The bullwhip effect is mainly caused by three underlying problems: 1) a lack of information, 2) the structure of the supply chain and 3) a lack of collaboration.
The three causes can be identified in an interactive session with the students by discussing the beergame experiences and then be corroborated with insights from practice and the literature.
1) Lack of information
In the beergame no information except for the order amount is perpetuated up the supply chain. Hence, most information about customer demand is quickly lost upstream in the supply chain.
With these characteristics the beergame simulates supply chains with low levels of trust, where only little information is being shared between the parties.
Without actual customer demand data, all forecasting has to rely solely on the incoming orders at each supply chain stage. In reality, in such a situation traditional forecasting methods and stock keeping strategies contribute to creating the bullwhip effect.
2) Supply chain structure
The supply chain structure itself contributes to the bullwhip effect. The longer the lead time, i.e. the longer it takes for an order to travel upstream and the subsequent delivery to travel downstream, the more aggravated the bullwhip effect is likely to be.
With traditional ordering, the point in time where an order is typically placed (the order point) is usually calculated by multiplying the forecasted demand with the lead time plus the safety stock amount, so that an order is placed so far in advance as to ensure service level during the time until the delivery is expected to arrive.
Hence, the longer the lead time is, the more pronounced an order will be as an reaction to an increase in forecasted demand (especially in conjunction with updating the safety stock levels, see above), which again contributes to the bullwhip effect.
3) Local optimisation
Local optimisation, in terms of local forecasting and individual cost optimisation, and a lack of cooperation are at the heart of the bullwhip problem.
A good example for local optimisation is the batch order phenomenon. In practice, ordering entails fix cost, e.g. ordering in full truck loads is cheaper then ordering smaller amounts. Furthermore, many suppliers offer volume discounts when ordering larger amounts.
Hence, there is a certain incentive for individual players to hold back orders and only place aggregate orders. This behaviour however aggravates the problem of demand forecasting, because very little information about actual demand is transported in such batch orders.
And batch ordering, of course, contributes directly to the bullwhip effect by unnecessarily inflating the orders.