Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/the-bullwhip-effect-in-supply-chain-reflections-after-a-decade/
A decade has passed since the publication of the two seminal papers by Lee, Padmanabhan and Whang (1997) that describes the “bullwhip effect” in supply chains and characterizes its underlying causes. The bullwhip phenomenon is observed in supply chains where the decisions at the subsequent stages of the supply chain are made greedily based on local information, rather than through coordination based on global information on the state of the whole chain. The first consequence of this information distortion is higher variance in purchasing quantities compared to sales quantities at a particular supply chain stage. The second consequence is increasingly higher variance in order quantities and inventory levels in the upstream stages compared to their downstream stages (buyers). In this paper, we survey a decade of literature on the bullwhip effect and present the key insights reported by researchers and practitioners. We also present our reflections and share our vision of possible future.
This study analyzed order fulfillment times for 1,000 products ordered from Amazon and other major online retailers to compare their performance. The results showed that on average, Amazon fulfilled orders in 1.92 days while other retailers took 4.81 days. Amazon delivered orders faster than competitors for 81% of products. Amazon had particularly strong fulfillment for books, delivering orders on average twice as fast as other retailers. The study concludes that Amazon's superior fulfillment infrastructure gives it a significant competitive advantage over retailers that have not invested similarly in online order fulfillment.
Potential competitors pose medium pressure as entry barriers are high due to Walmart's distribution systems and brand name. Rivalry among established companies is also medium as Target is Walmart's strongest competitor who has carved out its niche. Buyers and substitutes exert low pressure as Walmart offers convenience and low prices not found elsewhere while suppliers have low to medium bargaining power, depending on their size. Complementors also pose low pressure as they do not significantly affect Walmart's business model.
This document provides an overview of analyzing a company's external environment. It discusses key questions to consider regarding a company's industry and competitive environment, including:
1. The dominant economic traits of the industry.
2. The competitive forces facing industry members using Porter's Five Forces model.
3. The key factors driving change in the industry and their potential impacts.
4. The market positions occupied by competitors and their relative strengths.
The document provides frameworks and checklists to guide answering these questions to thoroughly understand the strategic situation facing a company.
Porter's Five Forces Model analyzes industry structure and competitive challenges. The five forces are: 1) threat of new entrants, 2) rivalry among existing competitors, 3) bargaining power of suppliers, 4) bargaining power of buyers, and 5) threat of substitute products. A sixth force of complementors was also proposed. The five forces framework helps managers understand industry attractiveness and form strategic responses accordingly.
The document discusses analyzing a company's external environment including opportunities and threats. It describes analyzing the general environment factors like demographic, economic, political/legal, socio-cultural, technological, and global influences. It also discusses analyzing the industry environment including the five competitive forces that shape industry competition and profitability: threat of new entrants, power of suppliers, power of buyers, threat of substitutes, and rivalry among existing competitors. Finally, it discusses analyzing competitor environments through assessing competitors' objectives, strategies, assumptions, capabilities, and responses.
This document discusses analyzing a company's external environment including the general environment, industry environment, and competitor environment. It defines opportunities and threats as conditions in the general environment that could help or hinder strategic competitiveness. It also describes Porter's five forces model for industry environment analysis and discusses analyzing competitors, strategic groups, and key success factors.
This study analyzed order fulfillment times for 1,000 products ordered from Amazon and other major online retailers to compare their performance. The results showed that on average, Amazon fulfilled orders in 1.92 days while other retailers took 4.81 days. Amazon delivered orders faster than competitors for 81% of products. Amazon had particularly strong fulfillment for books, delivering orders on average twice as fast as other retailers. The study concludes that Amazon's superior fulfillment infrastructure gives it a significant competitive advantage over retailers that have not invested similarly in online order fulfillment.
Potential competitors pose medium pressure as entry barriers are high due to Walmart's distribution systems and brand name. Rivalry among established companies is also medium as Target is Walmart's strongest competitor who has carved out its niche. Buyers and substitutes exert low pressure as Walmart offers convenience and low prices not found elsewhere while suppliers have low to medium bargaining power, depending on their size. Complementors also pose low pressure as they do not significantly affect Walmart's business model.
This document provides an overview of analyzing a company's external environment. It discusses key questions to consider regarding a company's industry and competitive environment, including:
1. The dominant economic traits of the industry.
2. The competitive forces facing industry members using Porter's Five Forces model.
3. The key factors driving change in the industry and their potential impacts.
4. The market positions occupied by competitors and their relative strengths.
The document provides frameworks and checklists to guide answering these questions to thoroughly understand the strategic situation facing a company.
Porter's Five Forces Model analyzes industry structure and competitive challenges. The five forces are: 1) threat of new entrants, 2) rivalry among existing competitors, 3) bargaining power of suppliers, 4) bargaining power of buyers, and 5) threat of substitute products. A sixth force of complementors was also proposed. The five forces framework helps managers understand industry attractiveness and form strategic responses accordingly.
The document discusses analyzing a company's external environment including opportunities and threats. It describes analyzing the general environment factors like demographic, economic, political/legal, socio-cultural, technological, and global influences. It also discusses analyzing the industry environment including the five competitive forces that shape industry competition and profitability: threat of new entrants, power of suppliers, power of buyers, threat of substitutes, and rivalry among existing competitors. Finally, it discusses analyzing competitor environments through assessing competitors' objectives, strategies, assumptions, capabilities, and responses.
This document discusses analyzing a company's external environment including the general environment, industry environment, and competitor environment. It defines opportunities and threats as conditions in the general environment that could help or hinder strategic competitiveness. It also describes Porter's five forces model for industry environment analysis and discusses analyzing competitors, strategic groups, and key success factors.
The bullwhip effect in supply chain: Reflections after a decadeGurdal Ertek
A decade has passed since the publication of the two seminal papers by Lee, Padmanabhan and Whang (1997) that describes the “bullwhip effect” in supply chains and characterizes its underlying causes. The bullwhip phenomenon is observed in supply chains where the decisions at the subsequent stages of the supply chain are made greedily based on local information, rather than through coordination based on global information on the state of the whole chain. The first consequence of this information distortion is higher variance in purchasing quantities compared to sales quantities at a particular supply chain stage. The second consequence is increasingly higher variance in order quantities and inventory levels in the upstream stages compared to their downstream stages (buyers). In this paper, we survey a decade of literature on the bullwhip effect and present the key insights reported by researchers and practitioners. We also present our reflections and share our vision of possible future.
http://research.sabanciuniv.edu.
This document summarizes a research paper that examines using fuzzy logic to reduce the bullwhip effect in supply chains caused by rationing and shortage gaming. It first defines the bullwhip effect and describes rationing and shortage gaming as a cause. It then discusses related work applying fuzzy logic to supply chain problems. Next, it examines the impact of rationing and shortage gaming on the bullwhip effect in more detail. It provides an overview of fuzzy logic and how it can be applied to model rationing and shortage gaming using a fuzzy logic-based approach. Finally, it proposes a fuzzy system model for resolving rationing and shortage gaming issues in the supply chain.
11.factors causing reversed bullwhip effect on the supply chains of kenyan firmsAlexander Decker
This study examined factors that cause supply chain variability, known as the reverse bullwhip effect, along the supply chain of Kenya Pipeline Company. The researchers conducted interviews and collected data through questionnaires from 5 depots along the supply chain. The findings suggested that capacity constraints were a major factor contributing to supply chain inefficiencies. Specifically, the downstream storage capacity was much larger than the upstream utilization capacity, limiting product flow to end sale points. The conclusion was that the supply chain was inefficient due to challenges related to capacity and government intervention. Recommendations included strategies to adjust capacity, upgrade equipment, add manpower and machine hours, and reduce disruptive government intervention.
Thomson Financial analyzed 75 recent instances of shareholder activism and found mixed results regarding the impact on stock price. Stocks targeted by activists showed higher returns after the activism in both short and long-term, but the results were not always statistically significant. Certain sectors like consumer discretionary were more frequent targets. Activists achieved at least one of their demands 45% of the time, with the highest success rate of nearly 80% for demands to remove the CEO. Stocks of targeted companies outperformed a control group after activism, suggesting shareholder monitoring leads to positive changes. However, the prominence rather than just substance of activism may influence stock prices.
S P R I N G 1 9 9 7Hau L. LeeV. PadmanabhanSeungjin W.docxrtodd599
S P R I N G 1 9 9 7
Hau L. Lee
V. Padmanabhan
Seungjin Whang
The Bullwhip
Effect in Supply
Chains
Distorted information from one end of a supply chain to the
other can lead to tremendous inefficiencies: excessive
inventory investment, poor customer service, lost revenues,
misguided capacity plans, ineffective transportation, and
missed production schedules. How do exaggerated order
swings occur? What can companies do to mitigate them?
Vol. 38, No. 3 Reprint #3837 http://mitsmr.com/1phEOiM
http://mitsmr.com/1phEOiM
93SLOAN MANAGEMENT REVIEW/SPRING 1997 LEE ET AL.
N
ot long ago, logistics executives at Procter &
Gamble (P&G) examined the order pat-
terns for one of their best-selling products,
Pampers. Its sales at retail stores were fluctuating, but
the variabilities were certainly not excessive. However,
as they examined the distributors’ orders, the execu-
tives were surprised by the degree of variability. When
they looked at P&G’s orders of materials to their sup-
pliers, such as 3M, they discovered that the swings
were even greater. At first glance, the variabilities did
not make sense. While the consumers, in this case,
the babies, consumed diapers at a steady rate, the de-
mand order variabilities in the supply chain were am-
plified as they moved up the supply chain. P&G
called this phenomenon the “bullwhip” effect. (In
some industries, it is known as the “whiplash” or the
“whipsaw” effect.)
When Hewlett-Packard (HP) executives examined
the sales of one of its printers at a major reseller, they
found that there were, as expected, some fluctuations
over time. However, when they examined the orders
from the reseller, they observed much bigger swings.
Also, to their surprise, they discovered that the orders
from the printer division to the company’s integrated
circuit division had even greater fluctuations.
What happens when a supply chain is plagued with
a bullwhip effect that distorts its demand information
as it is transmitted up the chain? In the past, without
being able to see the sales of its products at the distri-
bution channel stage, HP had to rely on the sales or-
ders from the resellers to make product forecasts, plan
capacity, control inventory, and schedule production.
Big variations in demand were a major problem for
HP’s management. The common symptoms of such
variations could be excessive inventory, poor product
forecasts, insufficient or excessive capacities, poor cus-
tomer service due to unavailable products or long back-
logs, uncertain production planning (i.e., excessive revi-
sions), and high costs for corrections, such as for expe-
dited shipments and overtime. HP’s product division
was a victim of order swings that were exaggerated by
the resellers relative to their sales; it, in turn, created
additional exaggerations of order swings to suppliers.
In the past few years, the Efficient Consumer Re-
sponse (ECR) initiative has tried to redefine how the
grocery supply chain should work.1 One motivation
fo.
Multi agent system for knowledge management in SCMGeorge Ogrinja
This document provides an overview of supply chain management and multiagent systems. It begins by introducing supply chain management as a business practice used to address industrial problems through inter-company collaboration. It then discusses multiagent systems, defining agents and comparing them to objects. Finally, it synthesizes these two areas by discussing how agent-based approaches can be applied to supply chain management problems.
This document summarizes a study on market efficiency and long-term stock returns. It discusses two key findings:
1) Studies of long-term stock returns show about as much evidence of overreaction to information as underreaction, which is consistent with the prediction of market efficiency.
2) Most anomalies in long-term returns tend to disappear or become marginal when using different models for expected returns or statistical techniques, suggesting they can reasonably be attributed to chance rather than inefficiency.
* Corresponding author. Tel.: 773 702 7282; fax: 773 702 9937; e-mail: [email protected]
edu.
1 The comments of Brad Barber, David Hirshleifer, S.P. Kothari, Owen Lamont, Mark Mitchell,
Hersh Shefrin, Robert Shiller, Rex Sinquefield, Richard Thaler, Theo Vermaelen, Robert Vishny, Ivo
Welch, and a referee have been helpful. Kenneth French and Jay Ritter get special thanks.
Journal of Financial Economics 49 (1998) 283—306
Market efficiency, long-term returns, and behavioral
finance1
Eugene F. Fama*
Graduate School of Business, University of Chicago, Chicago, IL 60637, USA
Received 17 March 1997; received in revised form 3 October 1997
Abstract
Market efficiency survives the challenge from the literature on long-term return
anomalies. Consistent with the market efficiency hypothesis that the anomalies are
chance results, apparent overreaction to information is about as common as underreac-
tion, and post-event continuation of pre-event abnormal returns is about as frequent as
post-event reversal. Most important, consistent with the market efficiency prediction that
apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique. ( 1998 Elsevier Science S.A. All rights
reserved.
JEL classification: G14; G12
Keywords: Market efficiency; Behavioral finance
1. Introduction
Event studies, introduced by Fama et al. (1969), produce useful evidence on
how stock prices respond to information. Many studies focus on returns in
a short window (a few days) around a cleanly dated event. An advantage of this
approach is that because daily expected returns are close to zero, the model for
expected returns does not have a big effect on inferences about abnormal returns.
0304-405X/98/$19.00 ( 1998 Elsevier Science S.A. All rights reserved
PII S 0 3 0 4 - 4 0 5 X ( 9 8 ) 0 0 0 2 6 - 9
The assumption in studies that focus on short return windows is that any lag
in the response of prices to an event is short-lived. There is a developing
literature that challenges this assumption, arguing instead that stock prices
adjust slowly to information, so one must examine returns over long horizons to
get a full view of market inefficiency.
If one accepts their stated conclusions, many of the recent studies on long-
term returns suggest market inefficiency, specifically, long-term underreaction
or overreaction to information. It is time, however, to ask whether this litera-
ture, viewed as a whole, suggests that efficiency should be discarded. My answer
is a solid no, for two reasons.
First, an efficient market generates categories of events that individually
suggest that prices over-react to information. But in an efficient market, appar-
ent underreaction will be about as frequent as overreaction. If anomalies split
randomly between underreaction and overreaction, they are consistent with
market efficiency. We shall see that a roughly even split between apparent
overreaction and underreact ...
This document summarizes a study examining the impact of mergers and acquisitions on research and development (R&D) intensity within high-tech industries from 1990-2014. The study uses an event study methodology to analyze stock price reactions around merger announcements. Key findings include:
1) 31.1% of acquiring firms saw positive abnormal stock returns around announcements, while 69% saw negative returns, surprisingly indicating investor pessimism about mergers.
2) 91.3% of target firms saw positive abnormal returns, as expected given they were being acquired.
3) The study aims to determine if event studies can help antitrust agencies evaluate potential pro-competitive or anti-competitive effects of mergers in innovative sectors
Market efficiency survives the challenge from the literature on long-term return anomalies. Consistent with the market efficiency hypothesis that the anomalies are chance results, apparent overreaction to information is about as common as under-reaction, and post-event continuation of preevent abnormal returns is about as frequent as post-event reversal. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique.
This document proposes a novel approach using Mamdani Fuzzy Logic to find the Mean Square Error (MSE) in supply chain management and reduce the Bullwhip Effect. It first discusses supply chain management and the Bullwhip Effect. It then reviews literature on quality chain management and related approaches. The document presents a system model using Mamdani Fuzzy Logic to group Mamdani and logical approaches, tune membership functions, and defuzzify results. It applies this approach to an export value forecasting problem using Iranian carpet trade data over 9 years. Simulation results show the approach can forecast reliability, payoffs, and change in variables to minimize price while reconciling customer demand with supplier capabilities.
Competing retailers and inventory an empirical investigation ofMakwana Suresh
This document summarizes a study investigating how competition influences inventory levels at General Motors (GM) dealerships. The researchers collected daily inventory and sales data over six months from over 200 GM dealerships located in isolated U.S. markets. They developed an empirical model to analyze how competition can affect inventory through two main channels: 1) Increased competition can lower a retailer's sales (a "sales effect"), and 2) Increased competition can influence a retailer's level of stocking inventory to meet customer demand (a "service level effect"). While theory is clear on the sales effect, it is ambiguous on the direction of the service level effect. The researchers used their detailed dealership-level data and instrumental variables to empirically estimate the direction
Adoption of supply chain management strategiesTapan Panda
This document discusses a survey of Indian retailers about adopting supply chain management strategies to address the bullwhip effect. The bullwhip effect occurs when small changes in customer demand result in large fluctuations in orders to suppliers further up the supply chain. The survey compares how small and medium retailers differ in their willingness to participate in supply chain information sharing. The findings could help companies reduce negative perceptions among retailers about supply chain practices.
The document discusses the bullwhip effect, which occurs when demand fluctuations are amplified up the supply chain. It provides the example of Procter & Gamble observing that distributor orders to factories for disposable diapers varied more than retail demand, and factory orders from distributors varied even more. This occurred even though demand for diapers is very consistent. The bullwhip effect causes inefficiencies like excess costs and variability in production and shipping. Ways to remedy it include improving demand forecasting, reducing order batching, and increasing communication between supply chain partners.
This document discusses the bullwhip effect in supply chains. It begins by defining the bullwhip effect and explaining how it can lead to excess inventory and production inefficiencies. The beer game simulation is presented as demonstrating how orders fluctuate more at each stage of a supply chain even with a constant demand. Four main reasons for the bullwhip effect are discussed: demand forecast updating, order batches, price fluctuations, and rationing and shortage gaming. The document will continue by analyzing formal models of the bullwhip effect and approaches to mitigate it such as information sharing, reducing lead times, and retailer collaboration.
This study examines how inventory shortages or stockouts affect customer purchase behavior and a retailer's profitability using data from an online grocer. The three main objectives are:
1) To empirically investigate the short-term and long-term impact of stockouts on customer purchase behavior.
2) To study how customer responses to stockouts differ across customer segments.
3) To analyze how the impact of stockouts varies across product categories.
The findings suggest stockouts have a complex, nonlinear effect on customer relationships and retailer profitability. Small decreases in stockout rates can achieve much of the potential benefit of eliminating stockouts entirely. Prioritizing inventory to reduce stockouts for key customer segments and product categories can
The document discusses the bullwhip effect in supply chains. It describes how demand variability increases as it moves up the supply chain from retailers to manufacturers. This occurs through factors like order batching, shortage gaming, and demand forecast inaccuracies. Countermeasures to reduce the bullwhip effect include vendor managed inventory programs, electronic data interchange for ordering, and sharing demand information. The bullwhip effect can increase costs and lower revenues for all members of the supply chain.
Summary of the article (50) Research problem and research ques.docxpicklesvalery
Summary of the article (50%)
Research problem and research question
Literature review/Theoretical framework
Methodology
Findings and Discussion
Theoretical implications
Practical and/or policy implication
Critical evaluation of article (50%)
Below are some of the ways you can critically evaluate the article: (choose one)
Criteria for quantitative paper: reliability, internal validity, construct validity, and external validity. (Required if doing a quantitative paper)
Criteria for qualitative paper: credibility, transferability, dependability, and confirmability. (Required if doing a qualitative paper)
World Development Vol. 39, No. 12, pp. 2119–2131, 2011
� 2011 Elsevier Ltd. All rights reserved.
0305-750X/$ - see front matter
www.elsevier.com/locate/worlddev
doi:10.1016/j.worlddev.2011.04.016
Local Means in Value Chain Ends: Dynamics of Product
and Social Upgrading in Apparel Manufacturing
in Guatemala and Colombia
SETH PIPKIN *
Massachusetts Institute of Technology, Cambridge, USA
Summary. — This paper contributes to existing discussions of global value chains (GVC) and industrial upgrading by examining obser-
vations from eight months of field research in Guatemala and Colombia, where upgrading firms have their own nationally distinct form
of labor relations, despite producing the same products for the same overseas buyers. Analysis of these observations leads to the con-
clusion that labor relations show significant leeway in relation to upgrading outcomes, and that local history merits more attention as a
driver of management strategy. The paper concludes with a discussion of relevant theory and implications for future research.
� 2011 Elsevier Ltd. All rights reserved.
Key words — global value chains, industrial upgrading, apparel manufacturing, Guatemala, Colombia, Latin America
* I would like to thank first and foremost Judith Tendler, Hugo Beteta, the
MIT Program on Human Rights and Justice, and the Institute for Work
and Employment Research (IWER) for allowing me to work in the field
and develop this project. Thanks are also due to Michael Piore, Richard
Locke, Susan Silbey, Suzanne Berger, Andrew Schrank, David Weil,
Alberto Fuentes, Regina Abrami, Ben Rissing, Matthew Amengual, Salo
Coslovsky, Roberto Pires, participants in the IWER seminar at MIT
Sloan, as well as the anonymous reviewers from World Development for all
of their feedback and help. For all of the support received in this work,
responsibility for errors is solely my own. Final revision accepted: March
1, 2011.
1. INTRODUCTION
Policy paradigms for development have reached a cross-
roads. The power of financial globalization to ‘eclipse’ the
power of nation-states has been both asserted and questioned
(Berger, 2000; Evans, 1997), while the Washington Consensus
seems to have fully fallen out of favor (Held, 2005; Kucszynski
& Williamson, 2003; Rodrik, 2006). The search for alternative
models is palpable. Such a project may not be acco ...
Supply refers to the quantity of a product offered for sale. The law of supply states that more of a product will be offered at a higher price and less at a lower price. Supply curves show the relationship between price and quantity supplied, and can be individual or market curves representing one firm or multiple firms. Factors that affect supply include costs of inputs, productivity, technology, taxes, expectations, government regulations, and number of sellers. When these factors change, the supply curve shifts representing a change in quantity supplied at each price level.
Rule-based expert systems for supporting university studentsertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/rule-based-expert-systems-for-supporting-university-students/
There are more than 15 million college students in the US alone. Academic advising for courses and scholarships is typically performed by human advisors, bringing an immense managerial workload to faculty members, as well as other staff at universities. This paper reports and discusses the development of two educational expert systems at a private international university. The first expert system is a course advising system which recommends courses to undergraduate students. The second system suggests scholarships to undergraduate students based on their eligibility. While there have been reported systems for course advising, the literature does not seem to contain any references to expert systems for scholarship recommendation and eligibility checking. Therefore the scholarship recommender that we developed is first of its kind. Both systems have been implemented and tested using Oracle Policy Automation (OPA) software.
Optimizing the electric charge station network of EŞARJertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/optimizing-the-electric-charge-station-network-of-esarj/
In this study, we adopt the classic capacitated p-median location model for the solution of a network design problem, in the domain of electric charge station network design, for a leading company in Turkey. Our model encompasses the location preferences of the company managers as preference scores incorporated into the objective function. Our model also incorporates the capacity concerns of the managers through constraints on maximum number of districts and maximum population that can be served from a location. The model optimally selects the new station locations and the visualization of model results provides additional insights.
More Related Content
Similar to The Bullwhip Effect In Supply Chain Reflections After A Decade
The bullwhip effect in supply chain: Reflections after a decadeGurdal Ertek
A decade has passed since the publication of the two seminal papers by Lee, Padmanabhan and Whang (1997) that describes the “bullwhip effect” in supply chains and characterizes its underlying causes. The bullwhip phenomenon is observed in supply chains where the decisions at the subsequent stages of the supply chain are made greedily based on local information, rather than through coordination based on global information on the state of the whole chain. The first consequence of this information distortion is higher variance in purchasing quantities compared to sales quantities at a particular supply chain stage. The second consequence is increasingly higher variance in order quantities and inventory levels in the upstream stages compared to their downstream stages (buyers). In this paper, we survey a decade of literature on the bullwhip effect and present the key insights reported by researchers and practitioners. We also present our reflections and share our vision of possible future.
http://research.sabanciuniv.edu.
This document summarizes a research paper that examines using fuzzy logic to reduce the bullwhip effect in supply chains caused by rationing and shortage gaming. It first defines the bullwhip effect and describes rationing and shortage gaming as a cause. It then discusses related work applying fuzzy logic to supply chain problems. Next, it examines the impact of rationing and shortage gaming on the bullwhip effect in more detail. It provides an overview of fuzzy logic and how it can be applied to model rationing and shortage gaming using a fuzzy logic-based approach. Finally, it proposes a fuzzy system model for resolving rationing and shortage gaming issues in the supply chain.
11.factors causing reversed bullwhip effect on the supply chains of kenyan firmsAlexander Decker
This study examined factors that cause supply chain variability, known as the reverse bullwhip effect, along the supply chain of Kenya Pipeline Company. The researchers conducted interviews and collected data through questionnaires from 5 depots along the supply chain. The findings suggested that capacity constraints were a major factor contributing to supply chain inefficiencies. Specifically, the downstream storage capacity was much larger than the upstream utilization capacity, limiting product flow to end sale points. The conclusion was that the supply chain was inefficient due to challenges related to capacity and government intervention. Recommendations included strategies to adjust capacity, upgrade equipment, add manpower and machine hours, and reduce disruptive government intervention.
Thomson Financial analyzed 75 recent instances of shareholder activism and found mixed results regarding the impact on stock price. Stocks targeted by activists showed higher returns after the activism in both short and long-term, but the results were not always statistically significant. Certain sectors like consumer discretionary were more frequent targets. Activists achieved at least one of their demands 45% of the time, with the highest success rate of nearly 80% for demands to remove the CEO. Stocks of targeted companies outperformed a control group after activism, suggesting shareholder monitoring leads to positive changes. However, the prominence rather than just substance of activism may influence stock prices.
S P R I N G 1 9 9 7Hau L. LeeV. PadmanabhanSeungjin W.docxrtodd599
S P R I N G 1 9 9 7
Hau L. Lee
V. Padmanabhan
Seungjin Whang
The Bullwhip
Effect in Supply
Chains
Distorted information from one end of a supply chain to the
other can lead to tremendous inefficiencies: excessive
inventory investment, poor customer service, lost revenues,
misguided capacity plans, ineffective transportation, and
missed production schedules. How do exaggerated order
swings occur? What can companies do to mitigate them?
Vol. 38, No. 3 Reprint #3837 http://mitsmr.com/1phEOiM
http://mitsmr.com/1phEOiM
93SLOAN MANAGEMENT REVIEW/SPRING 1997 LEE ET AL.
N
ot long ago, logistics executives at Procter &
Gamble (P&G) examined the order pat-
terns for one of their best-selling products,
Pampers. Its sales at retail stores were fluctuating, but
the variabilities were certainly not excessive. However,
as they examined the distributors’ orders, the execu-
tives were surprised by the degree of variability. When
they looked at P&G’s orders of materials to their sup-
pliers, such as 3M, they discovered that the swings
were even greater. At first glance, the variabilities did
not make sense. While the consumers, in this case,
the babies, consumed diapers at a steady rate, the de-
mand order variabilities in the supply chain were am-
plified as they moved up the supply chain. P&G
called this phenomenon the “bullwhip” effect. (In
some industries, it is known as the “whiplash” or the
“whipsaw” effect.)
When Hewlett-Packard (HP) executives examined
the sales of one of its printers at a major reseller, they
found that there were, as expected, some fluctuations
over time. However, when they examined the orders
from the reseller, they observed much bigger swings.
Also, to their surprise, they discovered that the orders
from the printer division to the company’s integrated
circuit division had even greater fluctuations.
What happens when a supply chain is plagued with
a bullwhip effect that distorts its demand information
as it is transmitted up the chain? In the past, without
being able to see the sales of its products at the distri-
bution channel stage, HP had to rely on the sales or-
ders from the resellers to make product forecasts, plan
capacity, control inventory, and schedule production.
Big variations in demand were a major problem for
HP’s management. The common symptoms of such
variations could be excessive inventory, poor product
forecasts, insufficient or excessive capacities, poor cus-
tomer service due to unavailable products or long back-
logs, uncertain production planning (i.e., excessive revi-
sions), and high costs for corrections, such as for expe-
dited shipments and overtime. HP’s product division
was a victim of order swings that were exaggerated by
the resellers relative to their sales; it, in turn, created
additional exaggerations of order swings to suppliers.
In the past few years, the Efficient Consumer Re-
sponse (ECR) initiative has tried to redefine how the
grocery supply chain should work.1 One motivation
fo.
Multi agent system for knowledge management in SCMGeorge Ogrinja
This document provides an overview of supply chain management and multiagent systems. It begins by introducing supply chain management as a business practice used to address industrial problems through inter-company collaboration. It then discusses multiagent systems, defining agents and comparing them to objects. Finally, it synthesizes these two areas by discussing how agent-based approaches can be applied to supply chain management problems.
This document summarizes a study on market efficiency and long-term stock returns. It discusses two key findings:
1) Studies of long-term stock returns show about as much evidence of overreaction to information as underreaction, which is consistent with the prediction of market efficiency.
2) Most anomalies in long-term returns tend to disappear or become marginal when using different models for expected returns or statistical techniques, suggesting they can reasonably be attributed to chance rather than inefficiency.
* Corresponding author. Tel.: 773 702 7282; fax: 773 702 9937; e-mail: [email protected]
edu.
1 The comments of Brad Barber, David Hirshleifer, S.P. Kothari, Owen Lamont, Mark Mitchell,
Hersh Shefrin, Robert Shiller, Rex Sinquefield, Richard Thaler, Theo Vermaelen, Robert Vishny, Ivo
Welch, and a referee have been helpful. Kenneth French and Jay Ritter get special thanks.
Journal of Financial Economics 49 (1998) 283—306
Market efficiency, long-term returns, and behavioral
finance1
Eugene F. Fama*
Graduate School of Business, University of Chicago, Chicago, IL 60637, USA
Received 17 March 1997; received in revised form 3 October 1997
Abstract
Market efficiency survives the challenge from the literature on long-term return
anomalies. Consistent with the market efficiency hypothesis that the anomalies are
chance results, apparent overreaction to information is about as common as underreac-
tion, and post-event continuation of pre-event abnormal returns is about as frequent as
post-event reversal. Most important, consistent with the market efficiency prediction that
apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique. ( 1998 Elsevier Science S.A. All rights
reserved.
JEL classification: G14; G12
Keywords: Market efficiency; Behavioral finance
1. Introduction
Event studies, introduced by Fama et al. (1969), produce useful evidence on
how stock prices respond to information. Many studies focus on returns in
a short window (a few days) around a cleanly dated event. An advantage of this
approach is that because daily expected returns are close to zero, the model for
expected returns does not have a big effect on inferences about abnormal returns.
0304-405X/98/$19.00 ( 1998 Elsevier Science S.A. All rights reserved
PII S 0 3 0 4 - 4 0 5 X ( 9 8 ) 0 0 0 2 6 - 9
The assumption in studies that focus on short return windows is that any lag
in the response of prices to an event is short-lived. There is a developing
literature that challenges this assumption, arguing instead that stock prices
adjust slowly to information, so one must examine returns over long horizons to
get a full view of market inefficiency.
If one accepts their stated conclusions, many of the recent studies on long-
term returns suggest market inefficiency, specifically, long-term underreaction
or overreaction to information. It is time, however, to ask whether this litera-
ture, viewed as a whole, suggests that efficiency should be discarded. My answer
is a solid no, for two reasons.
First, an efficient market generates categories of events that individually
suggest that prices over-react to information. But in an efficient market, appar-
ent underreaction will be about as frequent as overreaction. If anomalies split
randomly between underreaction and overreaction, they are consistent with
market efficiency. We shall see that a roughly even split between apparent
overreaction and underreact ...
This document summarizes a study examining the impact of mergers and acquisitions on research and development (R&D) intensity within high-tech industries from 1990-2014. The study uses an event study methodology to analyze stock price reactions around merger announcements. Key findings include:
1) 31.1% of acquiring firms saw positive abnormal stock returns around announcements, while 69% saw negative returns, surprisingly indicating investor pessimism about mergers.
2) 91.3% of target firms saw positive abnormal returns, as expected given they were being acquired.
3) The study aims to determine if event studies can help antitrust agencies evaluate potential pro-competitive or anti-competitive effects of mergers in innovative sectors
Market efficiency survives the challenge from the literature on long-term return anomalies. Consistent with the market efficiency hypothesis that the anomalies are chance results, apparent overreaction to information is about as common as under-reaction, and post-event continuation of preevent abnormal returns is about as frequent as post-event reversal. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique.
This document proposes a novel approach using Mamdani Fuzzy Logic to find the Mean Square Error (MSE) in supply chain management and reduce the Bullwhip Effect. It first discusses supply chain management and the Bullwhip Effect. It then reviews literature on quality chain management and related approaches. The document presents a system model using Mamdani Fuzzy Logic to group Mamdani and logical approaches, tune membership functions, and defuzzify results. It applies this approach to an export value forecasting problem using Iranian carpet trade data over 9 years. Simulation results show the approach can forecast reliability, payoffs, and change in variables to minimize price while reconciling customer demand with supplier capabilities.
Competing retailers and inventory an empirical investigation ofMakwana Suresh
This document summarizes a study investigating how competition influences inventory levels at General Motors (GM) dealerships. The researchers collected daily inventory and sales data over six months from over 200 GM dealerships located in isolated U.S. markets. They developed an empirical model to analyze how competition can affect inventory through two main channels: 1) Increased competition can lower a retailer's sales (a "sales effect"), and 2) Increased competition can influence a retailer's level of stocking inventory to meet customer demand (a "service level effect"). While theory is clear on the sales effect, it is ambiguous on the direction of the service level effect. The researchers used their detailed dealership-level data and instrumental variables to empirically estimate the direction
Adoption of supply chain management strategiesTapan Panda
This document discusses a survey of Indian retailers about adopting supply chain management strategies to address the bullwhip effect. The bullwhip effect occurs when small changes in customer demand result in large fluctuations in orders to suppliers further up the supply chain. The survey compares how small and medium retailers differ in their willingness to participate in supply chain information sharing. The findings could help companies reduce negative perceptions among retailers about supply chain practices.
The document discusses the bullwhip effect, which occurs when demand fluctuations are amplified up the supply chain. It provides the example of Procter & Gamble observing that distributor orders to factories for disposable diapers varied more than retail demand, and factory orders from distributors varied even more. This occurred even though demand for diapers is very consistent. The bullwhip effect causes inefficiencies like excess costs and variability in production and shipping. Ways to remedy it include improving demand forecasting, reducing order batching, and increasing communication between supply chain partners.
This document discusses the bullwhip effect in supply chains. It begins by defining the bullwhip effect and explaining how it can lead to excess inventory and production inefficiencies. The beer game simulation is presented as demonstrating how orders fluctuate more at each stage of a supply chain even with a constant demand. Four main reasons for the bullwhip effect are discussed: demand forecast updating, order batches, price fluctuations, and rationing and shortage gaming. The document will continue by analyzing formal models of the bullwhip effect and approaches to mitigate it such as information sharing, reducing lead times, and retailer collaboration.
This study examines how inventory shortages or stockouts affect customer purchase behavior and a retailer's profitability using data from an online grocer. The three main objectives are:
1) To empirically investigate the short-term and long-term impact of stockouts on customer purchase behavior.
2) To study how customer responses to stockouts differ across customer segments.
3) To analyze how the impact of stockouts varies across product categories.
The findings suggest stockouts have a complex, nonlinear effect on customer relationships and retailer profitability. Small decreases in stockout rates can achieve much of the potential benefit of eliminating stockouts entirely. Prioritizing inventory to reduce stockouts for key customer segments and product categories can
The document discusses the bullwhip effect in supply chains. It describes how demand variability increases as it moves up the supply chain from retailers to manufacturers. This occurs through factors like order batching, shortage gaming, and demand forecast inaccuracies. Countermeasures to reduce the bullwhip effect include vendor managed inventory programs, electronic data interchange for ordering, and sharing demand information. The bullwhip effect can increase costs and lower revenues for all members of the supply chain.
Summary of the article (50) Research problem and research ques.docxpicklesvalery
Summary of the article (50%)
Research problem and research question
Literature review/Theoretical framework
Methodology
Findings and Discussion
Theoretical implications
Practical and/or policy implication
Critical evaluation of article (50%)
Below are some of the ways you can critically evaluate the article: (choose one)
Criteria for quantitative paper: reliability, internal validity, construct validity, and external validity. (Required if doing a quantitative paper)
Criteria for qualitative paper: credibility, transferability, dependability, and confirmability. (Required if doing a qualitative paper)
World Development Vol. 39, No. 12, pp. 2119–2131, 2011
� 2011 Elsevier Ltd. All rights reserved.
0305-750X/$ - see front matter
www.elsevier.com/locate/worlddev
doi:10.1016/j.worlddev.2011.04.016
Local Means in Value Chain Ends: Dynamics of Product
and Social Upgrading in Apparel Manufacturing
in Guatemala and Colombia
SETH PIPKIN *
Massachusetts Institute of Technology, Cambridge, USA
Summary. — This paper contributes to existing discussions of global value chains (GVC) and industrial upgrading by examining obser-
vations from eight months of field research in Guatemala and Colombia, where upgrading firms have their own nationally distinct form
of labor relations, despite producing the same products for the same overseas buyers. Analysis of these observations leads to the con-
clusion that labor relations show significant leeway in relation to upgrading outcomes, and that local history merits more attention as a
driver of management strategy. The paper concludes with a discussion of relevant theory and implications for future research.
� 2011 Elsevier Ltd. All rights reserved.
Key words — global value chains, industrial upgrading, apparel manufacturing, Guatemala, Colombia, Latin America
* I would like to thank first and foremost Judith Tendler, Hugo Beteta, the
MIT Program on Human Rights and Justice, and the Institute for Work
and Employment Research (IWER) for allowing me to work in the field
and develop this project. Thanks are also due to Michael Piore, Richard
Locke, Susan Silbey, Suzanne Berger, Andrew Schrank, David Weil,
Alberto Fuentes, Regina Abrami, Ben Rissing, Matthew Amengual, Salo
Coslovsky, Roberto Pires, participants in the IWER seminar at MIT
Sloan, as well as the anonymous reviewers from World Development for all
of their feedback and help. For all of the support received in this work,
responsibility for errors is solely my own. Final revision accepted: March
1, 2011.
1. INTRODUCTION
Policy paradigms for development have reached a cross-
roads. The power of financial globalization to ‘eclipse’ the
power of nation-states has been both asserted and questioned
(Berger, 2000; Evans, 1997), while the Washington Consensus
seems to have fully fallen out of favor (Held, 2005; Kucszynski
& Williamson, 2003; Rodrik, 2006). The search for alternative
models is palpable. Such a project may not be acco ...
Supply refers to the quantity of a product offered for sale. The law of supply states that more of a product will be offered at a higher price and less at a lower price. Supply curves show the relationship between price and quantity supplied, and can be individual or market curves representing one firm or multiple firms. Factors that affect supply include costs of inputs, productivity, technology, taxes, expectations, government regulations, and number of sellers. When these factors change, the supply curve shifts representing a change in quantity supplied at each price level.
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The Bullwhip Effect In Supply Chain Reflections After A Decade
1. Ertek, G., Eryılmaz, E. (2008) “The bullwhip effect in supply chain: Reflections after a decade” .
CELS 2008, Jönköping, Sweeden. (presented by EmreEryılmaz).
Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as
above. You can download this final draft from http://research.sabanciuniv.edu.
THE BULLWHIP EFFECT IN SUPPLY CHAIN
Reflections after a Decade
Gürdal Ertek, Emre Eryılmaz
Sabancı University, Orhanlı, Tuzla, 34956, Turkey
Abstract A decade has passed since the publication of the two seminal papers by
Lee, Padmanabhan and Whang (1997) that describes the “bullwhip effect” in supply
chains and characterizes its underlying causes. The bullwhip phenomenon is
observed in supply chains where the decisions at the subsequent stages of the
supply chain are made greedily based on local information, rather than through
coordination based on global information on the state of the whole chain. The first
consequence of this information distortion is higher variance in purchasing
1
2. quantities compared to sales quantities at a particular supply chain stage. The
second consequence is increasingly higher variance in order quantities and
inventory levels in the upstream stages compared to their downstream stages
(buyers). In this paper, we survey a decade of literature on the bullwhip effect and
present the key insights reported by researchers and practitioners. We also present
our reflections and share our vision of possible future.
Keywords: Bullwhip Effect, Information Distortion, Information Flow, Production
and Inventory Management.
Introduction
The general opinion of a supply chain is that it is a channel that finished goods are produced
from raw materials and then transported to customers (Vollmann et al., 2000). Mentzer et al.
(2001) describes upstream and downstream flow of products, information and finances from
supplier to customer that occurs between three or more echelons. “According to a Georgia
Technical University study, because of supply chain problems, a firm loses its value between 9 and
20 percent in a six-month period” (Reddy, 2001). Due to the high competition of business
environment in the global world, most firms try to increase productivity and eliminate problems of
their supply chain systems. Some of the problems that firms face are excessive inventory, shortage
of the products, information distortion and insufficient transportation. One of the main reasons of
these problems is the “bullwhip effect”. The Bullwhip effect is the demand variance amplification
while moving through to upstream echelons from downstream echelons (Lee et al, 1997).
The concept of the bullwhip effect was first mentioned by Procter & Gamble to explain
increasing order behavior of Pamper diapers between customer and supplier (Lee et al., 1997).
Although customer demand is almost stable, Procter & Gamble realized that there is a significant
variance at wholesale orders. They also realized that the variance of orders placed to the raw
material suppliers is greater than the variance of orders placed to wholesalers. There are also other
firms that realized the “bullwhip effect” with respect to their companies’ order fluctuations such as
Hewlett-Packard, 3M, Eli-Lilly, DRAM market. The bullwhip effect causes inefficiency and this
returns as costs to firms. For example, “among various members of the $300 billion (annual)
2
3. grocery industry, there is $75 billion to $100 billion worth of inventory caught due to the
inefficiencies” (Fuller et al, 1993).
The “bullwhip effect” phenomenon is also known in different names such as “whiplash effect”,
“whipsaw effect” and “acceleration principle” but the “bullwhip effect” term is the most preferred
one. In this tutorial, we searched “bullwhip effect” in “ABI/INFORM Global (ProQuest)” database
system and we used only full text resources.
The Bullwhip Effect and the Beer Game
First studies on the bullwhip effect belong to Jay Forrester. He developed a computer
simulation model using the DYNAMO simulator that represents traditional supply chain. The
supply chain consists of three echelons, namely factory, distributor and retailer. He demonstrated
the amplification of demand in his model but did not call the phenomenon as “bullwhip effect”.
Forrester believed that irrational decision making is the main cause of the bullwhip effect which he
proved through his model. He also showed that time delays, random fluctuation of demand and
limited capacity can lead to the bullwhip effect.
Emergence of the Beer Game
Beer Distribution Game is one of the exercises that illustrate the dynamics of a supply chain
(Jacobs, 2000). The game was developed at the Massachusetts Institute of Technology’s Sloan
School of Management by System Dynamics Group (Sterman, 1989). The Beer Distribution Game
consists of four echelons which are customer, retailer, distributor and factory. Each echelon is
managed by a single player and communication between echelons is not allowed. In the game, the
customer requests beer from the retailer and, in turn, the retailer orders to the distributor.
Similarly, the distributor gives orders to the factory and then the beer is produced. Only the retailer
knows the actual customer demand and the other players base their decisions according to the
ordering patterns of their immediate downstream echelon. The time that is required for ordering,
process and delivering the beer are represented by ordering and shipping delays. The main
objective of the game is minimizing total cost, which is the combination of inventory holding and
backlogging costs. Sterman (1989) inferred three consequences from the game:
3
4. 1) Large oscillations appear in orders and inventories.
2) Demand amplification increases as one goes to upstream.
3) Order rate tends to peak from retailer to factory.
The following figures show the results of a beer game played by a diverse population of
industrial engineering and management science undergraduate students in Istanbul, Turkey. The
figures I and II in appendix part display the ordering patterns of 2 teams representing the supply
chain of Brand 1 and Brand 2.
Having observed outcomes of the beer game, Sterman (1989) claims that the bullwhip effect
occurs due to the irrational behavior of managers or feedback misperception.
Lee et al. (1997) identify the underlying causes of the bullwhip effect by developing a
mathematical model of serial supply chain. In contrast to Forrester (1961) and Sterman (1989),
they model the manager of each echelon as being rational and optimizing. In the follow-up paper,
Lee et al. (2004) demonstrate that the bullwhip effect is a result of strategic interactions among
rational supply chain members. Lee et al. (1997) demonstrate four reasons of the bullwhip effect:
1) Demand Signal Processing
2) Order Batching
3) Price Fluctuations
4) Shortage Gaming
We will shortly point out these four reasons because this model constitutes the backbone of
the bullwhip effect studies.
Demand Signal Processing: Most companies use forecasting to determine capacity planning,
production scheduling, material requirement and inventory control. Forecasts are often based on
historical data gathered from sales information of the company. When a downstream echelon
places an order, its immediate upstream firm considers this order as a signal about expected future
4
5. product demand. Subsequently, upstream firm adjusts its forecasts based on this signal. For
instance, if a retailer places an order to a distributor, the distributor adjusts its forecasts and places
an order to wholesaler. Similar relation occurs between the wholesaler and the factory. In this case,
orders have larger variance due to the updated forecasts. Moreover, safety stock is required as a
result of forecasted demand and the longer lead time results in the need of more safety stock. This
situation causes higher order variance than the actual demand, therefore the bullwhip effect
occurs.
Order Batching: Some inventory monitoring and control are used by each echelon in order to
place orders to its immediate upstream echelon in a supply chain (Lee et al., 1997). Generally,
companies do not immediately place orders to their suppliers. They often batch demands and use
periodic ordering or push ordering strategies. In periodic ordering, companies place orders once in
a week or in another period. For example, if a company places order monthly, supplier will face
erratic downstream orders, since there will be a spike in a month and no demand orders in the rest
of the month. “Obviously, the supplier faces higher demand variability than the company” (Lee et
al., 1997). This increased variability attests to the fact that periodical ordering causes the bullwhip
effect. In push ordering, salespeople regularly measures quarterly or yearly. As a result, most of the
companies have orders at the end of a quarter or a year. In this situation, the bullwhip effect
appears due to companies’ order patterns that indicate higher variance than customers’
consumption patterns.
Price Fluctuations: Price fluctuations are generally resulted by “forward buy” arrangements
between a company and its supplier. Lee et al. (1997) indicated that 80 percent of transaction
between manufacturer and distributor is forward buying in the grocery industry. Coupons, price
discounts, quantity discounts and rebates are frequently used in marketplace and these special
promotions also cause price fluctuations. This situation triggers customers to buy more than their
immediate needs and they stock products for their future needs. If prices return to its previous
level, customers do not buy products until their entire stock will be consumed. Because of the fact
that the buying pattern has higher variances than the normal pattern, it does not reflect actual
consumption pattern. Hence, the bullwhip effect occurs.
5
6. Shortage Gaming: If the demand of a product exceeds its supply, shortage gaming occurs.
Because there is insufficient amount of the product, supplier rations the product between
downstream members. After that, downstream members place demand orders more than their
needs to finally reach their actual needs. After shortage time passes, placed demand orders will be
canceled since they were inflated. Shortage gaming causes the bullwhip effect because the actual
demand variance is amplified as we moved from the customer to the supplier.
Literature on the Bullwhip Effect
There are numerous researches about the bullwhip effect. Most of the researches demonstrate
that the bullwhip effect exists and some others investigate how the bullwhip effect reacts according
to different conditions.
Impact of Forecasting on the Bullwhip Effect
The relationship between forecasting and the bullwhip effect is considered by many authors.
Hanssens (1998) empirically connects the bullwhip effect and forecasting. He illustrates that the
bullwhip effect exists as a result of forecasting and measures the impact of the effect. Graves (1999)
also shows that the bullwhip effect exists in consequence of forecasting under integrated demand
method. Chen et al. (1999) measure the magnitude of the bullwhip effect under different
forecasting techniques such as exponential smoothing and moving average. Also Chen et al. (2000)
quantify the impact of demand forecasting on the bullwhip effect in a two stage supply chain and
extend this study to a multistage supply chain. They demonstrated that the variance of orders
placed by the downstream echelons will be higher than the variance of demand if a downstream
echelon periodically updates the mean and the variance of demand that based on observed
customer demand data. They assume that exact demand value is not known. Dejonckheere et al.
(2004) have gained similar results with Chen.
Metters (1997) investigates the bullwhip effect in monetary terms. He proves that forecasting is
one of the main reasons of the bullwhip effect. Results of his research indicate that eliminating
forecast error may increase profitability between 5 – 10 percent. Miyooka and Hausman (2004)
6
7. deploy “stale” or old forecasts to determine base stock levels and use current forecasts to
communicate upstream and downstream stages. Their strategy can decrease expected inventory
level, shortage cost and production fluctuations in the decentralized strategy. In contrast to
benchmarking, their strategy results in higher shortage costs and inventory level but lower
variability at period to period production
Impact of Information on the Bullwhip Effect
Chen (1998) studies the importance of centralized demand information in a serial inventory
system. He compares the echelon stock and installation stock policies and shows that the value of
information is related to the system parameters namely lead times, batch sizes, number of stages,
demand variability and customer service level. Towill and McCullen (2001) study on the efficiency
of a supply chain and they used information transparency system as one of the methods that
reduce the bullwhip effect which consists of high information integrity between supply chain
members. Yu et al. (2001) discuss information sharing between supply chain members and
investigate its benefits to the each member of the chain. In their model, a retailer and a
manufacturer can both gain benefit by information sharing.
Disney and Towill (2003) examine the relation between the vendor-managed inventory and the
bullwhip effect in a traditional “serially-linked” supply chain. They demonstrate that some causes
of the bullwhip effect can be eliminated and the influence of other causes can be reduced by
applying VMI policy. Croson and Donohue (2003) focus on how point of sale (POS) data can help
to reduce the bullwhip effect in a multi-echelon supply chain. They found that POS information
across the supply chain can reduce the magnitude of the order oscillations and decrease the
magnitude of the order amplification between the wholesaler and the distributor. However, they do
not come to same conclusion in a retailer - wholesaler and a distributor – manufacturer relation.
Moreover, they prove that order oscillations of sharing POS information provide less benefit to
retailers and wholesalers than to manufacturers and distributors (Donahue et al., 2003).
Chen et al. (2000) study the impact of sharing centralized customer demand information on
the bullwhip effect in a multistage supply chain. They prove that by information sharing, it is
possible to reduce the bullwhip effect but they could not eliminate the bullwhip effect. Hayya et al.
(2004) use a simulation model to examine the effects of information quality and information
7
8. sharing on bullwhip effect and illustrate that information quality is also important besides
information sharing. They use the results of Chen (2000) and Dejonckheere et al. (2004) to verify
the accuracy of the simulation. Kulp et al. (2004) conclude that supply chain performance is better
when information sharing and collaborations occur.
Clark and Hammond (1997) make an empirical analysis which illustrates that investing in
Electronic Data Interchange (EDI) for information sharing provides less benefit than investing for
business process reengineering. Cachon et al. (2000) show that using information technology to
expand the flow of information provides less benefit than using information technology to
accelerate and smooth physical flow of goods in a supply chain. Lee et al. (2000) state that
information sharing of retail demand data decreases the cost of the manufacturer.
Impact of Seasonality and Order Batching on the Bullwhip Effect
Firms use production smoothing technique due to the increasing marginal cost or high cost of
changing the rate of production. Blinder (1981) claims that batching occurs due to the setup and
ordering costs. Jung et al. (1999) analyze order batching in terms of customer’s effect and claim
that infrequent orders in large lot sizes are preferred by firms even they are flexible supplier.
Cachon (1999) shows that if a retailer order is periodically in fixed lots, the order cycles and the
batch size influence the bullwhip effect proportionally. Moinzadeh and Nahmias (2000) observe
that the bullwhip effect can be reduced as a result of correlated ordering instead of order batching.
Gilbert and Chatpattananan (2006) study on ARIMA model and show how to optimally distribute
the bullwhip effect over smoothing periods. Lee et al. (1999) illustrate that batching contributes to
the bullwhip effect.
Cachon et al. (2006) have searched the bullwhip effect according to monthly data of U.S.
Census Bureau between January 1992 and February 2006 that consists of 6 retailers, 18
wholesalers and 50 manufacturing industries. They claim that the bullwhip effect is more
dominant than production smoothing if an industry’s production is more volatile than its demand.
“An industry’s incentive to production smooth should increase as its demand becomes more
seasonal” (Cachon et al., 2006). Most of the researchers observe the bullwhip effect in various
examples and conclude that it is consistent with the variance of sales is lower than variance of
production. However, Cachon et al. (2006) find that manufacturing demand is less volatile than
8
9. downstream echelons. Only wholesalers reflect amplified demand characteristics. Moreover, they
find some verification that retails demand is the most volatile in the supply chain which contradicts
with the bullwhip effect. They believe that they come to different conclusions because “seasonality
in combination with increasing marginal costs provides a strong motivation to smooth production
relative to demand, so it is not surprising that eliminating a primary reason to production smooth
leads to incorrect conclusion that most firms amplify” (Cachon et al. 2006). Also, they consider
that seasonality is one of the major causes of the bullwhip effect that should be analyzed. Similarly,
Metters (1997) depicts that seasonality is a major factor of the bullwhip effect.
Strategies for Dealing with the Bullwhip Effect
Lee et al. (1997) suggest that making demand data available at downstream site to an upstream
site is a remedy to mitigate demand signal processing. Thus, upstream site and downstream site
can use same data while updating their forecasts. Their strategy can be achieved by using electronic
data interchange (EDI) and point of sale systems (POS). They prove EDI can also help to break
order batching. Against price variations they recommend reducing price discount and using
strategies like everyday low pricing system. They add that sharing sales capacity and inventory data
can be helpful to eliminate shortage gaming.
Hayya et al. (2004) find out that information sharing decreases total and stage to stage
variance amplification. They also illustrate that information sharing decelerates variance
amplification as going to upstream site from downstream site. Additionally, the authors show that
information quality is an important factor to reduce the bullwhip effect. Chen et al. (2000)
demonstrate that smoother demand forecasts provide smaller variation increase. They also
illustrate that the retailer should use more demand data to reduce the bullwhip effect if longer lead
times is in process. They also have similar conclusion about using centralized information but they
demonstrate that the bullwhip effect is not completely eliminated by using information sharing
policies. Croson and Donohue (2003) confirm that behavioral impact of adding POS data sharing
improves performance of supply chains.
Ruggles (2005) suggests that using collaboration tools like vendor managed inventory (VMI)
can help to reduce the bullwhip effect because these systems make available the demand data and
inventory position information to members of the supply chain. Disney and Towill (2003)
9
10. recommend VMI as a remedy against the bullwhip effect. They argue that shortage gaming can be
completely eliminated because responsibilities in relation change. Also, they claim order batching
effect can be eliminated by the structure of the information flow. Moreover, VMI can reduce the
bullwhip effect that is caused by price variations. Finally, they claim that VMI supply chain causes
less variation than traditional supply chain.
Yu et al. (2001) investigate the benefits of information sharing to members of the chain. They
found that in “decentralized control” and “coordinated control”, retailer will obtain nothing but in
“centralized control” retailer can gain performance improvement. However, manufacturer can gain
inventory level reduction if information sharing occurs. Manufacturer gains more than retailer but
it is possible to provide pareto improvement (benefit of the two stages) by information sharing.
Towill (1999) demonstrates that removal of one or more intermediate echelons, encouraging
collaboration among supply chain members and reducing time delays can significantly reduce the
bullwhip effect. McCullen and Towill (2001) categorize various supply chain strategies into four
principles:
1) Control System Principle: Strengths dynamic stability of the supply chain.
2) Time Compression Principle: Aims reduction in material and information flow lead
times
3) Information Transparency Principle: Information sharing between members
4) Echelon Elimination Principle: Removal of echelons.
Authors embedded these principles in a company’s strategy and achieved 36 per cent reduction
in the bullwhip effect (Mc Cullen et al., 2001).
Kaipia et al. (2006) state that reducing “nervousness” can reduce the bullwhip effect. They have
three strategies.
1) Stabilize and simplify planning process.
2) Develop communication practices with suppliers
10
11. 3) Implementing VMI system
Donavan (2003) makes some managerial suggestions to minimize the bullwhip effect and
increase business performance. He suggests minimizing cycle time, monitoring actual demand for
products, increasing quality and frequency of collaboration, minimizing or eliminating information
queues, minimizing incentive promotions to customers and providing vendor-managed inventory.
Conclusion
Although the bullwhip effect phenomenon has been heavily considered in the last two decades,
nowadays it is a well known issue that is being investigated in various aspects. There are a lot of
research that demonstrate the bullwhip effect exists and that try to find reasons which cause the
bullwhip effect. Moreover, some researches seek remedies to eliminate or reduce the bullwhip
effect and some firms like Cisco and P&G explain the reasons of the inefficiencies they faced in
their supply chain by referring to the bullwhip effect (Lee et al. 2004).
The researches about the bullwhip effect come to a point but there is a long way that to go
through to clearly understand the effect because there are still contradictions. For example, Cachon
et al. (2006) find out significant contradictions to the definition of the effect that was discussed in
part 3. The fact that they come up with different results could not be fully explained. We do not
know if it is a special case or researches before Cachon’s have similar characteristics. Moreover,
there are possibly different choices that can be made regarding the way data was aggregated and
neither current literature nor business practice is clear about this matter. In addition, different
choices can be used while separating the data (Fransoo, 2000). Since different choices can be
considered we are not able to find the exact reflections of the bullwhip effect but it can be said that
similar databases and applied methods can be used as an initiative to eliminate the bullwhip effect
and to increase the efficiency of the supply chain systems.
11
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