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

Samaneh Beheshti-Kashi; Jürgen Pannek; Aseem Kinra

doi:10.1016/j.ifacol.2019.11.449

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

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

^*Corresponding author for this work

Universität Bremen

Research output: Contribution to journal › Conference article in journal › Research › peer-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.

Original language	English
Book series	IFAC-PapersOnLine
Volume	52
Issue number	13
Pages (from-to)	1721-1726
Number of pages	6
ISSN	2405-8963
DOIs	https://doi.org/10.1016/j.ifacol.2019.11.449
Publication status	Published - Sept 2019
Externally published	Yes
Event	9th IFAC Triannual Conference on Manufacturing Modeling, Management and Control. MIM 2019 - Berlin School of Economics and Law / Lichtenberg Campus, Berlin, Germany Duration: 28 Aug 2019 → 30 Aug 2019 Conference number: 9 https://blog.hwr-berlin.de/mim2019/

Conference

Conference	9th IFAC Triannual Conference on Manufacturing Modeling, Management and Control. MIM 2019
Number	9
Location	Berlin School of Economics and Law / Lichtenberg Campus
Country/Territory	Germany
City	Berlin
Period	28/08/2019 → 30/08/2019
Internet address	https://blog.hwr-berlin.de/mim2019/

Keywords

Decision support systems
Data processing
Production
Identification
Estimation

Access to Document

10.1016/j.ifacol.2019.11.449Licence: Unspecified

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Persistent link

Cite this

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title = "Complementing Decision Support and Forecasting Risk in Supply Chain with Unstructured Data",

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.",

keywords = "Decision support systems, Data processing, Production, Identification, Estimation, Decision support systems, Data processing, Production, Identification, Estimation",

author = "Samaneh Beheshti-Kashi and J{\"u}rgen Pannek and Aseem Kinra",

year = "2019",

month = sep,

doi = "10.1016/j.ifacol.2019.11.449",

language = "English",

volume = "52",

pages = "1721--1726",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

publisher = "Elsevier",

number = "13",

note = "9th IFAC Triannual Conference on Manufacturing Modeling, Management and Control. MIM 2019, MIM2019 ; Conference date: 28-08-2019 Through 30-08-2019",

url = "https://blog.hwr-berlin.de/mim2019/",

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AU - Beheshti-Kashi, Samaneh

AU - Pannek, Jürgen

AU - Kinra, Aseem

N1 - Conference code: 9

PY - 2019/9

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N2 - 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.

AB - 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.

KW - Decision support systems

KW - Data processing

KW - Production

KW - Identification

KW - Estimation

KW - Decision support systems

KW - Data processing

KW - Production

KW - Identification

KW - Estimation

U2 - 10.1016/j.ifacol.2019.11.449

DO - 10.1016/j.ifacol.2019.11.449

M3 - Conference article in journal

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SN - 2405-8963

VL - 52

SP - 1721

EP - 1726

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

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T2 - 9th IFAC Triannual Conference on Manufacturing Modeling, Management and Control. MIM 2019

Y2 - 28 August 2019 through 30 August 2019

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