The Bullwhip Effect, defined as the upstream amplification of demand variability, has received considerable interest in the field of Supply Chain Management in recent years. This phenomenon has been detected in various industries and sectors, and manifests itself with multiple inefficiencies and higher costs at upper echelons in the supply chain. As a result, this topic is of great importance for academics and practitioners alike. One root cause of the Bullwhip Effect is the need for firms to forecast demand in order to place their orders and base their inventory decisions. Despite the multitude of studies that have emerged tackling
this issue, the impact of the quality of forecasts on the Bullwhip Effect has received limited coverage in the literature. Modelling and forecasting the demand can be challenging, resulting in increased forecast uncertainty that contributes to the Bullwhip Effect.

This thesis aims at bridging this gap by investigating three main research questions:
(i) How can supply chain forecast uncertainty be captured at a firm level? (ii) How can the upstream propagation of forecast uncertainty from the Bullwhip Effect be measured? and
(iii) What customer demand information sharing strategy is the most effective in reducing upstream the forecast uncertainty and inventory costs resulting from the Bullwhip Effect?
We first propose an empirical approximation for measuring forecast uncertainty at a local level, which we show to outperform commonly used approximations for inventory purposes. We then propose a novel metric to capture the propagation of forecast uncertainty at higher echelons in the Supply Chain, which correlates strongly with upstream inventory costs, more so than the conventional Bullwhip measure. Using this, we evaluate alternative
information sharing strategies that have appeared in the literature, but have not been assessed comparatively. We find that relying solely on point of sales data results in the best forecasting accuracy and inventory cost performance for upstream members. The findings obtained are actionable and simple to implement, making them of great use and relevance
for supply chain practitioners and managers.