Kim Hua Tan, Guojun Ji, Chee Peng Lim & Ming-Lang Tseng

Introduction

Big data has become increasingly fashionable in recent years. Key trends led to the big data era are cheaper technology, increased use of smart phones and social media, more popular use of the cloud computing, internet of things (IoT) and Industry 4.0 (Priya and Ranjith Kumar 2015; Zhang et al. 2015; Pan et al. 2017). These global trends generate more volume, variety and velocity (3Vs) of data than ever before, which makes big data more difficult to manage and analyse. Manyika et al. (2011) pointed out that the big data analytics can be helpful to support global manufacturing and supply chain innovation by creating data transparency, improving human decision-making and promoting innovative business models. However, there is a lack of data analytics techniques available to help decision-makers and practitioners to capture and harvest the potential value of data (Tan et al. 2015; Tseng et al. 2015; Tan and Zhan 2017). Thus, a data analytic infrastructure that helps decision-makers to make use of the high volume of data to serve as inputs for decision-making is necessary. Although there is a variety of analytics techniques i.e. predictive analytics, data mining, case-based reasoning, exploratory data analysis, business intelligence, machine learning techniques, and so on, methods that are capable to handle vast volume of unstructured data is still not well established (Wong 2012).

The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high-quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation and expert system with applications span across online reviews, food retail chain to e-health.

In the paper, ‘Using Artifificial Neural Networks to Predict Container Flows between the Major Ports of Asia’, by Chuck Tsai and Linda Huang (National Ocean University, Taiwan), big data is used to support port operators and liners strategic planning. Chuck uses artificial neural networks to predict container flows between major ports of Asia by considering GDP, interest rates, the value of export and import trade, the numbers of export and import containers and the number of quay cranes. The forecasting results indicate that the prediction errors are relatively small in most selected ports, and thus, shipping companies can use the container flow prediction model to improve strategic planning.

To link to this article: https://doi.org/10.1080/00207543.2017.1331051