IDEAS home Printed from https://ideas.repec.org/a/ddj/fseeai/y2019i2p91-100.html
   My bibliography  Save this article

Technologies that through Synergic Development can support the Intelligent Automation of Business Processes

Author

Listed:
  • Vasile MAZILESCU

    (Dunarea de Jos University of Galati, Romania)

  • Adrian MICU

    (Dunarea de Jos University of Galati, Romania)

Abstract

Intelligent Automation (InA) activities using large and heterogeneous data and knowledge is an old concern in IT&C and computer science, being the next generation of Robotic Process Automation (RPA) at the enterprise level. Software systems and better solutions are developed to integrate business logic rules at the application level and to provide efficient work tools for accurate results. By designing such systems, the aim is to obtain better decisions as soon as possible in real time and make more efficient the process through which they can be made. InA is a holistic approach based on the variety of existing technologies based on Digital Transformation (DT) and automation of manual activities, using for this purpose digital workers, Artificial Intelligence (AI), cloud computing, Big Data. In this world of consumption, much of the technology we introduce into our daily lives is based on AI models and methods. Cognitive Technologies (CTs) are being developed using more and more specialized platforms. This is how different automation and intelligent capabilities are developed, which in turn adds a semantic connectivity between people and information systems [41]. The present paper analyzes the main important technologies that can effectively support the development, automation and robotization of business processes (BPA/R), identifying in a rational way how they can be applied for a synergic balance of tasks between people and computing systems, with a corresponding increase of the value added of business processes.

Suggested Citation

  • Vasile MAZILESCU & Adrian MICU, 2019. "Technologies that through Synergic Development can support the Intelligent Automation of Business Processes," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 91-100.
  • Handle: RePEc:ddj:fseeai:y:2019:i:2:p:91-100
    DOI: https://doi.org/10.35219/eai1584040937
    as

    Download full text from publisher

    File URL: http://www.eia.feaa.ugal.ro/images/eia/2019_2/Mazilescu_Micu.pdf
    Download Restriction: no

    File URL: https://libkey.io/https://doi.org/10.35219/eai1584040937?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Willcocks, Leslie P. & Lacity, Mary & Craig, Andrew, 2015. "The IT function and robotic process automation," LSE Research Online Documents on Economics 64519, London School of Economics and Political Science, LSE Library.
    2. Willcocks, Leslie P. & Lacity, Mary & Craig, Andrew, 2015. "Robotic process automation at Xchanging," LSE Research Online Documents on Economics 64518, London School of Economics and Political Science, LSE Library.
    3. Anagnoste Sorin, 2017. "Robotic Automation Process - The next major revolution in terms of back office operations improvement," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 11(1), pages 676-686, July.
    4. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    5. Allen H. Huang & Reuven Lehavy & Amy Y. Zang & Rong Zheng, 2018. "Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach," Management Science, INFORMS, vol. 64(6), pages 2833-2855, June.
    6. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexandru-Mihai Crijman, 2021. "Good Business Processes Candidates For Automation Future Of Work: Robotic Process Automation," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 63-71, August.
    2. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    3. Bennani, Hamza, 2018. "Media coverage and ECB policy-making: Evidence from an augmented Taylor rule," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 26-38.
    4. Joshua Zoen Git Hiew & Xin Huang & Hao Mou & Duan Li & Qi Wu & Yabo Xu, 2019. "BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability," Papers 1906.09024, arXiv.org, revised Jul 2022.
    5. Cristina-Claudia OSMAN, 2019. "Robotic Process Automation: Lessons Learned from Case Studies," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 23(4), pages 66-71.
    6. Uklańska Anna, 2023. "Robotic Process Automation (RPA) – Bibliometric Analysis and Literature Review," Foundations of Management, Sciendo, vol. 15(1), pages 129-140, January.
    7. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    8. Ahmed, Yousry & Elshandidy, Tamer, 2016. "The effect of bidder conservatism on M&A decisions: Text-based evidence from US 10-K filings," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 176-190.
    9. Rongjiang Cai & Tao Lv & Cheng Wang & Nana Liu, 2023. "Can Environmental Information Disclosure Enhance Firm Value?—An Analysis Based on Textual Characteristics of Annual Reports," IJERPH, MDPI, vol. 20(5), pages 1-21, February.
    10. Dim, Chukwuma & Koerner, Kevin & Wolski, Marcin & Zwart, Sanne, 2022. "Hot off the press: News-implied sovereign default risk," EIB Working Papers 2022/06, European Investment Bank (EIB).
    11. František Dařena & Jan Přichystal, 2018. "Analysis of the Association between Topics in Online Documents and Stock Price Movements," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(6), pages 1431-1439.
    12. Pastwa, Anna M. & Shrestha, Prabal & Thewissen, James & Torsin, Wouter, 2021. "Unpacking the black box of ICO white papers: a topic modeling approach," LIDAM Discussion Papers LFIN 2021018, Université catholique de Louvain, Louvain Finance (LFIN).
    13. Marc Gilbert Joseph Buchholzer, 2022. "Review of International Comparative Management Volume 23, Issue 1, March 2022 101 Value-ADDED Automation, a Solution for the Future of Work in Automotive Manufacturing in Romania," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 23(1), pages 101-111, March.
    14. Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
    15. Muhammad Farhan Malik & Yuan George Shan & Jamie Yixing Tong, 2022. "Do auditors price litigious tone?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(S1), pages 1715-1760, April.
    16. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
    17. Diego F. Téllez & Jesús M. Godoy, 2017. "Mission Power and Firm Financial Performance," Documentos de Trabajo de Valor Público 15655, Universidad EAFIT.
    18. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    19. Wenbing Luo & Ziyan Tian & Xusheng Fang & Mingjun Deng, 2024. "Can good ESG performance reduce stock price crash risk? Evidence from Chinese listed companies," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(3), pages 1469-1492, May.
    20. Andreou, Christoforos K. & Andreou, Panayiotis C. & Lambertides, Neophytos, 2021. "Financial distress risk and stock price crashes," Journal of Corporate Finance, Elsevier, vol. 67(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ddj:fseeai:y:2019:i:2:p:91-100. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gianina Mihai (email available below). General contact details of provider: https://edirc.repec.org/data/fegalro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.