Towards Lean Automation: Fine-Grained sentiment analysis for customer value identification

That customer value should drive product development and production is a basic tenet of the Toyota Production System and Lean. Traditional means to extract what the customer wants often focus on customer surveys. But surveys are time consuming and costly. At the same time, there exists a large amount of customer comments in online reviews that is easily accessible, whilst the advances of data science, for example as part of Lean Automation, provide new means to extract information from this data. In this context, a new approach to fine-grained sentiment analysis of Chinese consumer data is developed. The new approach integrates pre-training language model, conditional random field model and linguistic knowledge model. The new approach is shown to outperform traditional approaches in a comparison experiment, while an ablation experiment shows that our new approach is parsimonious, i.e., all three constituting components are needed. Finally, a use case is presented that exemplifies how our new approach can support managers in identifying customer value (through positive evaluations), and most importantly guide Lean improvement, through detailed information on characteristics that are evaluated negatively, ranked according to customer importance. Findings have important implications for research and practice.

Automated summary

The article introduces a new approach to fine-grained sentiment analysis of Chinese consumer data in the context of Lean manufacturing. The new approach extracts customer value from online reviews and guides Lean improvement. Experimental results show that the new approach outperforms traditional methods, and a use case demonstrates its practical importance.