IDEAS home Printed from https://ideas.repec.org/a/aes/infoec/v25y2021i2p29-39.html
   My bibliography  Save this article

A Classification Predictive Model to Analyze the Supply Chain Strategies

Author

Listed:
  • Elena PUICA

Abstract

Big Data Analytics (BDA) has the capacity to increase communications and better manage supply chain strategies. The main objective of this study developed, firstly was a systematic literature review, to understand how BDA has been investigated on supply chain strategies, which resources are handled by BDA and which Supply Chain Management strategies are positively affected by those technologies, and secondly, to apply a classification predictive model to foresee the level of implementation of innovative technologies in supply chain strategies. The applied predictive classification model helped to offer an understanding and to determine that in supply chain strategies there are innovative technologies implemented and their percentage of implementation will have an increasing value. This study, that is focused on BDA and supply chain strategies, offers new opportunities, and is adding value and operational excellence for existing supply chain practices. The adoption of big data technology in supply chain can create considerable value-added.

Suggested Citation

  • Elena PUICA, 2021. "A Classification Predictive Model to Analyze the Supply Chain Strategies," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 25(2), pages 29-39.
    • Handle: RePEc:aes:infoec:v:25:y:2021:i:2:p:29-39
    as

    Download full text from publisher

    File URL: http://revistaie.ase.ro/content/98/03%20-%20puica.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Souza, Gilvan C., 2014. "Supply chain analytics," Business Horizons, Elsevier, vol. 57(5), pages 595-605.
    2. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    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. Qiang Xing & Zhong Chen & Ziqi Zhang & Xiao Xu & Tian Zhang & Xueliang Huang & Haiwei Wang, 2020. "Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior," Energies, MDPI, vol. 13(6), pages 1-32, March.
    2. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    3. Lixing Chen & Xueliang Huang & Hong Zhang, 2020. "Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation," Energies, MDPI, vol. 13(7), pages 1-17, March.
    4. Gonzalez Venegas, Felipe & Petit, Marc & Perez, Yannick, 2021. "Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    5. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt, 2022. "Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development," Energies, MDPI, vol. 15(15), pages 1-27, August.
    6. Ran, Cuiling & Zhang, Yanzi & Yin, Ying, 2021. "Demand response to improve the shared electric vehicle planning: Managerial insights, sustainable benefits," Applied Energy, Elsevier, vol. 292(C).
    7. Gang Wang & Angappa Gunasekaran & Eric W. T. Ngai, 2018. "Distribution network design with big data: model and analysis," Annals of Operations Research, Springer, vol. 270(1), pages 539-551, November.
    8. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    9. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
    10. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    11. Leonardo de Assis Santos & Leonardo Marques, 2022. "Big data analytics for supply chain risk management: research opportunities at process crossroads," Post-Print hal-03766121, HAL.
    12. Wang, Yue & Shi, Jianmai & Wang, Rui & Liu, Zhong & Wang, Ling, 2018. "Siting and sizing of fast charging stations in highway network with budget constraint," Applied Energy, Elsevier, vol. 228(C), pages 1255-1271.
    13. Raiden Skala & Mohamed Ahmed T. A. Elgalhud & Katarina Grolinger & Syed Mir, 2023. "Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging," Energies, MDPI, vol. 16(10), pages 1-21, May.
    14. Ulrich Leicht-Deobald & Thorsten Busch & Christoph Schank & Antoinette Weibel & Simon Schafheitle & Isabelle Wildhaber & Gabriel Kasper, 2019. "The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity," Journal of Business Ethics, Springer, vol. 160(2), pages 377-392, December.
    15. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    16. Tino T. Herden & Steffen Bunzel, 2018. "Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study," Logistics, MDPI, vol. 2(2), pages 1-20, May.
    17. Tino T. Herden, 2020. "Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 163-214, April.
    18. Marzio Barresi & Edoardo Ferri & Luigi Piegari, 2023. "An MV-Connected Ultra-Fast Charging Station Based on MMC and Dual Active Bridge with Multiple dc Buses," Energies, MDPI, vol. 16(9), pages 1-23, May.
    19. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    20. Tino T. Herden & Benjamin Nitsche & Benno Gerlach, 2020. "Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations," Logistics, MDPI, vol. 4(1), pages 1-27, February.

    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:aes:infoec:v:25:y:2021:i:2:p:29-39. 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: Paul Pocatilu (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.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.