Complementing Decision Support and Forecasting Risk in Supply Chain with Unstructured Data

Samaneh Beheshti-Kashi*, Jürgen Pannek*, Aseem Kinra*

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

Abstract

Within this paper, we aim to highlight the potential of unstructured data sources such as blogs to complement and improve data triangulation and to support supply chain decision making. To this end, we combine the fields of supply chain management and control theory to provide a proof of concept for the utilization of unstructured data. An in-depth application example for the monitoring of trends on product features in fashion supply chains is developed using blog based textual data. The application example demonstrates predictive validity and forecasting risks of expert opinions found in textual data.
OriginalsprogEngelsk
BogserieIFAC-PapersOnLine
Vol/bind52
Udgave nummer13
Sider (fra-til)1721-1726
Antal sider6
ISSN2405-8963
DOI
StatusUdgivet - sep. 2019
Udgivet eksterntJa
Begivenhed9th IFAC Triannual Conference on Manufacturing Modeling, Management and Control. MIM 2019 - Berlin School of Economics and Law / Lichtenberg Campus, Berlin, Tyskland
Varighed: 28 aug. 201930 aug. 2019
Konferencens nummer: 9
https://blog.hwr-berlin.de/mim2019/

Konference

Konference9th IFAC Triannual Conference on Manufacturing Modeling, Management and Control. MIM 2019
Nummer9
LokationBerlin School of Economics and Law / Lichtenberg Campus
Land/OmrådeTyskland
ByBerlin
Periode28/08/201930/08/2019
Internetadresse

Emneord

  • Decision support systems
  • Data processing
  • Production
  • Identification
  • Estimation

Citationsformater