https://web.mit.edu/jsterman/www/SDG/beergame.html
Teaching Takes Off
Flight Simulators for Management Education
"The Beer Game"
Prof. John D. Sterman
Sloan School of Management
Massachusetts Institute of Technology
E51-351
30 Wadsworth Street
Cambridge, MA 02142
jsterman "at" mit.edu
Chaos reigns in my classroom. Eighty students are shouting,
gesturing, and laughing while counting poker chips and turning over
cards. A thick roll of $1 bills awaits the winners. A field trip to
Las Vegas? No, it's the "Beer Game", a role-playing simulation
designed to teach principles of management science.
We all know the world is growing more complex. Technological, social,
and environmental change are accelerating. Organizations, industries,
and government grow ever more tightly coupled. Today's students will
face a world that is more dynamic and more uncertain than ever
before.
Managers are not alone in facing such daunting tasks. Our society
depends on systems of enormous complexity, from nuclear power plants
to jumbo jets. Indeed, a popular metaphor likens managers and pilots.
Managers must fly their organizations through uncharted skies and
rough weather, constantly monitoring their information systems for
signs of trouble or opportunity, dogfighting with the competition,
preventing hijacking by hostile raiders - all the while giving the
stockholders in the back a smooth ride.
There is one difference between managers and pilots, however. No
airline would dream of sending pilots up in a real jumbo jet without
extensive training in the simulator. Yet managers are expected to fly
their organizations relying on management school - the equivalent of
ground school - and perhaps some experience as junior crew.
[beergame]
Figure 1. Beer Game board, showing initial conditions.
To meet these challenges we need to develop 'management flight
simulators', learning environments that motivate, that provide
experiential as well as cognitive lessons, that compress time and
space so that we may experience the long-term consequences of our
actions. The Beer Game is one of a number of management flight
simulators developed at MIT's Sloan School of Management for these
purposes. The game was developed by Sloan's System Dynamics Group in
the early 1960s as part of Jay Forrester's research on industrial
dynamics. Its has been played all over the world by thousands of
people ranging from high school students to chief executive officers
and government officials.
Of course, there is no beer in the beer game, and the game does not
promote drinking. Originally the 'production-distribution game', most
students are more excited about producing beer than widgets or
toasters. When played in, say, high schools, it easily becomes the
apple juice game.
[beerplay]
Managers in an executive workshop playing the Beer Game at MIT.
Playing the Game
The game is played on a board that portrays the production and
distribution of beer (figures 1-2). Each team consists of four
sectors: Retailer, Wholesaler, Distributor, and Factory (R, W, D, F)
arranged in a linear distribution chain. One or two people manage
each sector. Pennies stand for cases of beer. A deck of cards
represents customer demand. Each simulated week, customers purchase
from the retailer, who ships the beer requested out of inventory. The
retailer in turn orders from the wholesaler, who ships the beer
requested out of their own inventory. Likewise the wholesaler orders
and receives beer from the distributor, who in turn orders and
receives beer from the factory, where the beer is brewed. At each
stage there are shipping delays and order processing delays. The
players' objective is to minimize total team costs. Inventory holding
costs are $.50/case/week. Backlog costs are $1.00/case/week, to
capture both the lost revenue and the ill will a stockout causes
among customers. Costs are assessed at each link of the distribution
chain.
The game can be played with anywhere from four to hundreds of people.
Each person is asked to bet $1, with the pot going to the team with
the lowest total costs, winner take all. The game is initialized in
equilibrium. Each inventory contains 12 cases and initial throughput
is four cases per week. In the first few weeks of the game the
players learn the mechanics of filling orders, recording inventory,
etc. During this time customer demand remains constant at four cases
per week, and each player is directed to order four cases,
maintaining the equilibrium. Beginning with week four the players are
allowed to order any quantity they wish, and are told that customer
demand may vary; one of their jobs is to forecast demand. Players are
told the game will run for 50 simulated weeks, but play is actually
halted after 36 weeks to avoid horizon effects.
Each player has good local information but severely limited global
information. Players keep records of their inventory, backlog and
orders placed with their supplier each week. However, people are
directed not to communicate with one another; information is passed
through orders and shipments. Customer demand is not known to any of
the players in advance. Only the retailers discover customer demand
as the game proceeds. The others learn only what their own customer
orders.
These information limitations imply that the players are unable to
coordinate their decisions or jointly plan strategy, even though the
objective of each team is to minimize total costs. As in many real
life settings, the global optimization problem must be factored into
subproblems distributed throughout the organization.
The game is deceptively simple compared to real life. All you have to
do is meet customer demand and order enough from your own supplier to
keep your inventory low while avoiding costly backlogs. There are no
machine breakdowns or other random events, no labor problems, no
capacity limits or financial constraints. Yet the results are
shocking.
Typical Results: Boom and Bust
Figure 2 shows actual results from teams consisting of graduate
students and business executives. Each column shows the results of a
single team. The top four graphs show the orders placed by the
players, from the retailer (bottom) to factory (top). The bottom four
graphs show the players' inventories and backlogs (negative values),
in the same order. Average team costs are about $2000, though it is
not uncommon for costs to exceed $10,000; few ever go below $1000.
Optimal performance (calculated using only the information actually
available to players themselves) is about $200. Average costs are ten
times greater than optimal!
[Image1]
More revealing, the departures from optimality are not random. Though
individual games differ quantitatively, they always exhibit the same
patterns of behavior:
1. Oscillation: Orders and inventories are dominated by large
amplitude fluctuations, with an average period of about 20 weeks.
2. Amplification: The amplitude and variance of orders increases
steadily from customer to retailer to factory. The peak order rate at
the factory is on average more than double the peak order rate at
retail.
3. Phase lag: The order rate tends to peak later as one moves from
the retailer to the factory.
In virtually all cases, the inventory levels of the retailer decline,
followed in sequence by a decline in the inventory of the wholesaler,
distributor, and factory. As inventory falls, players tend to
increase their orders. Players soon stock out. Backlogs of unfilled
orders grow. Faced with rising orders and large backlogs, players
dramatically boost the orders they place with their supplier.
Eventually, the factory brews and ships this huge quantity of beer,
and inventory levels surge. In many cases one can observe a second
cycle.
Lessons of the game
During the game emotions run high. Many players report feelings of
frustration and helplessness. Many blame their teammates for their
problems; occasionally heated arguments break out. After the game I
ask the players to sketch their best estimate of the pattern of
customer demand, that is, the contents of the customer order deck.
Only the retailers have direct knowledge of that demand. The vast
majority invariably draw a fluctuating pattern for customer demand,
rising from the initial rate of 4 to a peak around 20 cases per week,
then plunging.
"After all, it isn't my fault", people tell me, "if a huge surge in
demand wiped out my stock and forced me to run a backlog. Then you
tricked me - just when the tap began to flow, you made the customers
go on the wagon, so I got stuck with all this excess inventory."
Blaming the customer for the cycle is plausible. It is
psychologically safe. And it is dead wrong. In fact, customer demand
begins at four cases per week, then rises to eight cases per week in
week five and remains completely constant ever after.
This revelation is often greeted by disbelief. How could the wild
oscillations arise when the environment is virtually constant? Since
the cycle isn't a consequence of fickle customers, players realize
their own actions must have created the cycle. Though each player was
free to make their own decisions, the same patterns of behavior
emerge in every game, vividly demonstrating the powerful role of the
system in shaping our behavior.
Research reported in Sterman (1989) shows how this occurs. Most
people do not account well for the impact of their own decisions on
their teammates - on the system as a whole. In particular, people
have great difficulty appreciating the multiple feedback loops, time
delays and nonlinearities in the system, using instead a very simple
heuristic to place orders. When customer orders increase
unexpectedly, retail inventories fall, since the shipment delays mean
deliveries continue for several weeks at the old, lower rate. Faced
with a growing backlog, people must order more than demand, often
trying to fix the problem quickly by placing huge orders. If there
were no time delays, this strategy would work well. But in the game,
these large orders stock out the wholesaler. Retailers don't receive
the beer they ordered, and grow increasingly anxious as their backlog
worsens, leading them to order still more, even though the supply
pipe line contains more than enough. Thus the small step in demand
from four to eight is amplified and distorted as it is passed to the
wholesaler, who reacting in kind, further amplifies the signal as it
goes up the chain to the factory. Eventually, of course, the beer is
brewed. The players cut orders as inventory builds up, but too late -
the beer in the supply line continues to arrive. Inventories always
overshoot, peaking at an average of about forty cases.
Faced with what William James called the "bloomin', buzzin'
confusion" of events, most people forget they are part of a larger
whole. Under pressure, we focus on managing our own piece of the
system, trying to keep our own costs low. And when the long-term
effects of our short-sighted actions hit home, we blame our customer
for ordering erratically, and our supplier for delivering late.
Understanding how well intentioned, intelligent people can create an
outcome no one expected and no one wants is one of the profound
lessons of the game. It is a lesson no lecture can convey.
The patterns of behavior observed in the game - oscillation,
amplification, and phase lag - are readily apparent in the real
economy (figure 4), from the business cycle to the recent boom and
bust in real estate. The persistence of these cycles over decades is
a major challenge to educators seeking to teach principles and tools
for effective management. Though repeated experience with cycles in
the real world should lead to learning and improvement, the duration
of the business cycle exceeds the tenure of many managers. In real
life the feedback needed to learn is delayed and confounded by change
in dozens of other variables. By compressing time and space, and
permitting controlled experimentation, management flight simulators
can help overcome these impediments to learning from experience.
But the biggest impediments to learning are the mental models through
which we construct our understanding of reality. By blaming outside
forces we deny ourselves the opportunity to learn - recall that
nearly all players conclude their roller coaster ride was caused by
fluctuating demand. Focusing on external events leads people to seek
better forecasts rather than redesigning the system to be robust in
the face of the inevitable forecast errors. The mental models people
bring to the understanding of complex dynamics sytematically lead
them away from the high leverage point in the system, hindering
learning, and reinforcing the belief that we are helpless cogs in an
overwhelmingly complex machine.
Thus to be effective, management flight simulators must be more than
just business games. They must be embedded in a learning environment
that encourages reflection on the perceptions, attributions, and
other mental models we use to interpret experience as well as the
substantive lessons of the situation. These issues are the focus of
current research at MIT and elsewhere (see Sterman and Morecroft 1992
for examples). In addition to growing use in education, management
flight simulators, both computer-based and manual, are finding
successful application in a wide range of firms. They have helped
stimulate improvement in hospital emergency room operations, raised
maintenance productivity in the chemicals industry, boosted service
quality in the insurance industry, and helped top management in high
tech, petrochemicals, and other industries to reformulate their
strategies. Though much further work lies ahead, flight simulators
may someday be as integral a part of the learning process for
managers as they are today for pilots.
Using the Beer Game and Other Management Flight Simulators
The beer game is particularly useful in classes on operations
management, production scheduling and related issues. The game
highlights the importance of coordination among levels in an
organization, the role of information systems in controlling complex
systems, and the implications of different production paradigms such
as Just-In-Time inventory management.
[Image2]
Figure 3. The 'beer game' in real life: The US economy exhibits
oscillation, amplification, and phase lag as one moves through the
distribution chain from production of consumer goods to intermediate
goods to raw materials.
But the game illustrates more general lessons as well. The game
creates a real organization, with 'teams' supposed to work together.
Yet the pressures of events and limited mental models of the players
quickly cause team cohesion to break down. The game provides a vivid
experience with a complex system, where players can see how the
collective results of individually sensible decisions can be
disastrous; where they can see the connection between the structure
of a system and the dynamics it generates. The game is often used by
firms in the service, financial, and other industries where there is
no inventory to manage. It is widely played as a team building
experience at all levels of management from the shop floor to the
boardroom.
A full analysis of the beer game appears in J. Sterman (1989),
"Modeling Managerial Behavior: Misperceptions of Feedback in a
Dynamic Decision Making Experiment", Management Science, 35(3),
321-339. Other management flight simulators and applications to real
organizations are described in Modelling for Learning Organizations,
John Morecroft and John Sterman, eds., 1994, Portland OR,
Productivity Press, 800/394-6868.
Instructions and a video tape of the beer game shown on the
MacNeil-Lehrer News Hour in 1989 are available from the System
Dynamics Group at MIT. John has also developed a number of additional
management flight simulators around other operational and strategic
issues. These simulators are computer-based and come with full
documentation and instructions:
* People Express Airlines. This computer simulation puts you in
command of the innovative but now defunct People Express
Airlines. You decide what prices to set, how fast to grow, how to
respond to the competition. Your hiring policies influence
morale, productivity, and turnover; your marketing efforts shape
the growth of demand; your competitors fight back. Widely used in
marketing, strategy, organizational behavior, operations, and
even law schools.
* B & B Enterprises. You are responsible for the management of a
new consumer durable from product launch through maturity. You
set price, marketing budgets, and build capacity as the product
goes through its lifecycle. You must forecast demand for the
product and respond to a simulated competitor in a dynamic world
including learning curves, word of mouth, product
differentiation, capacity acquisition lags, and price conscious
customers. Useful in marketing, strategy, industrial
organization, game theory, and modeling and simulation.
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A version of this paper appeared in OR/MS Today, October 1992, 40-44.
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