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Session 1a Bullwhip Effect Bhu

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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


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