Expanding Six Sigma to Suppliers - Abidian · Value Stream Mapping • Value Stream Mapping is the...
Transcript of Expanding Six Sigma to Suppliers - Abidian · Value Stream Mapping • Value Stream Mapping is the...
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Expanding Six Sigma to Suppliers
Elizabeth CudneyCQE, SSBB
December 5, 2005
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The Definition of a Lean Enterprise
A group of individuals, functions, which are sometimes separate but operationally synchronized organizations.
The objectives of the lean enterprise are:
• “Correctly specify value for the customer”
• “Identify all the action required to bring a product from concept to launch, from order to delivery, and from raw material into the hands of the customer and on through its useful life.”
• “Remove any actions which do not create value and make those actions which do not create value proceed in continuous flow as pulled by the customer.”
• “Analyze the results and start the evaluation process over again.”
Source: Womack, James P. and Jones, Daniel T., Lean Thinking: Banish Waste and Create Wealth in Your Corporation, Simon & Schuster, New York, NY, 1996.
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What is Lean?
• Lean Manufacturing focuses on eliminating waste and improving flow using various Lean principles and their respective approaches.
• VSM, standard work, 5S, SMED, visual management, etc.
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Lean Overview
• Lean emphasizes the elimination and prevention of waste.
• Lean is focused on the customer by addressing what is value added and what is non-value added.
• Products and services are delivered Just-in-Time meaning in the right amounts, at the right time and in the right condition.
• Products and services are produced only when a signal is received from the customer and are pulled through the system.
• A lean system allows for an efficient response to fluctuating customer demands and requirements.
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Lean Benefits
• Eliminate waste
• Reduce non-value added activities
• Improve process flow
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What is Six Sigma?
• Strategy to minimize variation towards the goal of 3.4 defects per million.
• A philosophy to promote excellence in all business processes.
• A 5 phase methodology for continuous improvement.
• A statistic which describes the amount of variation in a process.
• A tool to reduce or eliminate variation.
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Six Sigma Overview• Six Sigma is a customer focused continuous
improvement strategy and discipline that minimizes defects and variation towards an achievement of 3.4 defects per million opportunities in product design, production, and administrative processes.
• It is focused on customer satisfaction and cost reduction by reducing variation in processes.
• Six Sigma is also a methodology using a metric based on standard deviation.
• Six sigma targets aggressive goals.
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Six Sigma Benefits
• Stronger knowledge of products and processes
• Reduction in defects
• Increased customer satisfaction level that generates business growth and improves profitability
• Increased communication and teamwork
• Common set of tools
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Why should we combine them?
• By combining the Six Sigma DMAIC methodology with lean manufacturing tools, companies have a more appropriate toolkit to address all types of process problems and can reap even more dramatic gains.
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Lean vs. Six Sigma
Broad trainingLearn by doing
Specific trainingLearn by doing
Training
Dedicated resourcesAd-hoc – kaizen basedInfrastructure
Long-term cyclical improvementShort-term focusLength of Projects
Various approachesDriven by Value Stream MappingProject Selection
Generic problem solving approach using statistics
Lean technique specificBasic principles and best practices
Approach
All business processesMainly manufacturing processesApplication
Corporate cultureOperations level (at minimum)Culture
StrategicProject orientedOperations level
Business Scope
Reduce variationImprove process capability
Create flowEliminate waste
Goal
Six SigmaLean
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Lean, Six Sigma, or Both?
• Which is better?
• What do we first?
• Can the two approaches be combined?
• How do we reap the biggest reward?
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Systematic Approach
Standard Work
DEFINE
MEASURE
ANALYZE
IMPROVE
CONTROL
Create flow
Eliminate Variation
Quadratic Loss Function
Quality Characteristic
Design of Experiments
Quadratic Loss Function
Quality Characteristic
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DMAIC Approach to Lean and Six Sigma
D
M
A
I
C
Voice of the CustomerValue Stream Mapping
SPC, Standard Work, Control Plan
Kanban, Visual Management, Heijunka, Poke-Yoke,Design of Experiments
TAKT Time, Cause and Effect Diagram, FMEA,Hypothesis Testing
5S, Capability Analysis, Measurement System Analysis, Spaghetti Diagram, Process Flow Diagram
Project Selection
Understand the process and identify
potential factors
Confirm the vitalfew factors
Optimize and implement solutions
Sustain results
Phase Deliverables
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Define
• Value Stream Mapping
Source: Rother, Mike and Shook, John, Learning to See: Value Stream Mapping to Add Value and Eliminate Muda, The Lean Enterprise Institute, Brookline, MA, 1998.
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Value Stream Mapping
• Value Stream Mapping is the first building block to integrating Lean and Six Sigma.
• The purpose of Value Stream Mapping is to understand the big picture.
• The current value stream consists of all actions necessary to deliver a product including value added and non-value added.
• Value stream mapping must be conducted first to provide an effective blueprint for implementing an improvement strategy.
• A key step in creating the current state map is to calculate TAKT time.
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Measure• Quality Loss Function
• Specifications
Loss (L) (L) = k (µ - T)2
Targety (response
value)
Where, •k is a monetary constant,•
,•µ is the mean, and•T is the target.
20
0
∆=
Ak
EquallyGoodBad Bad
LSL USL
EVERYTHING IS NOT EQUALLY GOOD
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Beware of the “Goalpost” Mentality
• The value of manufacturing specifications to the customer is only important when they receive a product that is defective.
• Specifications create “goalposts” for product acceptance.
• To the customer, the specifications should be created based on their expectations and requirements.
• A benefit of using the quadratic loss function is that it is in monetary units.
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Analyze
• Design of Experiments– Structured method to determine the
relationship between factors (Xs) that affect a process and the output of the process (Y).
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Improve
Eliminate variationImprove flow
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Control
• Standard work– A lean tool that defines and documents the interaction
between people and their environment.Operation From: Raw Material STANDARD WORK SHEET Part No. Sequence To: Finished Material Part Name:
7
+
6
+5
+4
+3
+
2
+1
+ RM 13
8
+
9
+10
+11
+12
+ 14
15 16 FM Quality Check Safety Standard WIP # Pieces WIP TAKT Time Cycle Time +
10 474 944
Operator 1
Operator 2
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Case Study
• Six Sigma project performed on the casting process at a casting supplier to reduce center line shrinkage.
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Extended Value Stream
Raw Material Casting Supplier
Machining &
AssemblyCustomer Consumer/
End User
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Value ChainCasting Supplier
Manufacturing&
Assembly
CustomerQDC MetricsRipple effect throughout the entire value chain
Casting process capability and variation
Manufacturing and assembly variation
QDC – Quality, Delivery, Cost
Customer Satisfaction!!!!
Customer/End User
Variation in product performance in the hands of the customer
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Objective
• The objective of this case study was to utilize the Six Sigma DMAIC methodology in conjunction with lean manufacturing techniques to meet customer requirements in terms of both the level of quality performance and production requirements.
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Introduction
• Objectives* More Efficient
Process* Reduce Costs* Reduce Quality
Defects* Improve Delivery
• Performance Measures* Production Numbers* Scrap Numbers* On-time Delivery
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Goals
• Reduce internal PPM from 23,309 to 5,827 (75% reduction)
• Reduce annual COPQ from $200,100 to $50,025 (75% reduction)
• PPM savings of 17,482
• COPQ annual savings of $150,075
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Casting Porosity
R20.0008 =
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
Jan
Feb
Mar
Apr
May Jun
Jul
Aug
Sep
Oct
Nov
Dec Jan
Feb
Mar
Apr
May Jun
Jul
Aug
Month
PP
M
PPM
PPMPPM GoalLinear (PPM)
PPM
Changed suppliers
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Casting Porosity
R20.0297 =
$0
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
Jan
Feb
Mar
Apr
May Jun
Jul
Aug
Sep
Oct
Nov
Dec Jan
Feb
Mar
Apr
May Jun
Jul
Aug
Month
COPQ
/Kit
COPQ
COPQCOPQ GoalLinear (COPQ)
COPQ
Changed suppliers
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Variation Reduction Kaizen
• One week kaizen format used to kickoff the Six Sigma project.
• Kickoff the Six Sigma Black Belt project by applying Process Flow, Cause and Effect with CNX/SOP to reduce casting centerline shrinkage.
• Goal was to change the number of noise variables to constants by 50%.
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Process Flow Diagrams
• Process flow diagrams for all relevant supplier processes were documented.
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Sand Mullor Process Flow Diagram
Custom mix (Clay) Water Returned sand
Mull
Discharge fortesting
System adjustsfor last
5 readings
Using recipedetermine
charge
Add slagcoagulant
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Molding Machine Process Flow Diagram
Sand droppedfrom hopper
Flask rollsback over
Flask comes up and seals
Flask rollsover
Fill drag side
Feed bottomboard
Hopper carmoves out
Flask car movesout/dischargesprevious mold
A
A
B
Excess sandcollected asreturned sand
Hopper cardrawn back
Fills cope
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Molding Machine Process Flow Diagram Cont’d
B
Flask carpulled back
Cope and dragsqueezed
Is moldgood? Mold scrapped
No
Yes
Add cores andfilters as needed
Jacket added
Wait in transfersystem for hot
metal
Blow out drag, cores, filters
C
C
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A
Melting Process Flow DiagramReceiving inspection
of ferro alloys
Receiving inspection of raw materials
Collect scrap/ returns
Put into bins1) Steel2) Pig iron
Using recipeDetermine
charge
Load onto scale
Load into preheater
Dump into one of 3 furnaces
Add alloys
Melt
A
Temperaturetaken
At 2750°F? Continue
heating
B
No
Yes
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Melting Process Flow Diagram Cont’d
Sample pulled for LA test
Increase KW totap temperature
Add slagcoagulant
Remove slag
Tap intotransfer ladle
SampleGood?
Adjust by Adding steel and silicon
No
Yes
B
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Pouring Process Flow Diagram
Add alloy totreatment ladle
Weigh iron
Take to pouring zone via monorail
Distribute into oneof 3 pouring ladles
and inoculate
Receive ironin treatment ladle
Remove slag
Fill coupon (test slug)
Follow lastmold
Check formodularity
Begin pouring
Pour test bars eachshift for each class
of iron
Empty pouringladle completely
A
A
B
B
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Sand Mullor Cause and Effect Diagram
Measurement Method Machine
Manpower Materials Environment
Centerline Shrinkage
(N) Failed moisture test
(N) Failed permeability
(N) Failed green strength
(N) Failed compactibility
(N) Temperature of sand
(N) Failed sieve analysis
(N) Time on conveyor
(N) Missed a manual test
(N) Poor Gage R&R
(N) Failed clay wash
(N) Manual test Gage R&R
(N) Mullor ran manually
(N) Improperly mulled sand
(N) PLC control fails
(N) Mullor fails
(N) Hoppers not cleaned
(N) Untrained operator
(N) Temperature of sand
(N) Contaminated materials
(N) Shop Temperature
(N) Humidity
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Molding Machine Cause and Effect Diagram
Measurement Method Machine
Manpower Materials Environment
Center Line Shrinkage
(C) Squeeze pressure of mold machine
(N) Sand temperature
(N) Shake out time
(N) Cope (not enough sand)
(N) Drag (not enough sand)
(N) Window location (relative to shrinkage)
(N) Lack of support on top of casting
(N) Swell
(N) Cracked mold
(N) Storage conveyor temperature
(N) PLC control fails
(N) Weight Distribution
(N) Mold not inspected
(N) First mold not inspected
(N) Chills not set properly
(N) Chills not set at all
(N) Operator not trained
(N) Mold not blown out
(C) Pattern preparation
(C) Improper set-up
(C) Wet sand
(N) Wrong size filter
(N) Defective filter
(N) Dust (dry sand)
(N) Pattern not clean
(C) Green sand strength(N) Shop temperature
(C) Mold hardness
(N) Shop humidity
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Melting Cause and Effect DiagramMeasurement Method
Manpower Materials
Center Line Shrinkage
(N) Temperature of furnace
(C) Carbon equivalent
(N) Preheat temperature
(N) Preheat too short
(N) Scale
(N) Carbide tendency
(N) Lab test gage R&R
(N) Lab test error
(N) Improper slag removal
(N) Material falloff
(N) Melt too cold
(N) Transfer time
(N) Temperature control error
(N) Charge makeup error
(N) Training
(C) Alloys incorrect
(C) Chemistry out of balance
(N) Manganese Content
(N) Materials mixed
(N) % of returns used
(N) Material cleanliness
(N) Chemical composition
(N) Material size
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Pouring Cause and Effect Diagram
Measurement Method Machine
Manpower Materials Environment
Center Line Shrinkage
(N) Zero scale weight
(N) Failed nodularity test
(N) Spectrometer gage R&R
(N) Temperature of sand
(N) Incorrect weighing
(N) Pouring temperature
(C) Holding time
(N) Pouring time
(N) Runout
(N) Ladle not empty at start
(N) Failed ductile iron
(C) Weight of jacket/cleanliness
(N) Ladle not preheated
(N) Mold not clean (sand in sprue hole)
(N) Slag buildup on ladle
(N) Reverse taper sprue
(N) Ladle lip changes
(N) Sprue design
(N) Pouring time
(N) Turbulence of pour
(N) Short pour
(N) Operator not trained
(N) Velocity of pour material
(N) Proper temperature
(N) Proper inoculation
(N) Too much in treatment ladle
(N) Too little in treatment ladle
(N) Filter missing
(N) Failure to remove slag
(N) Shop temperature
(N) Shop humidity
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Noises to Constants
• Action plan created to change noises to constants
• i.e. Pattern Preparation and Set-up– Check sheet on Manufacturing Order– Check sheet used at every job changeover– Procedure established for molding machine setup– Standard gating procedure for pattern PM
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Process FMEA
483Visual inspection2Wrong material used
963Visual inspection4Charge hanging up in bucket
723Visual inspection3Operator error when adding charge material
1127PM2Scales out of calibration
8Chemistry out of spec
324Visual inspection4Charge hanging up in bucket
183Visual, scales3Operator error when adding charge material
287PM2Scales out of calibration
2Furnace too empty
273Visual, scales3Operator error when adding charge material
427PM2Scales out of calibration
3Furnace too fullIncorrect weight of charge material
Melting
Controlsof FailureVof FailureModeRequirements
ProcessMechanism(s)EEffect(s)FailureFunction
RPNDetec.CurrentOccPotential Cause(s)/ClassSPotentialPotentialProcess
Core Team:_____________________________________
Key Date_________________________________Model Year(s)/ Vehicle___________________________
Process Responsibility______________________Item _Melting______________________________________
PROCESS FMEA
POTENTIAL FAILURE MODE AND EFFECTS
ANALYSIS
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Design of Experiments
Casting Process
Pouring Temperature2650 - 2400°F
Squeeze Pressure/Mold Hardness1200 - 800
Silicon Content2.80 – 2.20
Shrink Free Part
INPUT PROCESS OUTPUT
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DOE Setup
80024002.208120024002.20780026502.206
120026502.20580024002.804
120024002.80380026502.802
120026502.801
Squeeze Pressure
Pour TempSi TargetRun
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DOE
• Optimal settings for pouring temperature, silicon content and squeeze pressure determined.
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Results
• PPM at the start of the project: 23,309• PPM at the end of the project: 221• Results are a 99.1% reduction in internal PPM
• COPQ at the start of the project: $200,100• COPQ at the end of the project: $238• Results are a 99.5% reduction in COPQ
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Key Points
• Build relationships with suppliers
• Train your suppliers
• You are only as good as your suppliers
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Thank you