Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements

Increasing application areas and depths of autonomous systems in logistics provide a new level of challenge for the analysis and design of human–machine interaction concepts. Due to scarce high-skilled personnel in several regions and the objectives of efficiency and sustainability improvement, logistics operators have to pursue technological progress like automation with all means. In order to distinguish between more or less performing human–artificial collaboration systems in logistics ex ante for investment decision purposes, a multi-dimensional conceptual framework is developed. A comprehensive case study regarding automated truck driving in logistics is provided in order to test the concept concerning practical implications. Results include the notion of four distinctive and increasing resistance levels before finally an efficient ‘trusted’ collaboration between human operators and artificial intelligence systems can be achieved. This is important for the design of many automated systems in logistics, among others for driving and piloting professions regarding autonomous driving supervision.

• Record URL:
  https://doi.org/10.1080/13675567.2017.1384451
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13675567
• Supplemental Notes:
  • © 2017 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Klumpp, Matthias
• Publication Date: 2018-5

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 224-242
• Serial:
  • International Journal of Logistics Research and Applications
  • Volume: 21
  • Issue Number: 3
  • Publisher: Taylor & Francis
  • ISSN: 1367-5567
  • EISSN: 1469-848X
  • Serial URL: http://www.tandfonline.com/toc/cjol20/current

Subject/Index Terms

• TRT Terms: Artificial intelligence; Automated vehicle control; Automation; Case studies; Human machine systems; Logistics
• Subject Areas: Freight Transportation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01672307
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 19 2018 9:33AM