

QUALITY

9


“σ” is a Greek letter used to represent a measure of a

process’ variation

Graphical meaning of Six Sigma

3σ

process

LSL USL

3σ

Cp=Cpk=1

σ

0.4

0.3

0.2

0.1

0.0

14131211109876


Graphical meaning of Six Sigma…

Centered 6σ

process

LSL USL

6σ

Cp=Cpk=2

σ

14131211109876

0.4

0.3

0.2

0.1

0.0

1615154

10


Graphical meaning of Six Sigma…

Official 6σ

processCp=2, Cpk=1.5

LSL USL

4.5σ

14131211109876

0.4

0.3

0.2

0.1

0.0

1615154

1.5σ


Defects per million opportunitiesProcess capability

3.46

2335

6,2104

66,8073

308,5372

PPMσ

Historic standard

US companies

New standard

World class standard evolution

11


Distinctive characteristics

CEO’s direct involvement and leading Six Sigma

Project evaluation, approval and following by the finance

function

Integration of existing techniques into a structured methodology

Six Sigma is an important part of the individual performance

evaluation

Of immediate application to GB or BB projects

It can be applied to any area within an organization

Dr. Edgardo J. Escalante

Metrics (Ys) linked to

CTQs

Define the Problem

Project Objective

Project Goal

PROCESS

X1X2X3X4

Y1Y2Y3

Establish Controls on the critical Xs so the

improvements will be maintained

Identify ways

to improve

the process

and validate

the solution

Measure and Analyze data

and process performance to

determine the critical

variables and root cause of

the problem

DMAIC Process

Little (2002)

22

12


Overall Approach

Practical Problem Statistical Problem

Statistical SolutionPractical Solution

y f x x xk= ( , , ... , )1 2

23

Zinkgraf S. y Snee R. (1999)


SIX SIGMA-DMAIC phases

Previous activities

Identify project, champion and project owner

Team defined and fully trained

Define customers and CTQs

Define project charter

Title, Business Case (Problem definition, COPQ,

baseline and entitlement), objective and goals, scope,

resource requirements, financial benefits, project approval

team members and estimated time

Define project plan

Develop a high-level process map

DEFINE

13


Develop a detailed process map

Identify inputs and outputs

Perform measurement system analysis

Establish process capability baseline and entitlement

Identify potential critical inputs

Determine the critical inputs

Adjust the process

Evaluate new process capability

MEASURE

ANALYZE


Optimize critical inputs

Generate and test possible solutions

Select the best solution

Design implementation plan


Develop a monitoring and control plan

Verify final process capability

Obtain owner sign-off

Elaborate a final report

IMPROVE

CONTROL

14


Define

problem

Describe process

Stable/capable

measurementImprove

N

Determine & validate significant

variables. Adjust process

Evalute process

stability & capability

Capable

process

N

D

M

M

A

A

OptimizeI

Stable

processEliminate

special

causes

N

Control

processC

Improve

continuouslyC

Y

Y

Y

Evaluate process capability & stabilityr

(baseline/entitlement)

M

DMAIC FLOW


Case development

Rent – A – Linen company

Products: bed sheets, towels, medical linen, etc.

Customers: major hospitals in a large metropolitan area

and in neighboring towns

One important customer satisfaction indicator (CTQ):

Response time to customer’s orders (OTD), target no

more than 8 hours since order reception

28

15


Problem description

The % of late shipments has increased as shown

SepAugJulJunMayAprMarFebJanDecNov

6

5

4

3

2

Month

%LateShipments

Time Series Plot of %LateShipments


OctSepJulJunMayAprMarFebJanDecNov

7

6

5

4

3

2

Index

Individual Value _

X=5.045

UCL=6.721

LCL=3.370

56

1

1

I Chart of %LateShpments

16


SepAugJulJunMayAprMarFebJanDecNov

7

6

5

4

3

2

Index2

Sample Mean

__X=5.788

UCL=6.953

LCL=4.622

1 2

Xbar Chart of Cap by Stage2

B

E


Project Charter

Problem definition/Response variable

� The % late shipments has significantly increased since Feb.

Response variable (Y): % Late shipments, measured as the

(number of late shipments divided by the total number of

shipments)*100 .

Business Case

Project title

Reduction of the percent of late shipments

17


Cost of poor quality (COPQ)

� The monthly average % late shipments is 5.79 (B, baseline)

since February. Each late shipment costs $300 and there are

an average of 400 shipments/month (100/weekly)

� The average monthly total cost is

0.0579*300*400=$6,948/month or $83,376 a year

Business Case (cont.)


Goal and target

� To reduce the % late shipments to no more than 2% (E)

(entitlement) by 1st March (6 months).

Scope and limitations

� Applicable to deliveries within the metropolitan area

Estimated resources

� $6,000 (tests, personnel, meeting room, statistical software)

Expected economic benefits

� Expected new COPQ=2%*300*400=$2,400/month or $28,800

yearly

� The expected yearly benefits are $83,376–$28,800=$54,576

� Benefits from first year will be $54,576–$6,000=$48,576

18


Champion (name/signature) Finance approval (name/signature)

H. Virtos J. Moredo

Process owner (name/signature) Estimated time

L. Moranteso 6 months

Black Belt (name/signature) Team members (name/signature)

R. Martecas R. Mataes, L. Recado, G. Gorid


% Late

shipments

(No. late shipments/total

No. shipments)*100Customer

requirements

Orders

on-time

Orders

delivered

on-time

ResponseResponsevariable

(Y)

Measurement

TargetNo more than 2%

of late shipments

Critical-to-quality characteristics (CTQ) tree

19


SIPOC DIAGRAM

S=Suppliers I=Inputs

P=Process O=outputs C=Customers

Suppliers Inputs Process Outputs Customers

Sales dept.

Suppliers of

services

Suppliers of

linens

Suppliers of eq.

& chemicals

HR dept.

Orders

Gas,

Water

Electricity

Linens

Chemicals

People

Order

reception

Order

processing

Schedule

delivery

Lots of

linens

delivered

Local and

neighbo-

ring

hospitals


Quick exercise

A tools distribution company has seen a decrease of its on-time

deliveries and decided to analyze the situation. Based on a sample

of its last 100 shipments the problem-solving team elaborated the

following histogram depicting the characteristic (CTD) “delivery

time”. Specs are less or equal 24h and the team set an

improvement target of 50%. Briefly define the problem.

20


12 24

0

10

20

Hours

Fre

cue

ncy

USLHISTOGRAM

0


DEFINE THE PROBLEM

Define the problem using one or more of the

following tools:

Pareto chart

Histogram

Run chartAudits

Critical-to-Quality (CTQ)

Problem

Target

21


Develop a detailed process map

Identify inputs and outputs

Perform measurement system analysis

Establish process capability baseline and entitlement

MEASURE


StartOrder

receptionSchedule

deliverySend

items

-Quantity & type-Processing time

-Delivery time-No. of

available trucks

-Informationaccuracy

-Scheduleddelivery time

Prepare

order

End

Example. Rent – A – Linen distribution company

42

PROCESS MAP

%Late shipments = f (Inf. accuracy, processing t,…, No. of avail. trucks)?

22


Measurement System Analysis

� Reliability and dependability of measurements

�Make appropiate decisions

Studies→ stability, linearity, bias, repeatability &

reproducibility



The order reception systems is automated and

remotely accessed by customers for placing orders.

This system is serviced at appropriate time intervals.

Order’s reception time is set by the customer when he

signs an electronic reception sheet connected directly

to Rent‒A‒Linen.

23


Order # 1 2 3 4 5 6 7 8 9 10

Time 1 {1} 7.1 2.3 5.9 4.3 6.6 8.1 9.2 5.4 8.4 7.8

Time 2 {2} 7.3 2.6 6.2 3.9 6.9 8.2 8.9 4.9 8.5 7.9


Gage R&R

Study Var %Study Var

Source StdDev (SD) (6 * SD) (%SV)

Total Gage R&R 0.20494 1.2296 9.75

Repeatability 0.20494 1.2296 9.75

Part-To-Part 2.09223 12.5534 99.52

Total Variation 2.10225 12.6135 100.00

{1} from automated system {2} by hand

MEASUREMENT SYSTEM EVALUATION


Process capability baseline and entitlement

USL

Based on 8 previous months of weekly data

3128252219161310741

10

8

6

4

2

Obser vation

Individual Value

_X=5.871

U C L=9.559

LC L=2.182

3128252219161310741

4

3

2

1

0

Obser vation

Moving Range

__M R=1.387

U C L=4.531

LC L=0

I-MR Chart of %LT-week

24


876543

99

95

90

80

70

60

50

40

30

20

10

5

1

%LT-week

Percent

Mean 5.871

StDev 1.026

N 32

AD 0.445

P-Value 0.267

Probability Plot of %LT-weekNormal


8765432

USL

LSL *

Target *

USL 2

Sample Mean 5.87074

Sample N 32

StDev (Within) 1.22952

StDev (O v erall) 1.02603

Process Data

C p *

C PL *

C PU -1.05

C pk -1.05

Pp *

PPL *

PPU -1.26

Ppk -1.26

C pm *

O v erall C apability

Potential (Within) C apability

PPM < LSL *

PPM > USL 1000000.00

PPM Total 1000000.00

O bserv ed Performance

PPM < LSL *

PPM > USL 999178.54

PPM Total 999178.54

Exp. Within Performance

PPM < LSL *

PPM > USL 999919.20

PPM Total 999919.20

Exp. O v erall Performance

Within

Overall

Process Capability of %LT-weekUSL=target<=02

25


Identify potential critical inputs

Determine the critical inputs

Adjust the process


Optimize critical inputs

Generate and test possible solutions

Select the best solution

Design implementation plan


ANALYZE

IMPROVE


Process variables 1 2 3 4 5 TOTAL Rank

Information accuracy 2 2 1 2 2 9

Quantity & type 3 1 2 3 3 12

Processing time 6 6 5 5 6 28 1

Scheduled delivery time 5 5 3 5 5 23 3

Delivery time 1 6 6 1 1 15

No. of available trucks 4 3 5 6 6 24 2

NOMINAL GROUP TECHNIQUE

GROUP MEMBERS

Process variables

26


TIME

PROCESSING

HIGH

ROUTES

PROCEDURES TRUCKS

PEOPLE

Errors

Lack of superv ision

Lack of motiv ation

Lack of training

Not enough

Broken down

Unav ailable

Not standardized

Not automated

O bsolete

Not appropriate

Wrong programming

Lack of driv ers

Suboptimized

Not up to date

Cause-and-Effect Diagram


Experiential exercise-product/process optimization

One wishes to build a helicopter prototype that has the

longest Y=Flying time {Processing time} once dropped

from a certain height.

A: Wing length (2”, 3”)

No. of trucks {a, b}

B: Body length (2”, 3”)

No. of routes {c, d}

C: Body width (1”, 1.5”)

No of drivers {e, f}Adapted from Box (1992)

What kind of design is it?

27


Design Matrix (coded units)

StdOrder WINGS BODY WIDTH TIME

1 -1 -1 -1

2 1 -1 -1

3 -1 1 -1

4 1 1 -1

5 -1 -1 1

6 1 -1 1

7 -1 1 1

8 1 1 1


StdOrder WINGS BODY WIDTH TIME

1 2 2 1

2 3 2 1

3 2 3 1

4 3 3 1

5 2 2 1.5

6 3 2 1.5

7 2 3 1.5

8 3 3 1.5

Design Matrix (uncoded units)

28


Cut

Fold

A

B

C (1”(-1), 1.5”(+1))

Material needed:

8 letter-sized sheets

8 scissors

8 small paper clips

2 glue sticks to share

1 chronograph

2” (-1)

3” (+1)

2” (-1)

3” (+1)

3”

1” (fixed)

Not to scale

Wings

Body

Width


Depending on the dimensions of your prototype it should look like this

Glue

here

Final prototype

Paper

clip

29


Design matrix in original or uncoded units (inches) in random order using Minitab®

Study of mean flight time

Factors influencing flying time:

Optimum levels to maximize flying time:


765432

Processing time

Processing time-new

Data

Dotplot of Processing time, Processing time-new {h}

30


4137332925211713951

10.0

7.5

5.0

2.5

0.0

Observation

Individual Value

_X=1.64

UC L=2.95

LC L=0.32

1 2

4137332925211713951

4

3

2

1

0

Observation

Moving Range

__MR=0.496

UC L=1.620

LC L=0

1 2

I-MR Chart of %LT-week by stage

Before improvements

After improvements


Process capability after improvements

121110987654321

3

2

1

0

Observation

Individual Value

_X=1.636

UC L=2.955

LC L=0.318

121110987654321

1.6

1.2

0.8

0.4

0.0

Observation

Moving Range

__MR=0.496

UC L=1.620

LC L=0

I-MR Chart of %LT-week

31


2.52.01.51.00.5

99

95

90

80

70

60

50

40

30

20

10

5

1

%LT-week3

Percent

Mean 1.636

StDev 0.3887

N 12

AD 0.241

P-Value 0.711

Probability Plot of %LT-week3Normal


Evaluate new process capability after adjustments

2.42.01.61.20.8

USL

LSL *

Target *

USL 2

Sample Mean 1.63615

Sample N 12

StDev (Within) 0.439504

StDev (O v erall) 0.388709

Process Data

C p *

C PL *

C PU 0.28

C pk 0.28

Pp *

PPL *

PPU 0.31

Ppk 0.31

C pm *

O v erall C apability

Potential (Within) C apability

PPM < LSL *

PPM > USL 83333.33

PPM Total 83333.33

O bserv ed Performance

PPM < LSL *

PPM > USL 203873.65

PPM Total 203873.65

Exp. Within Performance

PPM < LSL *

PPM > USL 174624.63

PPM Total 174624.63

Exp. O v erall P erformance

Within

Overall

Process Capability of %LT-week

32


Develop a monitoring and control plan

Verify final process capability

Obtain owner sign-off

Elaborate a final report

CONTROL


�In addition to optimizing the process, some

SOP where developed to keep them

enforced

�Control charts were set in place to monitor

KPIVs/KPOVs

33


Final remarks

Six Sigma Scientific

methodKnowledge

Improvement

ability

Quality &

productivity

improvement

Systematic

innovation

BUSINESS STRATEGY

Bisgaard and DeMast (2006); Escalante (2008)


REFERENCES

Bisgaard S., De Mast J. (2006). “After Six Sigma, what’s Next?”. Quality Progress, January.

Box G. (1995). “Total Quality: Its Origins and Its Future”. Report No. 123,

CQPI, UW. January.

Box G. (1992). “Teaching engineers experimental design with a paper helicopter” (George’s

Column). Quality Engineering, Vol. 4, No. 3.

Escalante E. (2008). Full Speed Ahead. Six Sigma Forum Magazine, May.

Little T. (2002). “Six Sigma Executive Overview”. Thomas Little Consulting.

Shewhart W. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand

Co. Inc.

Sullivan L. P. (1984). Reducing Variability: A New Approach to Quality. Quality Progress, July.

Taguchi G. (1987). Introduction to Quality Engineering. A.P.O.

Zinkgraf S. y Snee R. (1999). Institutionalizing Six Sigma in Large Corporations: A Leadership

Roadmap. Quality and Productivity Research Conference.

Documents

The Application of Six-Sigma DMAIC to a Distribution ...pasichile2013.auburn.edu/presentations/edgardo_escalante.pdfDMAIC to a Distribution System Edgardo J. Escalante, Ph.D. ... Critical-to-quality