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In this game, you act as a retailer within a beer supply chain.
Each week, customers place orders for beer. Based on this demand, you must decide how many units to order from your supplier.
You only have access to local information, such as your current inventory, outstanding backorders, and incoming deliveries.
You do not know future customer demand, and all orders and deliveries are subject to delays.
For example the units you ordered from your supplier (the Wholesaler) will not always arrive immediately the week after,
but could arrive in 2-3 weeks instead or even partially distributed over several weeks.
Your decisions therefore affect the system with a delay, and their full impact may only become visible several weeks later.
Your objective is to minimize your total costs over the course of the game. These consist of:
Total Cost = $200 + (Inventory x $0.50) + (Backlog x $1)
Example:
You have 300 beer units in your inventory and a backlog of 100 beer units.
$200 + (300 x $0.50) + (100 x $1) = $450 Total Costs
Balancing these two cost drivers by ordering not too much or too little is the central challenge of the game.
The game illustrates how coordination problems and delayed feedback can lead to inefficiencies in supply chains.
The dashboard summarizes your start-of-week situation using only the local information available to you as the retailer. It does not reveal future demand or the internal states of the other supply chain members.
Orders placed by your customers last week. This is the demand you need to respond to as quickly and reliably as possible.
Units you shipped to your customers last week. If inventory was too low, you may not have been able to fulfill all demand immediately.
Units received from the wholesaler last week. These deliveries increase your available inventory for future customer demand.
Orders you already placed but have not received yet. They are still moving through the supply chain and may arrive with delay.
Beer units currently in stock. This inventory is used to fulfill customer orders, but holding too much stock also creates costs.
Customer orders you could not fulfill because inventory was not sufficient. These units remain pending until stock becomes available.
Last week's expenses from inventory and backlog. These values show how your earlier decisions affected system performance.
The quantity you choose to order from your supplier this week. This decision shapes future inventory, service level, and total cost.
If not, please review the instructions once more before starting the game.
Thank you for taking part in this study.
We would now like to briefly explain the specific objectives of the research project.
This study investigates how people make decisions when they receive different levels of support from an AI system. We are particularly interested in whether and how various forms of assistance influence decision performance. Depending on the condition, participants either receive no support, a brief AI-generated summary of the current situation, or a more direct AI recommendation. Each participant experiences only one of these versions, but everyone performs the same underlying task.
Your participation helps us better understand how humans interact with AI in complex decision-making contexts. Thank you again for your contribution.
Please click the link below to complete the study:
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