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.

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Recent software developments in system modelling via transfer function analysis now enables a much broader understanding of the dynamics of aggregate planning to be gained. In particular it opens up the possibility of exploiting filter theory as a focal point during algorithm design. This is particularly attractive in view of the fact that we have established, via transfer function models, that there is commonality between HMMS and the order-up-to replenishment rules used extensively within both local and global supply chains. Filter theory allows us to relate these dynamics directly to present day production planning strategy as observed in much industrial practice. It covers the spectrum of production strategies recently identified as preferred industrial practice. These strategies range from "level scheduling" (i.e. lean production) right through to "pure chase" (i.e. agile manufacture) with appropriate simple algorithmic control support via APIOBPCS software.

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