Special issue on
Innovations in Production and Distribution Management with Big Data Analytics
Guest Editors:
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Dr. Abdelkader Benyettou Faculty of sciences and Technologies, University of Relizane, Algeria. |
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Dr. Khadidja Henni Institute of Applied Artificial Intelligence, Université TÉLUQ, Montréal, QC, Canada. |
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Dr. Hamdadou Jamila Department of Computer Science, University of Oran 1, Oran, Algeria. |
The IJMS (Interdisciplinary Journal of Management Studies) is a peer-reviewed open access journal published by the University of Tehran, Iran since the year 2007. The journal publishes scientific papers reporting original research and/or applications in the field of Management Sciences.
Production and distribution are two of the most important operational functions in a supply chain. Production and distribution management encompasses all the necessary activities to produce products and deliver them to the end consumers. Production and distribution channels include a chain of interdependent entities. These entities require centralized management for the seamless flow of goods and services. This requirement has attracted considerable attention for research in recent years.
The production and distribution of industry face several challenges at various levels of operation. These challenges include communication gaps, complexities in tracking the delivery of goods, difficulties in warehouse management and issues in forecasting the production operations. These challenges make it harder for making an effective decision. This issue emphasizes the need for innovation that facilitates intelligent methods for gathering and analyzing data. The number of data points generated in the supply chain industry has skyrocketed with the widespread deployment of intelligent sensors and linked devices. With this influx of data, it is no coincidence that big data analytics in manufacturing is a trending issue. All of these sensor-based and computational technologies collect and process large volumes of data. The large volume of available data, along with big data analytics tools, provides an unprecedented opportunity to optimize production and distribution management.
Big data analytics is a cutting-edge technology that provides an infinite amount of informative insights that can lead to operational improvements in the production and management of an enterprise. Big data analytics facilitates effective methods to aggregate and analyze the vast amount of data that can be used to automate and identify real-time trends in a supply chain. Although there are several operational benefits in adopting big data analytics models, some challenges need to be addressed. Despite a high hardware capacity, processing a huge volume of data is a challenging issue. Furthermore, there is a growing demand for computationally sophisticated and flexible ways for analyzing and filtering useful data from enormous datasets. Furthermore, managing data complexity is crucial for the efficient handling of production and distribution channels of a supply chain. For this reason, identifying ways to process unstructured and semi-structured data is important. Big data analytics involves the extraction of data from numerous resources and transferring them into a cloud infrastructure. This situation opens up opportunities for data security vulnerabilities. There is an urgent need for developing security mechanisms suitable for voluminous dynamic data. Papers are invited that consider, but are not limited to, the following themes:
Manuscript Preparation and Submission
All Manuscripts submitting in the SI should conform to the standard editorial and publication policies as mentioned in the journal. The authors should submit the manuscript via online system at https://ijms.ut.ac.ir/. Please select the article type “SI: IPDMBDA-2026” when submitting the manuscript.
Important Dates