A leading macroeconomic indicators’ based framework to automaticallygenerate tactical sales forecasts
以领先的宏观经济指标为基础的框架，自动生成战术销售预测
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Tactical sales forecasting is fundamental to production, transportation and personnel decisions at all levels of asupply chain. Traditional forecasting methods extrapolate historical sales information to predict future sales. Asa result, these methods are not capable of anticipating macroeconomic changes in the business environment thatoften have a significant impact on the demand. To account for these macroeconomic changes, companies adjusteither their statistical forecast manually or rely on an expert forecast. However, both approaches are notoriouslybiased and expensive. This paper investigates the use of leading macroeconomic indicators in the tactical salesforecasting process. A forecasting framework is established that automatically selects the relevant variables andpredicts future sales. Next, the seasonal component is predicted by the seasonal naive method and the long-termtrend using a LASSO regression method with macroeconomic indicators, while keeping the size of the indicator’sset as small as possible. Finally, the accuracy of the proposed framework is evaluated by quantifying the impactof each individual component. The carried out analysis has shown that the proposed framework achieves areduction of 54.5% in mean absolute percentage error when compared to the naive forecasting method.Moreover, compared to the best performing conventional methods, a reduction of 25.6% is achieved in thetactical time window over three different real-life case studies from different geographical areas.

[size=13.7143px]战术销售预测是生产，运输和人事决策在供应链的各个层次的基础。
传统的预测方法是通过历史销售信息来预测未来的销售。
因此，这些方法无法预测经常对需求产生重大影响的商业环境的宏观经济变化。
为了应对这些宏观经济变化，企业要么手工调整统计预测，要么依赖专家的预测。
然而，这两种方法都是出了名的偏颇和昂贵。
本文研究了领先宏观经济指标在战术销售预测过程中的应用。
建立了一个自动选择相关变量并预测未来销售的预测框架。
然后用季节朴素法预测季节分量，用带有宏观经济指标的LASSO回归法预测长期趋势，同时使指标s的设置尽可能小。
最后，通过量化每个组件的影响来评估所提出框架的准确性。
分析结果表明，与单纯预测方法相比，该方法的平均绝对误差降低了54.5%。
此外，与性能最好的传统方法相比，在来自不同地理区域的三个不同的现实案例研究的战术时间窗口中，实现了25.6%的减少。