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.

How to Minimize the Bullwhip Effect

The first step in minimizing the bullwhip effect is understanding customers’ demand planning and inventory consumption. Lack of demand visibility can be addressed by providing all key players in the supply chain with access to point of sale (POS) data. Suppliers and customers must then collaborate to improve  the quality and frequency of communication throughout the supply chain.

They also can share information through an arrangement such as vendor-managed inventory (VMI). Eliminating practices that cause demand spikes, such as order batching, also can help. The higher order cost associated with smaller or more frequent orders can be offset with Electronic Data Interchange (EDI) and computer aided ordering (CAO).

Pricing strategies and policies can also help reduce the bullwhip effect. Eliminating incentives that cause customers to delay orders, such as volume transportation discounts, and addressing the causes of order cancellations or reductions can help create smoother ordering patterns. Offering products at stable and fair prices can prevent buying surges triggered by temporary promotional discounts. Special purchase contracts can be implemented to encourage ordering at regular intervals to better synchronize delivery and purchase.