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Big Data Analytics in Operations Management
Author(s) -
Choi TsanMing,
Wallace Stein W.,
Wang Yulan
Publication year - 2018
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.12838
Subject(s) - big data , analytics , data science , computer science , revenue management , business analytics , revenue , data analysis , data management , software analytics , strengths and weaknesses , web analytics , supply chain management , supply chain , business , data mining , marketing , world wide web , the internet , philosophy , web application security , software , software system , web development , business model , software construction , accounting , epistemology , programming language , business analysis
Big data analytics is critical in modern operations management ( OM ). In this study, we first explore the existing big data‐related analytics techniques, and identify their strengths, weaknesses as well as major functionalities. We then discuss various big data analytics strategies to overcome the respective computational and data challenges. After that, we examine the literature and reveal how different types of big data methods (techniques, strategies, and architectures) can be applied to different OM topical areas, namely forecasting, inventory management, revenue management and marketing, transportation management, supply chain management, and risk analysis. We also investigate via case studies the real‐world applications of big data analytics in top branded enterprises. Finally, we conclude the study with a discussion of future research.