基于TCN和LightGBM的供应商主动维护客户构成模型

A Customer Composition Model for Supplier Proactive Maintenance Based on TCN and LightGBM

本文介绍了一种基于TCN和LightGBM的供应商主动维护客户构成模型,旨在解决传统的被动响应式服务无法满足当今竞争激烈市场需求的问题。该模型结合了时间序列分析和机器学习技术,使用TCN捕捉客户订单数据的时间依赖性,同时利用LightGBM学习非时序的复杂关系特征,以快速捕捉市场变化并提高服务体验,让客户既不断货,也不积压,实现“响应性服务”向“感知响应性主动服务”转变。本文的贡献在于提出了一种高效的供应商维护客户构成模型,具有较强的适应性和预测准确性。该模型的应用可以有效地提高供应商的竞争力,满足市场的需求。

This article introduces a customer composition model for supplier proactive maintenance based on TCN and LightGBM, aiming to solve the problem that traditional passive responsive services cannot meet the demands of today’s competitive market. The model combines time series analysis and machine learning techniques, using TCN to capture the time dependency of customer order data and utilizing LightGBM to learn non-time series complex relationship features, in order to quickly capture market changes and improve service experience, allowing customers to have continuous supply without inventory pile-up, achieving the transformation from “responsive service” to “per-ceptive responsive proactive service”. The contribution of this article is to propose an efficient sup-plier maintenance customer composition model, which has strong adaptability and prediction ac-curacy. The application of this model can effectively improve the competitiveness of suppliers and meet market demands.