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Company
LOGO
Push-Pull: Strategic Thinking for Operational Excellence
Push-Pull: Strategic Thinking for Operational Excellence
Yang Sun
GWC 521Department of Industrial Engineering
Arizona State University
IE@ASU 2
Teaching Philosophy
• Basics • Basics
• Intuition • Intuition
• Synthesis • Synthesis
Know HowKnow How Know WhyKnow Why
Major Source: Wally Hopp and Mark Spearman, Factory Physics, 2nd Ed., 2000
Company
LOGO Basics
IE@ASU 4
Essential Context
The IDIB Portfolio• Information
• Decision
• Implementation
• Buffer
Sources: Lee Schwarz, Lecture Notes, 2003Lee Schwarz, "A New Teaching Paradigm: The Information/Control/Buffer
Portfolio", Production and Operations Management 7:2, pp. 125-131, 1998Dan Shunk, “Knowledge Management”, Lecturer Notes.
Data
Information
Knowledge
Org. Learning
Wisdom
IE@ASU 5
Variability Basics
• Variability is a fact of life. Increasing variability (always) degrades system performance.
• Demand Variability• The Bullwhip Effect (Volatility Amplification Law)• Forecasting Laws
• Process Variability• Think about Little’s Law!
• Technology/Organization Variability• Fruit Flies (Clockspeed Amplification Law)
• Variability will be buffered by some combination of inventory, capacity, and time.
Additional Sources: Hau Lee et al., Information distortion in a Supply Chain: The Bullwhip Effect, Management Science, 43(4), 1997; or The
Bullwhip Effect in Supply Chains, Sloan Management Review 38(3), Spring1997Charley Fine, CLOCKSPEED: Winning Industry Control in the Age of Temporary Advantage, 1998
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Buffer Basics
» Inventory» Capacity» Time
• Buffer Strategy• Buffer Flexibility/Pooling• Buffer Location
• Definition: Lean = Minimal Buffer CostTOYOTA Lean Phases:
[Eliminate Direct Waste] (Value-Add)[Substitute Capacity for Inventory Buffers] (Push -> Pull) [Reduce Variability] [Reduce Capacity Buffers] (Cont. Improv.)
Sources: Wally Hopp, Supply Chain Sciences, 2005
IE@ASU 7
Lessons from History
• A history of buzzwords
EOQ; MRP; MPR-II; ERP;BPR; MES; APS; Kanban; JIT; TQM; CIM; FMS;
……
• What went wrong?
IE@ASU 8
Lessons from History (cont’d)
• Problems with traditional approaches:– Scientific Management has stressed math over
realism– MRP is fundamentally flawed, in the basics, not the
details– JIT is a collection of methods and slogans, not
systems
• Bottom lines:– Supply Chain/Manufacturing systems are large scale,
complex, and varied. – No “technological silver bullet” can save us.– Continuous improvement is essential.
IE@ASU 9
Definition: Supply Network
• A value-oriented network of processes and stockpoints that
deliveries goods and services to customers.
Sources: Wally Hopp, Supply Chain Sciences, 2005
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Definition: Push/Pull Production System
• Push Systems: schedule work releases based on demand.
• Pull Systems: authorize work releases based on system status.
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So, pull is not…
• Kanban– Kanban is a special case of pull– ConWIP is a generalized pull concept
• Make-to-Order: – MRP with firm orders on MPS is make-to-order.– But it does not limit WIP and is therefore a push system.
• Make-to-Stock:– Pull systems do replenish inventory voids.– But jobs can be associated with customer orders.
• Forecast Free: – Toyota’s classic system made cars to forecasts.– Use of tact times or production smoothing often involves
production without firm orders (and hence forecasts).
IE@ASU 12
The magic of pull…
• Cycle Time (Δt) ↓• Variability ↓• Cost ↓• Service ↑• Quality ↑• Flexibility ↑
You don’t never make nothin’ and send it no place. Somebody has to come get it.– Hall 1983
• I dislike this definition.
• The key is the WIP cap.• Why control the WIP?
• ConWIP• Observability, Efficiency, and Robustness• Overcoming rigidity of pull
WIP
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Exercise
• Are the following systems push or pull?– Kinko’s copy shop– Soda vending machine– “Pure” MRP system– Doctor’s office– Supermarket (goods on shelves)– Tandem line with finite interstation buffers– Runway at O’Hare during peak periods– Order entry server at Amazon.com
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Definition: Push/Pull Supply Chain
• A push supply chain makes production and distribution decisions based on forecasts (Build-to-stock)
• A pull supply chain drives production and distribution by customer orders (Build/Assembly-to-Order)
• Key concept: Location of the push/pull boundary (PPB) (strategic inventory point, inventory/order (I/O) interface)
Source: Simchi-Levi et al., Designing and Managing the Supply Chain, 2003
Material ProcessIntermediateInventory
FinishedGoods
Process Deliver
Push
Push Pull
Pull
IE@ASU 15
Push/pull Boundary Location
• Real World Examples:– IBM PCB Case– GM Case (WSJ Oct. 21, 96, A1)– HP DeskJet Case (See Lee, Billington, and Carter, HP gains control of inventory
and service through design for localization, Interfaces 23(4), 1993; Feitzinger and Lee, Mass
Customization at HP: The Power of Postponement, HBR Jan-Feb, 1997)
• Goal: eliminate entire portion of cycle time seen by customers by building to stock. (Need for responsiveness)
• Basic Tradeoff: Responsiveness vs. Inventory (Time vs. Cost)
• Levels: Product design (postponement) and process design (quick response mfg)
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Basic Takeaways
• Most systems are hybrid
• Push/pull supply chain is a strategic design– Key: where the push/pull boundary is located– Lead time is the primary driving factor. (Δt)
• Push/pull production is a control policy– Push keywords: Ctrl Release Utilization– Pull keywords: Ctrl WIP Cycle Time (Δt)– A pull thinking is always desired
Company
LOGO Intuition
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The problem is choice
• Yes, but not only…
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Fisher’s Matrix
Source: Marshall Fisher, “What is the right supply chain for your product”, Harvard Business Review, March-April 1997
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Hau Lee’s Matrix
Basic Appeals, Grocery, Food, Most Commodities
Fashion Appeals, Computers, Pop Music, Toys
Some Power, Some Food Produce, Precious Metals
M-commerce, Telecom, High-end Servers, Semiconductor
Demand UncertaintyLow
(Functional Product)
High(Innovative Product)
Low(Stable
Process)
Low(Functional
Product)
High(Evolving Process)
Su
pp
ly U
ncertain
ty
Efficiency, Information Integration, Auto-Replenishment, VMI
(Efficient SC)
Build-to-Order, Flexible Mfg, Accurate Response, Postponement
(Flexible SC)
Buffer Inventory, Shared Resources, Multi-Sourcing, Info Sharing
(Risk-Hedging SC)
Supply Network, Postponement, Design Collaboration
(Agile SC)
Demand UncertaintyLow
(Functional Product)
High(Innovative Product)
Low(Stable
Process)
Low(Functional
Product)
High(Evolving Process)
Su
pp
ly U
ncertain
ty
Source: Hau Lee, “Aligning supply chain strategies with product uncertainties”, California Management Review, 44(3), 2002
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Simchi-Levi’s Matrix
Source: David Simchi-Levi et al., Designing and Managing the Supply Chain, 2003
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Where to locate the PPB?
• Auto• Semiconductor/Electronics• Grocery• …• Everything
Material AssemblyPartsInventory
FinishedGoods
Process Deliver
Push
Push Pull
Pull
Push Pull
Push Pull
IE@ASU 23
Semiconductor Case Study
Fab Probe Assembly TestDieBank
FinalGoods
DeliveryMaterial
Push
Push Pull
Pull
Source: Yang Sun, Comparing Semiconductor Supply Chain Strategies under Demand Uncertainty and Process Variability, Master’s Thesis, ASU
IE@ASU 24
Forecasting and Demand Uncertainty
• There is a confusion between two kinds of forecasting: ‘what can be sold (WCBS)’ and ‘what will be sold (WWBS)’ (Montgomery et al. Forecasting and Time Series Analysis, 1990). The former represents the possible market trends. The latter always represents the company’s capacity and budget constraint. Since capacity utilization is extremely important in semiconductor manufacturing, it is always the WWBS forecasts that triggers the production plan (push).
• The semiconductor industry is always under stress: either ‘lack-for-sales’ (LFS) (WCBS < WWBS) or ‘lack-for-capacity’ (LFC) (WCBS > WWBS) (Shunk et al. Electronics Industry Drives of Intermediation and Disintermediation, submitted, 2005)
• Note that huge demand uncertainty EXISTS in the semiconductor industry.
WWBS
WCBS
WCBS (LFC)
(LFS)
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Process Variability
• Integrated into a cycle time distribution– Issues that can affect the variance of mfg cycle times: variable
capacity, shortage of material, variable priorities in lot release, scheduling and dispatching, frequent machine breakdowns, operator error, etc.
– Issues that can affect the variance of delivery time: globally distributed destination, regional traffic condition, variable 3PL/4PL, holding in custom
Fab Probe Assembly TestDieBank
FinalGoods
DeliveryMaterial
Front-end Back-end Delivery
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Performance Metrics: Cost and Service
• On-time delivery service is of critical importance in today’s semiconductor business, but companies are not doing very well today (case: Gateway penalized Intel by shifting business to AMD to blame Intel’s bad delivery service.)
• Cost (per product sold) performance= front-end mfg costs + back-end mfg costs + inventory (holding) costs + penalty costs based on tardiness
• Delivery cost ignored
• (Quality is a given in SC analysis.)
Penalty
L.T.Due
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The Simulation Model
Fab Probe Assembly TestDieBank
FinalGoods
DeliveryMaterial
Front-end Back-end Delivery
WWBS
Order Generator based on
WCBS
Forecasts
Push Pull(Pull Strategy)
Pull(Push-PullStrategy)
Pull(Push
Strategy)
Performance
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DOE Factors
Factors Level 1 Level 2 Level 3
Strategy Pull Push-Pull Push
Due-Date Lead Time Tight Medium Loose
Penalty Weight Light Heavy
Demand of Product A LFS (Low) LFC (High)
Demand of Product B LFS LFC
Front-end CT Variability Zero High
Back-end CT Variability Zero High
Delivery Time Variability Zero High
Front-end Mfg Cost Low High
Back-end Cost Prod A Low High
Back-end Cost Prod B Low High
5132 fractional factorial design
Assume: two products, same family, assembled from common generic parent die
IE@ASU 29
A General Case Instance
Due-date Lead Time or Cited Lead Time
Level 1: Tight Level 2: Medium Level 3: Loose
5 days 30 days 55 days
Penalty per delayed hour
Light Penalty $55 $30 $5
Heavy Penalty $275 $150 $25Process Cycle Time
(days)Level 1
Zero VariabilityLevel 2
High Variability
Front-end Constant (45) Triangular (35,45,55)
Back-end Constant (8) Triangular (5,7,12)
Final Product Delivery Constant (4) Triangular (2,3,7)
Mfg Cost ($/wafer) Level 1: Low Cost Level 2: High Cost
Front-end 2400 4000
Back-end for Commodity Goods 2400 4000
Back-end for High-end Goods 4800 8000
And other assumptions
Duarte, 2001
IC Knowledge, 2003
IE@ASU 30
The ‘Global’ Experiment: Effects
Since in simulation experiments almost all factors have none-zero effects, Sequential Bifurcation Analysis is suggested by Wan et al. 2003 (QSR Winner paper INFORMS Atlanta ‘03)
•Group Screening: Factors are grouped as ‘Important’ and ‘Unimportant’•Step-Down: In each step, a group of factors are tested for importance
Strategy
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Screened Factorial Effects
• Primary Factors: Due-date Lead Time and Penalty Weight (Δt is the game)
• Secondary Factors: Demand Pattern and Mfg Cycle Time Variability
• Unimportant Factor: Final Product Logistics Time Variability
• (Of course “costs” have significant effects, but do we need to analyze them?)
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Due-dates vs. Penalty Weights
0
2000
4000
6000
8000
10000
12000
1 2 3
Pull
Pushpull
Push
5days 30days 55days Due Day
To
tal C
ost p
er w
afe
r sold
in $
Further Step-Down Analysis
√√
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
1 2 3
Pull
Pushpull
Push
5days 30days 55daysDue Day
To
tal C
ost p
er w
afe
r sold
in
$
√
Light Penalty
Heavy Penalty
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Demand Pattern vs. Mfg C.T. Variability
7100
7200
7300
7400
7500
7600
7700
7800
7900
8000
8100
Pull
Push-Pull
Push
$ Total Cost per wafer sold – Product A
Low Variability
High Variability
Low Variability
High Variability
Low Variability
High Variability
6300
6400
6500
6600
6700
6800
6900
7000
7100
Pull
Push-Pull
Push
Low Demand Mid Demand High Demand
Low Demand Mid Demand High Demand
$ Total Cost per wafer sold – Product A
High Variability
Low Variability
High Variability
Low Variability
Low Variability
High Variability
High Variability
medium due-dateand light penalty
loose due-dateand heavy penalty
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The analytical results lead to a conceptual decision framework
Due-Date Lead TimeTight Medium
LooseIm
portance of on
-time delivery
service
Less Im
portan
t F
ar More
Imp
ortant
Push
Pull
Step-down to Layer Two Comparison
This is Layer One
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Layer Two: Push-Pull Can be Appropriate
Layer Two Low Demand (Lack-for-sales)
Average Demand High Demand (Lack-for-capacity)
Low Mfg* Variability
Pull Push Push
High Mfg* Variability
Push-Pull Push-Pull Push
* Manufacturing variability contains both front-end and back-end variability
medium due-date + light penaltyor
loose due-date + heavy penalty
Aggregate Demand
Pro
cess
Varia
bility
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What else can be done?
• Pooling/Postponement• Hybrid
Strategy Postponement of decision
Inventory at die-bank
Inventory at finished goods
No postponement (Push) X
Partial postponement (Push) X X
Die-bank push-pull X X
Hybrid X X X
Source: Alex Brown et al., Xilinx improves its semiconductor supply chain using product and process postponement, Interfaces, 30(4), 2000
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Technology Involvement
Wafer
ProductionTest
FPGA
Standard IC
ASSP
PLD
GA
Structured ASIC
CBIC
Full- Custom IC
System
Set-up
Assembly
& Packaging
Fabrication
& Probe
Process
Product
Wafer
ProductionTest
FPGA
Standard IC
ASSP
PLD
GA
Structured ASIC
CBIC
Full- Custom IC
System
Set-up
Assembly
& Packaging
Fabrication
& Probe
Process
Product
Push
Pull
Source: Joong-In Kim and Dan Shunk, working paper
IE@ASU 38
Intuition Takeaways
• Δt is the name of the game. From the semiconductor case, lead time customers require and the perceived importance of on-time delivery are the driving factors.
• We also need to understand not only the nature of the demands but that of the processes.
• Supply Chain Visibility (both Demand Stream and Supply Stream) is important.
• Implementation issues should be addressed.• Transition from push to pull needs tremendous
cultural change and technological support.
Company
LOGOSynthesis -- Push-Pull It All Together
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A Customer-Driven Supply Chain Framework
Semiconductor SC example
Source: Yang Sun, Dan Shunk, John Fowler, Proceedings of INFORMS Annual Meeting, San Francisco, Nov. 2005
Info Flow
Material Flow
IE@ASU 41
DieBank
Wafer Fabrication (W/F) Assembly & Test (A/T)Configuration& Shipment (C/S)
The Semiconductor Flow
RawMaterial
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Critical Decisions in the Semiconductor Supply Chain
Wafer Fabrication (W/F) Assembly & Test (A/T)Configuration& Shipment (C/S)
W/F – Build the right stock
•How much raw material inventory to hold?
•What categories of products to release? How many to release?
•What priorities are assigned to wafer lots?
•Which A/T facility to ship to?
A/T – Rough cut, loose allocation
•How much die bank inventory to hold? /How much package material inventory to hold?
•What rough cut allocations of lots to anticipated orders are made?
•What priorities are assigned to anticipated orders or factory released lots?
C/S – Who gets what!
•How much finished goods inventory to hold?
•What is the final priorities for firm orders?
•Which lots are assigned to which order, or Who gets what?
•What quantity and mix of products are shipped from which factory to which customer?
Source: Shunk et al., ASU DBR Survey, 2004
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How are they made?
Wafer Fabrication (W/F) Assembly & Test (A/T)Configuration& Shipment (C/S)
Optimization .45 .35 .20
Heuristics .33 .27 .29
Tacit Knowledge .22 .38 .51
Decision
Technique
Current
Ideal?
W/F A/T C/SOdds Ratio
≈0.25 2004 Survey Result
Logistic Estimation of Probabilities
IE@ASU 44
Inventory Management
• Key to Supply Chain Management
• Deterministic model – adjust solution- EOQ to compute order quantity, then add safety stock
– EOQ Assumptions (not realistic)
– Key Insight: There is a tradeoff between lot size and inventory
• Stochastic models- news vendor model
- base stock and (Q,r) models
- (s,S) models
- Multi-echelon and network models
IE@ASU 45
Prioritizing and Releasing
• There is sometimes confusion between the production planning domain and the shop floor control domain. We need to connect planning and execution.
• The releasing function is key to Push-Pull. It connects supply chain planning and factory operation.– Supply Chain: Release by forecast vs. by order– Factory: MRP Push vs. Kanban/ConWIP Pull
• Allocation is important for determining “who gets what”.
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Logistics
• Key to Supply Chain Management
• Often performed by a 3PL or 4PL
• Begin to contribute large portion to the GDP
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Within The Four Walls
Capacity
Release
Scheduling
Dispatching
$$$$
$$$
$$
$
Recommended reading: John Fowler et al., Workload Control in the Semiconductor Industry, Production Planning & Control, 13(7), 2002
Shop floor ctrl
Workforce planning
Quality ctrl
…
IE@ASU 48
Synthesis and Implementation
• The Strategic Importance of Details
• The Practice Matter of Implementation– System view– Means-ends analysis– Creative alternative generation– Modeling and optimization– Iteration
• Communication and Teamwork
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Coordination and Collaboration
• Value of Info Sharing/SC Visibility• Coordinated Decision Making• Knowledge Sharing and Communities of
Common Interests• Risk Sharing• Contract Management• VMI and CPFR• Remodeling the Supply Chains to pursue Supply
Network Collaboration
Recommended Reading: Gérard Cochan, Matching Supply with Demand, 2005
IE@ASU 50
We think in generalities, we live in detail.
–Alfred North Whitehead