Applied Stochastic Models in Business and Industry has just published a special issue on Data Science in Business and Industry, guest edited by David Banks, Alba Martínez-Ruiz, David F. Muñoz and Javier Trejos-Zelaya.
Statistical learning and stochastic modelling are the engines of data science. The models and methods help businesses leverage data to make better decisions and use resources more efficiently. Information and communications technologies have forever changed business planning and have led to the development of new industries. Companies use data science to manage supply chains, support dynamic pricing, optimize delivery, and control robotic manufacture. Data science also opens the door to greener industries with less pollution and more fuel efficiency.
This special issue includes applications of simulation-based and multi-objective optimization, kernel smoothing and random forests, hierarchical time series forecasting, Long Short-Term Memory, latent Dirichlet allocation, Bayesian linear models, probability distributions, and support vector machines. The papers examine real-world applications across various industries such as grocery stores, assembly plants, television advertising, internet activity, online markets, industrial recommendation systems, electricity consumption, and the agriculture sector.
Read the special issue here.
