关键词: 分销供应链, 多级库存优化, 需求预测, 机器学习, 抽样平均逼近

Abstract: This paper is concerned with distribution and inventory replenishment of a global consumer electronics manufacturing supply chain, which consists of a central warehouse, oversea central distribution centers, and retailers. Direct shipments from the central distribution centers to retailers leads to faster response but more risk of obsolete inventory, compared to shipment from central warehouse to the retailers by air transportation. The silent features of the problem such as lifecycle and sales pattern are analyzed in details, then demand forecasting model using real-time sales data, and the inventory replenishment optimization models are developed so as to tradeoff the service level and risks in the supply. Specifically, the demand forecast by machine learning algorithms is proposed, and considering the shipment paths, service levels, supply capacity and inventory levels, a nonlinear programming model to minimize the total quantity in the inventory system is developed. Moreover, the nonlinear programming model is then linearized as a linear programming model and the sample average approximation is employed to solve the problem. Also, a heuristic algorithm is proposed based on a modified base-stock policy. Numerical studies show that the heuristic algorithm can obtain results close to that of mathematical programming with much less computation effort. Compared with the real operations data, the proposed methods can effectively reduce inventory and increase the service level.

Key words: distribution supply chain, multi-echelon inventory optimization, demand forecasting, machine learning, sample average approximation