Case studies: a study of missing data imputation in predictive modeling of a wood composite manufacturing process
Zeng, Y, T. M. Young, D. J. Edwards, F. M. Guess, and C. -H. Chen.  2016.  Journal of Quality Technology, 48(3):284-296.

Abstract:
Real-time process data and destructive test data were collected and merged from a wood composite manufacturer in the southeastern US for the purpose of developing real-time predictive models for strength properties of manufactured particleboard. Sensor malfunction and other real-time data problems lead to null fields in the company’s data warehouse, resulting in information loss. Many manufacturers attempt to build accurate predictive models excluding entire records with null fields or use summary statistics such as the average or median in place of the null field. However, predictive-model errors in validation may be higher in the presence of information loss and may misguide the production process