Most organizations adjust their statistical forecasts (e.g. on sales) manually. Forecasting Support Systems (FSS) enable the related process of automated forecast generation and manual adjustments. As the FSS user interface connects user and statistical algorithm, it is an obvious lever for facilitating beneficial adjustments whilst discouraging harmful adjustments. This paper reviews and organizes the literature on judgemental forecasting, forecast adjustments, and FSS design. I argue that algorithmic transparency may be a key factor towards better, integrative forecasting and test this assertion with three FSS designs that vary in their degrees of transparency based on time series decomposition. I find transparency to reduce the variance and amount of harmful forecast adjustments. Letting users adjust the algorithm's transparent components themselves, however, leads to widely varied and overall most detrimental adjustments. Responses indicate a risk of overwhelming users with algorithmic transparency without adequate training. Accordingly, self-reported satisfaction is highest with a non-transparent FSS.

翻译：大多数组织会手动调整其统计预测（如销售预测）。预测支持系统（FSS）实现了自动预测生成与手动调整的相关流程。由于FSS用户界面连接了用户与统计算法，它显然是一个促进有益调整、同时抑制有害调整的有效杠杆。本文回顾并梳理了关于判断预测、预测调整及FSS设计的文献。我认为算法透明度可能是实现更优整合预测的关键因素，并通过三种基于时间序列分解、透明度不同的FSS设计验证了这一主张。研究发现透明度能够降低有害预测调整的方差和数量。然而，允许用户直接调整算法的透明组件会导致调整结果差异巨大且总体危害性最高。用户反馈表明，若缺乏充分培训，算法透明度存在令用户不堪重负的风险。相应地，非透明FSS获得了最高的用户自报告满意度。