The Beer Game is a simulation that models the bullwhip effect in supply chains. It involves four stages - retailer, wholesaler, distributor, and factory. The simulation showed increasing swings in orders as information moved up the supply chain, with the factory receiving the most volatile orders despite stable consumer demand. This caused high and fluctuating inventory levels. Stockouts also occurred, especially at the retailer level, further increasing costs. Overall, the simulation demonstrated how lack of coordination between supply chain stages can damage costs and service quality.

1. The Beer Game
More about production and decision-making,
Less about consumption……
Presented By : Group 8, Sales and Distribution Management, IIM Kozhikode
|Pranav Koundinya| |Payal Sachan| |Pathsamatla Sraavya| |Arjun Kemmu| |Karidi Sidhartha|

2. The Market
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The Market
Mean 5.12
Median 4
Mode 4
Standard Deviation 1.53622915
Sample Variance 2.36
Kurtosis -0.509030956
Skewness 0.981453618
Range 4
Minimum 4
Maximum 8
Confidence Level(95.0%) 0.634124226

3. The receiver of market
information
RETAILER
The first point of contact
for market information
WHOLESALER
The factory-contact for
production information
DISTRIBUTOR
The manufacturer who
receives information, the last
FACTORY
6
4
4 1 0
Week 1
Week 3 Week 5
Week 7
Information Flow
Week 1
Week 1Week 3Week 5Week 7
Goods Flow
6600

4. Retailer Wholesaler
Factory Distributor
Psyche of
the Human
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Incoming Order
Order Placed
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Incoming Order
Order Placed
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Incoming Order
Order Placed
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Incoming Order
Order Placed
Need to avoid Stock-Out Need to maintain lower
inventory
Need to maintain lower
inventory
Need to avoid under-
production

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Retailer Wholesaler Distributor Factory
Factory has
the biggest
incoming
order
The next
biggest order
is to the
distributor
Wholesaler is
also pressured
with bigger
orders
Evidence of Bull-Whip Effect
Incoming Orders

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Retailer Wholesaler Distributor Factory
Evidence of Bull-Whip Effect Factory
suffered highest
inventory for
long periods
Retailer was
most affected
by stock-outs
Inventory
Performance

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Retailer Wholesaler Distributor Factory
Cost
Cost – Performance of stages
Week 5 – 9 : Retailer
Wholesaler and
Distributor reduced their
costs, causing factory
inventory to pile up

8. What clicked for Group 8 ?
Over ordering due to
fear of stock-outs
avoided
The team suffered severe stock-
outs which added to cost but
saved on the cost due to over
ordering that could have
multiplied
Bull-whip effect
reduced by ordering
‘Just-Enough’
The Maximum order(10) in the
Retailer stage was 2 Standard
Deviations(2.06) from the
mean(5.12)
Zero Stock Out
Performance of Factory
A Stock out in the factory would
have caused a further 2 week
delay down the chain and been
catastrophic for all stages
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9. What did not click for Group 8 ?
Unprecedented
Pressure on Factory
The Factory underwent long
periods of heavy inventory was
almost pushed to a stock-out at
the end!
Batch Ordering
The retailer, in anticipation of
smaller/bigger orders started
ordering Zeros followed by huge
quantities!
Severe Stock-outs
A stock out here has just twice
the cost of inventory, however in
a real scenario the cost of losing
a customer may be much higher
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10. Cost Breakup
348, 27%
332, 26%
309, 24%
290, 23%
Retailer Wholesaler Distributor Factory
120
228
Retailer
Inventory Stock-Outs
252
80
Wholesaler
Inventory Stock-Outs
252
80
Wholesaler
Inventory Stock-Outs
288
2
Factory
Inventory Stock-Outs
Total Cost = 1279 Units

11. Major Problems What IF ? ….
High Inventory Levels
Low Service Levels (Stock-Outs)
High Cost
Demand Fluctuations
We could bring the market
data directly to the factory
Introduce
consumer
bidding for
future
consumption
Maintain constant
inventory and order
levels among all stages
(except factory)
Develop a
separate channel
for spike orders
fulfilled directly
from factory
IS THIS WHAT THE
E-COMMERCE GIANTS
ARE DOING???