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.

Big Data Analytics in Operations Management

Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda

Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach

A conceptual framework for measuring sustainability performance of supply chains

Assessing sustainability of supply chains by double frontier network DEA: A big data approach

Developing Double Frontier Version of Dynamic Network DEA Model: Assessing Sustainability of Supply Chains

Determining efficiency and ranking of production lines are important issues in operations management. In this paper, we calculate the efficiency of production lines using network data envelopment analysis (NDEA) model in the presence of desirable and undesirable outputs. However, in NDEA several production lines may become efficient which makes ranking impossible. For the first time, maximal balance index and NDEA are combined to rank production lines. A case study determines efficiency scores of seven Iranian factories. Case study shows advantage of our model over previous ones.

Taliva Badiezadeh

Papers

Assessing sustainability of supply chains by double frontier network DEA: A big data approach

Developing Double Frontier Version of Dynamic Network DEA Model: Assessing Sustainability of Supply Chains

Efficiency evaluation of production lines using maximal balance index