IDEAS home Printed from https://ideas.repec.org/a/spr/elmark/v32y2022i4d10.1007_s12525-022-00599-z.html
   My bibliography  Save this article

Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses

Author

Listed:
  • Md Shajalal

    (Fraunhofer-Institute for Applied Information Technology FIT
    University of Siegen)

  • Alexander Boden

    (Fraunhofer-Institute for Applied Information Technology FIT
    Bonn-Rhein-Sieg University of Applied Science)

  • Gunnar Stevens

    (University of Siegen
    Bonn-Rhein-Sieg University of Applied Science)

Abstract

Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain.

Suggested Citation

  • Md Shajalal & Alexander Boden & Gunnar Stevens, 2022. "Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2107-2122, December.
  • Handle: RePEc:spr:elmark:v:32:y:2022:i:4:d:10.1007_s12525-022-00599-z
    DOI: 10.1007/s12525-022-00599-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12525-022-00599-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12525-022-00599-z?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    2. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    3. Scott Thiebes & Sebastian Lins & Ali Sunyaev, 2021. "Trustworthy artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 447-464, June.
    4. Nowak Andrzej S. & Radzik Tadeusz, 1994. "The Shapley Value for n-Person Games in Generalized Characteristic Function Form," Games and Economic Behavior, Elsevier, vol. 6(1), pages 150-161, January.
    5. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    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. Christian Meske & Babak Abedin & Mathias Klier & Fethi Rabhi, 2022. "Explainable and responsible artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2103-2106, December.

    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. Alexander Mayr & Philip Stahmann & Maximilian Nebel & Christian Janiesch, 2024. "Still doing it yourself? Investigating determinants for the adoption of intelligent process automation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-22, December.
    2. Rainer Alt, 2021. "Electronic Markets on digital platforms and AI," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 233-241, June.
    3. Rainer Alt, 2022. "Electronic Markets on AI and standardization," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1795-1805, December.
    4. Jonas Wanner & Lukas-Valentin Herm & Kai Heinrich & Christian Janiesch, 2022. "The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2079-2102, December.
    5. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    6. Lukas-Valentin Herm & Theresa Steinbach & Jonas Wanner & Christian Janiesch, 2022. "A nascent design theory for explainable intelligent systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2185-2205, December.
    7. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    8. Dangxing Chen, 2023. "Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models," Papers 2309.13246, arXiv.org.
    9. Eduard Hartwich & Alexander Rieger & Johannes Sedlmeir & Dominik Jurek & Gilbert Fridgen, 2023. "Machine economies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    10. Najla Alharbi & Bashayer Alkalifah & Ghaida Alqarawi & Murad A. Rassam, 2024. "Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection," Future Internet, MDPI, vol. 16(10), pages 1-22, October.
    11. Araya-Córdova, P.J. & Vásquez, Óscar C., 2018. "The disaster emergency unit scheduling problem to control wildfires," International Journal of Production Economics, Elsevier, vol. 200(C), pages 311-317.
    12. Rui Ma & Jia Wang & Wei Zhao & Hongjie Guo & Dongnan Dai & Yuliang Yun & Li Li & Fengqi Hao & Jinqiang Bai & Dexin Ma, 2022. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    13. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    14. Dylan Norbert Gono & Herlina Napitupulu & Firdaniza, 2023. "Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    15. Tang, Pan & Tang, Tiantian & Lu, Chennuo, 2024. "Predicting systemic financial risk with interpretable machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    16. Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.
    17. Ritu Dutta & Souvik Roy & Surajit Borkotokey, 2023. "The Generalized Shapley Value of Cooperative Games as a Social Preference Function," Group Decision and Negotiation, Springer, vol. 32(2), pages 277-300, April.
    18. P. Herings & A. Predtetchinski & A. Perea, 2006. "The Weak Sequential Core for Two-Period Economies," International Journal of Game Theory, Springer;Game Theory Society, vol. 34(1), pages 55-65, April.
    19. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    20. Shuai Sang & Lu Li, 2024. "A Novel Variant of LSTM Stock Prediction Method Incorporating Attention Mechanism," Mathematics, MDPI, vol. 12(7), pages 1-20, March.

    More about this item

    Keywords

    eXplainable artificial intelligence (XAI); Backorder prediction; CNN; Local explanation; Global explanation;
    All these keywords.

    JEL classification:

    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

    Statistics

    Access and download statistics

    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:spr:elmark:v:32:y:2022:i:4:d:10.1007_s12525-022-00599-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.