@inproceedings{b87f91bc8eb9429e89b96ff2f80cf66b,
title = "Resource Productivity Taught Well: How Learning Factories Can Support Knowledge Transfer on a Multifaceted Subject",
abstract = "Resource productive operations are about more than just reducing waste and improving energy efficiency; they can create long-term advantages in costs, risk reduction, and innovation resulting in financial benefits and decreased environmental impact of industrial processes. Designed as an analytics-supported operations management approach that uses performance metrics, they assist decision making and can lead to operational excellence. We demonstrate how experiences from the process industry were incorporated into a new learning module of the LEAD Factory at Graz University of Technology. By introducing the key concepts of {\textquoteleft}profit per hour{\textquoteright} and {\textquoteleft}theoretical limit{\textquoteright}, participants are sensitized to {\textquoteleft}variable cost opportunities{\textquoteright} and can experience the complexity of multi-parameter systems and trade-offs between process variables. To master the complexity, a KPI tree and bubble diagram are developed during the learning module to identify patterns and interrelations of settings that influence profitability.",
keywords = "learning factory, multi-parameter systems, operations management, process control, resource productive operations",
author = "Kai R{\"u}dele and Markus Hammer and Matthias Wolf",
year = "2024",
month = jul,
day = "11",
doi = "10.1007/978-3-031-65411-4_5",
language = "English",
isbn = "978-3-031-65410-7",
volume = "1",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer, Cham",
pages = "36--44",
editor = "Sebastian Thiede and Eric Lutters",
booktitle = "Learning Factories of the Future",
note = "14th Conference on Learning Factories : CLF 2024, CLF 2024 ; Conference date: 17-04-2024 Through 19-04-2024",
}