Practice Exam #1

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The Bullwhip Effect
John H. Vande Vate
Fall, 2002
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Diagnosis & Treatment
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What is it?
Symptoms?
Causes?
Treatments
Follow-up
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What it is…
The Bullwhip Effect describes the
phenomenon in which order
variability is amplified as it moves up
the supply chain from end-consumers
through distribution and
manufacturing to raw material
suppliers.
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Example
Procter & Gamble: Pampers
• Smooth consumer demand
• Fluctuating sales at retail stores
• Highly variable demand on distributors
• Wild swings in demand on manufacturing
• Greatest swings in demand on suppliers
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Illustration
Consumer Sales at Retailer
Consumer demand
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100
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Retailer's Orders to Distributor
1000
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100
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Retailer Order
900
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Illustration
Retailer's Orders to Distributor
1000
Retailer Order
900
800
700
600
500
400
300
200
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Distributor's Orders to P&G
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100
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Distributor Order
1000
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Illustration
Distributor’s Orders to P&G
Distributor Order
1000
900
800
700
600
500
400
300
200
100
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P&G's Orders with 3M
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0
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P&G Order
700
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Illustration
Consumer Sales at Retailer
Consumer demand
1000
900
800
700
600
500
400
300
200
41
39
37
35
33
31
29
27
25
23
21
19
17
15
13
9
7
5
3
1
0
11
100
P&G's Orders with 3M
1000
900
800
600
500
400
300
200
100
40
37
34
31
28
25
22
19
16
13
10
7
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0
1
P&G Order
700
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What Are the Effects?
What problems, costs, challenges does
this create for the players in the
supply chain?
What problems does this create for the
product in the market place?
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The Effects
• Manufacturing Cost
– Capital Investment
– Operating costs
• Inventories
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Anticipatory
Cycle
Pipeline
Safety stock
Infrastructure
• Lead Time
– New product releases
– Order response time
• Shipping & Receiving Cost
– Order processing
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The Effects
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Customer Service Level
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Product availability
Transport Cost
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Economies of scale
Variability
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The Causes
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Order batching
Pricing Strategies
Uncertain Supply
Forecasting
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Causes
• Order Batching
– Reduce processing costs
– Exploit economies of scale in transport
– Ordering cycles and planning cycles
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Causes
• Pricing Strategies
– Promotions
• Reduce margin
• Advance demand
• Diversions
– Sales Targets & Revenue Targets
• Reduce price at end of quarter to meet
plans
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Uncertain Supply
Product on Allocation
• Customers place extra large orders to
ensure they get “their share”
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Forecasting
More variability
Poorer forecasts
Less reliable supply
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Treatments
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Information Sharing
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Channel Alignment
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Wal-Mart provides POS info to P&G
Coordination of promotions, transport, etc.
Operational Efficiency
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Reducing cost and leadtime
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Information Sharing
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Chrysler makes the cars
Shared schedule information
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Leer makes the seats
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Third party cuts & sews fabric
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Milliken makes the fabric
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Dupont makes raw material
…
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Information Sharing
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Chrysler makes the cars
Shared schedule information
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Leer makes the seats
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Third party cuts & sews fabric
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Milliken makes the fabric
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Dupont makes raw material
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BMW & Daimler
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Fiber Optic controls
Bosch: integration
Infineon: switches
Several other suppliers
Shared visibility of components and alerts
of shortages
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VMI/CRP
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Vendor managed inventory/Continuous
Replenishment
Dell requires its suppliers to hold
consignment inventory at a warehouse
near the factory --- Vendor responsible
for maintaining 2 weeks supply
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Consumer Contact
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Maintain contact with end consumer (source of
demand) to reduce reliability on information
from channels
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Loyalty programs
Coupons
BMW model of ordering
Disintermediate distribution
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Dell build-to-order
GM build-to-order in Brazil
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Information
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Information sharing from industrial
customers
VMI and CRP
Contact with end consumers
Disintermediate distributors
Faster replenishment
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Reducing Batch Sizes
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Reduce the cost of ordering: automated
ordering, VMI, etc.
Facilitate consolidation:
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multi-stop deliveries, pick-ups, milk-runs
Shared inventory and transport (Dell)
3PL’s help
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Stabilize Prices
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Eliminate promotions (Everyday low
prices)
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Stabilize Demand
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Auto manufacturers produce at a constant
rate and drive demand with 0% financing,
rebates, etc.
Dell adjusts its offerings and pricing to
reflect product availability
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Eliminate Gaming
• Allocate based on historical sales rather
than orders
• Intel case
• Promote orders far in advance
• Limit cancellations
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Follow-up
• How well have these cures worked?
• Enormous investment of energy and
money into these treatments
• The Bullwhip is alive and well
• Two “cases”
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Dell
• Hard drives
• Relies on several sources
– Competition: who gets what share
– Contingency: if one has a problem
– Cultivation: don’t want just 1 disk drive maker
• Contracts for share
– X% of volume to A, Y% to B, etc.
• Implementation
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Implementation
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Assume a 5 day production schedule
20% to A: one day a week
40% to B: two days a week
40% to C: two days a week
Mondays to A
Tuesdays & Wednesdays to B
Thursdays & Fridays to C
Comments?
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The Auto Industry
• Increasingly BTO
– Consumer contact
– Short replenishment cycles
– Small batch sizes
• Increasingly Lean
– As little as 2 hours inventory on site
– Sequencing: Send supplier locked production
schedule. Supplier sends parts in that order
– Frequent small deliveries (sometimes every 4 hrs)
– Coordinated supply with Milk runs, etc.
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Auto Industry
• Keep production level
– Target daily production, e.g., 1,000/day
– Promotions, rebates, low financing to drive
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Consequences
• BTO and shorted order-to-delivery means
smaller bucket of orders in hand to
sequence with:
Before
After
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Best Schedule: 3R, 3B, 3G, 3Y
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More Variable Usage
• Sequence under old method
• Sequence under BTO
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Lean Prevents Pooling
With releases every day or even several times
per day, variability is transmitted to
suppliers
Study of one OEM’s in-bound supply
showed up to 270% variation in day-to-day
volumes ordered
X today, 3X tomorrow, 1/3X next day…
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Consequences
• Supplier Capacity
• Supplier Inventory
• Transportation
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Confounding Factor
• Product Diversification
– GM plans to launch a new vehicle every 23
days.
– BMW makes 1017 versions of the 7 series
sedan
–…
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Next
• Inventory model to temper the Bull Whip
Effect in lean/BTO environments
• November 19th Visitor from
– Peach State Integrated Technologies
– What they are doing with location models
• Yuri and his team are working on using
those models to build low variance milk
runs for Ford based on location models
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