OPERATIONS MANAGEMENT DATA ANALYTICS
Mission Statement
With modern technology, data is playing an increasingly important role in daily operations in a wide range of organizations. This trend provides opportunities for not only to explore data to uncover the story behind them, but also to develop new models and methodologies that account for the generation of the data. In many cases, innovative data analytics require bringing together machine learning techniques with stochastic modeling and optimization to efficiently utilize the data and make the best decisions.
We look for innovative contributions to the core of production and operations management by integrating data analytics. This would differentiate the contributions from, for example, pure econometric based data analysis, pure deterministic or stochastic optimization models with predicted inputs, or pure data mining, storage, or computing developments. Significant contributions should emphasize how available information provided by data may invoke alternative ways of modeling and analysis, leading to new operations management models, policies, opportunities and implications.
Though we welcome contributions that make significant theoretical advancement, emphasis should be given to demonstrating the business value of utilizing data in efficient decision making for operations management. Developments for decision making with limited data or changing environment will be particularly valuable. While demonstrating the practicality of the model and methodology developed using real data is always valuable, use of real data is not a must.
Departmental Editor
Professor J. George Shanthikumar
Purdue University
shanthikumar@purdue.edu
Senior Editors
Gah-Yi Ban, Imperial College, London
Hamsa Bastani, University of Pennsylvania
Boxio (Beryl) Chen, University of Illinois, Chicago
Qi (Annabelle) Feng, Purdue University
Bora Keskin, Duke University
Pengyi Shi, Purdue University
Dennis Zhang, Washington University, St Louis
Huanan Zhang, University of Colorado, Boulder
