Characterizing Digital Dashboards for Smart Production LogisticsShow others and affiliations
2022 (English)In: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action: IFIP WG 5.7 International Conference, APMS 2022, Gyeongju, South Korea, September 25–29, 2022, Proceedings, Part II / [ed] Duck Young Kim; Gregor von Cieminski; David Romero, Cham: Springer Nature Switzerland AG , 2022, p. 521-528Conference paper, Published paper (Refereed)
Abstract [en]
Developing digital dashboards (DD) that support staff in monitoring, identifying anomalies, and facilitating corrective actions are decisive for achieving the benefits of Smart Production Logistics (SPL). However, existing literature about SPL has not sufficiently investigated the characteristics of DD allowing staff to enhance operational performance. This conceptual study identifies the characteristics of DD in SPL for enhancing operational performance of material handling. The study presents preliminary findings from an ongoing laboratory development, and identifies six characteristics of DD. These include monitoring, analysis, prediction, identification, recommendation, and control. The study discusses the implications of these characteristics when applied to energy consumption, makespan, on-time delivery, and status for material handling. The study proposes the prototype of a DD in a laboratory environment involving Autonomous Mobile Robots.
Place, publisher, year, edition, pages
Cham: Springer Nature Switzerland AG , 2022. p. 521-528
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X ; 664
Keywords [en]
Energy utilization, Mobile robots, Navigation, Autonomous Mobile Robot, Conceptual study, Corrective actions, Digital dashboard, Laboratory development, Material handling, Operational performance, Production logistics, Smart production logistic, Support staff, Materials handling, Autonomous mobile robots, Digital dashboards, Smart production logistics
National Category
Production Engineering, Human Work Science and Ergonomics Environmental Management Other Civil Engineering Other Mechanical Engineering Robotics
Research subject
User Centred Product Design
Identifiers
URN: urn:nbn:se:his:diva-21947DOI: 10.1007/978-3-031-16411-8_60ISI: 000869729400060Scopus ID: 2-s2.0-85138813083ISBN: 978-3-031-16410-1 (print)ISBN: 978-3-031-16411-8 (electronic)OAI: oai:DiVA.org:his-21947DiVA, id: diva2:1703334
Conference
International Conference on Advances in Production Management Systems, APMS 2022, Gyeongju, South Korea, September 25–29, 2022
Funder
Vinnova
Note
© 2022, IFIP International Federation for Information Processing.
© 2022 Springer Nature Switzerland AG. Part of Springer Nature.
The authors would like to acknowledge the support of Swedish Innovation Agency (VINNOVA), and its funding program Produktion2030. This study is part of the Explainable and Learning Production & Logistics by Artificial Intelligence (EXPLAIN) project.
2022-10-132022-10-132023-07-11Bibliographically approved