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(PDF) Artificial intelligence for last-mile logistics - Procedures and architecture
Source: researchgate.net
Article summary:
1. This paper explores the potential of using AI to improve delivery forecasting in last-mile logistics.
2. The Cross-Industry Standard Process for Data Mining (CRISP-DM) framework is adopted and discussed in detail to illustrate the complexity and importance of each task such as data preparation or evaluation.
3. Ideas for the integration of the solution into the complexity of real information systems for logistics are given.
Article analysis:
The article is generally reliable and trustworthy, as it provides a detailed overview of how artificial intelligence can be used to improve delivery forecasting in last-mile logistics. The authors provide a structured theoretical solution approach and a method for improving delivery forecasting using AI, which is based on the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Furthermore, they discuss ideas for integrating this solution into real information systems for logistics, which adds further credibility to their claims.
However, there are some potential biases that should be noted when considering this article. For example, the authors do not explore any counterarguments or present both sides equally when discussing their proposed solutions. Additionally, there is no mention of possible risks associated with using AI in last-mile logistics, which could lead readers to believe that it is risk free when this may not be the case. Finally, there is no evidence provided to support some of the claims made by the authors, which could lead readers to question their validity.