Lecture-1 slide.pdf

The Supply Chain Dynamics
David Simchi-Levi
Professor of Engineering Systems
Massachusetts Institute of Technology
Tel: 617-253-6160
E-mail: dslevi@mit.edu

©Copyright 2021 D. Simchi-Levi
Outline of the Presentation
 The Bullwhip Effect
 Causes
 Impact
 Strategies

The Bullwhip Effect
and its Impact on the Supply Chain
• Consider the order pattern of a single color
television model sold by a large electronics
manufacturer to one of its accounts, a
national retailer.
Figure 1. Order
Stream
Huang at el., Working paper, Philips Lab

Figure 2. Point-of-sales
Data-Original
Figure 3. POS Data After
Removing Promotions
The Bullwhip Effect

Figure 4. POS Data After Removing Promotion & Trend
The Bullwhip Effect

Higher Variability in Orders Placed by Computer
Retailer to Manufacturer Than Actual Sales
Retail sales versus Orders to PC Manufacturers

Increasing Variability of Orders
Up the Supply Chain
Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review

We Conclude ….
• Order Variability is amplified up the
supply chain; upstream echelons face
higher variability.
• What you see is not what they face.

The Bullwhip Effect
Customers
Retailers
P&G

What are the Causes….
• Promotional sales
• Volume and Transportation Discounts
– Forward Buying: Forward buying involves
purchasing during manufacturer
promotional periods enough products to
meet retailer demands for many months
• Inflated orders
- IBM Aptiva orders increased by 2-3 times
when retailers thought that IBM would be out
of stock over Christmas
- Same with Motorola’s Cellular phones

• Single retailer, single manufacturer.
– Retailer observes customer demand,
Dt.
– Retailer orders qt from manufacturer.
Retailer Manufacturer
Dt qt
L

Order-up-to-Policy
L
z
L
B σ
µ +
=
• = Expected daily demand
• = Standard deviation of daily demand
• L = Lead Time
• z = Safety Factor
µ
σ

• Promotional sales
• Volume and Transportation Discounts
• Inflated orders
• Demand Forecast
• Long cycle times

Var(q)/Var(D):
For Various Lead Times
L=5
L=3
L=1
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30
L=5
L=3
L=1
Var(Q)
Var(D)
Number of (Monthly) Demand Data used to Generate a Forecast

Consequences….
• Increased safety stock
• Reduced service level

Inventory and Service Levels
Service
Level
L H
H
L
Inventory
Level
100% service level

Service
Level
L H
H
L
Inventory
Level
For a given inventory level
service level is reduced

Service
Level
L H
H
L
Inventory
Level
For a given service level,
inventory level is
increased

Responsiveness
Efficiency
High
Cost
Low
Cost
Short
Response
Long
Response
A
C
B
Current
Strategy
Trade-Offs between Responsiveness vs. Efficiency
22

• Single retailer, single manufacturer.
– Retailer observes customer demand,
Dt.
– Retailer orders qt from manufacturer.
Consequences….
Retailer Manufacturer
Dt qt
L

Consequences….
• Increased safety stock
• Reduced service level
• Inefficient allocation of resources
• Increased transportation costs

Multi-Stage Supply Chains
Consider a multi-stage supply chain:
– Stage i places order qi to stage i+1.
– Li is lead time between stage i and
i+1.
Retailer
Stage 1
Manufacturer
Stage 2
Supplier
Stage 3
qo=D q1
q2
L1 L2

Multi-Stage
Systems:Var(qk)/Var(D)
0
5
10
15
20
25
30
0 5 10 15 20 25
Dec, k=5
Cen, k=5
Dec, k=3
Cen, k=3
k=1

The Bullwhip Effect:
Managerial Insights
• Exists, in part, due to the retailer’s need to estimate
the mean and variance of demand.
• The increase in variability is an increasing function of
the lead time.
• The more complicated the demand models, the
greater the increase.
• Centralized demand information can reduce the
bullwhip effect, but will not eliminate it.

Coping with the Bullwhip Effect
in Leading Companies
• Reduce Variability and Uncertainty
- POS
- Sharing Information
- Year-round low pricing
• Reduce Lead Times
- EDI
- Cross Docking
• Alliance Arrangements
– Vendor managed inventory
– On-site vendor representatives

Source: Barilla SpA (A), Janice H. Hammond, Harvard

Exhibit 12 Weekly Demand for Barilla Dry Products from Cortese’s Northeast Distribution Center to the
Pedrignano CDC, 1989.

Implementation at a CPG
Company

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