Big Data Analytics in Operations Management

TLDR

Big data analytics is critical in modern operations management. The study explores existing big data analytics techniques, identifying their strengths, weaknesses, and major functionalities. The authors review these techniques, discuss strategies to overcome computational and data challenges, map the methods to key OM domains such as forecasting, inventory, revenue, marketing, transportation, supply chain, and risk, and present case studies of real‑world applications in leading enterprises. The study concludes with a discussion of future research directions.

Abstract

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.