Beergame
Beer Game Debriefing
Dr. Kai Riemer
Experiencing the effects of systems dynamics
 Did you feel yourself controlled by forces in the system from
time to time? Or did you feel in control?
 Did you find yourself "blaming" the groups next to you for your
problems?
 Did you feel desperation at any time?
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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Some questions for discussion




What, if anything, is unrealistic about this game?
Why are there order delays?
Why are there production delays? Shipping delays?
Why have both distributor and wholesalers? Why not ship
beer directly from the factory to the retailer?
 Must the brewer be concerned with the management of the
raw materials suppliers?
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
3
Bullwhip Effect
100
90
80
70
Orders
Customer
60
Retailer
50
Wholesaler
Distributor
40
Factory
30
20
10
0
Week
Week
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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Other groups
Bullwhip Effect
Bullwhip Effect
Bullwhip Effect
70
70
70
60
60
60
50
50
50
Retailer
Wholesaler
30
Distributor
40
Retailer
Wholesaler
30
Distributor
Factory
Customer
Orders
40
Customer
Orders
Orders
Customer
Retailer
Wholesaler
30
Distributor
Factory
Factory
20
20
20
10
10
10
0
Week
0
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Week
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
Week
Bullwhip Effect
Week
Bullwhip Effect
Bullwhip Effect
40
90
60
80
35
50
70
30
40
20
Wholesaler
Distributor
15
Factory
Customer
Retailer
30
Wholesaler
Distributor
Factory
20
10
Orders
Retailer
Orders
Customer
25
Orders
40
60
Customer
50
Retailer
Wholesaler
40
Distributor
30
Factory
20
10
5
10
0
0
0
Week
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41
Week
Week
1
8
15
22
29
36
43
Week
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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What happened here?
 Average order size
ohne
mit
10,075
10,55
10,4
10,05
7,7
7,9
8
8,325
 Maximum order size
ohne
23
35
99
100
mit
20
13
23
20
 Numbers of small orders (0-2 items)
ohne
4
9
16
26
mit
6
5
3
4
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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Out of stock = Serious lack of service level!
150
100
Inventory
50
Retailer
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Wholesaler
Distributor
-50
Factory
-100
-150
-200
Week
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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What happened here?
 Inventory oscillation: maximum amplitude
ohne
73
101
193
230
mit
38
59
32
48
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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Total Cost
1400
1200
Cost
1000
Retailer
800
Wholesaler
Distributor
600
Factory
400
200
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Week
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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Bullwhip effect  problems




High inventory levels
Low service level (back orders)
High cost
High demand fluctuation causes more problems…
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
10
Bullwhip effect  problems
 Variation in demand along the supply chain requires
 Shipment capacity
 Production capacity
 Inventory capacity
 to cope with peaks.
 Most of the time this capacity will be idle.
 There’s significant cost and investments attached!
 In the end: high overall cost in the supply chain
 But competition between supply chains and networks, not just between
individual companies!
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
11
Structure creates behaviour
 different people in the same organizational structure produce the same
(or at least similar) results.
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
12
Real world reactions
 A typical organizational response would be to find the "person
responsible" (the guy placing the orders or the inventory
manager) and blame him.
 But the game clearly demonstrates how inappropriate this
response is
 different people following different decision rules for ordering
create similar oscillations.
 We have to change the structural setup!
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
13
Factors contributing to bullwhip effect
 Demand forecasting
 Usage of aggregate and thus inaccurate data does not allow for
good predictions
 High variability leads to continuous adaptations of order policies
and thus increases variability upstream
 Lead time
 High lead time creates uncertainty
 Requires high safety stock levels
 Reduces flexibility and adaptability to unforeseen changes in
demand
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
14
Factors contributing to bullwhip effect
 Batch ordering
 Batch ordering at one stage in SC leads to observing high variability at
next stage upstream:
 one week large order followed by weeks with no order
 Contributors: fixed ordering costs, transportation and price discounts
 Price fluctuation
 Stock up when prices are lower  large orders
 Promotions and discounts
 Inflated orders
 In time of shortages, suppliers place big orders when expecting to be
allocated proportionally
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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Lesson
 In traditional supply chains information about consumer demand is only




passed up the supply chain through the orders that are placed
 Or using aggregated figure
Information is therefore lost
High Buffer stocks result
Even if each party acts “optimally” individually the result is less than optimal
for the whole supply chain
Result is higher prices, less sales.
BUT:
 Competition is now supply chain against supply chain and
Network against network
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
16
Contact
 Dr. Kai Riemer
 http://www.beergame.org
Beergame Debriefing, by Kai Riemer, http://www.beergame.org
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