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A Simulation-Based Decision Support for Reverse Logistics of Remanufactured Spare Parts: A Case study of Diesel Particle Filters
KTH, School of Industrial Engineering and Management (ITM), Production engineering.
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Remanufacturing plays a crucial role in circular business models as it offers a sustainable approach to restoring products while reducing energy and resource consumption. In the automotive industry, remanufactured parts are widely used for spare parts, although their usage is still limited compared to newly produced parts. While remanufacturing is considered environmentally beneficial, it is essential to take into account the impact of reverse logistics practices when assessing the environmental and economic effects of remanufactured parts.

This research aims to develop a simulation-based decision support tool for assessing Scania’s aftermarket operations. It evaluates the environmental and economic impact of using remanufactured spare parts compared to newly produced ones. Using agent-based and discrete event methods, the simulation model analyzes the effectiveness of the reverse logistics network in terms of CO2 emissions and cost. The findings highlight the benefits of remanufacturing in terms of cost, CO2 emissions, and material savings compared to new production. These findings support the integration of remanufactured parts into the industry’s circular practices. It enables waste reduction, extends the lifespan of products, and contributes to overall sustainability efforts.

The simulation model developed in this study can serve as a valuable tool for decision-makers seeking to evaluate the environmental and economic implications of using remanufactured spare parts.

Place, publisher, year, edition, pages
2023. , p. 54
Series
TRITA-ITM-EX ; 2023:453
Keywords [en]
Circular Business Models, Reverse Logistics, Simulation, Remanufacturing, Agent Based Method, Discrete Event Method
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-334176OAI: oai:DiVA.org:kth-334176DiVA, id: diva2:1788309
Supervisors
Examiners
Available from: 2023-08-16 Created: 2023-08-16

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf