In today's complex and ever‐changing world, concerns about the lack of enough data have
been replaced by concerns about too much data for supply chain management (SCM). The …

The amount of data produced and communicated over the Internet is significantly increasing,
thereby creating challenges for the organizations that would like to reap the benefits from …

Vaccination offers health, economic, and social benefits. However, three major issues—
vaccine quality, demand forecasting, and trust among stakeholders—persist in the vaccine …

Research interests in machine learning (ML) and supply chain management (SCM) have
yielded an enormous amount of publications during the last two decades. However, in the …

We are concerned with the interaction and integration between demand forecasting and
inventory control, in the context of supply chain operations. The majority of the literature is …

This paper explores the applications of data mining techniques in accounting and proposes
an organizing framework for these applications. A large body of literature reported on …

In this study, a hybrid model based on ANFIS (Adaptive Neuro-Fuzzy Inference Systems), a
predictive intelligent-based technique, and TOPSIS (Technique for Order Performance by …

Artificial Intelligence (AI) has emerged as a complementary technology in supply chain
research. However, the majority of AI approaches explored in this context afford little to no …

Abstract The Supply Chain Operations Reference (SCOR) KPIs are widely used to measure
supply chain performances by industrial practitioners. However, it is still difficult to determine …

Inventory backorder prediction is widely recognized as an important component of inventory
models. However, backorder prediction is traditionally based on stochastic approximation …