A data mining approach to forecast behavior

Nihat Altintas () and Michael Trick ()

Annals of Operations Research, 2014, vol. 216, issue 1, 3-22

Abstract: This study presents a data mining analysis of forecasting patterns in a supply chain. Multiple customers who are auto manufacturers order from a large auto parts supplier. The auto manufacturers provide forecasts for future orders and update them before the due date. The supplier uses these forecasts to plan production in advance. The accuracy of the forecasts varies from customer to customer. We provide a framework to analyze the forecast performance of the customers. There are different complexities in forecasts that are captured from our data set. Daily flow analysis helps to transform data and obtain accuracy ratios of forecasts. Customers are then classified based on their forecast performances. We demonstrate the application of some recent developments in clustering and pattern recognition analysis to performance analysis of customers. Copyright Springer Science+Business Media New York 2014

Keywords: Data mining; Automotive industry; Forecasting (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10479-012-1236-9 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:216:y:2014:i:1:p:3-22:10.1007/s10479-012-1236-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-012-1236-9

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().