7 session 7_bulwhip effect cfvg 2012

Supply Chain Dynamics, Value of Supply 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

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

Case 1: P&GCase 1: P&G--Dynamics of the Supply Chain Dynamics of the Supply Chain O

Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998

CustomerDemand

Retailer OrdersDistributor Orders

Production Plan

Case 1: P&G: What Management Gets... Case 1: P&G: What Management Gets... O

CustomerDemand

Production PlanProduction Plan

Case 1 P&G: What Management WantsCase 1 P&G: What Management Wants……

Production PlanCustomerDemand

CustomerDemand

Bullwhip EffectBullwhip Effect

� Variability of demand amplified as we move up the supply chain from the retailer to the distributor to the manufacturer to the suppliers

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

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

Growth of demand variabilityGrowth of demand variability

Bullwhip Effect -- Retailer Demand

0 25 50 75 100

Bullwhip Effect -- Distributor Demand

0 25 50 75 100

� Retailer demand

Distributor demand �

Growth of demand variabilityGrowth of demand variability

Bullwhip Effect -- Distributor Demand

0 25 50 75 100

Bullwhip Effect -- Manufacturer Demand

0 25 50 75 100

� Distributor demand

Manufacturer demand �

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

•Forecasting (Forecast Updates)

Contributors to Bullwhip Effect#1Contributors to Bullwhip Effect#1

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.

�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.

�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.

Reorder Point with Variable DemandReorder Point with Variable Demand

stocksafety

yprobabilit level service toingcorrespond deviations standard ofnumber

demanddaily ofdeviation standard the

timelead

demanddaily average

pointreorder

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

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

At the end of week#1At the end of week#1

Let us represent WeekLet us represent Week--beginning and Weekbeginning and Week--

ending inventory as followsending inventory as follows

Prod Stock Order Stock Order Stock 100 100 100 100 100 100 (start)

100 (end)

Watch now, Watch now, very-very carefully, how Bullwhip , how Bullwhip

effect setseffect sets--inin

100 (end)

?? 100 95

100 (end)

90 100 95

Watch now, veryWatch now, very--very carefully, how very carefully, how

Bullwhip effect setsBullwhip effect sets--inin

Watch how Bullwhip effect is entering at

Retailer level

Prod Stock Order Stock Order Stock 100 100 100 100 100 100

90 100

(90+5)

(5)(90)

Watch how Bullwhip effect is entering at Watch how Bullwhip effect is entering at

Retailer levelRetailer level

Watch how Bullwhip effect sets in

90 100

(90+5)

90 100

(90+5)

90 100

(90+5)

90 100

Note how Bullwhip effect has found footNote how Bullwhip effect has found foot--hold at the hold at the

Retailer levelRetailer level

Prod Stock Order Stock Order Stock 100 100 100 100

100 100

90 100

Watch how Bullwhip effect is entering into Distributor Watch how Bullwhip effect is entering into Distributor

levellevel

100 100

?? 100 90 100

Watch how Bullwhip effect is entering into

Distributor level

100 100

80 100 90 100

Prod Stock Order Stock Order Stock 100 100

100 100

80 100

90 100

(10)(80)

Watch how Bullwhip effect is entering into Watch how Bullwhip effect is entering into

Distributor levelDistributor level

100 100

80 100

90 100

100 100

80 100

90 100

100 100

80 100

90 100

100 90

Watch how Bullwhip effect has found a footWatch how Bullwhip effect has found a foot--

hold at Distributor levelhold at Distributor level

100 100

80 100

90 100

100 90

Watch how Bullwhip effect is expanding its Watch how Bullwhip effect is expanding its

wings at Distributor levelwings at Distributor level

100 100

80 100

90 100

100 90

100 100

80 100

90 100

100 90

100 100

80 100

90 100

100 90

100 100

80 100

90 100

100 90

100 100

80 100

90 100

100 90

Watch how Bullwhip effect has settled badly at Watch how Bullwhip effect has settled badly at

100 100

80 100

90 100

100 90

Similarly try at Manufacturer level pleaseSimilarly try at Manufacturer level please

Prod Stock Prod Stock Prod Stock 100 100

100 100

Similarly try at Manufacturer level pleaseSimilarly try at Manufacturer level please

100 100

?? 100

80 100

90 100

Manufacturer levelManufacturer level

100 100

60 100

80 100

90 100

Watch how Bullwhip effect has entered into Watch how Bullwhip effect has entered into

Manufacturer levelManufacturer level

100 100

60 100

80 100

90 100

100 90

wings at Manufacturer levelwings at Manufacturer level

100 100

60 100

80 100

90 100

120 80

100 90

Watch how Bullwhip effect has expanded its Watch how Bullwhip effect has expanded its

wings at Manufacturer levelwings at Manufacturer level

100 100

60 100

80 100

90 100

120 80

100 90

?? 100

Watch how Bullwhip effect is aggravatingWatch how Bullwhip effect is aggravating

100 100

60 100

80 100

90 100

120 80

100 90

90 100

100 100

60 100

80 100

90 100

120 80

100 90

90 100

100 100

60 100

80 100

90 100

120 80

100 90

90 100

Watch how Bullwhip effect has aggravatedWatch how Bullwhip effect has aggravated

100 100

60 100

80 100

90 100

120 80

100 90

90 100

Watch how Bullwhip effect has aggravatedWatch how Bullwhip effect has aggravated

•LEAD TIME

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.

Measuring the Bullwhip EffectMeasuring the Bullwhip EffectBetween 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…..

QVar++≥

)(++≥

…if the retailer estimates the mean demand based on a five periodmoving 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….

…or at least 40% larger than the variance of customer demand. The following slide plots the relationship between the number ofperiods 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

Lower Bound of Increase VariabilityLower Bound of Increase Variability

3 5 10 15 20 25 30p (number of periods in moving average)

Var (Q)/Var (D)

L = 5 L = 3 L = 1

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.

ConsequencesConsequences……..

� Increased safety stock

� Reduced service level

� Inefficient allocation of resources

� Increased transportation costs

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.

RetailerStage 1

ManufacturerStage 2

SupplierStage 3

qo=D q1q2

∑∑

)(2)(2

MultiMulti--Stage Supply Chains Stage Supply Chains

(Formula)(Formula)

MultiMulti--Stage Systems: Var(qStage Systems: Var(qkk)/Var(D))/Var(D)

1015202530

0 10 20 30

Dec, k=5

Cen, k=5

Dec, k=3

Cen, k=3

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.

ORDER BATCHING

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

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

PRICE FLUCTUATION

Price fluctuationsPrice fluctuations

� Driven by

– Price discounts

– Quantity or volume discounts

– Coupons

– Rebates

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

•RATIONING & SHORTAGE GAMING

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

Impact of Impact 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 andproducts are returned.

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?

•HOW TO TAME?

Bullwhip Effect: ITS YOUR TURNBullwhip Effect: ITS YOUR TURN

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.

Avoid multiple demand forecast Avoid 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

Reducing Demand UncertaintyReducing Demand UncertaintyCentralized vs. Decentralized InformationCentralized vs. Decentralized Information

Dec K = 5

Cen K = 5

Dec K = 3

Cen K = 3

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).

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 thatsupports information sharing over the internet.

Reducing Demand VariabilityReducing Demand Variability

In addition to sharing information through EDI and XML technologies, 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 supplier responsible for inventory levels at customer locations.

This provides organizations up the chain with even greater visibility of demand patterns and product availability.

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.

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

Stabilize pricesStabilize prices

� Avoid price discounting and volume discounting

� Same day low prices (Wal-Mart)

Eliminate gaming in shortage situationsEliminate gaming in shortage situations

� Allocate product based on past salesnot on current orders

� Share information about capacity

� Long term contracting to allow vendors to adjust capacity

� Eliminate generous return and order cancellation policies

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.

Supply Chain Coordination InitiativesSupply Chain Coordination Initiatives

FrameworkFrameworkCauses 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)

�Activity Based Costing (ABC)

Shortage Gaming

�Sharing sales, capacity, and inventory data

�Allocation based on passed salesSource: Lee et al. (1997)

Coping with the Bullwhip Effect Coping with the Bullwhip Effect

� Reduce Variability and Uncertainty

- Sharing Information

- Year-round low pricing

� Reduce Lead Times

- Cross Docking

� Alliance Arrangements

– Vendor managed inventory

– On-site vendor representatives

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.

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.

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

You May Try This:You May Try This:

--Use Beer Distribution Game to Demonstrate and Analyze Use Beer Distribution Game to Demonstrate and Analyze

--Use CD given with the Text bookUse CD given with the Text book

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.

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.

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

Bullwhip Effect in Supply ChainsBullwhip Effect in Supply Chains

Example: 3 retailers with the same wholesaler– Periodic (s, Q) policy.

– Review Period = 1 day

Example Supply Chain StagesExample Supply Chain Stages

Supplier Manufacturer One

Distributor

RetailersMany

Customers

Bullwhip Effect in Supply Chains Bullwhip Effect in Supply Chains (Constant customer demands = 10+25+15=50 units/day)(Constant customer demands = 10+25+15=50 units/day)

Day Beg. Inv. Dem. Ord. Beg. Inv. Dem. Ord. Beg. Inv. Dem. Ord. Observed Cust. Dem.

1 20 10 ?? 100 25 150 15 ?? ??

2 10 10 75 25 135 15 0 50

3 50 10 50 25 ?? 120 15 125 50

4 40 10 25 25 105 15 0 50

5 30 10 125 25 90 15 0 50

6 20 10 ?? 100 25 75 15 50 50

7 10 10 75 25 60 15 0 50

8 50 10 50 25 ?? 45 15 125 50

9 40 10 25 25 30 15 ?? 120 50

10 30 10 125 25 15 15 0 50

11 20 10 ?? 100 25 120 15 50 50

12 10 10 75 25 105 15 0 50

13 50 10 50 25 ?? 90 15 125 50

14 40 10 25 25 75 15 0 50

15 30 10 125 25 60 15 0 50

16 20 10 ?? 100 25 45 15 50 50

17 10 10 75 25 30 15 ?? 120 50

18 50 10 50 25 ?? 15 15 125 50

19 40 10 25 25 120 15 0 50

20 30 10 125 25 105 15 0 50

Retailer 1 Retailer 2 Retailer 3 Demands to Wholesaler

1 20 10 50 100 25 150 15 50 50

2 10 10 75 25 135 15 0 50

3 50 10 50 25 125 120 15 125 50

4 40 10 25 25 105 15 0 50

5 30 10 125 25 90 15 0 50

6 20 10 50 100 25 75 15 50 50

7 10 10 75 25 60 15 0 50

8 50 10 50 25 125 45 15 125 50

9 40 10 25 25 30 15 120 120 50

10 30 10 125 25 15 15 0 50

11 20 10 50 100 25 120 15 50 50

12 10 10 75 25 105 15 0 50

13 50 10 50 25 125 90 15 125 50

14 40 10 25 25 75 15 0 50

15 30 10 125 25 60 15 0 50

16 20 10 50 100 25 45 15 50 50

17 10 10 75 25 30 15 120 120 50

18 50 10 50 25 125 15 15 125 50

19 40 10 25 25 120 15 0 50

20 30 10 125 25 105 15 0 50

1 20 10 50 100 25 150 15 50 50

2 10 10 75 25 135 15 0 50

3 50 10 50 25 125 120 15 125 50

4 40 10 25 25 105 15 0 50

5 30 10 125 25 90 15 0 50

6 20 10 50 100 25 75 15 50 50

7 10 10 75 25 60 15 0 50

8 50 10 50 25 125 45 15 125 50

9 40 10 25 25 30 15 120 120 50

10 30 10 125 25 15 15 0 50

11 20 10 50 100 25 120 15 50 50

12 10 10 75 25 105 15 0 50

13 50 10 50 25 125 90 15 125 50

14 40 10 25 25 75 15 0 50

15 30 10 125 25 60 15 0 50

16 20 10 50 100 25 45 15 50 50

17 10 10 75 25 30 15 120 120 50

18 50 10 50 25 125 15 15 125 50

19 40 10 25 25 120 15 0 50

20 30 10 125 25 105 15 0 50

Bullwhip Effect in Supply ChainsBullwhip Effect in Supply Chains(Constant demands)(Constant demands)

R e t a i l e r 1 D e m a n d

0 5 10 15 20 25 30 35 40 45 50

0 5 10 15 20 25 30 35 40 45 50R e t a i l e r 3 D e m a n d

0 5 10 15 20 25 30 35 40 45 50

Demands Observed by Wholesaler

0 5 10 15 20 25 30 35 40 45 50

Bullwhip Effect in Supply ChainsBullwhip Effect in Supply Chains(Random demands)(Random demands)

0 5 10 15 20 25 30 35 40 45 50

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0

Demands O bserved by Wholesaler

0 5 10 15 20 25 30 35 40 45 50

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.

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.

7 session 7_bulwhip effect cfvg 2012

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