IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i6p2373-d1356232.html
   My bibliography  Save this article

Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability

Author

Listed:
  • Syed Mithun Ali

    (Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh)

  • Amanat Ur Rahman

    (Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA)

  • Golam Kabir

    (Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada)

  • Sanjoy Kumar Paul

    (UTS Business School, University of Technology Sydney, Sydney, NSW 2007, Australia)

Abstract

The performance of supply chains significantly impacts the success of businesses. In addressing this critical aspect, this article presents a methodology for analyzing and predicting key performance indicators (KPIs) within supply chains characterized by limited, imprecise, and uncertain data. Drawing upon an extensive literature review, this study identifies 21 KPIs using the balanced scorecard (BSC) methodology as a performance measurement framework. While prior research has relied on the grey first-order one-variable GM (1,1) model to predict supply chain performance within constrained datasets, this study introduces an artificial intelligence approach, specifically a GM (1,1)-based artificial neural network (ANN) model, to enhance prediction precision. Unlike the traditional GM (1,1) model, the proposed approach evaluates performance based on the mean relative error (MRE). The results demonstrate a significant reduction in MRE levels, ranging from 77.09% to 0.23%, across various KPIs, leading to improved prediction accuracy. Notably, the grey neural network (GNN) model exhibits superior predictive accuracy compared to the GM (1,1) model. The findings of this study underscore the potential of the proposed artificial intelligence approach in facilitating informed decision-making by industrial managers, thereby fostering economic sustainability within enterprises across all operational tiers.

Suggested Citation

  • Syed Mithun Ali & Amanat Ur Rahman & Golam Kabir & Sanjoy Kumar Paul, 2024. "Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability," Sustainability, MDPI, vol. 16(6), pages 1-31, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2373-:d:1356232
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/6/2373/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/6/2373/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    2. Agarwal, Ashish & Shankar, Ravi & Tiwari, M.K., 2006. "Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach," European Journal of Operational Research, Elsevier, vol. 173(1), pages 211-225, August.
    3. Xianghuan Zu & Chuanlei Yang & Hechun Wang & Yinyan Wang, 2018. "An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-15, January.
    4. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    5. Shashank Thanki & Jitesh Thakkar, 2018. "A quantitative framework for lean and green assessment of supply chain performance," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 67(2), pages 366-400, February.
    6. Hendrik Reefke & Mattia Trocchi, 2013. "Balanced scorecard for sustainable supply chains: design and development guidelines," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 62(8), pages 805-826, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moktadir, Md Abdul & Ren, Jingzheng, 2024. "Global semiconductor supply chain resilience challenges and mitigation strategies: A novel integrated decomposed fuzzy set Delphi, WINGS and QFD model," International Journal of Production Economics, Elsevier, vol. 273(C).

    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. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    2. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    3. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    4. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    5. Fildes, Robert & Petropoulos, Fotios, 2013. "An evaluation of simple forecasting model selection rules," MPRA Paper 51772, University Library of Munich, Germany.
    6. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    7. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
    8. Vikash Sharma & Rakesh D. Raut & Sachin Kumar Mangla & Balkrishna E. Narkhede & Sunil Luthra & Ravindra Gokhale, 2021. "A systematic literature review to integrate lean, agile, resilient, green and sustainable paradigms in the supply chain management," Business Strategy and the Environment, Wiley Blackwell, vol. 30(2), pages 1191-1212, February.
    9. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.
    10. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    11. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    12. Svetunkov, Ivan & Kourentzes, Nikolaos, 2015. "Complex Exponential Smoothing," MPRA Paper 69394, University Library of Munich, Germany.
    13. J W Taylor, 2011. "Multi-item sales forecasting with total and split exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 555-563, March.
    14. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    15. Kourentzes, Nikolaos & Petropoulos, Fotios, 2016. "Forecasting with multivariate temporal aggregation: The case of promotional modelling," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 145-153.
    16. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251.
    17. Mun, Mak Kit & Chong, Choo Wei, 2018. "Forecasting Movie Demand Using Total and Split Exponential Smoothing," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 81-94.
    18. Shuang Yao & Donghua Yu & Yan Song & Hao Yao & Yuzhen Hu & Benhai Guo, 2018. "Dry Bulk Carrier Investment Selection through a Dual Group Decision Fusing Mechanism in the Green Supply Chain," Sustainability, MDPI, vol. 10(12), pages 1-19, November.
    19. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    20. Madden, Gary & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," Telecommunications Policy, Elsevier, vol. 31(1), pages 31-44, 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:gam:jsusta:v:16:y:2024:i:6:p:2373-:d:1356232. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.