A Computerized Beer Game and Decision Making Experiments

Abstract

This paper has two goals: The first is to present a computerized version of "Beer Game" originally developed as a board game to teach managers the principles of supply chain management. The multiplayer interactive simulation game we develop is 100 percent faithful to the original game, so that experimental results from the physical and computerized environments can be safely compared. The simulation model used to represent the game also illustrates some subtleties that a model builder must be careful about while simulating a discrete and physical game. Secondly, the game was used as an experimental platform and experiments were done in order to analyze game medium (computer vs. board), demand pattern and learning effects on performances of players. One striking result is the fact that subjects who played the board game scored significantly better than those who played the computerized version in the same conditions. 1

Figures (22)

In the board game, customer orders have the classical pattern of 4,4,4,4,8,8,8,8,8,... , where there is a step increase in the beginning of fifth period. Since the fifth period begins at time 4, the orders must become 8 at time 4 in order to be consistent with the original game instructions. ©! Shipment Formulations

Table 1. Board game records for benchmarking

Table 2. Orders and inventory levels of alternative model 1 The rationale behind the above structure is that two delay stocks match the two delay boxes that exist in the board version. But simulation is needed for full verification. The Retailer order at time | is 10 units and at time 2, this order passes to the stock “R_ Orders Placed” and at time 3 to the “WIncoming Orders” stock. Again at time 3, “Wh Ship Ratel” is 10. However, there is a major problem due to calculation sequence of the simulation software: This 10-unit order influences the “Wholesaler Inventory” at time 4, i.e. the wholesaler inventory decreases at time 4. Nevertheless, in the board game it decreases at time 3. So, in this model there is one redundant delay structure, which results in a shift of inventory levels by one time period.

Figure 3. Ordering-delay structure of alternative model 1

Table 3. Orders and inventory levels of alternative model 2

Figure 4. Ordering-delay structure of alternative model 3

Table 4. Orders and inventory levels of alternative model 3

Figure 5. Wholesaler user interface The computer network simulation game is the computerized version of the board game in which players decide on their orders for each period. Since the user interfaces are very similar, wholesaler interface will be explained also representing the other sectors. Below is the user interface used in the game:

Figure 8. Order rate graphs of group 7 as an example to phase lag Another major observation regarding the comparison of the board and computerized games was that the players performed better in the board game compared to the computerized game. This was figured out by comparing the board game results with the computerized game results belonging to the same demand pattern group. The variables used in the analysis were:

The results of the experiments sectioned with respect to the demand pattern applied are summarized in Table 8. II-) Step-Up-and-Down Customer Demand (4,4,4,4,12,8,8,8.8.8......): After a one-period peak of 12 at time five followed by a step-down of four units, the demand pattern stabilized at the upgraded level of 8.

Table 10. Anova results for minimum inventory value

Table 12. Anova results for maximum amplitude values

The learning subjects performed better also compared to the same demand pattern results in the computerized games (p-value: 0,039). Regarding total cost values, all of their results were even better than the best performance achieved by the relevant demand pattern group, which is a cost figure of 1770 (See Table 8). Table 13 shows the detailed results of these learning subjects.