The bright side of consumers' opinions of improving reverse logistics decisions: a social media analytic framework

Mitigating wastes, manufacturers must make the best decisions when it comes to reusing and recycling returned products. As unsatisfactory products are not going to be bought by customers, managers would be faced with a paradoxical decision on reusing or recycling these products. The proposed framework demonstrates how to analyse positive/negative feedback from consumers to form the most effective disposition decision strategies for managers in reverse logistics by means of sentiment analysis algorithms. Applying the framework, companies will be able to extract, categorise, and analyse their consumers' opinion and sentiment to make a strategic decision in reverse logistics to minimise returned products, waste, inventory, and cost, while maximising efficiency, profit, SC sustainability, and customer satisfaction. While the framework is broad enough to be used in different industries, such as the electronics and automobile, the probability of biased opinion that may arises by limitation in considering a specific language or location has been greatly reduced. This paper focuses on social media data to optimise the decision-making process in reverse logistics through a big data analysis approach. In this research, a case study was conducted on Apple mobile phones Twitter data, including models and features.

Automated summary

This paper presents a framework that utilizes sentiment analysis algorithms to analyze consumer feedback, helping managers make effective decision strategies in reverse logistics. Applying this framework can minimize waste, cost, and inventory, while maximizing efficiency and customer satisfaction.