Towards a Predictive Approach for Omni-channel Retailing Supply Chains

Marina Meireles Pereira; Enzo Morosini Frazzon

doi:10.1016/j.ifacol.2019.11.235

Towards a Predictive Approach for Omni-channel Retailing Supply Chains

Marina Meireles Pereira, Enzo Morosini Frazzon

Federal University of Santa Catarina

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › peer review

Abstrakt

The adoption of omni-channel strategy has changed the relation between retailers and customers and brought more complexity to the retailing supply chains. To address the increasing complexity, it is necessary to adopt innovative approaches based on information technologies and intelligent decision methods. The challenges to retailers are improving the accuracy of offline and online channels demand forecasting, better managing offline and online customer's needs, thus reducing the uncertainties of the omni-channel retailing supply chain. In this context, this research paper aims to propose a predictive approach for omni-channel retailing supply chain combining clustering with artificial neural network to handle demand uncertainty.

Originalsprog	Engelsk
Bogserie	IFAC-PapersOnLine
Vol/bind	52
Udgave nummer	13
Sider (fra-til)	844-850
DOI	https://doi.org/10.1016/j.ifacol.2019.11.235
Status	Udgivet - sep. 2019
Udgivet eksternt	Ja
Begivenhed	9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019 - Berlin, Tyskland Varighed: 28. aug. 2019 → 30. aug. 2019

Konference

Konference	9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019
Land/Område	Tyskland
By	Berlin
Periode	28/08/2019 → 30/08/2019
Sponsor	et al., IFAC TC 1.3. Discrete Event and Hybrid Systems, IFAC TC 3.2. Computational Intelligence in Control, IFAC TC 4.3. Robotics, IFAC TC 5.1. Manufacturing Plant Control, International Federation of Automatic Control (IFAC) - Technical Committee on Manufacturing Modelling for Management and Control, TC 5.2

Bibliografisk note

Funding Information:
This study was financed in part by the Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - Brasil (CAPES) - Finance Code 001.

Funding Information:
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Publisher Copyright:
© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Dokumenter og links

10.1016/j.ifacol.2019.11.235

Citationsformater

@inproceedings{501ab644c07b4a2f8bcade8db5c7540f,

title = "Towards a Predictive Approach for Omni-channel Retailing Supply Chains",

abstract = "The adoption of omni-channel strategy has changed the relation between retailers and customers and brought more complexity to the retailing supply chains. To address the increasing complexity, it is necessary to adopt innovative approaches based on information technologies and intelligent decision methods. The challenges to retailers are improving the accuracy of offline and online channels demand forecasting, better managing offline and online customer's needs, thus reducing the uncertainties of the omni-channel retailing supply chain. In this context, this research paper aims to propose a predictive approach for omni-channel retailing supply chain combining clustering with artificial neural network to handle demand uncertainty.",

keywords = "Artificial Neural Network, Clustering, Machine Learning, Omni-channel, Retail supply chain, Supply Chain Management",

author = "Pereira, {Marina Meireles} and Frazzon, {Enzo Morosini}",

note = "Publisher Copyright: {\textcopyright} 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.; 9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019 ; Conference date: 28-08-2019 Through 30-08-2019",

year = "2019",

month = sep,

doi = "10.1016/j.ifacol.2019.11.235",

language = "English",

volume = "52",

pages = "844--850",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

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TY - GEN

T1 - Towards a Predictive Approach for Omni-channel Retailing Supply Chains

AU - Pereira, Marina Meireles

AU - Frazzon, Enzo Morosini

PY - 2019/9

Y1 - 2019/9

N2 - The adoption of omni-channel strategy has changed the relation between retailers and customers and brought more complexity to the retailing supply chains. To address the increasing complexity, it is necessary to adopt innovative approaches based on information technologies and intelligent decision methods. The challenges to retailers are improving the accuracy of offline and online channels demand forecasting, better managing offline and online customer's needs, thus reducing the uncertainties of the omni-channel retailing supply chain. In this context, this research paper aims to propose a predictive approach for omni-channel retailing supply chain combining clustering with artificial neural network to handle demand uncertainty.

AB - The adoption of omni-channel strategy has changed the relation between retailers and customers and brought more complexity to the retailing supply chains. To address the increasing complexity, it is necessary to adopt innovative approaches based on information technologies and intelligent decision methods. The challenges to retailers are improving the accuracy of offline and online channels demand forecasting, better managing offline and online customer's needs, thus reducing the uncertainties of the omni-channel retailing supply chain. In this context, this research paper aims to propose a predictive approach for omni-channel retailing supply chain combining clustering with artificial neural network to handle demand uncertainty.

KW - Artificial Neural Network

KW - Clustering

KW - Machine Learning

KW - Omni-channel

KW - Retail supply chain

KW - Supply Chain Management

U2 - 10.1016/j.ifacol.2019.11.235

DO - 10.1016/j.ifacol.2019.11.235

M3 - Conference article

AN - SCOPUS:85078927171

VL - 52

SP - 844

EP - 850

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 13

T2 - 9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019

Y2 - 28 August 2019 through 30 August 2019

ER -

Towards a Predictive Approach for Omni-channel Retailing Supply Chains

Abstrakt

Konference

Bibliografisk note

Dokumenter og links

Fingeraftryk

Citationsformater