TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Shajalal, Md
A1  - Boden, Alexander
A1  - Stevens, Gunnar
T1  - Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses
JF  - Electronic Markets
N2  - 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.
KW  - CNN
KW  - eXplainable artifcial intelligence (XAI)
KW  - Global explanati
KW  - Local explanation
KW  - Backorder prediction
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-65077
SN  - 1019-6781
SS  - 1019-6781
U6  - https://doi.org/10.1007/s12525-022-00599-z
DO  - https://doi.org/10.1007/s12525-022-00599-z
VL  - 32
IS  - 4
SP  - 2107
EP  - 2122
PB  - Springer
ER  -