| Title |
Unlocking the potential of quality as a core marketing strategy in remanufactured circular products: A machine learning enabled multi-theoretical perspective |
| ID_Doc |
20245 |
| Authors |
Govindan, K |
| Title |
Unlocking the potential of quality as a core marketing strategy in remanufactured circular products: A machine learning enabled multi-theoretical perspective |
| Year |
2024 |
| Published |
|
| DOI |
10.1016/j.ijpe.2023.109123 |
| Abstract |
Remanufacturing processes are inevitably associated with sustainable development. To unleash the potential of remanufacturing for circular economy transition, practitioners have introduced several strategies. Despite the important role of remanufacturing in circular economy, the final sales of remanufactured products are often less than anticipated targets. While several challenges may impact smaller sales, a primary challenge is a lack of focus on marketing strategies. Accordingly, only a small number of published studies explore marketing in the remanufacturing field. This study explores potential marketing opportunities in remanufacturing and focuses on improving warranty management; one approach is through increasing the reliability of quality by integrating smart technologies and, specifically, machine learning (ML). To achieve effective integration of machine learning in a new application, such as remanufacturing, more primary assessments are required. This study is the first to explore critical success factors (CSF) of machine learning with the integration of remanufacturing. A Danish case context has been chosen to explore the CSFs on machine learning integration in the quality process, especially with inspection of end-of-life (EoL) brake calipers. The study employs various theories, including CSF theory, Technology-Organization-Environment (ToE) theory, and stakeholder theory to analyze problem. 22 common CSFs were collected from existing studies, and they are validated and categorized based on ToE theory. The results show that 'expand the reach of algorithms' (T5), 'scalability' (O2), and 'inspection policy' (E2) are the most important success factors under these three dimensions, respectively. Several contributions were drawn from the results obtained that could directly help industrial leaders with the effective integration of ML in remanufacturing. |
| Author Keywords |
Remanufacturing; Circular economy; CSF; Marketing strategy; Machine learning; Best -worst method |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001164550500001 |
| WoS Category |
Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science |
| Research Area |
Engineering; Operations Research & Management Science |
| PDF |
https://doi.org/10.1016/j.ijpe.2023.109123
|