Camilla Lundgren
Chalmers University of Technology
12 Papers
13 Citations
Camilla Lundgren is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Performance indicator & Computer science. The author has an hindex of 5, co-authored 11 publications.
Chat about Author
Papers
Challenges Building a Data Value Chain to Enable Data-Driven Decisions: A Predictive Maintenance Case in 5G-Enabled Manufacturing
Magnus Åkerman,Camilla Lundgren,Maja Bärring,Mats Folkesson,Viktor Berggren,Johan Stahre,Ulrika Engström,Martin Friis +7 more
TL;DR: Results show that, just as the literature suggests, the knowledge gaps between different domains is a key component to manage for succeeding when building Big Data applications in the context of future manufacturing and maintenance.
39
Quantifying the Effects of Maintenance – a Literature Review of Maintenance Models
TL;DR: A structured literature review of existing maintenance models is presented and how to increase their applicability for practitioners in industry is discussed.
29
A strategy development process for Smart Maintenance implementation
TL;DR: The proposed process provides industry practitioners with a step-by-step guide for the development of a clear smart maintenance strategy, based on the current state of their maintenance organization, which creates employee engagement and is a new way of developing maintenance strategies.
5G Enabled Manufacturing Evaluation for Data-Driven Decision-Making
Maja Bärring,Camilla Lundgren,Magnus Åkerman,Björn Johansson,Johan Stahre,Ulrika Engström,Martin Friis +6 more
TL;DR: This paper will address the requirements of data by domain experts, in the context of more real-time data available, and assess the key factors of big data; volume, velocity, and variety of data.
25
Performance indicators for measuring the effects of Smart Maintenance
TL;DR: This paper suggests 13 categories of PI to facilitate the selection of PIs for Smart Maintenance, based on 170 PIs which were analysed according to the anticipated effects of Smart Maintenance.