Supply Chain Risk Management: an Interactive Simulation Model in a Big Data Context
| Autor: | Jose A. Puppim de Oliveira, Maribel Yasmina Santos, Guilherme Pereira, António Amaro Costa Vieira, Luís Dias |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Rok vydání: | 2020 |
| Předmět: |
Big Data
Supply chain risk management 0209 industrial biotechnology Decision support system Industry 4.0 Computer science Supply chain Big data Context (language use) 02 engineering and technology computer.software_genre Industrial and Manufacturing Engineering 020901 industrial engineering & automation 0203 mechanical engineering Artificial Intelligence Supply Chain Science & Technology business.industry Virtualization 020303 mechanical engineering & transports Risk analysis (engineering) Scenario testing business computer Simulation |
| Zdroj: | Procedia Manufacturing. 42:140-145 |
| ISSN: | 2351-9789 |
| DOI: | 10.1016/j.promfg.2020.02.035 |
| Popis: | Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing. Aligned with the Industry 4.0 research and innovation agenda, a Decision Support System is currently being developed with the purpose of enhancing decision-making in risk scenarios at Supply Chains. It is comprised of a Big Data Warehouse and a simulation model. The former stores and provides integrated real data to the simulation model, which models the respective materials and information flows. Thus, the purpose of this paper is to present such tool being used to test scenarios that, contrarily to the traditional simulation approach, incorporate disruptions in an interactive way, meaning that users may fire such events at any desired simulation time and with different parameters. Thus, the tool is used to assess the impact of disruptions in the performance of the system. The conclusions of this paper highlight the benefits that can be obtained with the proposed interactive approach, as it allows a virtualization of the real system to be obtained and, at the same time, use the simulation model to assess what would be the impact of certain disruptions. This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|