Economics at your fingertips  

Data mining and fault tolerance in warehousing

Christopher Reining, Omar Bousbiba, Svenja Jungen and Michael Ten Hompel

A chapter in Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment, 2017, pp 215-232 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: This paper surveys the significance of data mining techniques and fault tolerance in future materials flow systems with a focus on planning and decision-making. The fundamental connection between data mining, fault tolerance, and materials flow is illustrated. Contemporary developments in warehousing are assessed to formulate upcoming challenges. In particular, the transition towards distributed systems and the increasing data volume is examined. The significance of taking fault tolerance into account is emphasized. Ultimately, research issues are derived by conflating the previous findings. They comprise a holistic approach towards the integration of data science and fault tolerance techniques into future materials flow systems. Tackling these research issues will help to proactively harmonize the data representation to specific data mining techniques and increase the reliability of such systems.

Keywords: materials flow system; data mining; fault tolerance; survey (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf)

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:

Access Statistics for this chapter

More chapters in Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL) from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().

Page updated 2020-04-02
Handle: RePEc:zbw:hiclch:209310