Browsing by Author "Carvalho, M. Sameiro"
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- A context-aware decision support system for selecting explainable artificial intelligence methods in business organizationsPublication . Reis, Marcelo I.; Gonçalves, João N. C.; Cortez, Paulo; Carvalho, M. Sameiro; Fernandes, João M.Explainable Artificial Intelligence (XAI) methods are valuable tools for promoting understanding, trust, and efficient use of Artificial Intelligence (AI) systems in business organizations. However, the question of how organizations should select suitable XAI methods for a given task and business context remains a challenge, particularly when the number of methods available in the literature continues to increase. Here, we propose a context-aware decision support system (DSS) to select, from a given set of XAI methods, those with higher suitability to the needs of stakeholders operating in a given AI-based business problem. By including the human-in-the-loop, our DSS comprises an application-grounded analytical metric designed to facilitate the selection of XAI methods that align with the business stakeholders’ desiderata and promote a deeper understanding of the results generated by a given machine learning model. The proposed system was tested on a real supply chain demand problem, using real data and real users. The results provide evidence on the usefulness of our metric in selecting XAI methods based on the feedback and analytical maturity of stakeholders from the deployment context. We believe that our DSS is sufficiently flexible and understandable to be applied in a variety of business contexts, with stakeholders with varying degrees of AI literacy.
- Improving freight quoting through business analytics: a case study of a logistics service providerPublication . Gonçalves, João N. C.; Correia, Miguel; Carvalho, M. SameiroIn the transportation sector, the process of estimating the profit margins to be applied to customer freight quotation requests is a problem of particular interest, which impacts strongly on customer relationship management. In practice, this process is typically conducted based on the subjective business experience of transport managers, posing challenges and delays in decision-making that can damage the customer relationship. This article explores a multivariate predictive analytics approach to support the process of estimating profit margins applied to customers for road freight transportation requests. Our approach consists of developing statistical learning models that make it possible to generalize historical relationships between a set of independent variables related to quotation requests and the respective profit margin applied and accepted by customers. The proposed approach is tested on empirical data from a portuguese logistics service provider. The results show that the proposed models have a good generalization capacity when tested on independent data under a rolling window evaluation mechanism. We discuss the managerial implications of the proposed approach and how it can serve as a decision support tool for applying profit margins to future requests for road freight transport quotation.