Predicting Future Inbound Logistics Processes Using Machine Learning
| Autor: | Gunther Reinhart, Dino Knoll, Marco Prüglmeier |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
0209 industrial biotechnology
Humanitarian Logistics Supply chain 02 engineering and technology Logistics Machine learning computer.software_genre Modelling 020901 industrial engineering & automation Predictive Model Artificial Intelligence Manufacturing 0502 economics and business General Environmental Science business.industry 05 social sciences Variance (accounting) Purchasing ddc Product (business) Algorithm Production planning Knowledge Management New product development Production Planning General Earth and Planetary Sciences Artificial intelligence business computer 050203 business & management |
| Zdroj: | Procedia CIRP. :145-150 |
| ISSN: | 2212-8271 |
| DOI: | 10.1016/j.procir.2016.07.078 |
| Popis: | Manufacturing industry is highly affected by trends of globalization and increasing dynamics of product life-cycles which results in global supply chain networks. For inbound logistics, a high variance of parts from different suppliers and locations needs to be delivered to the assembly line. Planning these inbound logistics processes depends on frequently changing information of product development, assembly line planning and purchasing. Currently, a high amount of time is spent for gathering information during planning and existing knowledge from previous planning processes is scarcely used for future planning. Therefore, this paper presents an approach for predictive inbound logistics planning. Using machine learning, generic knowledge of logistics processes can be extracted and used to predict future scenarios. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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