Oscillations and Amplifications in Demand-Supply Network Chains

Oscillations and Amplifications in Demand-Supply Network Chains

 

From Modeling and Measuring the Bullwhip Effect

Demand variability and uncertainty is a driver of supply chain inventory. Managing supply chains can be a challenge when demand variability and uncertainty is high. For a company in a supply chain consisting of multiple stages, each of which is run by a separate organization (or company), the variability of demand faced by this company can be much higher than the variability faced by downstream stages (where “downstream stages” refers to the stages closer to the final consumption of the product). The bullwhip effect refers to the phenomenon where demand variability amplifies as one moves upstream in a supply chain (Lee et al, 1997a, or LPW). LPW described this as a form of demand information distortion. Lee et al (1997b) further discussed the managerial and practical aspects of the bullwhip effect, giving more industry examples. The bullwhip effect phenomenon is closely related to studies in systems dynamics (Forrester, 1961; Sterman, 1989; Senge, 1990). Sterman (1989) observed a systematic pattern of demand variation amplification in the Beer Game, and attributed it to behavioral causes (i.e., misperceptions of feedback). Macroeconomists have also studied the phenomenon (Holt et al, 1968; Blinder, 1981; Blanchard, 1983).

 

From Operational and Behavioral Causes of Supply Chain Instability

 

Supply chain instability is often described as the bullwhip effect, the tendency for variability to increase at each level of a supply chain as one moves from customer sales to production (Lee et al. 1997, Chen et al. 2000). While amplification from stage to stage is important, supply chain instability is a richer and more subtle phenomenon. The economy, and the networks of supply chains embedded within it, is a complex dynamic system and generates multiple modes of behavior. These include business cycles (oscillation), amplification of orders and production from consumption to raw materials (the bullwhip), and phase lag (shifts in the timing of the cycles from consumption to materials). High product returns and spoilage are common in industries from consumer electronics to hybrid seed corn (Gonçalves 2003). Many firms experience pronounced hockey-stick patterns in which orders and output rise sharply just prior to the end of a month or quarter as the sales force and managers rush to hit revenue goals. Boom and bust dynamics in supply chains are often worsened by phantom orders—orders customers place in response to perceived shortages in an attempt to gain a greater share of a shrinking pie (T. Mitchell 1923, Sterman 2000, ch. 18.3, Gonçalves 2002, Gonçalves and Sterman 2005).

What are the causes of supply chain instability? Why does supply chain instability persist, despite the lean revolution and tremendous innovations in technology? What can be done to stabilize supply chains and improve their efficiency?

Here I describe the origins of supply chain instability from a complex systems perspective. The dynamics of supply chain networks arise endogenously from their structure. That structure includes both operational and behavioral elements.

 

From Operational and Behavioral Causes of Supply Chain Instability

Oscillation, Amplification, and Phase Lag

Exhibit 1 shows industrial production in the US. The data exhibit several modes of behavior. First, the long-run growth rate of manufacturing output is about 3.4%/year. Second, as seen in the bottom panel, production fluctuates significantly around the growth trend. The dominant periodicity is the business cycle, a cycle of prosperity and recession of about 3–5 years in duration, but exhibiting considerable variability.

The amplitude of business cycle fluctuations in materials production is significantly greater than that in consumer goods production (exhibit 2). The peaks and troughs of the cycle in materials production also tend to lag behind those in production of consumer goods. Typically, the amplitude of fluctuations increases as they propagate from the customer to the supplier, with each upstream stage tending to lag behind its customer. These three features, oscillation, amplification, and phase lag, are pervasive in supply chains.

 

From Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

A central question in operations management is whether the oscillations, amplification and phase lag observed in supply chains arise as the result of operational or behavioral causes.

Operational theories assume that decision makers are rational agents who make optimal decisions given their local incentives and information.  Supply chain instability must then result from the interaction of rational actors with the physical and institutional structure of the system.

  • Physical structure includes the network linking customers and suppliers and the placement of inventories and buffers within it, along with capacity constraints and time delays in production, order fulfillment, transportation, and so on.
  • Institutional structure includes the degree of horizontal and vertical coordination and competition among firms, the availability of information to decision makers in each organization, and the incentives faced by each decision maker.

Behavioral explanations also capture the physical and institutional structure of supply chains, but view decision makers as boundedly rational actors with imperfect mental models, actors who use heuristics to make ordering, production, capacity acquisition, pricing and other decisions (Morecroft 1985, Sterman 2000, Boudreau et al. 2003, Gino & Pisano 2008, Bendoly et al. 2010, Croson et al. 2013).

Amplifications and Phase Lag

Amplification and phase lag arise from the presence of basic physical structures including stocks of inventory and delays in adjusting production or deliveries to changes in incoming orders.

Oscillations

Oscillations, however, are not inevitable. They arise from boundedly rational, behavioral decision processes

The difference matters: if supply chain instability arises from operational factors and rational behavior, then policies must be directed at changing the physical and institutional structure of the system, including incentives.

If, however, instability arises from bounded rationality and emotional arousal such policies may not be sufficient.

 

Key People:

  • Jay W Forrester
  • John Sterman
  • Rogelio Oliva
  • Hau L Lee

 

Key Terms:

  • Bullwhip Effect
  • Oscillations
  • Amplifications
  • Negative Feedback Loop
  • Positive Feedback Loop
  • Phase Lag
  • Supply Chain Networks
  • Inventory Management
  • Production Smoothening
  • Beer Distribution Game
  • Industrial Dynamics
  • Operational and Institutional Structures
  • Behavioral causes
  • Instability
  • Variability
  • Uncertainty

 

Key Sources of Research:

 

Behavioral Causes of Demand Amplification in Supply Chains: “Satisficing” Policies with Limited Information Cues

Rogelio Oliva

Paulo Gonçalves

 

Click to access 1889_087eb4f1-0532-4a7d-a206-3d565efc02af_2005-OLIVA133.pdf

 

 

REDUCING THE IMPACT OF DEMAND PROCESS VARIABILITY WITHIN A MULTI-ECHELON SUPPLY CHAIN

Francisco Campuzano Bolarín1,Lorenzo Ros Mcdonnell1, Juan Martín García

2008

 

Click to access CAMPU215.pdf

 

 

The impact of order variance amplification/dampening on supply chain performance

 

Robert N. Boute, Stephen M. Disney, Marc R. Lambrecht and Benny Van Houdt

 

Click to access KBI_0603.pdf

 

 

Coping with Uncertainty: Reducing ”Bullwhip” Behaviour in Global Supply Chains

 

Rachel Mason-Jones, and Denis R. Towill

Click to access 24557527da7aa9da7de238fe7f4a463b2af6.pdf

 

 

Bullwhip in Supply Chains ~ Past, Present and Future

Steve Geary Stephen M Disney and Denis R Towill

 

Click to access 492a6e6ae1d0f186fe2570b7477428e8e467.pdf

 

 

Shrinking the Supply Chain Uncertainty Circle

R Mason-Jones

Click to access 19980901d.pdf

 

 

THE BULLWHIP EFFECT IN SUPPLY CHAIN Reflections after a Decade

Gürdal Ertek, Emre Eryılmaz

 

Click to access ertek_eryilmaz_cels2008.pdf

 

 

Information distortion in a supply chain: The bullwhip effect

Hau L Lee; V Padmanabhan; Seugjin Whang

Management Science; Apr 1997; 43, 4;

Click to access f26117d56ab96aabe2d6cee4c390ab20ee18.pdf

 

 

 

THE SUPPLY CHAIN COMPLEXITY TRIANGE: UNCERTAINTY GENERATION IN THE SUPPLY CHAIN

 

Click to access 140687.pdf

 

 

The Bullwhip Effect in Supply Chains

Hau L. Lee, V. Padmanabhan and Seungjin Whang

1997

http://sloanreview.mit.edu/article/the-bullwhip-effect-in-supply-chains/

 

 

The Bullwhip Effect: Analysis of the Causes and Remedies

 

Jonathan Moll

Rene Bekker

 

Click to access werkstuk-moll_tcm243-354834.pdf

 

 

‘BULLWHIP’ AND ‘BACKLASH’ IN SUPPLY PIPELINES

Vinaya Shukla, Mohamed M Naim, Ehab A Yaseen

 

https://hal.archives-ouvertes.fr/hal-00525857/document

 

 

How human behaviour amplifies the bullwhip effect – a study based on the beer distribution game online

Joerg Nienhaus, Arne Ziegenbein*, Christoph Duijts

 

Click to access Bullwhip_Effect_Article.pdf

 

 

The Bullwhip Effect in Different Manufacturing Paradigm: An Analysis

Shamila Nabi KHAN1 Mohammad Ajmal KHAN2 Ramsha SOHAIL

 

Click to access 11.pdf.pdf

 

 

On replenishment rules, forecasting and the bullwhip effect in supply chains

Stephen M. Disney1 and Marc R. Lambrecht

 

Click to access Disney%20-%20On%20replenishment%20rules%20forecasting%20and%20the%20bullwhip%20effect%20in%20supply%20chains%20pre%20print.pdf

 

 

Causes and Remedies of Bullwhip Effect in Supply Chain

Sivakumar Balasubramanian Larry Whitman Kartik Ramachandran Ravindra Sheelavant

 

Click to access 2001IERCBullwhip.pdf

 

 

Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

John Sterman

 

Click to access Sterman%20Beh%20Ops%20Handbook%20Chapter%20140210.pdf

 

 

Modeling and Measuring the Bullwhip Effect

Li Chen and Hau L. Lee

2015

Click to access Chen_Lee_Bullwhip_2015.pdf

 

 

Operational and Behavioral Causes of Supply Chain Instability

John D. Sterman

Click to access 2a3118c5c7d2bd475335549b0b943009d66c.pdf

 

 

Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

Rachel Croson, Karen Donohue, Elena Katok, and John Sterman

Click to access Order%20Stability%20in%20SCs050212.pdf

Click to access Order_Stability_070505.pdf

 

 

SUPPLY CHAIN DYNAMICS, THE “BEER DISTRIBUTION GAME” AND MISPERCEPTIONS IN DYNAMIC DECISION MAKING

John D. Sterman

Click to access E6-63-01-02.pdf

 

 

When Do Minor Shortages Inflate To Great Bubbles?

Paulo Gonçalves

2002

 

Click to access Gonca1.pdf

 

 

A new technology paradigm for collaboration in the supply chain

Branko Pecar and Barry Davies

Click to access c522d454d1dc036a22db29b2dee005dbc44e.pdf

 

 

MANAGERIAL INSIGHTS ON THE IMPACT OF FORECASTING AND INFORMATION ON VARIABILITY IN A SUPPLY CHAIN

Frank Chent, Zvi Drezner2 , Jennifer K. Ryan3 and David Simchi-Levi

 

Click to access 4%20chen.pdf

 

 

Supply and Production Networks: From the Bullwhip Effect to Business Cycles

Dirk Helbing Stefan Laemmer

2004

 

Click to access 04-12-033.pdf

 

 

Inventory dynamics and the bullwhip effect : studies in supply chain performance

Udenio, M.

2014

 

Click to access 776508.pdf

 

 

RECENT WORK ON BUSINES CYCLES IN HISTORICAL PERSPECTIVE: REVIEW OF THEORIES AND EVIDENCE

VictorZarnowitz

1984

 

Click to access w1503.pdf

 

 

THEORY AND HISTORY BEHIND BUSINESS CYCLES:ARE THE 1990S

THE ONSET OF A GOLDEN AGE?

 

Victor Zarnowitz

WorkingPaper7010 htp:/w.nber.org/papers/w7010

1999

 

Click to access w7010.pdf

 

 

The Beginning of System Dynamics

Jay W. Forrester

 

Click to access D-4165-1.pdf

 

 

Profiles in Operations Research: Jay Wright Forrester

David C. Lane John D. Sterman

 

Click to access jwf-profile-in-op.pdf

 

 

SYSTEM DYNAMICS MODELLING IN SUPPLY CHAIN MANAGEMENT: RESEARCH REVIEW

2000

 

Click to access 54fe11ea0aaa47f4c8e08959be2ef52d50a6.pdf

 

 

INDUSTRIAL DYNAMICS-AFTER THE FIRST DECADE

JAY W. FORRESTER

 

Click to access Forrester68.pdf

 

 

Industrial Dynamics

Jay W Forrester

1961

 

 

Business Dynamics

John Sterman

2000

 

George Dantzig and History of Linear Programming

George B. Dantzig and History of Linear Programming

Also, History of Optimal allocation of Resources  and Optimization

Key People

  • Tjalling C Koopmans
  • George B Dantzig
  • Leonid V Kantorovich
  • John Von Neumann
  • Wassily Leontief

 

Koopmans and Kantorovich got the 1975 Nobel prize for their work in optimal allocation of resources.

 

From Linear Programming  The Story about How It Began:

Some legends, a little about its historical significance, and comments about where its many mathematical programming extensions may be headed

 

Linear programming can be viewed as a part of a great revolutionary development which has given mankind the ability to state general goals and to lay out a path of detailed decisions to take in order to ‘best’ achieve its goals when faced with practical situations of great complexity. Our tools for doing this are ways to formulate real-world problems in detailed mathematical terms (models), techniques for solving the models (algorithms), and engines for executing the steps of algorithms (computers and software).

This ability began in 1947, shortly after World War II, and has been keeping pace ever since with the extraordinary growth of computing power. So rapid has been the advance in decision science that few remember the contributions of the great pioneers that started it all. Some of their names are von Neumann, Kantorovich, Leontief, and Koopmans. The first two were famous mathematicians. The last three received the Nobel Prize in economics for their work.

In the years from the time when it was first proposed in 1947 by the author (in connection with the planning activities of the military), linear programmming and its many extensions have come into wide use. In academic circles decision scientists (operations researchers and management scientists), as well as numerical analysts, mathematicians, and economists have written hundreds of books and an uncountable number of articles on the subject.

 

Key Sources of Research:

 

George B. Dantzig (1914–2005)

Richard Cottle, Ellis Johnson, and Roger Wets

 

Click to access Projekt_7-1DantzigAMS.pdf

 

Linear Programming and Its Extensions

G Dantzig

1963

Click to access R366part1.pdf

 

Linear Programming: Theory and its extensions Part 2

G Dantzig and M Thapa

Click to access ebooksclub.org__Linear_Programming_2__Theory_and_Extensions.pdf

 

Linear Programming: Introduction  Part 1

G Dantzig and M Thapa

Click to access linear_programming_vol1-dantzig_thapa.pdf

 

LINEAR PROGRAMMING

 

GEORGE B. DANTZIG

 

Click to access Dantzig2002.pdf

 

George B. Dantzig 1914–2005

By J. Dupaˇcov ́a and D.P. Morton

 

Click to access dupacova_morton_05.pdf

 

Linear Programming

The Story about How It Began:
Some legends, a little about its historical significance, and comments about where its many mathematical programming extensions may be headed

George B. Dantzig

 

Click to access dantzig.pdf

 

Linear Programming under uncertainty

G Dantzig

1955

Click to access dantzig.pdf

 

ORIGINS OF THE SIMPLEX METHOD

by

George B. Dantzig

TECHNICAL REPORT SOL 87-5

May 1987

 

Click to access a182708.pdf

 

A Brief History of Linear and Mixed-Integer Programming Computation

Robert E. Bixby

2010

 

Click to access 25_bixby-robert.pdf

 

Biography of George Bernard Dantzig

 

Click to access dantzigGeorge.pdf

 

Activity Analysis of Production and Allocation

Cowles Commision Research in Economics Monograph 13

T Koopmans

1951

Click to access m13-all.pdf

 

THE FIRST LINEAR-PROGRAMMING SHOPPE

SAUL I. GASS

 

Click to access LP%20Gass.pdf

 

Special Issue of Discrete Optimization

In memory of George Dantzig

2008

http://www.sciencedirect.com/science/journal/15725286/5/2

 

George B. Dantzig and systems optimization

Philip E. Gilla, Walter Murray, Michael A. Saunders, John A. Tomlin, Margaret H. Wright,

Discrete Optimization 5 (2008) 151–158

http://www.sciencedirect.com/science/article/pii/S1572528607000321

 

George Dantzig in the development of economic analysis

Kenneth J. Arrow

http://www.sciencedirect.com/science/article/pii/S1572528607000436

 

Solving Real-World Linear Programs: A Decade and More of Progress.

Robert E. Bixby,

(2002)

Operations Research 50(1):3-15. http://dx.doi.org/10.1287/opre.50.1.3.17780

 

 

”On the Shoulders of Giants”
A brief excursion into the history of mathematical programming 

R. Tichatschke

 

Click to access Tichaschke.pdf

 

Is the constancy of technical coefficients

a matter of tolerance ?

A methodological inquiry
about the justification of a controversial assumption 1936-1952

Amanar Akhabbar

 

Click to access akhabbar.pdf

 

Mathematical Methods of Organizing and Planning Production

Author(s): L. V. Kantorovich

Source: Management Science, Vol. 6, No. 4 (Jul., 1960), pp. 366-422

 

Click to access ManSci-v6_n4-366_422-1960.pdf

 

Wassily W. Leontief, Leonid V. Kantorovich, Tjalling C. Koopmans and J. Richard N. Stone
Pioneering Papers of the Nobel Memorial Laureates in Economics series

Edited by Howard R. Vane,  Chris Mulhearn,

Publication Date: 2009
ISBN: 978 1 84720 840 8

http://www.e-elgar.com/shop/wassily-w-leontief-leonid-v-kantorovich-tjalling-c-koopmans-and-j-richard-n-stone?___website=uk_warehouse