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

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.

Tendencias del uso de la Inteligencia Artificial en Empresas Globales en el periodo 2018-2023. Una revisión sistemática de la literatura científica

“…[15], [23], [27], [62] y [63]. La inteligencia artificial implementada en la robótica y otras tecnologías con un alto grado de autonomía deben ser reguladas y respaldadas por seguridad para la confianza del individuo y posible cliente de las empresas dotadas de IA.…”

Explainable and responsible artificial intelligence

“…In the first paper, "Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses", the authors Md Shajalal, Alexander Boden and Gunnar Stevens (Shajalal et al, 2022) explore how intelligent predictive models could be made explainable to the stakeholders in strategic inventory management. It proposes a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction.…”

“…New XAI methods were proposed, such as a novel global XAI approach by means of linguistic rules based on NLP building block (Binder et al, 2022), while Zacharias et al (2022) suggested an innovation regarding the pre-processing stage of AI by a new feature selection method based on XAI. Regarding the impact of XAI, it has been shown that transparent AI can be a very effective tool for decision support in different business domains such as inventory management (Shajalal et al, 2022) or human resources (Hofeditz et al, 2022), and others. For instance, the latter study highlighted that with XAI, bias and discrimination in decision making can be mitigated.…”