[TFKN+] A03c - Remanufacturing Business Models (test)

Type of course:

Digital learning, Lesson

Language:

Duration:

20 minutes

Workload:

15 hours

Proficiency:

Intermediate

Target:

Manager, Professionals, Students

SUMMARY

Welcome to the TFKN+, “Overcoming Remanufacturing Challenges with Discrete Event Simulation: A Case-Based Introduction” Lesson!

This lesson explores how Discrete Event Simulation (DES) can assist with addressing key challenges in remanufacturing, such as variability in returned product quality and complex process interdependencies.

Whether you’re an engineer, designer, or sustainability advocate, you’ll gain valuable insights into how to leverage Discrete Event Simulation (DES) to enhance decision-making in remanufacturing through a real-world-inspired case study, illustrating how DES enables decision-makers to optimize strategies under uncertain conditions.

Moreover, feel free to explore a wide array of videos, articles, papers and books, in the extra materials part of the lesson. Let us get started!

About The Author

MADE is a Competence Center for Industry 4.0 created to implement Orientation, Training, and Finalization activities for technology transfer projects with companies on Industry 4.0 issues. The ultimate goal of the Competence Center is to keep the profile of companies high, competitive, and sustainable. Moreover, MADE supports manufacturing companies, especially small and medium enterprises, on the path of digital transformation to factory 4.0: smart, connected, and sustainable, by providing a wide range of knowledge, methods and tools on digital technologies.

Learning outcomes

After the end of the lesson, learners are able to identify the inherent challenges of remanufacturing, including variability in returned product quality, multi-stage process complexities, and fluctuating supply and demand dynamics.
After the end of the lesson, learners are able to explain how Discrete Event Simulation (DES) can assist with addressing key challenges in remanufacturing, such as variability in returned product quality and complex process interdependencies.