ABSTRACT
This chapterdescribes a research study where sensors are embedded into products to track key data relating to the products during their life cycles. The underlying objective is to establish a mechanism that could track, assimilate, and assess meaningful information to eliminate or reduce the quality and quantity uncertainty associated with reverse supply chain systems. The data collated by sensors can include condition information about the products and their components. By using this data, the remaining life of a product can be estimated, and any future failure risks can be predicted. In addition, remaining life information can be used once the product completes its life cycle. A product is typically disassembled for further use if it is in good condition, and its components are subsequently remanufactured and/or refurbished. If the conditions of the product components are known prior to disassembly, these end-of-life processes can be planned accordingly so that associated costs are reduced. To convert the benefits of sensors into meaningful numbers, sensor-embedded systems and regular systems were modeled using discrete event simulation, and experiments were carried out for both systems. During the experimental phase, the disassembly cost, inspection cost, maintenance cost, and revenues were observed, and pairwise t-tests were performed to assess the extent to which the differences between the systems were significant. The results of the research indicate that sensor-embedded systems perform better than regular systems in terms of disassembly, inspection, and maintenance costs.
