Forecasting Supply Chain Demand by Clustering Customers

被引：22

作者：

Murray, Paul W. ^{[1
]}

Agard, Bruno ^{[1
]}

Barajas, Marco A. ^{[2
]}

机构：

[1] Ecole Polytech, Montreal, PQ H3C 3A7, Canada

[2] Mem Univ Newfoundland, Fisheries & Marine Inst, St John, NF, Canada

来源：

IFAC PAPERSONLINE | 2015年 / 48卷 / 03期

关键词：

Data Models; Exogenous variables; Forecasting; Segmentation; Vendor Managed Inventory; DECISION-SUPPORT-SYSTEM; NETWORK; IMPACT;

D O I：

10.1016/j.ifacol.2015.06.353

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Demand forecasts are essential for managing supply chain activities but arc difficult to create when collaborative information is absent. Many traditional and advanced forecasting tools are available. but applying them to a large number of customers is not manageable. In our research, we use data mining techniques to identify segments of customers with similar demand behaviors. Historical usage is used to cluster customers with similar demands. Once customer segments are identified a manageable number of forecasting models can be built to represent the customers within the segments. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：1834 / 1839

页数：6

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