Intermittent demand, inventory obsolescence, and temporal aggregation forecasts
研究了在库存过时风险下如何改进间歇性需求预测，评估了多种时间聚合方法，并提出了两种新的组合预测方法，通过模拟和真实数据验证了其有效性。
Forecasting for intermittent demand is considered a difficult task and becomes even more challenging in the presence of obsolescence. Traditionally the problem has been dealt with modifications in the conventional parametric methods such as Croston. However, these methods are generally applied at the observed frequency, ignoring any additional information, such as trend that becomes prominent at higher levels of aggregation. We evaluate established Temporal Aggregation (TA) methods: ADIDA, Forecast Combination, and Temporal Hierarchies in the said context. We further employ restricted least-squares estimation and propose two new combination approaches tailored to decreasing demand scenarios. Finally, we test our propositions on both simulated and real datasets. Our empirical findings support the use of variable forecast combination weights to improve TA’s performance in intermittent demand items with a risk of obsolescence.