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.
| Original language | English |
|---|---|
| Book series | IFAC-PapersOnLine |
| Volume | 52 |
| Issue number | 13 |
| Pages (from-to) | 844-850 |
| DOIs | |
| Publication status | Published - Sep 2019 |
| Externally published | Yes |
| Event | 9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019 - Berlin, Germany Duration: 28. Aug 2019 → 30. Aug 2019 |
Conference
| Conference | 9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019 |
|---|---|
| Country/Territory | Germany |
| City | Berlin |
| Period | 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 |
Bibliographical note
Publisher Copyright:© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Keywords
- Artificial Neural Network
- Clustering
- Machine Learning
- Omni-channel
- Retail supply chain
- Supply Chain Management