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

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 31944

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Special Issue Editors

Dr. Kamalakanta Muduli

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Guest Editor

Department of Mechanical Engineering, Papua New Guinea University of Technology, Lae, Papua New Guinea
Interests: decision making; Industry 4.0/5.0; healthcare waste management; machining; net zero economy
Special Issues, Collections and Topics in MDPI journals

Dr. Rakesh Raut

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Guest Editor

Department of Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai 400087, India
Interests: partner selection; collaborative network organization; managing supplier relations; service supply chain management; modeling and analysis of healthcare service operations; reverse logistics management
Special Issues, Collections and Topics in MDPI journals

Dr. Balkrishna Eknath Narkhede

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Guest Editor

Industrial Engineering & Manufacturing Systems, National Institute of Industrial Engineering (NITIE), Near Vihar Lake, Mumbai 400087, India
Interests: production; industrial engineering; operations management
Special Issues, Collections and Topics in MDPI journals

Dr. Himanshu Shee

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Guest Editor

Victoria University Business School, Victoria University, Melbourne 3000, Australia
Interests: supply chain strategies; digitalisation; sustainable operations; firm competitiveness; qualitative and quantitative methods; circular economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
big data
supply chain management
sustainable performance
human factors

Published Papers (10 papers)

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Research

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20 pages, 1924 KiB

Open AccessArticle

Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

by Siti Nurasyikin Shamsuddin, Noriszura Ismail and R. Nur-Firyal

Sustainability 2023, 15(13), 10737; https://doi.org/10.3390/su151310737 - 7 Jul 2023

Viewed by 2044

Abstract

Owning life insurance coverage that is not enough to pay for the expenses is called underinsurance, and it has been found to have a significant influence on the sustainability and financial health of families. However, insurance companies need to have a good profile of potential policyholders. Customer profiling has become one of the essential marketing strategies for any sustainable business, such as the insurance market, to identify potential life insurance purchasers. One well-known method of carrying out customer profiling and segmenting is machine learning. Hence, this study aims to provide a helpful framework for predicting potential life insurance policyholders using a data mining approach with different sampling methods and to lead to a transition to sustainable life insurance industry development. Various samplings, such as the Synthetic Minority Over-sampling Technique, Randomly Under-Sampling, and ensemble (bagging and boosting) techniques, are proposed to handle the imbalanced dataset. The result reveals that the decision tree is the best performer according to ROC and, according to balanced accuracy, F₁ score, and GM comparison, Naïve Bayes seems to be the best performer. It is also found that ensemble models do not guarantee high performance in this imbalanced dataset. However, the ensembled and sampling method plays a significant role in overcoming the imbalanced problem. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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17 pages, 3651 KiB

Open AccessArticle

Metaheruistic Optimization Based Ensemble Machine Learning Model for Designing Detection Coil with Prediction of Electric Vehicle Charging Time

by Abdulaziz Alshammari and Rakan C. Chabaan

Sustainability 2023, 15(8), 6684; https://doi.org/10.3390/su15086684 - 14 Apr 2023

Cited by 2 | Viewed by 1529

Abstract

An efficient charging time forecasting reduces the travel disruption that drivers experience as a result of charging behavior. Despite the machine learning algorithm’s success in forecasting future outcomes in a range of applications (travel industry), estimating the charging time of an electric vehicle (EV) is relatively novel. It can help the end consumer plan their trip based on the estimation data and, hence, reduce the waste of electricity through idle charging. This increases the sustainability factor of the electric charging station. This necessitates further research into the machine learning algorithm’s ability to predict EV charging time. Foreign object recognition is an essential auxiliary function to improve the security and dependability of wireless charging for electric vehicles. A comparable model is used to create the object detection circuit in this instance. Within this research, the ensemble machine learning methods employed to estimate EV charging times included random forest, CatBoost, and XGBoost, with parameters being improved through the metaheuristic Ant Colony Optimization algorithm to obtain higher accuracy and robustness. It was demonstrated that the proposed Ensemble Machine Learning Ant Colony Optimization (EML_ACO) algorithm achieved 20.5% of R², 19.3% of MAE, 21% of RMSE, and 23% of MAPE in the training process. In comparison, it achieves 12.4% of R^2, 13.3% of MAE, 21% of RMSE, and 12.4% of MAPE during testing. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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25 pages, 939 KiB

Open AccessArticle

Challenges Facing Artificial Intelligence Adoption during COVID-19 Pandemic: An Investigation into the Agriculture and Agri-Food Supply Chain in India

by Debesh Mishra, Kamalakanta Muduli, Rakesh Raut, Balkrishna Eknath Narkhede, Himanshu Shee and Sujoy Kumar Jana

Sustainability 2023, 15(8), 6377; https://doi.org/10.3390/su15086377 - 7 Apr 2023

Cited by 4 | Viewed by 3400

Abstract

The coronavirus (COVID-19) pandemic has witnessed a significant loss for farming in India due to restrictions on movement, limited social interactions and labor shortage. In this scenario, Artificial Intelligence (AI) could act as a catalyst for helping the farmers to continue with their farming. This study undertakes an analysis of the applications and benefits of AI in agri-food supply chain, while highlights the challenges facing the adoption of AI. Data were obtained from 543 farmers in Odisha (India) through a survey, and then interpreted using “Interpretive Structural Modelling (ISM)”; MICMAC; and “Step-Wise-Assessment and Ratio-Analysis (SWARA)”. Response time and accuracy level; lack of standardization; availability of support for big data; big data support; implementation costs; flexibility; lack of contextual awareness; job-losses; affordability issues; shortage of infrastructure; unwillingness of farmers; and AI safety-related issues are some challenges facing the AI adoption in agri-food supply chain. Implications were drawn for farmers and policy makers. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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19 pages, 993 KiB

Open AccessArticle

Prioritizing the Solutions to Overcome Lean Six Sigma 4.0 Challenges in SMEs: A Contemporary Research Framework to Enhance Business Operations

by Priyanshu Kumar Singh, R. Maheswaran, Naveen Virmani, Rakesh D. Raut and Kamalakanta Muduli

Sustainability 2023, 15(4), 3371; https://doi.org/10.3390/su15043371 - 12 Feb 2023

Cited by 3 | Viewed by 3141

Abstract

The research aims to prioritize the solutions to overcome the challenges of Lean Six Sigma 4.0 (LSS 4.0). It is an integrated approach with lean, six sigma, and Industry 4.0 attributes. This integrated approach helps to achieve organizational excellence and sustainable development goals. Fuzzy stepwise weight assessment ratio analysis (fuzzy-SWARA) was used to estimate the weights of LSS 4.0 challenges. Furthermore, fuzzy-weighted aggregated sum product assessment (fuzzy-WASPAS) was used to prioritize the LSS 4.0 solutions. In this study, 23 challenges and 23 solutions of LSS 4.0 implementation were identified with the help of an extensive literature review and discussion with the area experts having vast experience. Management participation in LSS 4.0 implementation and planning for long-term vision were found to be the topmost solutions to overcome LSS 4.0 challenges. To the best of our knowledge, to date, the prioritization of solutions to overcome the challenges of LSS 4.0 have not yet been investigated in the developing economic context. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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23 pages, 618 KiB

Open AccessArticle

Antecedents of Big Data Analytic Adoption and Impacts on Performance: Contingent Effect

by Abdalwali Lutfi, Akif Lutfi Al-Khasawneh, Mohammed Amin Almaiah, Ahmad Farhan Alshira’h, Malek Hamed Alshirah, Adi Alsyouf, Mahmaod Alrawad, Ahmad Al-Khasawneh, Mohamed Saad and Rommel Al Ali

Sustainability 2022, 14(23), 15516; https://doi.org/10.3390/su142315516 - 22 Nov 2022

Cited by 29 | Viewed by 3120

Abstract

The adoption of big data analytics (BDA) is increasing pace both in practice and in theory, owing to the prospects and its potential advantages. Numerous researchers believe that BDA could provide significant advantages, despite constant battles with the constraints that limit its implementation. Here, we suggest an incorporated model to investigate the drivers and impacts of BDA adoption in the Jordanian hotel industry based on the technology–organisation–environment framework and the resource-based view theory. The suggested model incorporates both the adoption and performance components of BDA into a single model. For data collection, in this study, we used an online questionnaire survey. The research model was verified based on responses from 119 Jordanian hotels. This study yielded two significant findings. First, we discovered that relative advantage, organizational readiness, top management support, and government regulations have a major impact on BDA adoption. The study results also reveal a strong and favourable association between BDA adoption and firm performance. Finally, information sharing was found to have a moderating effect on the association between BDA adoption and firm performance. The data revealed how businesses might increase their BDA adoption for improved firm performance. The present study adds to the limited but growing body of literature investigating the drivers and consequences of technology acceptance. The findings of this study can serve as a resource for scholars and practitioners interested in big data adoption in emerging nations. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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19 pages, 855 KiB

Open AccessArticle

The UAE Employees’ Perceptions towards Factors for Sustaining Big Data Implementation and Continuous Impact on Their Organization’s Performance

by S. M. F. D. Syed Mustapha

Sustainability 2022, 14(22), 15271; https://doi.org/10.3390/su142215271 - 17 Nov 2022

Cited by 2 | Viewed by 1493

Abstract

The UAE has officially launched the Big Data initiative in the year 2022; however, the interest in and adoption of Big Data technologies and strategies had started much earlier in the private and public sectors. This research aims to explore the perceptions of the UAE employees on factors needed to implement sustainable Big Data and the continuous impact on their organizational performance. A total of 257 employees were randomly selected for an online survey, and data were collected using a Likert-style five-point scale that was tested for validity and reliability. The findings indicate that employees believe that Big Data Sustainable Implementation leads to Business Performance. Additionally, employees consider factors such as Big Data Architecture Quality, Human Cognitive Factors, and Organizational Readiness to significantly impact on Sustainable Implementation. Further, a moderating impact of Human Cognitive Factors was found on the relationship between Big Data Architecture Quality and Sustainable Implementation. The study provides managerial insights and recommendations for policymaking. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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16 pages, 890 KiB

Open AccessArticle

Artificial Intelligence and Exploratory-Data-Analysis-Based Initial Public Offering Gain Prediction for Public Investors

by Manushi Munshi, Manan Patel, Fayez Alqahtani, Amr Tolba, Rajesh Gupta, Nilesh Kumar Jadav, Sudeep Tanwar, Bogdan-Constantin Neagu and Alin Dragomir

Sustainability 2022, 14(20), 13406; https://doi.org/10.3390/su142013406 - 18 Oct 2022

Cited by 1 | Viewed by 2349

Abstract

An initial public offering (IPO) refers to a process by which private corporations offer their shares in a public stock market for investment by public investors. This listing of private corporations in the stock market leads to the easy generation and exchange of capital between private corporations and public investors. Investing in a company’s shares is accompanied by careful consideration and study of the company’s public image, financial policies, and position in the financial market. The stock market is highly volatile and susceptible to changes in the political and socioeconomic environment. Therefore, the prediction of a company’s IPO performance in the stock market is an important study area for researchers. However, there are several challenges in this path, such as the fragile nature of the stock market, the irregularity of data, and the influence of external factors on the IPO performance. Researchers over the years have proposed various artificial intelligence (AI)-based solutions for predicting IPO performance. However, they have some lacunae in terms of the inadequate data size, data irregularity, and lower prediction accuracy. Motivated by the aforementioned issues, we proposed an analytical model for predicting IPO gains or losses by incorporating regression-based AI models. We also performed a detailed exploratory data analysis (EDA) on a standard IPO dataset to identify useful inferences and trends. The XGBoost Regressor showed the maximum prediction accuracy for the current IPO gains, i.e., 91.95%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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23 pages, 1416 KiB

Open AccessArticle

Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries

by Sudhanshu Joshi, Manu Sharma, Rashmi Prava Das, Joanna Rosak-Szyrocka, Justyna Żywiołek, Kamalakanta Muduli and Mukesh Prasad

Sustainability 2022, 14(18), 11698; https://doi.org/10.3390/su141811698 - 18 Sep 2022

Cited by 11 | Viewed by 4390

Abstract

This study work is among the few attempts to understand the significance of AI and its implementation barriers in the healthcare systems in developing countries. Moreover, it examines the breadth of applications of AI in healthcare and medicine. AI is a promising solution for the healthcare industry, but due to a lack of research, the understanding and potential of this technology is unexplored. This study aims to determine the crucial AI implementation barriers in public healthcare from the viewpoint of the society, the economy, and the infrastructure. The study used MCDM techniques to structure the multiple-level analysis of the AI implementation. The research outcomes contribute to the understanding of the various implementation barriers and provide insights for the decision makers for their future actions. The results show that there are a few critical implementation barriers at the tactical, operational, and strategic levels. The findings contribute to the understanding of the various implementation issues related to the governance, scalability, and privacy of AI and provide insights for decision makers for their future actions. These AI implementation barriers are encountered due to the wider range of system-oriented, legal, technical, and operational implementations and the scale of the usage of AI for public healthcare. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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25 pages, 8454 KiB

Open AccessArticle

Blockchain Technology and Artificial Intelligence Based Decentralized Access Control Model to Enable Secure Interoperability for Healthcare

by Sumit Kumar Rana, Sanjeev Kumar Rana, Kashif Nisar, Ag Asri Ag Ibrahim, Arun Kumar Rana, Nitin Goyal and Paras Chawla

Sustainability 2022, 14(15), 9471; https://doi.org/10.3390/su14159471 - 2 Aug 2022

Cited by 29 | Viewed by 3889

Abstract

Healthcare, one of the most important industries, is data-oriented, but most of the research in this industry focuses on incorporating the internet of things (IoT) or connecting medical equipment. Very few researchers are looking at the data generated in the healthcare industry. Data are very important tools in this competitive world, as they can be integrated with artificial intelligence (AI) to promote sustainability. Healthcare data include the health records of patients, drug-related data, clinical trials data, data from various medical equipment, etc. Most of the data management processes are manual, time-consuming, and error-prone. Even then, different healthcare industries do not trust each other to share and collaborate on data. Distributed ledger technology is being used for innovations in different sectors including healthcare. This technology can be incorporated to maintain and exchange data between different healthcare organizations, such as hospitals, insurance companies, laboratories, pharmacies, etc. Various attributes of this technology, such as its immutability, transparency, provenance etc., can bring trust and security to the domain of the healthcare sector. In this paper, a decentralized access control model is proposed to enable the secure interoperability of different healthcare organizations. This model uses the Ethereum blockchain for its implementation. This model interfaces patients, doctors, chemists, and insurance companies, empowering the consistent and secure exchange of data. The major concerns are maintaining a history of the transactions and avoiding unauthorized updates in health records. Any transaction that changes the state of the data is reflected in the distributed ledger and can be easily traced with this model. Only authorized entities can access their respective data. Even the administrator will not be able to modify any medical records. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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Review

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25 pages, 3429 KiB

Open AccessReview

A Review on the Adoption of AI, BC, and IoT in Sustainability Research

by Susie Ruqun WU, Gabriela Shirkey, Ilke Celik, Changliang Shao and Jiquan Chen

Sustainability 2022, 14(13), 7851; https://doi.org/10.3390/su14137851 - 28 Jun 2022

Cited by 19 | Viewed by 4482

Abstract

The rise of artificial intelligence (AI), blockchain (BC), and the internet of things (IoT) has had significant applications in the advancement of sustainability research. This review examines how these digital transformations drive natural and human systems, as well as which industry sectors have been applying them to advance sustainability. We adopted qualitative research methods, including a bibliometric analysis, in which we screened 960 publications to identify the leading sectors that apply AI/BC/IoT, and a content analysis to identify how each sector uses AI/BC/IoT to advance sustainability. We identified “smart city”, “energy system”, and “supply chain” as key leading sectors. Of these technologies, IoT received the most real-world applications in the “smart city” sector under the dimensions of “smart environment” and “smart mobility” and provided applications resolving energy consumption in the “energy system” sector. AI effectively resolved scheduling, prediction, and monitoring for both the “smart city” and “energy system” sectors. BC remained highly theoretical for “supply chain”, with limited applications. The technological integration of AI and IoT is a research trend for the “smart city” and “energy system” sectors, while BC and IoT is proposed for the “supply chain”. We observed a surge in AI/BC/IoT sustainability research since 2016 and a new research trend—technological integration—since 2020. Collectively, six of the United Nation’s seventeen sustainable development goals (i.e., 6, 7, 9, 11, 12, 13) have been the most widely involved with these technologies. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Analytics for enhanced Business Operations: A Contemporary Research Framework and Modelling)

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