Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

ABSTRACT

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

KEYWORDS:

• Customer demand prediction
• service-oriented manufacturing
• particle swarm optimization
• hybrid kernel
• least square support vector machine

Acknowledgements

The authors express sincere appreciation to the designers and engineers for their support in collecting the data on customer requirements and establishing the quality function deployment.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The project was supported by National Natural Science Foundation, China [grant number 70932004], [grant number 71602081].