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Supply Chain SystemSupply Chain System
Supply Chain Dynamics & Supply Chain Dynamics &
Bullwhip EffectBullwhip Effect
Dr. Ravi Shankar
Dr. RAVI SHANKARProfessor
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: [email protected], [email protected]://web.iitd.ac.in/~rshankar
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Supply Chain StagesSupply Chain Stages
Supply Chain encompasses all activities associated with the flow
and transformation of materials and informationfrom the raw material stage through to the end user.
Supplier Manufacturer Distributor Retailer Customer
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Case 1: P&G-Dynamics of the Supply ChainCase 1: P&G-Dynamics of the Supply Chain
OrderSize
Time
Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998
Customer
Demand
Retailer Orders
Distributor Orders
Production Plan
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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
Production Plan
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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 PlanCustomer
Demand
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Growth of demand variabilityGrowth of demand variability
Bullwhip Effect -- Retailer Demand
0
50
100
150
200
250
300
0 25 50 75 100
Time
Demand
Bullwhip Effect -- Distributor Demand
0
50
100
150
200
250
300
0 25 50 75 100
Time
Demand
Retailer demand
Distributor demand
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Growth of demand variabilityGrowth of demand variability
Bullwhip Effect -- Distributor Demand
0
50
100
150
200
250
300
0 25 50 75 100
Time
Demand
Bullwhip Effect -- Manufacturer Demand
0
50
100
150
200
250
300
0 25 50 75 100
Time
Demand
Distributor demand
Manufacturer demand
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Bullwhip EffectBullwhip Effect
Variability of demand amplified as we
move up the supply chain from the
retailer to the distributor to themanufacturer to the suppliers
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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
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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
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Five areas of supply chain management can contribute to
increased demand variability:
Forecasting (Forecast Updates)
Lead Times
Order Batching
Price Fluctuations
Shortage Gaming
Lets consider each.
Contributors to VariabilityContributors to Variability
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Impact of Forecasting on the Bullwhip EffectImpact of Forecasting on the Bullwhip Effect
Let us understand this withperiodic reviewpolicywhere 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 isreviewed, and the warehouse orders enough
to raise the inventory position to the base-
stock level.
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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
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Impact of Forecasting on the Bullwhip EffectImpact of Forecasting on the Bullwhip Effect
The base-stock level is typically set equal tothe average demand during lead time and
review period plus a multiple of the standard
deviation of demand during lead time andreview period.
The latter quantity is referred to as safetystock. Typically, managers use standard
forecast smoothing techniques to estimate
average demand and demand variability.
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Impact of Forecasting on the Bullwhip EffectImpact of Forecasting on the Bullwhip Effect
An important characteristic of all forecastingtechniques is that as more data are observed,
the estimates of the mean and the standard
deviation (or variability) of customerdemands are regularly modified.
Since safety stock, as well as the base-
stock level, strongly depends on theseestimates, the user is forced to change order
quantities, thus increasing variability.
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ExampleExample
Amplification of demand changes
that affect upstream operations
within the supply chainAssumes stocks of one week
demand
Lead time= 1 weekBackorder allowed
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At the start of the week#1At the start of the week#1
Manufact12
3
4
5
Week
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At the start of the week#1At the start of the week#1
Manufact12
3
4
5
Week
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Ma1
2
3
4
5
Watch how Bullwhip effect has aggravatedWatch how Bullwhip effect has aggravated
Week
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Ma1
2
3
4
5
Watch how Bullwhip effect has aggravatedWatch how Bullwhip effect has aggravated
Week
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Ma1
2
3
4
5
Watch how Bullwhip effect has aggravatedWatch how Bullwhip effect has aggravated
Week
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Manufact1
2
3
4
5
Watch how Bullwhip effect has aggravatedWatch how Bullwhip effect has aggravated
Week
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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 reviewperiod.
Thus, with longer lead times, a small change in
the estimate of demand variability implies a
significant change in safety stock and base-stocklevel, leading to a significant change in order
quantities.
This, of course, leads to an increase in variability.
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Measuring the Bullwhip EffectMeasuring the Bullwhip EffectBetween Retailer and ManufacturerBetween Retailer and Manufacturer
Assuming a moving average forecasting technique based on pobservations, every period the retailer calculates a new meanand standard deviation based on the p most recentobservations of demand, the target inventory also changes. The
ratio between orders to the manufacturer (Q) and retailer demand(D) is:
Therefore..
2
2221
)(
)(
p
L
p
L
DVar
QVar++
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2
2
5
)1(2
5
)1(21
)(
)(++
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 EffectBetween Retailer and ManufacturerBetween Retailer and Manufacturer
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Lower Bound of Increase VariabilityLower Bound of Increase Variability
0
2
4
6
8
10
12
3 5 10 15 20 25 30p (number of periods in moving average)
Var (Q)/Var (D)
L = 5 L = 3 L = 1
2
2221
)(
)(
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 graphsuggests 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.
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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
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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 notoverlap, but they often do
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Price fluctuationsPrice fluctuations
Driven by
Price discounts
Quantity or volume discounts
Coupons
Rebates
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Price fluctuationsPrice fluctuations
Create
Swings in demand (high during low
price periods; low during normal priceperiods)
Problems include
Overtime and idle production time Premium freight charges
Inventory accumulations
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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
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Impact ofImpact ofInflated 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 ofproduct(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 ordersplaced earlier (created to enhance allocation) are cancelled and
products are returned.
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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 andoperations, manufacturing and distribution capacity
requirements among other areas.
What action can we take to counteract the Bullwhip effect?
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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.
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Avoid multiple demand forecastAvoid multiple demand forecast
updatesupdates
Share consumption data with upstreammembers Point of sale data given to distributors and
manufacturers Use EDI and internet to share data
Vendor managed inventory or continuousreplenishment programs
Direct sale techniques to get downstreamdemand info.
Share sales, capacity and inventory data toreduce gaming
R d i D d U t i tR d i D d U t i t
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Reducing Demand UncertaintyReducing Demand UncertaintyCentralized 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).
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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.
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Reducing Demand VariabilityReducing Demand Variability
In addition to sharing information through EDI and XMLtechnologies, companies are closing the supply chain gap throughinitiatives 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 supplierresponsible for inventory levels at customer locations.
This provides organizations up the chain with even greater visibilityof demand patterns and product availability.
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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 levelfor placement to the supplier. As the orders are delivered to theretail distribution center, they are immediately staged for storedelivery, thus eliminating DC inventories.
Postponementdelays the differentiation of products until the timeof order. A basic system may be manufactured (say a base PCunit). 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 cycletimes.
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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
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Stabilize pricesStabilize prices
Avoid price discounting and volume
discounting
Same day low prices (Wal-Mart)
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Eliminate gaming in shortage situationsEliminate gaming in shortage situations
Allocate product based on past sales
not on current orders
Share information about capacityLong term contracting to allow
vendors to adjust capacity
Eliminate generous return and ordercancellation policies
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Establishing PartnershipsEstablishing Partnerships
Each of the methods outlined earlier rely on closer relationshipsbetween customers and suppliers in order to support greaterinformation sharing and the development of trust between theorganizations.
An additional strategy involving partnerships is the concept ofEvery Day Low Pricing (EDLP).
EDLP eliminates the pattern of promotion offered by suppliers.
By trading off the promotional efforts with a consistent and lowerprice the incentive for customers to place forward buys iseliminated and reduced variability in demand helps the supplierlower costs and maintain profitable margins.
Supply Chain Coordination InitiativesSupply Chain Coordination Initiatives
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Supply Chain Coordination InitiativesSupply Chain Coordination Initiatives
FrameworkFrameworkCauses of theBullwhip Effect
Information Sharing Channel Alignment Operational Efficiency
DemandForecastingUpdate
Understanding systemdynamics
Using point of sale (POS)data
EDI, XML (internet)
Computer AssistedOrdering
Vendor Managed Inventory(VMI)
Information sharing
Consumer direct
Lead-time reduction
Echelon-based inventorycontrol
Order Batching EDI
Extensible MarkupLanguage (XML). Internetordering
Discounts for assortmentplanning
Delivery appointments
Consolidation
Logistics outsourcing
Reducing order costs
Computer AssistedOrdering
PriceFluctuations
ContinuousReplenishment Programs(CRP)
Every Day Low Pricing(EDLP)
Every Day Low Pricing(EDLP)
Activity Based Costing(ABC)
ShortageGaming
Sharing sales, capacity,and inventory data
Allocation based onpassed salesSource: Lee et al. (1997)
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You May Try This:You May Try This:
--Use Beer Distribution Game to Demonstrate and AnalyzeUse Beer Distribution Game to Demonstrate and AnalyzeBullwhip EffectBullwhip Effect
-Use CD given with the Text book-Use CD given with the Text book