Abstract:

The demand for after-sales parts in large manufacturing enterprises is characterized by sparse distribution and high volatility， with high uncertainty in both demand frequency and demand quantity， and the demand sequences present typical intermittent characteristic. However， in actual operation and maintenance， the demand for parts fluctuates greatly in terms of frequency and quantity， resulting in various demand patterns. The existing intermittent demand prediction mainly uses single model or static combination of fixed prediction models， which is difficult to fully explore the evolution laws of demand sequences under different demand patterns， and the prediction accuracy and stability are hard to guarantee. To solve the above problems， an intermittent demand forecasting method based on adaptive matching of demand patterns was proposed， in which demand patterns were adaptively matched， and the prediction effect of intermittent sequences was improved by dynamically identifying and matching demand patterns. The method included two stages. In the model training stage， firstly， according to the intermittent characteristics of the historical demand data of parts， it was divided into demand sequences and interval sequences， and the two types of sequences were clustered separately to capture the different demand and interval patterns corresponding to each type of sequence. Secondly， a prediction model library containing statistical analysis models， shallow machine learning models， and deep learning models was established， and the prediction effects of different models on each demand pattern were tested to identify and mark the optimal prediction model for each type of demand pattern. In the prediction stage， the sequence to be predicted was divided into demand sequences and interval sequences， the demand pattern was identified and matched with the optimal prediction model， and the predicted values of demand and interval were combined to form the final prediction result. The experimental validation was carried out on the intermittent parts demand datasets of the American Automobile Company and the Royal Air Force， and the results showed that the proposed method could be applied to the historical data of parts with different demand patterns， and effectively improved the prediction accuracy by adaptively matching the demand pattern and the optimal prediction model.

Key words: intermittent sequence, demand forecasting, time series forecasting, demand pattern recognition, accessory management

摘要：

大型制造企业售后配件的需求分布稀疏、波动性大，需求频率和需求数量不确定性较高，序列呈现出典型的间歇性特点。在实际运维中，配件需求在频率和数量方面存在较大波动，从而产生变化多样的需求模式，而现有间歇性需求预测主要采用单一或静态组合的固定预测模型，难以充分挖掘不同需求模式下需求序列的演化规律，预测精度和稳定性均难以保证。为解决上述问题，提出一种基于需求模式自适应匹配的间歇性需求预测方法，通过动态识别和匹配需求模式提升间歇性序列预测效果。该方法包括两个阶段：在模型训练阶段，首先，根据配件历史需求数据的间歇性特征，将它划分为需求量序列和间隔量序列，并对两类序列分别进行聚类，以捕获每类序列对应的不同需求和间隔模式；其次，建立包含统计学分析模型、浅层机器学习模型及深度学习模型的预测模型库，测试各模型对每种需求模式的预测效果，识别并标记每类需求模式的最优预测模型。在预测阶段，将待预测序列划分为需求量序列和间隔量序列，确定需求模式并匹配最佳预测模型，进而将需求量和间隔量的预测值合并，形成最终预测结果。在美国汽车公司和英国空军的间歇性配件需求数据集上的实验结果表明，所提方法可适用于不同需求模式的配件历史数据，通过自适应匹配需求模式和最优预测模型，有效提升了预测精度。

关键词: 间歇性序列, 需求预测, 时间序列预测, 需求模式识别, 配件管理