Volume 9, 2021: Issue 1

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Title:

Artificial intelligence for last-mile logistics - Procedures and architecture

Author(s):

André Rosendorff, University of Applied Sciences for Telecommunications (HfTL), Germany

Alexander Hodes, University of Applied Sciences for Telecommunications Leipzig (HfTL), Germany

Benjamin Fabian, Technical University of Applied Sciences Wildau (TH Wildau), Germany

Abstract:

Artificial Intelligence (AI) is becoming increasingly important in many industries due to its diverse areas of application and potential. In logistics in particular, increasing customer demands and the growth in shipment volumes are leading to difficulties in forecasting delivery times, especially for the last mile. This paper explores the potential of using AI to improve delivery forecasting. For this purpose, a structured theoretical solution approach and a method for improving delivery forecasting using AI are presented. In doing so, the important phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, a standard process for data mining, are adopted and discussed in detail to illustrate the complexity and importance of each task such as data preparation or evaluation. Subsequently, by embedding the described solution into an overall system architecture for information systems, ideas for the integration of the solution into the complexity of real information systems for logistics are given.

Keywords:

Supply chain management, logistics, artificial intelligence, machine learning, business intelligence.

DOI:

https://doi.org/10.36965/OJAKM.2021.9(1)46-61

Type:

Research paper

Journal:

The Online Journal of Applied Knowledge Management (OJAKM), ISSN: 2325-4688

Publisher:

International Institute for Applied Knowledge Management (IIAKM)

Received:

1 March 2021

Revised:

27 May 2021; 7 July 2021

Accepted:

19 October 2021

Accepting Editor:

Meir Russ

Pages:

46-61