Assessing stakeholder perspectives on the explainability of AI solutions for smart production planning with just-in-time logistics

  • In recent years, intelligent systems have increased their capabilities greatly increasing their practical applicability. However, for the foreseeable future, such AI-powered agents will not act autonomously but assist a human that will ultimately responsible. Here, the explainability of agents’ suggestions becomes paramount to provide trust and acceptance by their human co-workers. For the field of stochastic/evolutionary optimization, it has not yet been investigated what levels of explainability real human stakeholders without deep technical knowledge of these systems actually request. In this article, we report an exploratory case study where we questioned a group of production planners (n = 11) about their needs for AI assistance and what types of explanations they would require to integrate AI into their day-to-day work-flow and still feel comfortable with the cooperation. While five participants expect their individual position to be threatened by these systems in the mid-term,In recent years, intelligent systems have increased their capabilities greatly increasing their practical applicability. However, for the foreseeable future, such AI-powered agents will not act autonomously but assist a human that will ultimately responsible. Here, the explainability of agents’ suggestions becomes paramount to provide trust and acceptance by their human co-workers. For the field of stochastic/evolutionary optimization, it has not yet been investigated what levels of explainability real human stakeholders without deep technical knowledge of these systems actually request. In this article, we report an exploratory case study where we questioned a group of production planners (n = 11) about their needs for AI assistance and what types of explanations they would require to integrate AI into their day-to-day work-flow and still feel comfortable with the cooperation. While five participants expect their individual position to be threatened by these systems in the mid-term, all participants agree that AI is beneficial for safeguarding the location against competitors or migration. We find that AI-based assistance is requested to a large degree across all age groups and that stakeholders greatly request explainability of the agent’s recommendations. From this real-world empirical evidence it becomes evident that implementing explainable optimization in production planning is a crucial next step towards Industry 5.0.show moreshow less

Download full text files

Export metadata

Statistics

Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Michael HeiderORCiDGND, Marcus AlbrechtGND, Johannes SchilpGND, Jörg HähnerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1282257
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/128225
ISSN:0957-4174OPAC
Parent Title (English):Expert Systems with Applications
Publisher:Elsevier BV
Place of publication:Amsterdam
Type:Article
Language:English
Year of first Publication:2026
Publishing Institution:Universität Augsburg
Release Date:2026/02/18
Volume:314
First Page:131352
DOI:https://doi.org/10.1016/j.eswa.2026.131352
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Ingenieurinformatik mit Schwerpunkt Produktionsinformatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung