JDA Demand Decomposition

构造准确的需求基线绝非易事，因为促销、产品的季节性以及竞争对手产品的影响等因素会增加其复杂程度。JDA Demand Decomposition 采用高级统计算法，可将由原因因素（如促销）引发的需求与基本需求历史加以区分，从而为作出更准确的基线预测和提高本地化产品的可用性奠定基础。

该解决方案如何解决您的业务难题？

如果促销的成果与基本需求没有分开，预测便可能引入误导性的季节因素，进而导致经常性的库存过剩和缺货。JDA Demand Decomposition 能够处理频繁多变的促销策略和价格，因而能够将季节性促销期间的季节影响与促销影响分离。同时，该方案还能考虑到其它产品的光环或抢食效应造成的需求高峰和低谷，以及促销前后的需求变化。

该解决方案的优势何在？

• 提高预测准确性并完善需求管理流程
• 减少库存过剩和缺货现象
• 降低底线库存成本
• 增加总收入

为何选择 JDA？

JDA Demand Decomposition 能够将销售历史数据拆分成常规时间序列和市场营销/特定事件两部分，从而更清晰地看到对需求起到推动作用的实际因素。常规时间序列包括趋势和季节性影响，而市场营销/特定事件则包括促销、价格波动、光环和抢食效应、宏观和微观市场情况以及特别事件，从而能够以前所未有的深度了解需求数据。

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