Role of artificial intelligence in operations environment: a review and bibliometric analysis

Abstract

Purpose“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.Design/methodology/approachAs rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).FindingsResults obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.Originality/valueThe result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation

“…The methods at this level may include network-based algorithms (Elhoone et al 2020;Hosseini andIvanov 2020), rough set theory (Mehdizadeh 2020), and tree-based clustering (Zanjani et al 2016). Finally, exploring techniques involve more advanced AI algorithms such as agentbased systems (Baryannis et al 2019), model predictive control (Belhadi et al 2019), robotic process automation (Schniederjans et al 2020) and computer vision (Dhamija and Bag 2020;Grover et al 2020). These techniques revolutionize exploring problems and solutions and promoting fast prototyping and evaluation of innovative solutions.…”

“…Further, embedding advanced AI techniques into supply chain innovation empowers innovation activities to be human-centric, creative, and effective in leveraging iterations (Verganti et al 2020). However, there are various technology-driven innovations based on new digital technologies; significantly fewer studies have addressed AI-driven SCI, Computer vision Grover et al (2020), Dhamija and Bag (2020) specifically how they are implemented and substitute prevailing current supply chain management practices (Beltagui et al 2020). Overall, AI-driven innovation may benefit several aspects of supply chain management and attract attention to essential elements of the supply chain that received insufficient attention in the past, i.e., supply chain resilience and performance.…”

Adopting a digital transformation strategy to enhance business network commons regeneration: an explorative case study

“…In general, on considering the digital transformation phenomenon, attention is first paid to operational efficiency or process simplification (Belvedere et al, 2018;Hess et al, 2016). However, organizations may adopt new digital technologies to achieve more sustainable performance (Bag et al, 2020a;Bressanelli et al, 2018;Dhamija and Bag, 2020;Gupta et al, 2020a;Hsu et al, 2018;Jabbour et al, 2018;Telukdarie et al, 2018), even in the context of the commons (Bonomi et al, 2019;Rossignoli et al, 2018). Thus, the issue of adopting digital technologies to promote more sustainable firm performance is relevant.…”

Artificial Intelligence in Tourism: A Review and Bibliometrics Research

“…Bibliometric methods are used to assess the impact of researchers, institutions, countries, or journals (Cunill et al, 2019) and they are useful to gain a macroscopic view of large amounts of academic literature (van Nunen et al, 2018). Bibliometric methods are powerful for assessing journal performances (Cunill et al, 2019;García-Lillo et al, 2016;Guzeller & Celiker, 2019;Merigó et al, 2019), evaluating the progress of a specific field at a given time period (Askun & Cizel, 2019;Dhamija & Bag, 2020;Koseoglu et al, 2016;van Nunen et al, 2018) and especially in evaluation of international scientific influence of an agent (van Raan, 2003). Bibliometrics is used across different disciplines and it's complementary to traditional methods (Zupic & Čater, 2015).…”