Impact of Forecasting on the Bullwhip.pdf

8/11/2019 Impact of Forecasting on the Bullwhip.pdf

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Erkan BAYRAKTAR

Baheehir University, stanbul

Kazm SARI

Beykent University, stanbul

Impact of Forecasting on the Bullwhip

Effect


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Impact of Demand Forecasting onthe Bullwhip Effect Introduction

Previous Studies

Our Study:

Winters Method (Triple Exponential Smoothing)for demands with linear trend and seasonality

Simulation Model

Design of Experiment Analysis of Simulation Ouput

Conclusion


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The Bullwhip Effect

The bullwhip effect represents the phenomenon

where orders to supplier tend to have larger

variance than sales to the buyer (i.e., demand

distortion) and this distortion propagates upstream

in an amplified form.


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Retailers Orders to Wholesaler

Increasing Variability of Orders up the Supply Chain (Lee et al. 1997b)


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DecreaseProfitability

DecreaseCustomer service

rate

IncreaseLead Time

IncreaseTransportation cost

IncreaseInventory cost

IncreaseProduction cost

High Bullwhip EffectPerformance

Measurement

(Chopra et al.2001, p.363)


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Metters (1997) studied the impact of the bullwhip

effect on profitability by establishing an empiricallower bound on the cost excess of the bullwhip

effect. Results indicate that the importance of the

bullwhip effect to a firm greatly depending on thespecific business environments and eliminating the

bullwhip effect can increase product profitability by

10-30%.


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Causes of the Bullwhip Effect(Lee et al. 1997a, 1997b)

Demand forecasting Order batching

Price fluctuation

Rationing and shortage gaming


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2. Previous Studies: Simple

Exponential Smoothing

Chen et al. (2000b):

Correlated demand AR(1) pattern

Order-up-to inventory control policy

The larger the smoothing parameter (), the larger the

bullwhip effect,

Longer lead times (L) lead to larger increase in bullwhip

effect,

A retailer facing a longer lead time, L, must use a smallersmoothing parameter (), in order to reduce the bullwhip

effect


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3.Previous Studies: Douple

Exponential Smoothing

Chen et al. (2000b):

Demand with a linear trend

Order-up-to inventory policy

Smoothing parameters , , and lead time have asignificant impact on the bullwhip effect,

Larger the smoothing parameters, the larger thebullwhip effect,

The increase in variability (bullwhip effect) doesnot depend on the magnitude of the linear trend.


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Winters Method (Triple

Exponential Smoothing) for

Demands with Linear Trend &Seasonality

A Simulation Study


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Design Of Experiment

The purpose of the design of experiment is to

analyze the impact of :

Smoothing parameters (, , ),

Lead time (L),

Strength of the seasonality,

on the bullwhip ratio.


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Independent Variables

Smoothing parameters Alpha (0.01, 0.25, 0.50),

Beta (0.01, 0.25, 0.50),

Gamma (0.01, 0.25, 0.50),

Lead time Low ( 1 week ),

Medium (3 weeks),

High (5 weeks),

Strength of seasonality Low seasonality Medium seasonality

High seasonality


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Demand Types

Demand generatorsnormal()noise

season

t)]52

2(sin[season

t)slopebase(Demandt +

+

+=

30

15

5

SEASON

10021000Low

Seasonality

10021000Medium

Seasonality

10021000High

Seasonality

NOISESLOPEBASEDEMAND TYPE


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1000

1200

1400

1600

1800

2000

2200

2400

0 10 20 30 40 50

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

0 10 20 30 40 50

1300

1400

1500

1600

1700

1800

1900

2000

2100

2200

0 10 20 30 40 50

High Seasonality Medium Seasonality

Low Seasonality

Demand types with

different strength of

seasonality that is used insimulation analysis


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Simulation Study: Supply Chain Structure

Consist of two members:

one retailer and one manufacturer,

In each period, t, the retailer observes his inventoryposition and places an order, qt , to the manufacturer

After the order is placed, the retailer observes andfills customer demand for that period, denoted by Dt

Any unfilled demand is backlogged

There is a fixed lead time between the time an orderis placed by the retailer and when it is received at theretailer


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Simulation Study: Forecasting Technique

It is assumed that retailer uses the winters (triple

exponential smoothing) method to forecast demandwhich is formulated as

n= 1,2...s

Ft+n : forecast at period t+n,

Lt : level component of demand at period t,Tt : trend component of demand at period t ,

Set+n-s : seasonality index for the same period in previous year,

(Abraham and Ledolter, 1983, p.170)

sntettent SnTLF ++ += )(


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Simulation Study: Forecasting Technique

s-1t

1

11t

e

11

t

s-1t

1t1t

S)1(S

)1()(

)()1(S

+

+

+

+

++

+

++

+=

+=

++=

t

t

tttt

te

L

D

TLLT

TLDL

(Abraham and Ledolter, 1983, p.170)


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Simulation Study: Ordering Policy

We assume that the retailer follows a simple order-up-to inventory policy in which order-up-to point isestimated from the observed demand as

: Forecasted demand over L periods

L : lead time + review period ( 1 )

: standard deviation of the demand over L periods

z : is a constant chosen to meet a desired service level,

L

t

L

tt zDS

+=

L

tD

L

t


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Simulation Output Analysis

The length of the simulation run is 520 weeks.

First 156 weeks are used to estimate the initial

parameters for the forecasting model,

To reduce the impact of random variations,ten replicates were conducted for each

combination of the independent variables.


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Simulation Output Analysis

Simulation Output Analysis indicates that

bullwhip ratio significantly influenced by

the

Smoothing Parameters (, , ), Lead Time,

Strength of the seasonality.


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Multiple Range Test


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Interaction Effect

Between SmoothingParameters & Lead

Time


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Interaction Effect

Between SmoothingParameters &

Seasonality


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Further Analysis of Gamma Parameter

Bullwhip Ratio for Various Levels of Gama

In order to better understand the influence of the

gamma parameter on bullwhip ratio we further

make an analysis for various levels of gammaunder different conditions.


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Further Analysis of Gamma Parameter :Bullwhip

Ratio for Various Levels of GammaEstimated Marginal Means of Bullwhip Effect

Gama

,91,81,71,61,51,41,31,21,11,01Es

tima

t

ed

Marg

ina

l

Means

17

16

15

14

13

12

11

Although, generally increasing values of gamma() increases thebullwhip ratio, in some points at the medium level of gamma()

the bullwhip ratio is smallest, not at small values of gamma ()

parameter

Duncana,b

120 2,3621

120 2,3736120 2,3866

120 2,4154

120 2,4177

120 2,4702

120 2,4831

120 2,5469120 2,6271

120 2,7147

,110 ,872 ,372 1,000 1,000 1,000

Gama,31,21

,41

,11

,51

,61,01

,71,81

,91

Sig.

N 1 2 3 4 5 6

Subset

Natural logarithm(Ln) transformation is made to

the dependent variable


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Conclusion

While seasonality gets stronger, the bullwhip effect goes

down. Intuitively, it can be said that the bullwhip effect is compensated by

the variability generated by the seasonality.

The impact of gamma ( ) parameter on the bullwhip ratio is

minor when compared with the other smoothing parameters Increase in lead time (L) leads to an increase in the bullwhip

ratio, but when alpha () and beta () parameters are chosen small, this

increase is very small when compared with higher values of alpha (),and beta ()


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Conclusion...

Choosing alpha (), and beta () parameter small is

important in order to reduce the bullwhip ratio,especially when the lead time (L) is long or/andstrength of seasonality is low.

The impact of the gamma( ) parameter on thebullwhip ratio is different form the alpha () andbeta () parameters. While smaller alpha () and beta() lead to smaller bullwhip ratio, for the gamma( )parameter, small and high values lead to largerbullwhip ratio, but medium values of gamma( )lead to smaller bullwhip ratio.


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Future Area of Research

Assess the impact of bullwhip effect on the

performance measures of the supply chain

(e.g., total cost of the members, total chain

cost, service level of chain members, andservice level of the chain).

Investigate other techniques of time series

analysis for seasonality.


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Thank you...


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Impact of Forecasting on the Bullwhip.pdf

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