Lean Six Sigma An Overview

S. Zaman Khan, Ph. D.

March 28, 2009

Agenda

• History of quality

• History of Six Sigma

• Basic Statistics

• What is Six Sigma

• What is Lean Thinking

• Lean (LSS) Methodology

• LSS Application strategy

• Basic Process Improvement Tools

2

Lean Six Sigma and change

• In order to develop, sustain, and become competitive,

we have to make changes.

• Lean Six Sigma is all about:

– Changing the culture of an organization

– Changing the processes to meet new customer requirements

and to remove constraints.

Lean Six Sigma is a physical transformation of the processes, and

it is a transformation of the organizational cultural

3

“To cherish traditions, old buildings, ancient cultures and graceful

lifestyles is a worthy thing -

but in the world of technology,

to cling to outmoded methods of manufacture, old product lines,

old markets or old attitudes among management and workers,

is a prescription for suicide.”

Sir Leuan Maddock

Change is necessary

4

―In order to allow ourselves to be creative, we have to relinquish

control and overcome fear. WHY?

Because real creativity is life-alerting. It threatens the status-quo; it

makes us see things differently.

It brings about change and we are terrified of change.‖

Madeleine L’Engle

―The world hates change, yet it is the only thing that has brought

progress."

Charles F. Kettering

There is always resistance to creativity

and change

5

Evolution - Quality Gurus

• Walter A. Shewhart (1891 – 1967): The work on quality is pioneered by

Dr. Walter A. Shewhart (Bell Tel. Labs) in 1920’s. The field of quality control got its name from his book ―Economic Control of Quality of Manufactured Product‖.

• Edward Deming (1900 – 1993): Dr. Shewhart’s colleague (and student) Dr.

Deming introduced new ideas in the field of quality control. His fundamental philosophy based on defect prevention rather than detection.

• Josef Juran (1904 - 2008): Introduced new ideas in the quality improvements

and taught quality-improvement methods in Japan and the US. Deming’s fourteen points and Juran’s four points are the backbone of modern quality concepts.

• Philip B. Crosby (1926 - 2001): Diversified the quality concepts on all levels

in an organization. He insists on exclusive use of facts, not judgment or guesswork, in making decisions regarding quality.

• Kaoru Ishikawa (1915 – 1989): Known for his work in finding out the factors

contributing to quality (fishbone or cause-and-effect diagram) and also his contemporary concepts of total quality control and total quality management.

• Genichi Taguchi (1924 - ): Famous for his ideas of loss function and novel

approaches to design of experiments as applied to manufacturing quality.6

Evolution - Quality Paramid

Pre-Industrial period

Scientific management

Shewhart SPC (1920s)

Sampling methods (AQL)

Deming/Juran

SPC & QM (1950s)

1920s –

1950s

1960s

Lean principles

JIT (1950s)

1970s

Void

Quality

revolution

1980s

and

Beyond

TOC

(1970s)

TOK

If Japan can, Why can’t we? Zero Defect

movement

Qaulity is free

(70s/80s)

Throughput strategies

Quick response

methodology (1970s)

SQC

TQM

Lean Six Sigma

Six Sigma

DFSS

NPD

BenchmarkingQuality

control

ISO 9000

Baldrige National

Quality

Lean

manufacturing

Quality team

Project teamsMotivational

theories

DRIVE

MBWA

7-S framework

Taguchi

methodology

Tuckman’s

model

Sharon’s model

Quality

councils

Quality circles

TQC

QFD

Quick production strategies (1800s)

VBM

Value engineering

Work-Out

TRIZ

BPR

PDCA

Tim

e lin

e

Bu

sin

ess Im

pro

vem

en

t P

rog

ram

s

Cost & Complexity

Note: Some of the terms are trademarks of other organizations but widely used in literature

Industrial revolution & Inspection (18th century)

7

Phases of Quality Evolution • Pre-Industrial age

• Industrial revolution (17th & 18th centuries) – Manual to machine culture

– Inspection

– Does it work approach …

• Birth of quality management (19th century) – Quality control

– Variation control

– Quality assurance …

– Motivational programs • If Japanese can do, Why can’t we

• Zero defect movement

• Our goal is 0.001 defect

• Quality is free …

– Scientific, statistical and management approaches • SPC/SQC

• TOC, TRIZ, TQM

• ISO 9000

• Six Sigma …

– Common sense • Do it right the first time

• Self-inspections

• Lean principles …

– LEAN SIX SIGMA

SPC – Statistical process control SQC – Statistical quality control TOC – Theory of constraint TRIZ – Theory of inventive problem solving TQM – Total quality management 8

The birth of Six Sigma quality standard

• 1980s: Electronic industries faced challenges from

Japanese competitors.

• Motorola management selected a team of professionals

including Bill Smith and Mikel Harry to evaluate the

current quality approaches.

• The team emphasized the correlation between the

performance of a product in the market with the amount

of rework required at the point of manufacturing.

• In 1986, it was recommended to raise the Motorola

quality standard from ±3 sigma to ±6 sigma and a new

quality metric ―six sigma‖ was introduced.

9

The birth of Six Sigma quality standard

• 1993: Six Sigma tools at Asea Brown Boveri (ABB).

• 1994: Mikel Harry developed Six Sigma Breakthrough Strategy

(MAIC) and established Six Sigma Academy in Arizona, USA.

• 1994: Allied Signal (now Honeywell) implemented Six Sigma at

corporate level.

• 1994-95: GE started corporate wide six sigma initiative.

• Many companies followed Allied Signal and GE’s successful

deployment of Six Sigma.

• 1990s: Methodology improved to become DMAIC.

• 1999: Combination of Lean and Six Sigma experimented.

• 2002: Lean Six Sigma (LSS) became a standard approach in many

industries.

DMAIC – Define, Measure, Analyze, Improve, Control

10

Basic Statistical notations

11

Mean, Variance, and Standard deviation Mean (μ for population and x-bar for sample) is arithmetic average of a set of values.

Data: 17, 16, 21, 18, 13, 16, 12, 11

Variance (σ2 for population and s2 for sample)

Standard deviation (σ for population and s for sample)

12

Properties of Normal Distribution

What is the difference among these three normal distributions?

• Normal Distribution: In statistics, the normal distribution or Gaussian

distribution is a continuous probability distribution that describes

data that clusters around a mean or average.

• First property: A normal distribution can be described completely by

knowing the mean and standard deviation.

• Second Property: The area under sections of the curve can be used

to estimate the cumulative probability.

13

What is Six Sigma?

14

Six Sigma Metric

• Six Sigma as a metric

– A process running at Six

Sigma quality level

produces no more than

3.4 defective parts per

million opportunities

(DPMO).

– As the sigma quality level

increases, the DPMO

decreases and the rolled

throughput yield (RTY)

increases.

• Sigma (σ), a Greek letter, denotes standard deviation.

• Six Sigma is a metric that measures the performance of

a process.

15

Metrics and terms enforced by Six Sigma

• A defect is a shortfall that causes inadequacy or failure by not

meeting customer specification.

• An opportunity is the total quantity of chances for a defect.

• Defect per unit (DPU)

• Total opportunities (TO)

• Defects per opportunities (DPO)

• Defects per million opportunities (DPMO)

units ofnumber Total

defects ofnumber TotalDPU

iesOpportunitunits ofnumber TotalTO

iesopportunit ofnumber Total

defects ofnumber TotalDPO

1,000,000DPODPMO 16

Rolled throughput yield

• Throughput yield: The percentage of the good pieces divided by

the total pieces sent into the process.

• First pass yield (FPY): The percent of good pieces resulting from a

process step. It is the percentage of good pieces divided by total

pieces started into the process step.

• Rolled throughput yield (RTY): Rolled Throughput Yield (RTY) is

the probability that a single unit can pass through a series of

process steps free of defects. It is the product of first pass yield

(FPY) of each process step.

• Traditional throughput yield focuses on the final outcome of a

process and allows a ―hidden factory‖ flourish (percentage of good

piece/total pieces sent into process).

• RTY allows to understand what areas/steps of the process are

creating defects and how the process output is impacted by those

defects. 17

Cost of poor quality and Lean Six Sigma

• Cost of poor quality (COPQ) is directly linked to the

defects per million opportunities (DPMO) or sigma level.

• Typical three-sigma company spends about 25 percent

of each sales dollar on the COPQ.

• The COPQ exceeds the % profit margin where COPQ is

not known.

Exercise: Brainstorm the cost of quality (COQ) and COPQ.

Document at least five types in both categories.

18

Six Sigma basics

• Six Sigma quality level is derived from Gaussian curve

for normal distribution (in use since 1733) and widely

promoted by Carl Friedrich Gauss since 1794.

• In 1922, Dr. Shewhart developed statistical process

control techniques and a +/- three sigma quality

standard was adopted by industry.

• The six sigma quality standard emphasizes on shrinking

the variation in the process so that it does not produce

defects 99.99966% of the time.

19

Area under the normal curve

Large variation

• The total area under the curve

(between -∞ and +∞) is 100%.

• Area between +/- 1 standard

deviation is 68.27%.

• Area between +/- 2 standard

deviation is 95.45%.

• Area between +/- 3 standard

deviation is 99.73%.

• A process running at +/- 3 Sigma

quality level produces 66,000

PPM defective (after drifts, the in

long term).

)(

2

)(exp

2

1)(

2

2

xz

xxf

x

20

Area under the normal curve

Reduced variability

• More data points would lie

closer to the mean if

variation is reduced.

• If all the data points lie

within +/- 6 standard

deviations, than the

throughput yield of the

process is 99.9999998%.

21

Area under the normal distribution curve beyond z » 4.57 is close to zero (1-

99.99966%). This translate in to 3.4 PPM & defined as Six Sigma Quality

Level.

Six Sigma Quality level

PPB – Parts per billionPPM – Parts per million ST – Short term LT – Long Term

22

Six Sigma quality level and process capability

A process running at Six Sigma quality level produces no

more than 3.4 parts per million defectives. PPB – Parts per billionPPM – Parts per million ST – Short term LT – Long Term

23

6-Sigma99.99966% Good

• 20,000 lost articles of mail per hour.

• 5,000 incorrect surgical operations

per week.

• Two short or long landings at most

major airports each day.

• 200,000 wrong drug prescriptions

each year.

• Seven articles lost per hour.

• 1.7 incorrect operations per

week.

• One short or long landing every

five years.

• 68 wrong drug prescriptions per

year.

3.8-Sigma99% Good

.

N

3.4 defects per

million

opportunities

Based on U.S. statistics in the 1990s

Why to raise the quality standard?

24

What quality level we want to be at?

• The goal of SSQL depends on nature of the process,

business needs, cost, and customer requirements.

• Most companies use LSS for:

– Problem solving

– Cost reduction and increase in profit margins

– Process optimization

– New process/ product development

– Personnel development and leadership

– Growth.

25

What is Lean Thinking? • Lean Thinking is also known as lean, lean production, lean

manufacturing, Toyota production system (TPS), Just-in-time (JIT)

etc.

• It is a common sense approach.

• Lean ideas originally developed in the United States (Ford Motors,

1914) and than widely used by Japanese (Toyota, 1950).

• Lean is focused at eliminating the waste in the processes that in turn

increases the speed, improves the quality, and reduces the cost.

• ―Strategy that uses less of everything compared with traditional

manufacturing: half the human effort, half the space, half the

investment in tools, half the engineering hours to develop a new

product. Also it requires keeping far less than half the needed

inventory on site, results in many fewer defects and produces a

greater and ever growing variety of products.‖ Machine that changed the world by James

Womack (1990)26

Commonly used Lean Thinking tools• Value stream mapping (VSM) and process mapping

• Kaizen events

• Total productive maintenance (TPM)

• Single minute exchange of dies (SMED)

• 5 S (sort, set in order, Shine, Standardize, Sustain)

• Load balancing

• Kanban

• Pull systems

• Point-of-use inventory (as opposed to warehouses)

• Vendor managed inventory (VMI)

• Mistake-proofing

• … 27

8 Wastes

Overproduction WaitingUnwanted Transportation

Overprocessing

Over InventoryUnwanted Movement

DefectsUnused Employee Creativity

Understanding Wastes

28

Exercise

• How to remember 8 wastes

29

Variation and COPQ

A good process running at

traditional high quality has a

potential to produce defects.

Cost of poor quality (COPQ)

=2666.19*Cost per part .

Over time COPQ multiplies.

A process running at a Six Sigma

quality level has less opportunity

to produce the defects.

The process will produce less

defects even after shifts and drifts

over time.

COPQ: cost of poor quality

USL – Upper specification limit

LSL – Lower specification limit

30

Focus of Lean Six Sigma

• Every process has a target that is measured around the mean.

• Variability is inherent to the processes that makes the mean

dynamic. The measure of this variability is standard deviation.

• Every process has a constraint that directly impacts the purpose or

profit.

• Every process has a waste that makes it slow.

• Lean Six Sigma helps to

– Move the mean to the target

– Shrink the variation for consistency

– Reduce and eliminate the constraints

– Eliminate waste.

31

What does Lean Six Sigma Means to a

business

• Metric

– Produce no more than 3.4 parts per million opportunities (cost, quality,

& delivery).

• Problem solving

– Use DMAIC breakthrough methodology to reduce variation, eliminate

waste, and remove the bottlenecks (cost, quality & delivery).

Understand customer requirements and reduce variation to meet those

requirements.

• Management system, strategy and Vision

– Reduce cost, increase value, increase revenues, develop human

resources, win over competitions, new business/ product development,

…

– A high performance system for executing business strategy - Motorola

DMAIC: Define, Measure, Improve, Control

32

Lean Six Sigma defined

• Lean Six Sigma is a rigorous, disciplined, and data

driven business process optimization and problem

solving methodology which aims to reduce variability,

eliminate non-value added activities (waste), and

reduce cost.

• Lean Six Sigma is applicable to any process/activity.

• Used world-wide and is Well-proven methodology.

33

Lean Six Sigma and financial benefits

• From 1986 – 2001, Motorola saved $16 billions.

• From 1996 – 1999, GE saved $4.4 billions.

• From 1998 – 2000, Honeywell saved $ 1.8 billions.

• From 2000 - 2000 Ford saved $1 billion.

• Over the past 20 years Six Sigma saved Fortune 500

companies an estimated $427 billion.

34

Industry embraced Six Sigma

• 53 percent of Fortune 500 companies are currently using

Six Sigma-and that figure rises to 82 percent if we look

at just the Fortune 100 – 2006 survey

• True six sigma produced 40% more savings than those

with less rigorous programs – 2006 survey

• More than 55% of 418 enterprises interviewed,

implement lean six sigma – 2006 survey

35

Harvesting the Fruit of Lean Six Sigma

Difficult to Reach Fruit Design for Six Sigma (DFSS)

Middle FruitLean and Six Sigma

Low Hanging FruitLean, Basic quality tools

Ground FruitLogic and Intuition Basic tools

Degree

of

Complexity

36

Lean Six Sigma - Training strategy

• Training strategy

– Plan

– Train

– Apply

– Review

Lean Six Sigma Change Agents go through different levels

of rigorous training, coaching and mentoring

Plan

Train

Apply

Review

37

Lean

Six

Sig

ma M

eth

od

olo

gy

Characterization

Optimization

Phase 2

Measure

Phase 3

Analyze

Phase 4

Improve

Phase 5

Control

Phase 1

Define

Lean Six Sigma MethodologyApplication Strategy

Practical

Problem

Analytical

Problem

Analytical

Solution

Practical

Solution

Lean Six Sigma uses the DMAIC (Define, Measure, Analyze, Improve,

Control) process as a disciplined and methodological approach for problem

solving and process improvement 38

Lean Six Sigma Application Process

Define

• Project identification

• Value Stream Mapping

• VOC and Kano Analysis

• Project Approval Form

• COPQ analysis

• Internal Rate of Return Analysis

• Cash Flow Analysis

• RACI

• Stake holder analysis

Measure

• Process Mapping

• Data Collection plans

• Constraint Identification

• Setup Reduction

• Generic Pull

• C&E Diagrams

• C&E Matrix

• Kaizen

• TPM

• Control Charts

• MSA and Gage R&R

• Process Capability Indices

Analyze

• Brainstorming

• Basic Tools

• Components of Variation

• FMEA

• Multi-Vari

• Box Plots

• Interaction Plots

• Regression

• ANOVA

• C&E Matrices

• Hypothesis testing

Improve

• VA Improvement

• Brainstorming

• Replenishment Pull

• Process Flow

• Benchmarking

• DOE/ RSM

• Stocking, Purchasing and Sales Strategy

• Supply-chain optimization

• Batch Sizing

• Line Balancing

• Piloting and Simulation

• Training

Control & Sustain

• EWMA and CuSumControl Charts

• Pareto Charts

• Visual Process Control

• Poka-Yoke

• Process Control Plans

• Project Commissioning

• Procedures & policies

• Safety measures

• Training

• Final Control Plan

• Identify Problem

• Develop List of Customers

• Develop List of CTQ’s

• Finalize Project Focus and Key Metrics

• Financial benefits

• Complete Project approval form

• Map Business Process

• Value stream mapping

• Qualify measurement systems

• Collect Data

• Determine process stability

• Conduct process Capability analysis

• Baseline analysis

• Propose Critical X’s

• Prioritize Critical X’s

• Verify Critical X’s

• Estimate the Impact of Each X on Y

• Quantify the Opportunity

• Prioritize Root Causes

• Conduct Root Cause Analysis on Critical X’s

• Critical X’s Confirmed

• Develop Potential Solutions

• Select Solution

• Optimize Solution

• Pilot Solution

• Process capability analysis

• Implement Process Changes and Controls

• Write Control Plan

• Calculate Financial Impact

• Process Metrics

• Transition Project to Future Owners

• Identify Project

• Translation Opportunities

VOC – Voice of customer COPQ – Cost of poor quality RACI – Responsible, Accountable,

Consulted, Informed

TPM – Total productive maintenance C&E – Cause and Effect MSA – Measurement system analysis R&R – Repeatability and Reproducibility

FMEA – Failure mode and effect analysis ANOVA – analysis of variance VA – value add DOE – Design of experiment

EWMA – Exponentially Weighted Moving Average

CuSum - cumulative sum

CTQ – Critical to quality RSM – Response surface methodology

X’s – Input variables Y’s – Output variables

39

Define Phase

• Define the problem.

• Identify the customer(s).

• Organize the team and define its roles and responsibilities.

• Establish goals and milestones.

• Establish the scope of the LSS project.

• Define the metrics.

• Map the process.

• Develop data collection plan.

What is important to customers OR business goals?

40

Measure Phase

• Collect data on current process.

• Confirm the customer’s needs, and expectation.

• Validate measurement system.

• Determine input variables (X’s) that may impact output (Y’s).

• Establish baseline measurement of current process.

How is the process performing? How does it look / feel like to the customer? How good is the data?

41

Analyze Phase

• Narrow the focus to specific issues.

• Develop a mechanism to analyze data.

• Identify what is causing defects, waste and variation. Characterize the variables (X’s).

• Find improvement opportunities.

• Based on data analysis, revisit problem statement and assess the need to further scope the issues.

The shape has a bell shape.

It is symmetric.

The shape has two humps.

It is bimodal.

The shape has a long tail.

It is not symmetric.

The shape is flat. There are one or more outliers.

41Q

Histogram Interpretations

40E

Graphical Analysis Tools – Box Plot

5 6 7 8 9 1 0 1 1 1 2 1 3 1 4

Mean (8.16)

Mode

22 points of data

(Half of the distribution)

22 points of data

Median

41P

Describing the Distributions

What are the most important causes of process waste, defects & variation?

42

Improve Phase

• Validate hypothesis about the root cause of the problem

• Identify critical variables (X’s)

• Identify alternate solutions

• Determine optimal solution

• Perform cost/benefit analysis

• Design improvements

• Pilot improvements

• Implement and validate improvements

Move the mean. Shrink the variance. Eliminate the waste.

43

Control Phase

• Ensure corrective actions are taken.

• Mistake-proof the process.

• Transition the control of the new process to the process

owner.

• Provide techniques to sustain the improvements.

• Measure the final capability.

• Monitor performance. How can we maintain the process improvements?

7.5

8.5

9.5

10.5

11.5

12.5

0 10 20 30

44

Design for Six Sigma (DFSS)

• DFSS is used to determine the customer and business

needs and translating those needs into new process or

product in the most optimal way to achieve most optimal

and sustaining results results.

• DFSS is process generation (as opposed to process

improvement).

• Also called DMADV or new product or process

development (NPD).

MeasureDefine DesignAnalyze Verify

45

Sponsor

• Own the vision, direction, integration, and results

• Identify Black Belts/Green Belts, and help in project identification

• Apply Lean Six Sigma to specific projects

• Lead and direct teams to execute projects

• Support Champion in effective project scoping

• Train, coach, and develop Black Belts/Green belts

• Work on complex projects

• Approve BB certification

Champions

Black Belts

Green Belts

Master Black

Belts

Process

Owners

• Identify and assist in scoping projects

• Own the process

• Ensure changes are sustained

LSS organizational structureRoles and responsibilities

Lean Six Sigma practitioners are assisted by financial experts to estimate and verify savings

46

Training requirements

• Champions training: 2 days – one week.

– Some companies include another week of on-the-job training.

• Green Belt training: Two weeks and one project.

• Black Belt training: Four – five weeks and two projects.

• Master Black Belt training: Three – five weeks training,

coaching, mentoring and facilitation.

47

LSS Practitioner qualities

• Customer focus, self-motivated and positive personality

• Leadership skills

• Excellent communication skills

• Excellent presentation skills

• Project management skills

• Process and product knowledge is preferred

• Team player

• Result oriented

• Data mining

• Passionate

• Patience

• Learner 48

• Late Delivery

• Poor Product Reliability

• High Cost of Quality

• Incoming Product Quality

Problems

• Unpredictable Quality or Product

Performance

• Poor Process Capability

• High Incidence of ―Past Due‖

notices

• High Maintenance Costs

• Low Machine Utilization

• Transactional Defects

• Low Customer Satisfaction

• Excessive Variation

• Poor Design

• High Operating Costs

• Excessive Scrap/Rework

• High rate of rejections

• High Inventories

• Long Cycle Times

• Capacity Constraints

• Excessive Set-Up Costs

• Waste

• Low Rolled Yield Rate

Typical Lean Six Sigma project areas

49

Lean Six Sigma Project Selection Criteria

• A high value project

• A repeatable process

• Strong management sponsorship

• Strategic linkage

• Process is within your control

• Data availability

• Compelling problem statement

• Despite attempts, process owner could not solve the problem

• Workable scope

• Short completion period

• Firm defect definition 50

Common Causes of Project Failures

• Inadequate management support.

• Inadequate time for Green Belts/ Black Belts and other team

members .

• Project Scope Is Too large

• ―Boiling the ocean‖

• Scope Creep.

• Project Scope Is Too small

• Projects with little business impact.

• Solution-in-Mind

• ―Just Do It‖ projects do not require the rigors of the LSS DMAIC process.

• Data not available or not valid.

• Politics (pet projects).

• Lack of ―soft skills‖ (communication, leadership, team building, and

change management).

51

Comply

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

Commit Embed

Marginal Improvement

Encode

Incremental Improvement

Major Improvement

Lean Six Sigma deploymentModified from Lean Six Sigma by Michael L. George, 2002, pp., 227

Vision

Planning

Benchmarking

Initiating

Executive teams

….

Invest Believe and

publicize

Embrace

and spread

Part of DNA

52

Tool Time

S.M.A.R.T. Goals

• Specific: A specific goal has a greater chance of success.

• Measureable: Criteria to measure the progress.

• Attainable: Identify goals that are most important to you.

• Realistic: Set a goal that is realistic.

• Time bound: Set a timeframe to achieve the goal.

54

Project charter• Precisely and quantitatively define the problem, establish the

objectives, both in one or two sentences.

• Problem statement: On-time delivery performance of all ABC units is

only 60%. This results in customer complaints and shipment

rejections that in turn increases the inventory levels.

• Objectives: Improve on-time delivery to 95% by the end of June

2009.

• In scope/Out of scope.

• Financial and other Benefits.

• Identify customers.

• Select a cross functional team and get the support of the subject

matter experts.

• …55

Seven basic tools of quality

• Ishikawa diagram (also called cause-and-effect diagram

or fishbone diagram)

• Pareto chart

• Check sheet

• Control chart

• Flowchart (or process map)

• Histogram

• Scatter diagram

56

Make the process visible

• Helps to see the abnormalities immediately.

• Uncovers the hidden factory.

• Helps to become proactive.

• Use a simple chart to show how the process is

performing.

• Display the primary metric close to the process.

• Display the process map or flow chart of the process.

57

RS On-time Delivery Performance

RS 2005 On-time Delivery performance

0

10

20

30

40

50

60

70

80

90

100

Jan Feb M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec

Customer M etric (Performance to request), % RS M etric (Performance to schedule), % Goal, %

On-time performance:

RS metric: 60%

Customer metric: 35%

Goal (by end of June 2006): 95% by end of June 2009 58

Process map

• Identify start and end point (process boundaries).

• Document the process steps by creating the flow chart.

• Identify the inputs and outputs of each step.

• Characterize the inputs

– Controllable (C)

– Standard operating procedure (SOP)

– Noise (N).

• Document data such as cycle time of the process steps,

number of operators, etc.

59

Types of variables

• Controllable (C): These inputs can be adjusted or

controlled during the process (e.g., speed, feed,

temperature).

• Standard Operating Procedures (SOP): Common

sense items (e.g., cleaning, safety).

• Noise (N): Things that we can’t control or don’t want to

control (too expensive or too difficult; e.g., ambient

temperature, humidity, wind etc.).

Process

Input (X’s) Output (Y’s)

Characterize

Input variables

Identify

Output variables

60

Value-added and non value-added activities

• Value Added: Activity that increases the value.

• To determine if an activity is value-added ask:

– Is this something the customer is willing to pay for?

– Does the activity positively change the form, fit, or function of the

product?

• Non Value Added: Any action which consumes

resources without directly contributing to the product.

• Non value added but necessary: Activities that do not

add value but are necessary to complete the job.

• Optimize value-added, reduce non value-added but

necessary, and eliminate non value-added activities. 61

High level process map

RS Valve manufacturing process

CustomersSales &

MarketingPlanning

Purchasing

Scheduling/

WarehouseProduction

Shipping/

Receiving

- Daily orders

- 24 different sizes and Types

- No forecast

- Domestic and International

- No LT for commercials

- Dynamic LT for specials (1 to 60 days)

Suppliers

- Offices at strategic locations

- Divided by regions

- Forecast

- 15 Overseas (LT = 24 weeks)

- 25 Domestic (LT = 8 weeks)

- Bulk orders (8 weeks SS)

- MRP

- MRP

- MRP

- Overnight schedule print out

- Bulk pull from stock

- Staging processes

- 2 shifts

- Machining and sub-processes

- Assembly

- Testing

- Daily trucks

- Various shippers

Note: Compare this high-level process map with the Org chart

LT – Lead-time

SS – Safety stock

MRP – Material requirement planning

62

Detailed RS Valve assembly process map (As Is)

Enter customer

orders

Planning

review/prioritize

overnight orders

Scheduling

create, print and

distribute WO

Legend

WO – Work order

BOM – Bill of materials

Kit – Production order package

C – Controllable

N – Noise

SOP – Standard Op. Procedure

Av. - Average

VA – Value added

CT – Cycle time

NVA – Non-value added

Average CT per product in indicated

Warehouse –

pull and stage

the material

Complete

BOM ?

Incomplete kit

staging

Staging

Pick prioritized

production

orders

Match the

paperwork and

pick the parts

Take the

paperwork and

material to Shop

Deliver the kit in

the shop staging

area

Daily and weekly

production

schedules

Assembler –

pick the material

with paperwork

Place the parts

on the assembly

line

Assemble (liner,

disc, stem,

bushing, body)

Leakage test

Rework

Torque test OK ?

Rework

Place

assemblies in

the staging area

QC OK ?Take parts to

ShippingPack and ship

Assembly to

rework (or scrap)

SOP , C, 3 days Av.

Manual

Daily

5 people

VA

SOP , C, 4 days av.,

Daily

3 people

SOP , C, System

Overnight

3 people

NVA

Y

N

Y

Y

Y

N

N

N

C, NVA, 2 people,

3 days Av.

C, 5 days Av. C, Daily, 150 yards one way, 45 minutes

C, 50 yards one way

25 minutes

C, 45 days Av. waiting

SOP, C, VA ,

45 minutes Av. SOP, 5 min. Av.

C, NVA, 30 min Av.

C, NVA, 3 min Av.

C, NVA, 30 min Av.

C, NVA, 5 min Av. SOP, C, NVA, 5 min Av.

C, NVA, 30 min Av.

C, VA, 2 days Av. SOP, C, 5 min Av.

OK ?

Processing time

Transactions = 16 days

Processing = 3.74 hours per product

Waiting time = ? 63

Exercise

• Mr. Z is attending SEF2009 from Bahrain. Develop a

process map showing steps from receiving forum

information to attending the forum. Write down estimated

time for steps (where applicable) and other

characterizations and data.

64

Rolled throughput yield versus throughput yield

Manufacture

part 1

Manufacture

part 2

Inspect

Manufacture

part 3Assemble

Rework/Scrap

95%

customer

quality

Hidden Factory

Yield = 95%

Manufacture

part 1

Manufacture

part 2

Manufacture

part 3Assemble

RTY = 82 %

and not 95%

Process step yield (First pass yield)

95% 97% 94% 95%

Real yield or RTY

95% 95*97=92% 95*97*94=86% 95*97*94*95=82%

OKNot OK

RTY helps to uncover

the hidden factory and

enforces prevention

50,000 PPM wasted 30,000 PPM wasted 60,000 PPM wasted 50,000 PPM wasted

Total wasted opportunities: 190,000 PPM

Process Sigma = 2.37

65

Defects per million opportunities (DPMO)

• Assuming that a process produces 30 assemblies

– Each assembly has 5 parts

– Number of opportunities for defect in each assembly = 5 + 1 = 6 (five for each

part and a sixth for appearance).

• Total opportunities, TO = 30*6 = 180

• Number of units checked = 30

– Defects found = 5

• DPO = 5/180 = 0.0277778

• DPMO = 0.0277778*1,000,000 = 2,7777.78 or 27,778

• This means that if the process has produced 1,000,000 assemblies,

we expect to end up with 27,778 defects in those assemblies.

• Add up DPMO for each sub process to find overall PPM defective.

66

5 W and 2 H problem identification

approach

Who? Identify customers complaining about the problem

What? Define the problem accurately

When? Timing - When did the problem start?

Where? Location - Where is it occurring?

Why? Identify the causes (5 WHYs)

How? In what mode the problem occur

How many? Magnitude or frequency of the problem

67

5 Whys help to find the root cause?

1. Why did the system fail?

A: The motor burned out.

2. Why did the motor burn out?

A: The shaft seized.

3. Why did the shaft seize?

A: There was no lubrication.

4. Why was there no lubrication?

A: The line filter was clogged.

5. Why was the line filter clogged?

A: It was the wrong sized mesh!

68

TimeTime is money.

Will never wait.

You may delay, but time will not.Benjamin Franklin

There is one kind of robber whom the law does not strike at, and who

steals what is most precious to people—time.

Make use of time, let not advantage slip.

Better three hours too soon, than one minute too late.

William Shakespeare

Time and tide wait for no man.

Geoffrey Chaucer

Money, I can only gain or

lose. But time I can only

lose. So, I must spend it

carefully

Time stays long enough for those who use it.

- Leonardo Da Vinci

We must use time as a tool, not as a couch.

- John F. Kennedy

One thing you can't recycle is wasted time.

Time is the supreme Law of nature.

- Arthur Stanley Eddington

Time is the wisest counselor of all.

- Pericles

Time is the school in which we learn,

time is the fire in which we burn.

- Delmore Schwartz

Time is what we want most,

but what we use worst.

- William Penn

Time is Money

Cycle time (CT)

• Time it takes an operator to go through all of his/her

work elements before repeating them.

• It is the time measured by direct observation in which a

good item or good product is completed by the process.

Finished parts

Cycle time

Operation

1Operation

2Operation

3Operation

4

Work in process (WIP)

70

Little’s Law to approximate Process CT

• Little’s law provides a quick and reliable mean to

measure the process cycle time (PCT) also called Lead

Time (LT).

• The time from work order release into the process until

completion and measured as:

Operation

1Operation

2Operation

3Operation

4

Work in process (WIP)Finished parts

Exits/day

Process Cycle Time (PCT) or Lead Time (LT)

71

Process cycle time (PCT)

Operation

1Operation

2Operation

3Operation

4

Work in process (WIP)Finished parts

• WIP is the number of pieces or transactions being worked in the

process at any given time, WIP = 14 pieces.

• Exits is the amount of work completed over a given period of time

(Weekly, Daily, hourly etc.), Exits = 7 units/day.

• CT or LT = (WIP/Exits per day) = 14/7 = 2 days.

Exits/day

Process Cycle Time (PCT) or Lead Time (LT)

• A reduction in WIP leads to cycle time reduction.

72

Takt Time (TT)

• Takt time is the maximum time allowed to produce a

product or process a transaction in order to meet

customer demand.

• Time available – Time available per day minus break, wash-up, set up etc.

• Customer demand– Number of units on order on a given day.

• Takt Time metric helps to synchronize the pace of

production to the pace of sales.

𝑻𝒂𝒌𝒕 𝑻𝒊𝒎𝒆 =𝑻𝒊𝒎𝒆 𝒂𝒗𝒂𝒊𝒍𝒂𝒃𝒍𝒆

𝑪𝒖𝒔𝒕𝒐𝒎𝒆𝒓 𝒅𝒆𝒎𝒂𝒏𝒅

73

Understanding TT

• CT = TT, ideal situation. Customer receives on-time.

• CT >> TT, Hidden factory, too much waste.

• CT < TT, over capacity. Producing faster than customer

order pace is waste of resources.

74

Takt Time (TT), example

• Customer demand = 100 units/day.

• Assuming one shift of eight hours and lunch time is not

paid.

• Total time = 8 hours/day.

• Breaks = 2*10 minutes, wash up = 10 minutes, meetings

= 20 minutes, operator maintenance = 10 minutes.

• Time available = (8*60) – 60 = 420 minutes /day.

• TT = 420/100 = 4.20 minutes.

• In order to meet the customer demand on-time, each unit

should be completed in 4.20 minutes. 75

Cause-and-Effect diagram• Two approaches:

– Use check sheets based on data collected by team

– Brainstorming without previous preparation.

• Two way to construct:

– Use a flip chart, write down the problem (effect) on the right side of a

main line with arrow on it and draw lines with major headings and than

brainstorm causes under each topic.

– Gather team thoughts on cause in a tabular form. Each column heading

will represent the major cause and sub-causes will be recorded.

• Construct the C&E diagram

problem

agreed

Write the

Major cause 4

Major cause 3

Major cause 2

Major cause 1

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

sub-sause

Cause-and-Effect Diagram Example

76

Exercise

• Assume that the local industries and KFUPM want to

increase the interaction and cooperation to make the

research and development more effective and useful.

Develop a cause and effect diagram.

77

Determine critical causes

• Brainstorm with the cross functional team and subject

matter experts to determine the critical causes.

• Use data (if available) to determine the impact and

criticality of a cause.

• Use multi voting techniques and knowledge of subject

matter experts to narrow the list of causes.

78

Check sheet

• A simple tool to collect the real time data at the location

where it is generated.

• Typically a blank form is used to record each occurrence

of interest.

• Check sheet is an effective data collection tool.

Defects in outside diameter of a steel disc

Machine# Sat Sun Mon Tue Wed Total

Machine 1 II I I II III 9

Machine 2 III I I I II 9

Machine 3 II III II II II 11

Machine 4 III IIII III III IIIII 18

Total 10 10 7 8 12 47

79

Scatter plot

• Scatter plot is used to display the relationship between

two variables.

• We can use MS Excel to display the scatter plot.

• The graph shows that there is a positive relationship

between number of hours studied each day and exam

score.

StudentsStudy Hours

Math Score

A 3 82

B 5 92

C 2 77

D 6 82

E 7 92

F 1 52

G 2 67

H 7 87

I 1 42

J 7 102

80

Pareto chart

0

10

20

30

40

50

60

Chart of mistake categories• Based on team input and/or

data, display the impact of

critical causes on the output of

the process using a Pareto

chart or bar chart.

• Pareto analysis provides

information on what 20% of the

variables cause 80% of the

problem.

• In many cases you may need a

second and third level Pareto

analysis

81

Implementation plan

• Develop a solution to eliminate or reduce the impact of

the critical causes on the output of the process.

• Develop an implementation (or action plan). It can be a

simple Excel spread sheet.

– Action item (to reduce the impact of critical causes)

– Impact

– Responsibility

– Start date

– End date

– Status

– …

82

Specifications and process data

• Statistical software help to use various tools (such as control

charts, process capability analysis, descriptive statistics etc).

• Such tools require training and practice.

• Simple line chart can be used to see the data with reference to the

specifications (use MS Excel).

1

1.5

2

2.5

3

3.5

4

4.5

1 2 3 4 5 6 7 8 9 10

Upper specification

limit (USL)

Lower specification

limit (LSL)

Mean

83

Final thoughts

Lean Six Sigma

• Process centered and project focused.

• Focuses on customer requirements.

• Emphasizes permanent change and transformation.

• Fact based and data driven.

• Applicable to transactional as well as manufacturing processes.

• Requires planning, training, coaching and mentoring efforts.

• Helps in sustaining and consistent change across functions.

• Requires leadership involvement/ commitment and line

management buy-in.

• Top down approach is most successful

• Brings about breakthroughs. 84

85

Appendix

Thank You

Six Sigma Process

• PPM expected = 0.002

Distribution span = 12 Sigma (±6*/2)

• The variance shrunk

• The standard deviation reduced to half ( = 0.0019)

• The data distribution around mean (first quartile) is

higher

Three Sigma Process

• PPM expected = 2700

Distribution span = 6 Sigma (±3)

• Large variance

• Higher standard deviation ( = 0.0038)

• Smaller data distribution around mean (less

frequency distribution close to mean)

• Data spread along the z line

• Data points exist close to upper and lower

specs i.e., at 3 and –3 distance from the

mean.

Six Sigma and PPM

²2

)(exp

2

1)(

xxf

x

)x(scorez

.001 PPM .001 PPM

1300 PPM1300 PPM

53

6 4 3 2 1

2 1 3

1 2 3 4 5 6

1 2

Difference between ±3and ±6data distribution (no shift, short term)

0

Six Sigma (new standard) versus

Three Sigma (old standard)

87

5- Why AnalysisWhy Because

Why did some Trouble

Tickets exceed 3 days to

Resolve!?

1- Spare Parts Availability (35.10%)

2- Unclear Process! (21.22%)

3- Cost Estimation (15.08%)

4- Wrong Assignment (10.61%)

5- Customer Availability (39.10%)

6- Lack of Technical skills (28.49%)

7- Restricted/ Remote areas (23.46%)

8- Incorrect Descriptions! (21.78%)

5- Why Analysis (Cont..)

Eight wastes in a business

must be measured and eliminated or reduced

T Transportation O Over production

W Waiting I Inventory S Skills D Defect O Over processing M Movement

Another Acronym used to remember 8 waists is

TIM P WOOD

90

Mean Shift and Variance Reduction

On CenterLarge Spread

LSL USLT

On CenterSmall Spread

LSL USLTLSL USLT

Off CenterSmall Spread

Off CenterLarge Spread

LSL USLT

91

Understanding and reducing variation

Lower Specification

Limit (LSL)Upper Specification

Limit (USL)

# o

f G

oals

92

Understanding and reducing variation

Lower Specification

Limit (LSL)Upper Specification

Limit (USL)

# o

f G

oals

93

Acronyms used on slide#3

94

SPC – statistical process control

TOK – theory of knowledge

TOC – theory of constraint

TRIZ – Theory of inventive problem solving

TQC – total quality control

SQC – statistical quality control

QM – quality management

PDCA – plan-do-check-act

QFD – quality function deployment

TQM – total quality management

MBWA – management by walking about

BPR – business process re-engineering

VBM – value based management

DFSS – design for six sigma

DMADV – design, measure, improve, control, verify

DRIVE – define, review, identify, verify, execute (TQM)

AQL – Acceptable quality level

NPD – New product/process development (same as DFSS)

Tuckman’s model – forming, storming, sorming, performing model

McKinsey 7-S framework – shared Value, structure, system, style, staff, skills,

strategy

Industrial Revolution – a period in the late 18th and early 19th centuries when

major changes in agriculture, manufacturing, production, and transportation had a

profound effect on the socioeconomic and cultural conditions in Britain.