Technology is advancing overtime and sizes of data sets generated are considered big and complex. The current database management tools and methods used to process data are inadequate; this paves way for big data analytics evolution and innovation. There is a growing need to develop big data tools and techniques to build capabilities to solve problems better than ever before. In current practices a number of industries are readily leveraging big data to their benefit. Even there are high expectations for big data analytics in supply chain, yet the extant supply chain literature has failed to explore the moderating and mediating linkage of big data due to a lack of volume and variety of data for more informed and timely decisions in supply chain.
In the current practices, data is generally collected to test the theoretical model which we derive after extensive literature review, opinion from experts and experience. This traditional approach has several limitations. Moreover, social media and online data collection and analysis have created opportunities to utilize data for making real-time and more accurate decisions in supply chain management.
The big data analytics architecture in supply chain is characterized by five vertical dimensions or ‘5Vs’ – Volume, Velocity, Variety, Veracity and Value. In the past, due to limitations of data and tools, most of the time researchers have to limit their scope of the study in supply chain. Recently, Fosso Wamba et al. (2015) extended the 3Vs and defined big data as a “holistic approach to manage, process and analyze 5 Vs (i.e., volume, variety, velocity, veracity and value) in order to create actionable insights for sustained value delivery, measuring performance and establishing competitive advantages”. Hence, there is need for revisiting existing theories in supply chain management with data powered by 5Vs.
The aim of this special issue of Computers and Industrial Engineering (CAIE) is to attract manuscripts which are firmly grounded in supply chain theories, using big data and predictive analytics to take the current supply chain theory and practice to the next level of excellence in terms of supporting suitable supply design and operations in the 21st century organizational competitiveness. These deliverables will re-align the supply chain into a more coordinated and integrated network with collaborative, efficient and secure service systems. It includes adaptive tracing in RFID-enables large scale supply chain, assessing risk management and mitigating disaster through GPS tracking, while improving demand driven operations through facility and ware house integration across supply chain by forecasting and planning through bid data integration and coordination.
The topics to be discussed in the special issue may include suggested topics but are not limited to the following:
Theory Building in Supply Chains;
Exploring possible moderation linkages;
Revisiting current institutional theory, resource dependence theory, transaction cost economics theory, agency theory, resource based view theory and ecological modernization theory using big data;
Exploring social capital theory using big data in supply chain network design;
Redefining supply chain coordination mechanism using social actors network theory supported by big data;
Building robust supply chain risk model using big data and predictive analytics;
The design of ethical supply chains using big data;
Developing performance measurement systems (PMS) using big data to address complex interface of supply chains with dynamic environment;
Designing dynamic supply chain network using big data and predictive analytics;
Improving forecasting models using big data and business analytics;
Exploring behavioral supply chains design theory using big data and predictive analytics;
Supply Chain Agility;
Supply Chain Adaptability;
Dynamic Supply Chain Alignment;
Resilient supply chain network design;
Tracking and traceability analysis
Supply chain security and big data
Collaborative service system for supply chain with multi-tenant data architecture design
Scalable and high efficiency new storage services
Inventory predictions using machine learning techniques
Optimizing Supply chain and distribution logistics.
Knowledge integration of distributed enterprises
Predictive maintenance for industrial products
All papers must be original and have not published, submitted and/or are currently under review elsewhere. Manuscripts should be submitted through the publisher’s online system, Elsevier Editorial System (EES) at http://ees.elsevier.com/caie/. Please follow the instructions described in the “Guide for Authors”, given on the main page of EES website. Please make sure you select “Special Issue” as Article Type and “Big Data in Supply Chain” as Section/Category. In preparing their manuscript, the authors are asked to closely follow the “Instructions to Authors”. Submissions will be reviewed according to C&IE’s rigorous standards and procedures through double-blind peer review by at least two qualified reviewers. Accepted papers become the property of C&IE’s publisher, Elsevier.