An automated packaging planning approach using machine learning
| Autor: | Dino Knoll, Daniel Neumeier, Marco Prüglmeier, Gunther Reinhart |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Mass customization 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences ddc 020901 industrial engineering & automation Manufacturing General Earth and Planetary Sciences Planning approach Product (category theory) Artificial intelligence business computer 0105 earth and related environmental sciences General Environmental Science |
| Zdroj: | Procedia CIRP. 81:576-581 |
| ISSN: | 2212-8271 |
| DOI: | 10.1016/j.procir.2019.03.158 |
| Popis: | The manufacturing industry is highly affected by trends of mass customization and increasing dynamics of product life-cycles which result in a large set of part variants. Thus, the required effort for logistics planning and, in particular, for packaging planning is increasing. This paper proposes an approach to automate the assignment of packaging for an individual part based on its characteristics using machine learning. We use the historical data of product parts and their packaging specifications to train our two-step machine learning model. Consequently, the model is able to propose a packaging with an accuracy of 84% in comparison with real-world data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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