{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:49:19Z","timestamp":1781689759695,"version":"3.54.5"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["955422"],"award-info":[{"award-number":["955422"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fraunhofer-Institut f\u00fcr Angewandte Informationstechnik FIT"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Electron Markets"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2019s 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\u2019 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.<\/jats:p>","DOI":"10.1007\/s12525-022-00599-z","type":"journal-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T14:02:49Z","timestamp":1668002569000},"page":"2107-2122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9011-708X","authenticated-orcid":false,"given":"Md","family":"Shajalal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6470-1151","authenticated-orcid":false,"given":"Alexander","family":"Boden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gunnar","family":"Stevens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"599_CR1","doi-asserted-by":"crossref","unstructured":"Abedin, B., Klier, M., Meske, C., & Rabhi, F. (2022) Introduction to the minitrack on explainable artificial intelligence (XAI). Proceedings of the 55th Hawaii International Conference on System Sciences, 1\u20132. http:\/\/hdl.handle.net\/10125\/70765","DOI":"10.24251\/HICSS.2022.182"},{"key":"599_CR2","doi-asserted-by":"publisher","unstructured":"Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (xai). IEEE Access, 6, 52138\u201352160. https:\/\/doi.org\/10.1109\/ACCESS.2018.2870052","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"599_CR3","doi-asserted-by":"publisher","unstructured":"Alvarez-Melis, D., & Jaakkola, T.S. (2017) A causal framework for explaining the predictions of black-box sequence-to-sequence models. Arxiv. https:\/\/doi.org\/10.48550\/arXiv.1707.01943","DOI":"10.48550\/arXiv.1707.01943"},{"key":"599_CR4","doi-asserted-by":"publisher","unstructured":"Arya, V., Bellamy, R. K., Chen, P.-Y., Dhurandhar, A., Hind, M., Hoffman, S. C., Houde, S., Liao, Q. V., Luss, R., Mojsilovi\u0107, A., Mourad, S., Pedemonte, P., Raghavendra,  R., Richards, J., Sattigeri, P., Shanmugam, K., Singh, M., Varshney, K. R., Wei, D., & Zhang, Y. (2019). One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques. Arxiv.\nhttps:\/\/doi.org\/10.48550\/arXiv.1909.03012","DOI":"10.48550\/arXiv.1909.03012"},{"key":"599_CR5","doi-asserted-by":"publisher","unstructured":"Bartoletti, I. (2019). AI in healthcare: Ethical and privacy challenges.  In:  D. Ria\u00f1o, S. Wilk, A. ten Teije (Eds.),\u00a0Artificial Intelligence in Medicine\u00a0(vol. 11526, pp. 7\u201310). AIME 2019. Lecture Notes in Computer Science. Springer. https:\/\/doi.org\/10.1007\/978-3-030-21642-9_2","DOI":"10.1007\/978-3-030-21642-9_2"},{"issue":"1","key":"599_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12525-022-00537-z","volume":"32","author":"RE Bawack","year":"2022","unstructured":"Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in e-commerce: A bibliometric study and literature review. Electronic Markets, 32(1), 1\u201342. https:\/\/doi.org\/10.1007\/s12525-022-00537-z","journal-title":"Electronic Markets"},{"key":"599_CR7","doi-asserted-by":"publisher","unstructured":"B\u0142aszczy\u0144ski, J., & Stefanowski, J. (2015). Neighbourhood sampling in bagging for imbalanced data. Neurocomputing, 150, 529\u2013542. https:\/\/doi.org\/10.1016\/j.neucom.2014.07.064","DOI":"10.1016\/j.neucom.2014.07.064"},{"key":"599_CR8","doi-asserted-by":"publisher","unstructured":"Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57(1), 203\u2013216. https:\/\/doi.org\/10.1016\/j.ins.2019.05.042","DOI":"10.1016\/j.ins.2019.05.042"},{"key":"599_CR9","doi-asserted-by":"publisher","unstructured":"Cao, Y., Geddes, T. A., Yang, J. Y. H., & Yang, P. (2020). Ensemble deep learning in bioinformatics. Nature Machine Intelligence, 2(9), 500\u2013508. https:\/\/doi.org\/10.1038\/s42256-020-0217-y","DOI":"10.1038\/s42256-020-0217-y"},{"key":"599_CR10","doi-asserted-by":"publisher","unstructured":"Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Obl\u00e9, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information sciences, 557, 317\u2013331. https:\/\/doi.org\/10.1016\/j.ins.2019.05.042","DOI":"10.1016\/j.ins.2019.05.042"},{"key":"599_CR11","doi-asserted-by":"publisher","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321\u2013357. https:\/\/doi.org\/10.5555\/1622407.1622416","DOI":"10.5555\/1622407.1622416"},{"key":"599_CR12","unstructured":"Cliff, D., Brown, D., & Treleaven, P. (2011). Technology trends in the financial markets: A 2020 vision. UK Government Office for Science. http:\/\/www.bis.gov.uk\/assets\/bispartners\/foresight\/docs\/computer-trading\/11-1222-dr3-technology-trends-in-financial-markets.pdf"},{"key":"599_CR13","doi-asserted-by":"publisher","unstructured":"de Santis, R. B., de Aguiar, E. P., & Goliatt, L. (2017). Predicting material backorders in inventory management using machine learning. 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1\u20136. https:\/\/doi.org\/10.1109\/LA-CCI.2017.8285684","DOI":"10.1109\/LA-CCI.2017.8285684"},{"key":"599_CR14","doi-asserted-by":"publisher","unstructured":"Do\u0161ilovi\u0107, F. K., Br\u010di\u0107, M., & Hlupi\u0107, N. (2018). Explainable artificial intelligence: A survey. 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) pp. 0210\u20130215. https:\/\/doi.org\/10.23919\/MIPRO.2018.8400040","DOI":"10.23919\/MIPRO.2018.8400040"},{"key":"599_CR15","unstructured":"F\u00f6rster, M., Klier, M., Kluge, K., & Sigler, I. (2020). Fostering human agency: A process for the design of user-centric XAI systems. 41st International Conference on Information Systems (ICIS). https:\/\/aisel.aisnet.org\/icis2020\/hci_artintel\/hci_artintel\/12"},{"key":"599_CR16","doi-asserted-by":"publisher","unstructured":"Hajek, P., & Abedin, M. Z. (2020). A profit function-maximizing inventory backorder prediction system using big data analytics. IEEE Access, 8, 58982\u201358994. https:\/\/doi.org\/10.1109\/ACCESS.2020.2983118","DOI":"10.1109\/ACCESS.2020.2983118"},{"key":"599_CR17","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). Adasyn: Adaptive synthetic sampling approach for imbalanced learning. 2008 IEEE International Joint Conference on Neural Networks. IEEE World Congress On Computational Intelligence, pp. 1322\u20131328. https:\/\/doi.org\/10.1016\/j.ins.2019.05.042","DOI":"10.1016\/j.ins.2019.05.042"},{"key":"599_CR18","doi-asserted-by":"publisher","unstructured":"Hussain, W., Merig\u00f3, J. M., & Raza, M. R. (2021). Predictive intelligence using anfisinduced owawa for complex stock market prediction. International Journal of Intelligent Systems. https:\/\/doi.org\/10.1002\/int.22732","DOI":"10.1002\/int.22732"},{"key":"599_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-022-07297-z","volume":"34","author":"W Hussain","year":"2022","unstructured":"Hussain, W., Gao, H., Raza, M. R., Rabhi, F. A., & Merigo, J. M. (2022a). Assessing cloud QoS predictions using OWA in neural network methods. Neural Computing and Applications, 34, 1\u201318. https:\/\/doi.org\/10.1007\/s00521-022-07297-z","journal-title":"Neural Computing and Applications"},{"key":"599_CR20","doi-asserted-by":"publisher","unstructured":"Hussain, W., Merig\u00f3, J. M., Raza, M. R., & Gao, H. (2022b). A new QoS prediction model using hybrid IOWA-ANFIS  with fuzzy C-means, subtractive clustering and grid partitioning. Information Sciences, 584, 280\u2013300. https:\/\/doi.org\/10.1016\/j.ins.2021.10.054","DOI":"10.1016\/j.ins.2021.10.054"},{"key":"599_CR21","doi-asserted-by":"publisher","unstructured":"Islam, S., & Amin, S. H. (2020). Prediction of probable backorder scenarios in the supply chain using distributed random forest and gradient boosting machine learning techniques. Journal of Big Data, 7(1), 1\u201322. https:\/\/doi.org\/10.1186\/s40537-020-00345-2","DOI":"10.1186\/s40537-020-00345-2"},{"key":"599_CR22","doi-asserted-by":"publisher","unstructured":"Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685\u2013695.\u00a0https:\/\/doi.org\/10.1007\/s12525-021-00475-2","DOI":"10.1007\/s12525-021-00475-2"},{"key":"599_CR23","doi-asserted-by":"publisher","unstructured":"Jiao, L., & Zhao, J. (2019). A survey on the new generation of deep learning in image processing. IEEE Access, 7, 172231\u2013172263.\u00a0https:\/\/doi.org\/10.1109\/ACCESS.2019.2956508","DOI":"10.1109\/ACCESS.2019.2956508"},{"key":"599_CR24","doi-asserted-by":"publisher","unstructured":"Kingma, D. P., Salimans, T., & Welling, M. (2015). Variational dropout and the local reparameterization trick. Advances in neural information processing systems, vol 28.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1506.02557","DOI":"10.48550\/arXiv.1506.02557"},{"key":"599_CR25","doi-asserted-by":"publisher","unstructured":"\u0141adyy\u017cy\u0144ski, P., \u017bbikowski, K., & Gawrysiak, P. (2019). Direct marketing campaigns in retail banking with the use of deep learning and random forests. Expert Systems with Applications, 134, 28\u201335. https:\/\/doi.org\/10.1016\/j.eswa.2019.05.020","DOI":"10.1016\/j.eswa.2019.05.020"},{"key":"599_CR26","unstructured":"Lawal, S., & Akintola, K. (2021). A product backorder predictive model using recurrent neural network. IRE Journals, 4(8)."},{"key":"599_CR27","unstructured":"Li, Y. (2017). Backorder prediction using machine learning for danish craft beer breweries.  [PhD disseration, Aalborg University]."},{"key":"599_CR28","doi-asserted-by":"publisher","unstructured":"Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019). Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods, 166, 4\u201321. https:\/\/doi.org\/10.1016\/j.ymeth.2019.04.008","DOI":"10.1016\/j.ymeth.2019.04.008"},{"key":"599_CR29","doi-asserted-by":"publisher","unstructured":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, vol 30. https:\/\/doi.org\/10.48550\/arXiv.1705.07874","DOI":"10.48550\/arXiv.1705.07874"},{"key":"599_CR30","doi-asserted-by":"publisher","unstructured":"Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2022). Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53\u201363. https:\/\/doi.org\/10.1080\/10580530.2020.1849465","DOI":"10.1080\/10580530.2020.1849465"},{"key":"599_CR31","doi-asserted-by":"publisher","unstructured":"Moscato, V., Picariello, A., & Sperl\u00ed, G. (2021). A benchmark of machine learning approaches for credit score prediction. Expert Systems with Applications, 165, 113986. https:\/\/doi.org\/10.1016\/j.eswa.2020.113986","DOI":"10.1016\/j.eswa.2020.113986"},{"key":"599_CR32","doi-asserted-by":"publisher","unstructured":"Nowak, A. S., & Radzik, T. (1994). The shapley value for n-person games in generalized characteristic function form. Games and Economic Behavior, 6(1), 150\u2013161. https:\/\/doi.org\/10.1006\/game.1994.1008","DOI":"10.1006\/game.1994.1008"},{"key":"599_CR33","doi-asserted-by":"publisher","unstructured":"Ntakolia, C., Kokkotiis, C., Moustakidis, S., & Papageorgiou, E. (2021). An explainable machine learning pipeline for backorder prediction in inventory management systems. 25th Pan-Hellenic Conference on Informatics, pp. 229\u2013234. https:\/\/doi.org\/10.1145\/3503823.3503866","DOI":"10.1145\/3503823.3503866"},{"key":"599_CR34","doi-asserted-by":"publisher","unstructured":"Ntakolia, C., Kokkotis, C., Karlsson, P., & Moustakidis, S. (2021). An explainable machine learning model for material backorder prediction in inventory management. Sensors, 21(23), 7926.\u00a0https:\/\/doi.org\/10.3390\/s21237926","DOI":"10.3390\/s21237926"},{"key":"599_CR35","doi-asserted-by":"publisher","unstructured":"Panesar, A. (2019). Machine learning and AI for healthcare\u00a0(pp. 1\u2013407). Springer. https:\/\/doi.org\/10.1007\/978-1-4842-3799-1","DOI":"10.1007\/978-1-4842-3799-1"},{"key":"599_CR36","doi-asserted-by":"publisher","unstructured":"Ramachandran, P., Zoph, B., & Le, Q. V. (2017). Searching for activation functions. Arxiv. \nhttps:\/\/doi.org\/10.48550\/arXiv.1710.05941","DOI":"10.48550\/arXiv.1710.05941"},{"key":"599_CR37","doi-asserted-by":"publisher","unstructured":"Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using adaboost and majority voting. IEEE Access, 6, 14277\u201314284. https:\/\/doi.org\/10.1109\/ACCESS.2018.2806420","DOI":"10.1109\/ACCESS.2018.2806420"},{"key":"599_CR38","doi-asserted-by":"publisher","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135\u20131144. https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"599_CR39","doi-asserted-by":"publisher","unstructured":"Saraogi, G., Gupta, D., Sharma, L., & Rana, A. (2021). An un-supervised approach for backorder prediction using deep autoencoder. Recent Advances in Computer Science and Communications Formerly: Recent Patents on Computer Science, 14(2), 500\u2013511. https:\/\/doi.org\/10.2174\/2213275912666190819112609","DOI":"10.2174\/2213275912666190819112609"},{"key":"599_CR40","doi-asserted-by":"crossref","unstructured":"Shajalal, M., Abedin, M. Z., & Uddin, M. M. (n.d.). Handling class imbalance data in business domain. The essentials of machine learning in finance and accounting\u00a0(pp. 199-210). Routledge.","DOI":"10.4324\/9781003037903-11"},{"key":"599_CR41","doi-asserted-by":"publisher","unstructured":"Shajalal, M., Hajek, P., & Abedin, M. Z. (2021). Product backorder prediction using deep neural network on imbalanced data. International Journal of Production Research, 1\u201318. https:\/\/doi.org\/10.1080\/00207543.2021.1901153","DOI":"10.1080\/00207543.2021.1901153"},{"issue":"2","key":"599_CR42","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s12525-020-00441-4","volume":"31","author":"S Thiebes","year":"2021","unstructured":"Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31(2), 447\u2013464. https:\/\/doi.org\/10.1007\/s12525-020-00441-4","journal-title":"Electronic Markets"},{"key":"599_CR43","unstructured":"Srivastava, N. (2013). Improving neural networks with dropout.\u00a0[Thesis, University of Toronto, 182(566), 7]."},{"key":"599_CR44","doi-asserted-by":"publisher","unstructured":"Wu, H., & Gu, X. (2015). Max-pooling dropout for regularization of convolutional neural networks. International Conference on Neural Information Processing, pp 46\u201354. https:\/\/doi.org\/10.48550\/arXiv.1512.01400","DOI":"10.48550\/arXiv.1512.01400"}],"container-title":["Electronic Markets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12525-022-00599-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12525-022-00599-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12525-022-00599-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T05:51:18Z","timestamp":1676613078000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12525-022-00599-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,9]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["599"],"URL":"https:\/\/doi.org\/10.1007\/s12525-022-00599-z","relation":{},"ISSN":["1019-6781","1422-8890"],"issn-type":[{"value":"1019-6781","type":"print"},{"value":"1422-8890","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,9]]},"assertion":[{"value":"8 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}