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

by Klumpp, Matthias (2018)

Abstract

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.

Key Passage

Between ever-increasing expectations and requirements and real human competence levels a ‘gap’is developing as required training for humans has for each and every person to start anew – learningcannot be automated for human workers: Longer education and training programmes are needed in order to arrive at required higher competence levels for a modern-day logistics and business environment. This constitutes a knowledge accumulation gap (grey field in Figure 2) that arises due to thefact that humans are not able to accumulate knowledge over generations – as opposed to machinesand computers which are able to do so. (p.226)

Keywords

Artificial Intelligence, Machine Learning, Worker Replacement, Worker Education, Logistics, Human Machine Interaction

Themes

Employment, Automation

Links to Reference

• https://doi.org/10.1080/13675567.2017.1384451
• http://dx.doi.org/10.1080/13675567.2017.1384451
• https://www.tandfonline.com/doi/abs/10.1080/13675567.2017.1384451?casa_token=N64G9HNQjhwAAAAA:EoMPfhyviGNTpGzOFb3l5evfUkRl9mC49b1hnhh99c-R1mKEaF8CFLAVj0FOyqAQQADfl8qu1NcrpQ
• https://www.tandfonline.com/doi/pdf/10.1080/13675567.2017.1384451?casa_token=UjHyHEUZm3UAAAAA:8YNexwxnTRGHnGC4T0AV-iU5RcT8QTfeJ_xREw-LwIkF_Z2VE0pXDlKDsIX5kw8DqrWcHO9oZIcBDA

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