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, 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.
- Jay W Forrester
- John Sterman
- Rogelio Oliva
- Hau L Lee
- Bullwhip Effect
- 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
Key Sources of Research:
Behavioral Causes of Demand Amplification in Supply Chains: “Satisficing” Policies with Limited Information Cues
REDUCING THE IMPACT OF DEMAND PROCESS VARIABILITY WITHIN A MULTI-ECHELON SUPPLY CHAIN
Francisco Campuzano Bolarín1,Lorenzo Ros Mcdonnell1, Juan Martín García
The impact of order variance amplification/dampening on supply chain performance
Robert N. Boute, Stephen M. Disney, Marc R. Lambrecht and Benny Van Houdt
Coping with Uncertainty: Reducing ”Bullwhip” Behaviour in Global Supply Chains
Rachel Mason-Jones, and Denis R. Towill
Bullwhip in Supply Chains ~ Past, Present and Future
Steve Geary Stephen M Disney and Denis R Towill
Shrinking the Supply Chain Uncertainty Circle
THE BULLWHIP EFFECT IN SUPPLY CHAIN Reflections after a Decade
Gürdal Ertek, Emre Eryılmaz
Information distortion in a supply chain: The bullwhip effect
Hau L Lee; V Padmanabhan; Seugjin Whang
Management Science; Apr 1997; 43, 4;
THE SUPPLY CHAIN COMPLEXITY TRIANGE: UNCERTAINTY GENERATION IN THE SUPPLY CHAIN
The Bullwhip Effect in Supply Chains
Hau L. Lee, V. Padmanabhan and Seungjin Whang
The Bullwhip Effect: Analysis of the Causes and Remedies
‘BULLWHIP’ AND ‘BACKLASH’ IN SUPPLY PIPELINES
Vinaya Shukla, Mohamed M Naim, Ehab A Yaseen
How human behaviour amplifies the bullwhip effect – a study based on the beer distribution game online
Joerg Nienhaus, Arne Ziegenbein*, Christoph Duijts
The Bullwhip Effect in Different Manufacturing Paradigm: An Analysis
Shamila Nabi KHAN1 Mohammad Ajmal KHAN2 Ramsha SOHAIL
On replenishment rules, forecasting and the bullwhip effect in supply chains
Stephen M. Disney1 and Marc R. Lambrecht
Causes and Remedies of Bullwhip Effect in Supply Chain
Sivakumar Balasubramanian Larry Whitman Kartik Ramachandran Ravindra Sheelavant
Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains
Modeling and Measuring the Bullwhip Effect
Li Chen and Hau L. Lee
Operational and Behavioral Causes of Supply Chain Instability
John D. Sterman
Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock
Rachel Croson, Karen Donohue, Elena Katok, and John Sterman
SUPPLY CHAIN DYNAMICS, THE “BEER DISTRIBUTION GAME” AND MISPERCEPTIONS IN DYNAMIC DECISION MAKING
John D. Sterman
When Do Minor Shortages Inflate To Great Bubbles?
A new technology paradigm for collaboration in the supply chain
Branko Pecar and Barry Davies
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
Supply and Production Networks: From the Bullwhip Effect to Business Cycles
Dirk Helbing Stefan Laemmer
Inventory dynamics and the bullwhip effect : studies in supply chain performance
RECENT WORK ON BUSINES CYCLES IN HISTORICAL PERSPECTIVE: REVIEW OF THEORIES AND EVIDENCE
THEORY AND HISTORY BEHIND BUSINESS CYCLES:ARE THE 1990S
THE ONSET OF A GOLDEN AGE?
The Beginning of System Dynamics
Jay W. Forrester
Profiles in Operations Research: Jay Wright Forrester
David C. Lane John D. Sterman
SYSTEM DYNAMICS MODELLING IN SUPPLY CHAIN MANAGEMENT: RESEARCH REVIEW
INDUSTRIAL DYNAMICS-AFTER THE FIRST DECADE
JAY W. FORRESTER
Jay W Forrester