Demand forecasting is essential for businesses to effectively plan and manage their supply chain (SC) operations. Judgmentally adjusting statistically derived forecasts is common industry practice and some evidence suggests that this process can improve forecast accuracy; particularly in the presence of retail promotions. Research pertaining to the bullwhip effect (BWE) suggests that accurate forecasting can improve SC stability by reducing demand distortion and order/inventory amplification. However, this stream of research has overlooked that forecasts are often adjusted by demand planners and very little is known about the impact that it has on the SC holistically. In this thesis, an experiment is designed to assess the effects that judgmental forecast adjustments have on upstream SC operations including production and inventory replenishment decisions. An empirical investigation is also undertaken at Coca-Cola Amatil, Australia, consisting of observations of operations and semi-structured interviews with management. Archival data is collected and utilised to generate experiment parameters and enhance the external validity of findings. The results from 240 participants indicate that forecast adjustments can improve SC performance, but their efficacy is dependent on the level of information sharing. When adjusted forecasts are only available to the retailer, SC costs increase while service levels decrease. However, costs reduce and service levels increase when adjusted forecasts are transparently shared across the SC. The most significant benefits are realised in the presence of promotions. In contrast to the demand/inventory variability amplification often observed in SCs, smoothing behaviour and improved SC stability is found to occur when adjusted forecasts are shared. The findings of this study narrow the current gap between judgmental forecasting and BWE streams of research and provide theoretical and managerial insights for more informed SC decision making.