The impact of bullwhip on supply chains: Performance pathways, control mechanisms, and managerial levers

• Date: 01 May 2015
• DOI: http://doi.org/10.1016/j.jom.2015.02.003
• Author: Manoj K. Malhotra,Alan W. Mackelprang
• Published date: 01 May 2015

Journal of Operations Management 36 (2015) 15–32

Contents lists available at ScienceDirect

Journal of Operations Management

journal homepage: www.elsevier.com/locate/jom

The impact of bullwhip on supply chains: Performance pathways,

control mechanisms, and managerial levers

Alan W. Mackelpranga,∗, Manoj K. Malhotra b,1

aCollege of Business Administration, Georgia Southern University, P.O. Box 8151, Statesboro, GA 30460-8151, USA

bDepartment of Management Science, Moore School of Business, University of South Carolina, Columbia, SC 29208, USA

article info

Article history:

Received 17 February 2014

Received in revised form 9 January 2015

Accepted 25 February 2015

Available online 9 March 2015

Accepted by Thomas Younghoon Choi

Keywords:

Bullwhip effect

Supplier performance

Empirical

Supply chain

abstract

Even though few empirical studies have tried to actually explicate the relationship between the bull-

whip effect and performance of the supplier firm, there exists a common perception for over 30 years

among both practitioners and academics that the bullwhip effect naturally results in decreased firm

profitability. Anecdotal evidence further suggests that this decline in profitability arises from a decline

in operational performance. However, the results of our study, which empirically examines the bullwhip

effect across supply chain partners through an analysis of 383 actual customer base-supplier dyads, chal-

lenge this commonly held position by suggesting that while traditional bullwhip often yields reduced

ROA, it ultimately has no relationship with the firm’s operating margin. Additionally, our results also call

into question whether or not production coordination between customers and suppliers can minimize

the need for inventory and capacity buffers, which are the two commonly utilized methods for battling

the bullwhip effect. Thus the relationship between bullwhip and firm performance is far more nuanced

and complicated than previously believed. We also show how the managerial bullwhip levers of coor-

dinating production across supply chain partners, or deploying inventory and capacity buffer control

mechanisms, can help maximize a firm’s performance along different dimensions.

© 2015 Elsevier B.V. All rights reserved.

1. Introduction

It is commonly perceived that bullwhip reduces a firm’s per-

formance by wreaking operational havoc. We challenge this

conventional wisdom by suggesting that while traditional bullwhip

generally does result in lower return on assets (ROA), it has no rela-

tionship with the firm’s operating margin, a primary indicator of

operational performance. Such a finding suggests that how bull-

whip actually impacts ROA is not as straightforward as previously

thought.

This study dives deeply into the bullwhip–performance rela-

tionship by proposing and evaluating a new dimension of bullwhip,

which captures the acceleration (or deceleration) of bullwhip

between supply chain partners. Such an evaluation allows us to

gain performance insights into not only the level of bullwhip a firm

faces, but also the extent to which an acceleration (or decelera-

tion) of the bullwhip impacts that firm’s performance. Results from

∗Corresponding author. Tel.: +1 9124780379.

E-mail addresses: Amackelprang@georgiasouthern.edu (A.W. Mackelprang),

Malhotra@moore.sc.edu (M.K. Malhotra).

1Tel.: +1 803 777 2712; fax: +1 803 777 6876.

this analysis will also challenge the conventional wisdom that cus-

tomers and suppliers in a supply chain that synchronize/coordinate

their production can minimize the need for inventory and capacity

buffers that represent traditional mechanisms utilized to combat

bullwhip. As such, we will show that with respect to the bullwhip

effect, leaning too heavily upon what many would consider to be

“already known” or “well understood” information may not always

yield contextually accurate insights.

At its most fundamental level, the bullwhip effect increas-

ingly distorts the pattern of actual end customer demand to

upstream supply chain partners (Lee et al., 1997a, 1997b; Zhang

and Burke, 2011), who are then often forced into “boom or bust”

production cycles. Such extreme production cycles resulting from

bullwhip have been shown to significantly increase supply chain

costs and lower performance (Sterman, 1989). Likewise, the Eco-

nomic Theory of Production Smoothing (ETPS) suggests that firms

have an economic incentive to avoid such extreme production

cycles and instead should smooth production (Holt et al., 1960;

Blanchard, 1983; Blinder, 1986; Miron and Zeldes, 1988). Numer-

ous researchers over several decades have highlighted a number of

countermeasures for combating the bullwhip.

Despite logical evidence that bullwhip behavior should be

avoided and recommendations on how to minimize it, there

http://dx.doi.org/10.1016/j.jom.2015.02.003

0272-6963/© 2015 Elsevier B.V. All rights reserved.

16 A.W. Mackelprang, M.K. Malhotra / Journal of Operations Management 36 (2015) 15–32

remains significant evidence that bullwhip still continues to occur

in about 67% of firms (Bray and Mendelson, 2012; Shan et al., 2014).

If firms are incentivized to smooth production to avoid the “boom

and bust” production cycles yet most do not do so, it suggests from

a rational perspective that these firms must have a larger incentive

not to smooth production, or from an irrational perspective, the

majority of firms are subject to the same widespread behaviorally

driven irrational actions. This apparent contradiction between the-

ory and practice raises an interesting research question. Does the

bullwhip effect actually result in reduced firm performance? If so,

what are the specific pathways through which this decreased firm

performance occurs? Finally, are there appropriate control mech-

anisms or managerial levers that can be applied to ameliorate the

bullwhip effect?

2. Bullwhip definitions and assessment

Before developing the theoretical model, it is important to first

establish definitions and foundational concepts underlying this

research. We subscribe to the traditional conceptual definition of

bullwhip as described by Lee et al. (1997a) as “the phenomenon

where orders to the supplier tend to have larger variance than sales

to the buyer (i.e., demand distortion), and the distortion propagates

upstream in an amplified form (i.e., variance amplification).”

Building upon this conceptual definition of bullwhip, it is clear

that bullwhip fundamentally consists of not only a level of demand

distortion, but also the extent of variance amplification. In other

words, while it is important to know if a firm is experiencing

demand distortion, it is also important to know if such demand

distortion is accelerating (or decelerating) along the supply chain.

For the purpose of this study, we describe these two aspects of bull-

whip simply as first and second order bullwhip effects. First order

bullwhip effect is the absolute level of demand variation amplifi-

cation for a given firm relative to the variance of actual demand for

the furthest downstream partner (e.g. demand distortion); while

second order bullwhip captures the rate of change (e.g. variance

amplification) in demand distortion (first order bullwhip) between

tiers in the supply chain. When evaluated together, first and sec-

ond order bullwhip not only capture the level of demand distortion

at a given node in the supply chain, but also the extent to which

demand distortion is accelerating (or decelerating) between tiers

in the supply chain. Thus, it is possible to capture all possible bull-

whip supply chain configurations by simultaneously utilizing these

two aspects of bullwhip.

We will first provide the mathematical equations we utilize to

assess first and second order bullwhip effects, then we will provide

a numerical example to further illustrate these calculations. First

order bullwhip for supplier sin year yis calculated as shown in

Eq. (1), where CV (Psy) is the coefficient of variation of production

of supplier firm sin the supply chain across the four quarters in

year y, and CV (Dcy) is the coefficient of variation of demand for the

supplier’s customer cacross the four quarters in year y.

Supplier First Order Bullwhip =CV(Psy )−CV(Dcy)

CV(Dcy)(1)

It is important to point out that in line with extant literature

(Bray and Mendelson, 2012; Shan et al., 2014), both demand and

production are calculated at the quarterly level. As configured in

Eq. (1), the first order bullwhip measure represents the percentage

change in upstream production variation relative to downstream

demand variation. As such, positive values indicate an increase in

the absolute level of demand variation or amplification, while a

negative number indicates a decrease in variation with respect to

downstream demand variation or an absolute dampening. Given

that first order bullwhip is consistent with how bullwhip has

been examined in extant literature (Cachon et al., 2007; Bray and

Table 1

Numerical illustration of bullwhip calculations using contrived data.

CustomeraSupplier (Tier 1)a

Known information Market Demand

Variance= 5

Production

Variance of

Customer= 7

Production Variance of

Supplier= 8

First order bullwhipb0.4= ((7 −5)/5)

(Production

Variance of

Customer is 40%

higher than Market

Demand Variance)

0.60= ((8 −5)/5)

(Production Variance of

Supplier is 60% higher than

Market Demand Variance)

Second order bullwhipcN/A 0.50= ((8 −5) −(7 −5))/|7−5|

(Supplier first order

bullwhip is 50% higher

than their Customer’s First

order Bullwhip indicating

that bullwhip is

accelerating between

customer and supplier)

aBoth customers and suppliers are manufacturers in our study.

bPositive values indicate amplifying, while negative values indicate dampening.

cPositive values indicate accelerating bullwhip, while negative values indicate

decelerating bullwhip.

Mendelson, 2012), we will interchangeably use the terms bullwhip

and first order bullwhip.

Whereas first order bullwhip captures the production variabil-

ity of the supplier with respect to end customer demand variability,

second order bullwhip captures the supplier’s production variabil-

ity as compared to the production variability of their customer.

By doing so, it is possible to determine how the firm is behaving

on both an absolute (first order) and relative (second order) basis

regarding production variability. Second order bullwhip for sup-

plier sand customer cin year yis calculated as shown in Eq. (2),

where CV (Psy) is the coefficient of variation of production for sup-

plier firm sin the supply chain across the four quarters in year y,

CV (Dcy) is the coefficient of variation of demand for the supplier’s

customer cacross the four quarters in year y, and CV (Pcy)isthe

coefficient of variation in production for customer cacross the four

quarters in year y.

Supplier Second Order Bullwhip

=(CV(Psy)−CV(Dcy)) −(CV(Pcy )−CV(Dcy))

|CV(Pcy)−CV(Dcy)|(2)

When this variable is positive, the supplier’s first order bullwhip

is larger than their customer’s first order bullwhip, thus indicat-

ing that the supplier is accelerating variance amplification relative

to their customer. When this measure is negative, the supplier is

decelerating variance amplification with respect to their customer.

Eqs. (1) and (2) make it possible to distinguish all combinations of

first and second order bullwhip effects, such as firms that may be

amplifying (first order) but at a decelerating rate (second order)

and vice versa.

We use a numerical example with contrived data in Table 1 to

illustrate the calculation of first and second order bullwhip using

Eqs. (1) and (2). The first order bullwhip for the customer is 0.4

as shown in Table 1, or in other words the customer’s production

variance is 40% larger than market demand variance. First order

bullwhip for the supplier is 0.6, or in other words their production

variance is 60% larger than market demand variance. Second order

bullwhip is 0.50, indicating that the supplier is amplifying at a 50%

higher rate than which the customer is amplifying, thus confirming

that the bullwhip is accelerating between the customer and the

supplier.