Supply Chain Dynamics, Value of Information & Bullwhip Effect
1. Supply Chain Dynamics, Value ofSupply Chain Dynamics, Value of
Information & Bullwhip EffectInformation & Bullwhip Effect
Dr. Ravi Shankar
Dr. RAVI SHANKAR
Professor
Department of Management Studies
Indian Institute of Technology Delhi
Hauz Khas, New Delhi 110 016 India
Phone: +91-(11) 2659-6421 (O)
Fax: (+91)-(11) 26862620
Email: ravi1@dms.iitd.ac.in, r.s@rediffmail.com
http://web.iitd.ac.in/~ravi1
2.
3. Supply Chain StagesSupply Chain Stages
Supply Chain encompasses all activities associated with the flow
and transformation of materials and information
from the raw material stage through to the end user.
Supplier Manufacturer Distributor Retailer Customer
4. Case 1: P&GCase 1: P&G--Dynamics of the Supply ChainDynamics of the Supply Chain
OrderSize
Time
Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998
Customer
Demand
Customer
Demand
Retailer Orders
Distributor Orders
Production Plan
5. Case 1: P&G: What Management Gets...Case 1: P&G: What Management Gets...
OrderSize
Time
Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998
Customer
Demand
Customer
Demand
Production PlanProduction Plan
6. Case 1 P&G: What Management WantsCase 1 P&G: What Management Wants……
Volumes
Time
Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998
Production Plan
Customer
Demand
Customer
Demand
8. Impacts of the Bullwhip EffectImpacts of the Bullwhip Effect
Increased inventory
Overtime production and idle
production scheduling
Excessive or insufficient capacity
Poor customer service due to
unavailable products
Expedited shipments
9. The Bullwhip effect is a phenomenon illustrated in
distribution channels where variability of product orders
increase at each subsequent echelon (stage) in the
channel.
Even though retail sales may fluctuate little, orders from
retailer to distributor fluctuate more and orders from the
distributor to the manufacturer fluctuate more yet.
Consider the following graph …..
The Bullwhip EffectThe Bullwhip Effect
12. Five areas of supply chain management can contribute to
increased demand variability:
• Forecasting (Forecast Updates)
• Lead Times
• Order Batching
• Price Fluctuations
• Shortage Gaming
Let’s consider each.
Contributors to VariabilityContributors to Variability
14. Impact of Forecasting on the Bullwhip EffectImpact of Forecasting on the Bullwhip Effect
Let us understand this with periodic review
policy where the inventory policy is
characterized by a single parameter, the base-
stock level.
That is, the warehouse determines a target
inventory level, the base-stock level, and each
review period, the inventory position is
reviewed, and the warehouse orders enough
to raise the inventory position to the base-
stock level.
15. Impact of Forecasting on the Bullwhip EffectImpact of Forecasting on the Bullwhip Effect
The base-stock level is typically set equal to
the average demand during lead time and
review period plus a multiple of the standard
deviation of demand during lead time and
review period.
The latter quantity is referred to as safety stock.
Typically, managers use standard forecast
smoothing techniques to estimate average
demand and demand variability.
16. Impact of Forecasting on the Bullwhip EffectImpact of Forecasting on the Bullwhip Effect
An important characteristic of all forecasting
techniques is that as more data are observed,
the estimates of the mean and the standard
deviation (or variability) of customer
demands are regularly modified.
Since safety stock, as well as the base-stock
level, strongly depends on these estimates,
the user is forced to change order quantities,
thus increasing variability.
17. Reorder Point with Variable DemandReorder Point with Variable Demand
stocksafety
yprobabilitlevelservicetoingcorresponddeviationsstandardofnumber
demanddailyofdeviationstandardthe
timelead
demanddailyaverage
pointreorder
where
=
=
=
=
=
=
+=
LZ
Z
L
d
R
LZLdR
d
d
d
σ
σ
σ
18. ExampleExample
Amplification of demand changes
that affect upstream operations
within the supply chain
Assumes stocks of one week
demand
Lead time= 1 week
Backorder allowed
19. At the start of the week#1At the start of the week#1
Manufacturer Distributor Retailer Demand
Prod Stock Order Stock Order Stock
100 100 100 100 100 100 100
95
1
2
3
4
5
Week
22. Impact of Lead Times on the Bullwhip EffectImpact of Lead Times on the Bullwhip Effect
To calculate safety stock levels and base-stock
levels, we in effect multiply estimates of the average
and standard deviation of the daily customer
demands by the sum of the lead time and the review
period.
Thus, with longer lead times, a small change in the
estimate of demand variability implies a significant
change in safety stock and base-stock level, leading
to a significant change in order quantities.
This, of course, leads to an increase in variability.
23. Measuring the Bullwhip EffectMeasuring the Bullwhip Effect
Between Retailer and ManufacturerBetween Retailer and Manufacturer
Assuming a moving average forecasting technique based on “p”
observations, every period the retailer calculates a new mean
and standard deviation based on the “p” most recent
observations of demand, the target inventory also changes. The
ratio between orders to the manufacturer (Q) and retailer demand
(D) is:
Therefore…..
2
2
22
1
)(
)(
p
L
p
L
DVar
QVar
++≥
24. 2
2
5
)1(2
5
)1(2
1
)(
)(
++≥
DVar
QVar
…if the retailer estimates the mean demand based on a five period
moving average (p = 5), and that an order placed by the retailer at the
end of period t is received at the start of period t + 1 (L = 1) then the
variance of the orders placed by the retailer will be….
4.1
)(
)(
≥
DVar
QVar
…or at least 40% larger than the variance of customer demand.
The following slide plots the relationship between the number of
periods included in the moving average forecast and the ratio
between consumer demand and retail orders.
=
Measuring the Bullwhip EffectMeasuring the Bullwhip Effect
Between Retailer and ManufacturerBetween Retailer and Manufacturer
25. Lower Bound of Increase VariabilityLower Bound of Increase Variability
0
2
4
6
8
10
12
3 5 10 15 20 25 30
p (number of periods in moving average)
Var (Q)/Var (D)
L = 5 L = 3 L = 1
2
2
22
1
)(
)(
p
L
p
L
DVar
QVar
++≥
LSTDzAVGL ××+×=minBased on an order-up-to inventory policy where,
L = Lead time (number of periods)
p = Periods in moving average forecast
Q = Retail order quantity
D = Consumer demand
Extending the logic presented in the previous algorithm, this graph
suggests that forecasting techniques that incorporate more history in the
forecasts, (“p” periods), and hence a smoother forecast, can help to reduce
the ratio of variability in orders. It also indicates (colored lines) that the lead
time used in the inventory algorithm can have a significant impact on order
variability.
27. MultiMulti--Stage Supply ChainsStage 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
30. The Bullwhip Effect:The Bullwhip Effect:
Managerial InsightsManagerial 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
and the forecasting techniques, the greater
the increase.
Centralized demand information can reduce
the bullwhip effect, but will not eliminate it.
32. Order batchingOrder batching
Driven by
– Economies of scale in order costs
– Economies of scale in transportation
(TL vs. LTL)
– MRP systems (updated monthly or
periodically)
– Push ordering (e.g., to meet a quarterly
sales quota) drive order batching
33. Order batchingOrder batching
Increases the variability of demand
as seen by the upstream member of
the supply chain
– No demand in some periods, large
demands in others
– Mitigated if customer cycles do not
overlap, but they often do
36. Price fluctuationsPrice fluctuations
Create
– Swings in demand (high during low
price periods; low during normal price
periods)
Problems include
– Overtime and idle production time
– Premium freight charges
– Inventory accumulations
38. Rationing and Shortage GamingRationing and Shortage Gaming
During shortages rationing is often
based on a fraction of the orders
placed by a firm
– Incentive to increase orders during
shortages, place orders with multiple
firms, and cancel orders once inventory
arrives
– Large swings in perceived demand at
upstream components of supply chain
39. Impact ofImpact of Inflated ordersInflated orders on the Bullwhipon the Bullwhip
Shortage gaming occurs in an environment of tight supply.
Supply chain customers may order larger quantities with the
expectation that they will receive a greater allocation quantity of
product(s) in short supply.
The impact on the supply chain is a significant increase in
forecasted demand as the inflated orders are received. When
products become available an oversupply can occur as orders
placed earlier (created to enhance allocation) are cancelled and
products are returned.
40. Bullwhip EffectBullwhip Effect
In summary, the bullwhip effect will occur to some degree in
most all supply chains. The extent of the effect will vary and
will impact inventory requirements, production scheduling and
operations, manufacturing and distribution capacity
requirements among other areas.
What action can we take to counteract the Bullwhip effect?
42. Managing the Bullwhip EffectManaging the Bullwhip Effect
We can…
…reduce uncertainty
…reduce demand variability
…reduce lead-times
…establish strategic partnerships
Information sharing
Channel alignment
Operational efficiencies.
43. Avoid multiple demand forecastAvoid multiple demand forecast
updatesupdates
Share consumption data with upstream
members
– Point of sale data given to distributors and
manufacturers
– Use EDI and internet to share data
– Vendor managed inventory or continuous
replenishment programs
– Direct sale techniques to get downstream
demand info.
– Share sales, capacity and inventory data to
reduce gaming
44. Reducing Demand UncertaintyReducing Demand Uncertainty
Centralized vs. Decentralized InformationCentralized vs. Decentralized Information
Dec K = 5
Cen K = 5
Dec K = 3
Cen K = 3
K = 1
p, number of periods in moving average
Var (Qk) / Var (D)
(K = stage in chain)
This graph compares the lower
bound of variability in a multi-
echelon supply chain when demand
is not shared between customers
(dashed line) and suppliers, and
when demand is shared (solid line).
45. Reducing UncertaintyReducing Uncertainty
Practices that support effort to reduce uncertainty involve the
implementation of systems such as
Electronic Data Interchange (EDI),
Extensible Markup Language (XML).
Both these technologies allow companies to share information
(such as consumer sales) with partner companies in the supply
chain.
EDI uses specific network services, XML is a new technology that
supports information sharing over the internet.
46. Reducing Demand VariabilityReducing Demand Variability
In addition to sharing information through EDI and XML
technologies, companies are closing the supply chain gap through
initiatives such as
Vendor Managed Inventories (VMI),
Quick Response (QR), and
Efficient Consumer Response (ECR).
Each of these initiatives offers a means to more closely coordinate
supply chain inventories, in some cases making the supplier
responsible for inventory levels at customer locations.
This provides organizations up the chain with even greater visibility
of demand patterns and product availability.
47. Reducing Lead Times (Cycle Time)Reducing Lead Times (Cycle Time)
Two strategies that help to reduce lead times include cross-
docking and postponement.
Cross-docking establishes order requirements at the store level
for placement to the supplier. As the orders are delivered to the
retail distribution center, they are immediately staged for store
delivery, thus eliminating DC inventories.
Postponement delays the differentiation of products until the time
of order. A basic system may be manufactured (say a base PC
unit). Key components are then added at the time of order.
Manufacturers are able to combine demand for the base product,
hold less expensive inventories of components, and reduce cycle
times.
48. Reduce Order batchingReduce Order batching
Reduce order costs
– Use EDI and standardize ordering
processes
– Innovative transportation (3PL)
• TL with products from multiple suppliers
• TL with same product to multiple customers
50. Eliminate gaming in shortage situationsEliminate gaming in shortage situations
Allocate product based on past sales
not on current orders
Share information about capacity
Long term contracting to allow
vendors to adjust capacity
Eliminate generous return and order
cancellation policies
51. Establishing PartnershipsEstablishing Partnerships
Each of the methods outlined earlier rely on closer relationships
between customers and suppliers in order to support greater
information sharing and the development of trust between the
organizations.
An additional strategy involving partnerships is the concept of
Every Day Low Pricing (EDLP).
EDLP eliminates the pattern of promotion offered by suppliers.
By trading off the promotional efforts with a consistent and lower
price the incentive for customers to place forward buys is
eliminated and reduced variability in demand helps the supplier
lower costs and maintain profitable margins.
52. Supply Chain Coordination InitiativesSupply Chain Coordination Initiatives
FrameworkFramework
Causes of
the Bullwhip
Effect
Information Sharing Channel Alignment Operational Efficiency
Demand
Forecasting
Update
Understanding system
dynamics
Using point of sale (POS)
data
EDI, XML (internet)
Computer Assisted
Ordering
Vendor Managed
Inventory (VMI)
Information sharing
Consumer direct
Lead-time reduction
Echelon-based
inventory control
Order
Batching
EDI
Extensible Markup
Language (XML). Internet
ordering
Discounts for assortment
planning
Delivery appointments
Consolidation
Logistics outsourcing
Reducing order costs
Computer Assisted
Ordering
Price
Fluctuations
Continuous
Replenishment Programs
(CRP)
Every Day Low Pricing
(EDLP)
Every Day Low Pricing
(EDLP)
Activity Based Costing
(ABC)
Shortage
Gaming
Sharing sales, capacity,
and inventory data
Allocation based on
passed salesSource: Lee et al. (1997)
53. Coping with the Bullwhip EffectCoping with the Bullwhip Effect
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
54. Example:Example:
Quick Response at BenettonQuick Response at Benetton
Benetton, the Italian sportswear manufacturer,
was founded in 1964. In 1975 Benetton had 200
stores across Italy.
Ten years later, the company expanded to the
U.S., Japan and Eastern Europe. Sales in 1991
reached 2 trillion.
Many attribute Benetton’s success to successful
use of communication and information
technologies.
55. Example:Example:
Quick Response at BenettonQuick Response at Benetton
Benetton uses an effective strategy, referred
to as Quick Response, in which
manufacturing, warehousing, sales and
retailers are linked together. In this strategy
a Benetton retailer reorders a product
through a direct link with Benetton’s
mainframe computer in Italy.
Using this strategy, Benetton is capable of
shipping a new order in only four weeks,
several week earlier than most of its
competitors.
56. How Does BenettonHow Does Benetton
Cope with the Bullwhip Effect?Cope with the Bullwhip Effect?
1. Integrated Information Systems
• Global EDI network that links agents with
production and inventory information
• EDI order transmission to Head Quarter
• EDI linkage with air carriers
• Data linked to manufacturing
2. Coordinated Planning
• Frequent review allows fast reaction
• Integrated distribution strategy
57. You May Try This:You May Try This:
--Use Beer Distribution Game to Demonstrate and AnalyzeUse Beer Distribution Game to Demonstrate and Analyze
Bullwhip EffectBullwhip Effect
--Use CD given with the Text bookUse CD given with the Text book
58. Supply Chain StructureSupply Chain Structure
Supply chains typically consist of the
following “players”:
– Retailers
– Wholesalers
– Distributors
– Manufacturers
Each player typically makes decision
independently or in a decentralized
manner.
59. Demands in Supply ChainsDemands in Supply Chains
Retailers respond to customer demands.
Orders placed by retailers become
demands for wholesalers.
Order placed by wholesalers become
demands for distributors
Order placed by distributors become
demands for manufacturers.
– Manufacturers must produce items to meet
these demands.
60. Bullwhip Effect in Supply ChainsBullwhip Effect in Supply Chains
Example: 2 retailers with the same wholesaler
– Periodic (s, Q) policy.
– Review Period = 1 day
Retailer-1
– Demand at Retailer 1 = 10/day (constant)
– Policy of Retailer 1 = (20,50)
– Beginning Inventory= 20 Items
61. Bullwhip Effect in Supply ChainsBullwhip Effect in Supply Chains
Example: 3 retailers with the same wholesaler
– Periodic (s, Q) policy.
– Review Period = 1 day
Retailer-2
– Demand at Retailer 2 = 25/day (constant)
– Policy of Retailer 2 = (50,125)
– Beginning Inventory= 100 Items
Retailer-3
– Demand at Retailer 3 = 15/day (constant)
– Policy of Retailer 1 = (30,120)
– Beginning Inventory= 120 Items
62. 62
Example Supply Chain StagesExample Supply Chain Stages
Supplier Manufacturer One
Distributor
Three
Retailers
Many
Customers
66. Bullwhip Effect in Supply ChainsBullwhip Effect in Supply Chains
(Constant demands)(Constant demands)
R e t a ile r 1 De m a n d
0
5
10
15
20
25
30
0 5 10 15 20 25 30 35 40 45 50
R e t a i l e r 2 D e m a n d
0
5
10
15
20
25
30
0 5 10 15 20 25 30 35 40 45 50Re t a i l e r 3 De ma nd
0
5
10
15
20
25
30
0 5 10 15 20 25 30 35 40 45 50
Demands Observed by Wholesaler
0
50
100
150
200
250
300
0 5 10 15 20 25 30 35 40 45 50
67. Bullwhip Effect in Supply ChainsBullwhip Effect in Supply Chains
(Random demands)(Random demands)
R e t a i l e r 1 D e m a n d
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 40 45 50
R e t a i l e r 2 D e m a n d
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 40 45 50
R e t a i l e r 3 D e m a n d
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 4 0 45 50
Demands Observed by Wholesaler
0
50
100
150
200
250
300
0 5 10 15 20 25 30 35 40 45 50
68. ObservationObservation……
Different chain phases have different
calculations of demand quantity, thus
the longer the chain between the
customer and manufacturer the bigger
the demand variation.
– Increases the level of inventory
– Prolongs the lead time
– Demands more efficient transportation
– Increases labor costs
– Decreases the level of product availability
– Leads to distrust among participants.
69. Bullwhip EffectBullwhip Effect
In 2001, Cisco
was forced to
write down $2.2
billion worth of
obsolete
inventory, due to
uncertain
variations in its
demand in its
supply chain.
