Date of Award

Fall 2021

Publication Type

Dissertation

Degree Name

Ph.D.

Department

Mechanical, Automotive, and Materials Engineering

First Advisor

W. El Maraghy

Second Advisor

H. El Maraghy

Third Advisor

Z. Pasek

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Product recovery and remanufacturing have received increasing attention in the past two decades due to environmental, legal and economic drivers. The concept of reverse logistics has evolved from an initiative to recycle raw materials such as paper and glass to a broad sustainable product recovery and remanufacturing approach to enable a transition to circular economy. Advanced manufacturing companies need to develop smart manufacturing-remanufacturing systems to maximize product value recovery utilizing Industry 4.0 principles. Research articles have accumulated over the years to suggest solutions to many product recovery and remanufacturing implementation problems. However, the common case of a family of products with modular structure has been scarcely considered. Moreover, articles focusing on product recovery and remanufacturing systems from circular economy and Industry 4.0 perspectives have been very limited. One of the objectives of this research is to provide a brief background of the early stage of research and analyze recent articles in the literature to summarize their scope, methods and models. Furthermore, this research attempts to develop a decision-making framework for product recovery and remanufacturing systems from the perspectives of the recent megatrends of circular economy and Industry 4.0. The proposed decision-making framework is explained in three chapters. First, an Industry 4.0 implementation framework for circular economy manufacturing systems is outlined to assist in identifying Industry 4.0 technologies and the interconnected network of cyber-physical systems across the manufacturing, remanufacturing and supply chain systems. The framework can be used as an input for further analysis using a conceptual decision-making framework and mathematical optimization models.

Effective closed-loop supply chain network is a prerequisite for circular economy manufacturing systems as it helps in establishing adequate quantity and quality levels of returned products. To determine the preferred network configuration, a multi-criteria decision-making framework is proposed to address the strategic planning issues in manufacturing-remanufacturing closed loop systems. The conceptual framework utilizes an analytical hierarchy process (AHP) model to help the decision makers in selecting the best alternatives for multiple strategic decisions including the implementation level of Industry 4.0 technologies. The model application is illustrated using a case study from the washing machine industry sector.

Once the configuration of manufacturing-remanufacturing closed-loop system is determined, a mathematical optimization model is used to complement the conceptual decision-making framework. The optimization model was applied to the case of washing machine manufacturing to determine the optimal product mix for three cases of remanufactured product portion to fulfill market demands. The case study involves the production of seven product variants and 14 modules. The results show that the company net profit varies with the remanufactured product portion requirement in the product mix. The model can be used in cloud computing within an Industry 4.0 framework to determine the optimal product mix based on real-time data feedback in a given production period. A second optimization model is developed to determine the optimal technology selection. Six scenarios of remanufactured product portion requirements were studied for the case of washing machines. The results show that the net profit decreases as the remanufactured product portion increases. The output of the mathematical optimization model can be used by decision makers to enhance the performance of the closed-loop system.

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