A social media analytics-based approach to customer-centric reverse logistics management of electronic devices: A case study on notebooks

Abstract

With the advancement of technology, the rate of replacement of unused electronic devices by consumers has increased. Throwing away these devices wastes the scarce natural resources used in their production and also has harmful effects on the environment. Using the concept of reverse logistics leads to the reduction of waste of electronic products such as laptops and the reuse of their parts. To implement the reverse logistics process in the production process, companies need to know which parts of their products have problems or breakdowns when used by consumers so that they can make the right decision about reusing, repairing or recycling the returned product parts. In this paper, the most effective decision-making strategies in reverse logistics based on consumer feedback are discussed. The result of using this model presented in this research is reducing costs and increasing producer productivity, which will ultimately lead to consumer satisfaction. This paper focuses on social media data to gather consumer feedback to optimize the decision-making process in reverse logistics through a data analytics approach. A case study is conducted on Twitter data regarding consumer opinions about MacBook Pro and MacBook Air. By using DEMATEL and network analysis techniques, factors affecting the improvement of the quality of MacBooks have been prioritized. The results showed that the proposed approach can help manufacturers by (a) finding effective features on consumers' behaviour and attitudes through their feedback in social networks, (b) identifying how factors influence each other (c) prioritizing factors, to establish the reverse logistics process which leads to increase consumer satisfaction and improve their performance.