Abstract(2) Logistics Systems Dynamics Group, Cardiff Business School, Cardiff University, Aberconway Building, Colum Drive, Cardiff -UK, CFIO 3ED. Wales, UK DisneySM@Cardiff.ac.uk An important contributory factor to the bullwhip effect (i.e. the variance amplification of order quantities observed in supply chains) is the replenishment rule implemented by supply chain members. We analyse the bullwhip effect induced by the use of different forecasting methods in order-up-to replenishment policies. We not only quantifY the variance amplification, but we prove that the bullwhip effect is guaranteed irrespective of the forecasting method used. Avoiding the bullwhip effect consequently means avoiding the order-up-to policies. In a second part of the paper we introduce a general decision rule that avoids variance amplification and succeeds in generating smooth ordering patterns, even when demand has to be forecasted. The methodology is based on control systems engineering and allows us to gain important insights in the dynamic behaviour of replenishment rules.

This paper examines the beneficial impact of information sharing in multi-echelon supply chains. We compare a traditional supply chain, in which only the first stage in the chain observes end consumer demand and upstream stages have to base their forecasts on incoming orders, with an information enriched supply chain where customer demand data (e.g. EPOS data) is shared throughout the chain. Two types of replenishment rules are analysed: orderup-to policies and smoothing policies (policies used to reduce or dampen variability in the demand). For the class of order-up-to policies, we will show that information sharing helps to reduce the bullwhip effect (variance amplification of ordering quantities in supply chains) significantly, especially at higher levels in the chain. However, the bullwhip problem is not completely eliminated and it still increases as one moves up the chain. For the smoothing policies, we show that information sharing is necessary to reduce order variance at higher levels of the chain. The methodology is based on control systems engineering and allows us to gain valuable insights into the dynamic behaviour of supply chain replenishment rules. We also introduce a control engineering based measure to quantify the variance amplification (bullwhip) or variance reduction.

The paper compares the expected performance of a Vendor Managed Inventory (VMI) supply chain with a traditional "serially-linked" supply chain. The emphasis of this investigation is the impact these two alternative structures have on the "Bullwhip Effect" generated in the supply chain. We pay particular attention to the manufacturer's production ordering activities via a simulation model based on difference equations. VMI is thereby shown to be significantly better at responding to volatile changes in demand such as those due to discounted ordering or price variations. Inventory recovery as measured by the Integral of Time * Absolute Error (ITAE) performance metric is also substantially improved via VMI. Noise bandwidth, that is a measure of capacity requirements, is then used to estimate the order rate variance in response to random customer demand. Finally, the paper simulates the VMI and traditional supply chain response to a representative retail sales pattern. The results are in accordance with "rich picture" performance predictions made from deterministic inputs.

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