Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling

Dear Colleagues,

Artificial Intelligence (AI) has been applied for several uses in machine learning to achieve autonomy in business entities. The augmentation and automation of processes enhance productivity and profitability by introducing automated chatbots that have seen applications in procurement, task management, and many more areas. Machine learning, a subset of AI, has been implemented in inventory management and supply chain planning such that the demand and supply can be automated through data-driven forecasting. Similarly, this forecasting of demand and supply has been used in preventing the understocking of inventories, as well as improved material handling in warehouses (Kumar et al., 2021). AI has also been the bedrock of autonomous logistics and shipping, which helps reduce delivery periods, lead time and logistics costs and improve supplier relationship management. Improved tracking and stock keeping facilities also help in improved reuse and recyclable opportunities, thus improving sustainable performance (Swain et al., 2021).

Big data collected through IoT and other connected devices (Choi et al., 2021) can also be analysed through AI to optimize operational efficiency in supply chain management (Yigitcanlar, & Cugurullo, 2020). While traditional tools and techniques are unable to manage complex big data, big data analytics (BDA) is revolutionizing the manufacturing industry through the analytics of usage pattern. As customers’ purchase behaviour affects managerial decisions targeted at user demand, the identification of variables impacting production rate, warehouse localization based on demand, product traceability through bar codes and RFID tags are becoming prominent. Further, manufacturing data could be used to determine the root cause of failure in machine components, schedule optimization and job allocation.

More research will help in the development of an ICT-integrated industry-specific cleaner process by reducing potential roadblocks. The successful implementation of these digital technologies is influenced by the regions' geographical and political conditions where the business entities operate. People experience a dilemma as to whether these emerging and intelligent applications could ever replace human intuition and empathy. In that sense, sector-specific empirical studies need to be conducted to compare AI-based decision making with that of human intelligence-based decision making to explore whether the former adds something innovative to the decision-making process and to what extent. Further, although few researchers have attempted to understand the interplay between BDA and firm performance, research regarding sector-specific challenges in adopting these advanced analytics could reveal and make easier the methods of adoption processes (Arunachalam et al., 2018). Empirical studies also need to discuss how big data influences decision making through algorithm-based computational intelligence techniques. It has also been argued that the extensive use of these disruptive technologies in business will generate less ambitious jobs for employees. Hence, studies also need to be conducted regarding how to make jobs interesting for employees in AI-driven intelligent business environments.

In this regard, this Special Issue encourages authors to contribute both qualitative and quantitative studies that demonstrate pathways to overcome bottlenecks, outline policy guidelines and develop a framework for the effective integration of AI and BDA into current practices.

The topics of interest include but are not limited to:

• Challenges and opportunities of AI and BDA adoption;
• AI and big data-enabled innovative sales and business forecasting models;
• AI and big data-driven purchase predictions and product recommendations;
• AI and big Data for detection and prevention of fraud in online transactions;
• Customer segmentation using AI and big data;
• Predictive customer service using AI;
• Social semantics and sentiment analysis using AI;
• Role of different data sources on operational practices and decision making;
• Advanced tracking technologies and their applications in the supply chain context;
• Human factors in AI-enabled data-driven business environment;
• Modelling challenges offered by digitalization towards human resource management;
• Impact of AI on the operational performance of Small and Medium-sized Enterprises (SMEs);
• Impact of open innovation possibilities of AI and other digital technologies on business operations in different sectors;
• AI-integrated innovative business models for improving sustainable performance;
• Potential role of Twitter in SCM (i.e., professional networking, stakeholder engagement, demand management, product development, risk management) using AI and BDA;
• Industry practices and benchmarking against the leaders in the field and drawing on the guidance for the followers.

References

• Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436.
• Choi, S. W., Lee, E. B., & Kim, J. H. (2021). The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects. Sustainability, 13(18), 10384.
• Kumar, S., Raut, R.D., Narwane, V.S., Narkhede, B.E. and Muduli, K. (2021), "Implementation barriers of smart technology in Indian sustainable warehouse by using a Delphi-ISM-ANP approach", International Journal of Productivity and Performance Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPPM-10-2020-0511
• Swain, S., Oyekola, P. O., Muduli, K. (2021)Intelligent Technologies for Excellency in Sustainable Operational Performance in Health Care Sector, International Journal of Social Ecology and Sustainable Development 14(6)(In Press)
• Yigitcanlar, T., & Cugurullo, F. (2020). The sustainability of artificial intelligence: An urbanistic viewpoint from the lens of smart and sustainable cities. Sustainability, 12(20), 8548.

Dr. Kamalakanta Muduli
Dr. Rakesh Raut
Dr. Balkrishna Eknath Narkhede
Dr. Himanshu Shee
Guest Editors

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• artificial intelligence
• big data
• supply chain management
• sustainable performance
• human factors