Resource Productivity Taught Well: How Learning Factories Can Support Knowledge Transfer on a Multifaceted Subject

Kai Rüdele, Markus Hammer, Matthias Wolf

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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 ‘profit per hour’ and ‘theoretical limit’, participants are sensitized to ‘variable cost opportunities’ 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.
Original languageEnglish
Title of host publicationLearning Factories of the Future
Subtitle of host publicationProceedings of the 14th Conference on Learning Factories 2024
EditorsSebastian Thiede, Eric Lutters
Place of PublicationCham
PublisherSpringer, Cham
Pages36-44
Number of pages9
Volume1
ISBN (Electronic)978-3-031-65411-4
ISBN (Print)978-3-031-65410-7
DOIs
Publication statusPublished - 11 Jul 2024
Event14th Conference on Learning Factories: CLF 2024 - University of Twente, Enschede, Netherlands
Duration: 17 Apr 202419 Apr 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1059

Conference

Conference14th Conference on Learning Factories
Abbreviated titleCLF 2024
Country/TerritoryNetherlands
CityEnschede
Period17/04/2419/04/24

Keywords

  • learning factory
  • multi-parameter systems
  • operations management
  • process control
  • resource productive operations

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering
  • Computer Networks and Communications

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