Yi, Clara & Tan, Puay & Zhaoyu, Ma. (2025). Leveraging Learning Factories to Develop Multi-site Dashboards for Decentralised Smart Manufacturing in Industry 5.0. 10.1007/978-3-031-98883-7_4.
Abstract:
With the new focus in Industry 5.0 on human-centric and resilient man- ufacturing, there is increasing interest in a value chain approach, where enter- prises operate across multiple sites or with value chain partners, potentially with decentralised decision-making. However, collaboration across sites presents challenges, as each site often develops its own shopfloor dashboard, leading to inconsistencies in data collection, KPI definitions, and metric displays, prevent- ing a unified cross-site performance view. This paper explores how Learning Factories can be used to develop industry-grade multi-site dashboards, which standardise and consolidate data from diverse systems, enabling a comprehensive perspective for global operations managers. We present a case study of a Learn- ing Factory network spanning three sites with varying products, processes, and protocols. This network was used to develop a multi-site manufacturing dash- board, later deployed regionally to a multinational corporation in high-volume production. The collaboration with the Learning Factory allowed the company to co-innovate a dashboard tailored to their specific needs, improving oversight and optimising decision-making. These dashboards enable more agile real-time drill- down analysis, making it easier to identify underperforming sites and enhance decision-making agility. The collaborative nature of Learning Factories also fa- cilitates the development of multi-enterprise dashboards, addressing challenges such as data privacy, security, and the standardisation of metrics across organi- sations. Learning Factories are key enablers of innovation in dashboard technol- ogy, driving the realisation of distributed smart value chains and supporting com- panies in their transition to Industry 5.0.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - RIE2025 Manufacturing, Trade and Connectivity (MTC) Industry Alignment Fund-Pre-Positioning (IAF-PP) Distributed Smart Value Chain (Award M23L4a0001)
Grant Reference no. : M23L4a0001
Description:
This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Networks and Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-031-98883-7_4.