Decision Analytics Journal最新文献

{"title":"A machine learning and explainability-driven methodology for identifying winning strategies in Rugby Union","authors":"Arnaud Odet ,&nbsp;Thomas Bechard ,&nbsp;Pierre Moretto ,&nbsp;Sebastien Dejean ,&nbsp;Cristian Pasquaretta","doi":"10.1016/j.dajour.2025.100568","DOIUrl":"10.1016/j.dajour.2025.100568","url":null,"abstract":"<div><div>Interest in predicting sports match outcomes has grown significantly, driven by advancements in machine learning techniques and widespread adoption. However, the utilization of these predictive models in enhancing tactical team performance remains relatively limited. We propose a methodology that combines machine learning and algorithm explainability techniques, which were demonstrated through a case study on Rugby Union. Our study unfolds in two phases: first, we identify the most suitable modeling approach for our data by establishing a prediction model based on performance indicators observed during games. Subsequently, we applied an analysis based on SHapley Additive exPlanations (SHAP) values to interpret the predictions of this model. Our findings serve three primary purposes: (i) from a global standpoint, identifying performance indicators that primarily determine match outcomes; (ii) from an aggregated point of view highlighting strengths and weaknesses of any given team; and (iii) from a local perspective, offering technical staff diagnostic analyses of past games.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100568"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An investigation of Frequentist and Ensemble Bayesian-aided techniques for prioritizing anomaly detection methods in time-series data","authors":"Vignesh Divakaran ,&nbsp;Vipasha Rana","doi":"10.1016/j.dajour.2025.100566","DOIUrl":"10.1016/j.dajour.2025.100566","url":null,"abstract":"<div><div>Accurately detecting anomalous points in time-series data is critical, as false positives can mislead business stakeholders, waste valuable resources, and diminish the overall impact of the detection system. While various statistical and machine learning techniques are employed to flag potential anomalies, the challenge lies in evaluating the significance of each approach and refining the results to isolate definitive anomalies. This paper examines multiple anomaly tagging techniques and introduces novel weightage assignment methods to prioritize the most effective approaches, filtering out less reliable ones. Specifically, we explore two methods: simple Frequentist approach and Ensemble Bayesian-aided approach, with an emphasis on why the latter is particularly well-suited for anomaly detection. The proposed methodology is validated both theoretically and empirically on time-series datasets. Our findings demonstrate that the Ensemble Bayesian-aided approach significantly improves detection accuracy by accounting for future uncertainty and addressing edge case fallacies inherent in individual tagging methods. This research provides a robust framework for anomaly detection, offering a powerful solution that enhances precision and reliability across diverse applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100566"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A predictive modelling approach to decoding consumer intention for adopting energy-efficient technologies in food supply chains","authors":"Brintha Rajendran,&nbsp;Manivannan Babu,&nbsp;Veeramani Anandhabalaji","doi":"10.1016/j.dajour.2025.100561","DOIUrl":"10.1016/j.dajour.2025.100561","url":null,"abstract":"<div><div>The transition towards energy-efficient practices in the food supply chain (FSC) is essential for addressing the dual imperatives of sustainability and cost-effectiveness. As consumers become increasingly aware of the environmental impact of their food choices, their willingness to support energy-efficient technologies (EET) has become a critical factor in shaping the future of sustainable FSC. This study empirically investigates consumer intention and desire to pay for food products characterized by a reduced energy footprint, utilizing machine learning (ML) algorithms to predict consumer preferences within the FSC. Association rule mining (ARM) was employed to uncover key patterns in consumer intentions, while multiple ML algorithms were compared to identify the most effective algorithm for predicting willingness to pay. The results reveal that the Random Forest algorithm achieved the highest accuracy at 82%, significantly outperforming other models. These findings underscore the potential of ML to refine marketing strategies and operational decisions, facilitating the broader adoption of EET within the FSC (EET-FSC). The study offers valuable implications for industry professionals seeking to enhance sustainability efforts through data-driven decision-making. The research contributes to optimizing FSC through improved decision-making, resource allocation, and sustainability initiatives. Future research directions include expanding the dataset scope, exploring advanced ML techniques, and examining the economic impacts of EET-FSC.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100561"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reinforcement learning and predictive analytics approach for enhancing credit assessment in manufacturing","authors":"Abdul Razaque ,&nbsp;Aliya Beishenaly ,&nbsp;Zhuldyz Kalpeyeva ,&nbsp;Raisa Uskenbayeva ,&nbsp;Moldagulova Aiman Nikolaevna","doi":"10.1016/j.dajour.2025.100560","DOIUrl":"10.1016/j.dajour.2025.100560","url":null,"abstract":"<div><div>The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities, and less favorable client conditions. To overcome this issue, a comprehensive credit assessment scoring system should be implemented to increase importers’ confidence. The article proposes a predictive-based reinforcement learning (PRL) model to help manufacturers and importers acquire more accurate and dependable credit scores while avoiding default risk. Furthermore, the proposed PRL model enhances decision-making, system efficiency, and risk-tolerant financial conditions. To attain these cutting-edge objectives, the proposed PRL model combines three algorithms. Algorithm 1 collects and aggregates data to indicate areas for improvement if credit scoring is poor. Algorithm 2 uses reinforcement learning to validate and enhance bank scores. Algorithm 3 focuses on predictive modeling for bank scoring, ensuring that the credit decision-making system is operational and constantly improving. Furthermore, reinforcement learning leverages the features from local interpretable model-agnostic explanations (LIME) and shapely additive explanations (SHAP) to generate locally reliable explanations and attribute the contribution of each feature for determining the output of the model. The Python platform tests the proposed PRL to achieve the objectives. Based on the results, The PRL model markedly enhances credit assessment precision, achieving an accuracy of over 99.5%, which outstrips current methodologies such OCLA (96.12%), PSML (84.12%), and EMPCC (91.67%). Furthermore, the PRL model augments leverage ratios, rising from 2.75% in 2015 to 3.36% in 2024.5, and increases accounts receivable turnover from 4.38% in 2015 to 7.4% in 2024.5, surpassing alternative credit evaluation methodologies. This research highlights the novelty of combining predictive analytics and reinforcement learning to revolutionize credit assessment, providing a scalable and reliable solution for manufacturers and importers. The findings establish the PRL model as a transformative approach for creating risk-tolerant and efficient financial environments.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100560"},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multiple criteria analysis approach for assessing regional and territorial progress toward achieving the Sustainable Development Goals in Italy","authors":"Idiano D’Adamo ,&nbsp;Massimo Gastaldi ,&nbsp;Antonio Felice Uricchio","doi":"10.1016/j.dajour.2025.100559","DOIUrl":"10.1016/j.dajour.2025.100559","url":null,"abstract":"<div><div>Sustainability is a pressing global challenge demanding an integrated approach balancing economic, environmental, and social perspectives. Numerous indicators have been proposed in the literature to assess progress toward the Sustainable Development Goals (SDGs), including equitable and sustainable well-being (BES). To effectively manage and monitor these indicators, robust analytical models are essential. The present study proposed an integrated analytical framework combining the 0–1 (min–max) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The analysis examined 61 indicators from the 2024 Equitable and Sustainable Well-being of the Territories, a dataset developed by the Italian National Institute of Statistics (ISTAT) to assess regional social, economic, and environmental sustainability across Italy. The results confirmed a persistent north–south divide, with average scores of 3.9 in the north and 1.4 in the south on a 1–5 scale, and central Italy demonstrating an intermediate performance of 3.1. At the regional level, Trentino-Alto Adige, Lombardia, and Valle d’Aosta emerged as top performers, while at the territorial level, Milano, Bologna, and Trieste stood out. These insights highlight the need for stronger synergies between territories to enhance competitiveness and elevate “Made in Italy” on the global stage. Effective regional collaboration may optimize resource allocation, harmonize territorial disparities, and accelerate progress toward the SDGs by leveraging local strengths and fostering sustainable development.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100559"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interval-valued Pythagorean fuzzy approach to mitigate traffic congestion in densely populated cities with implications for sustainability","authors":"Md. Fahim Bin Alam ,&nbsp;A.B.M. Mainul Bari ,&nbsp;Saifur Rahman Tushar ,&nbsp;K.M. Ariful Kabir","doi":"10.1016/j.dajour.2025.100558","DOIUrl":"10.1016/j.dajour.2025.100558","url":null,"abstract":"<div><div>Traffic congestion (TC) disrupts everyday life, elevating stress levels, extending commute durations, diminishing productivity, hampering air quality, and reducing the overall quality of life. Congestion exacerbates already-existing problems in densely populated cities and makes efficient urban planning more difficult. Hence, reducing TC is necessary to create sustainable and livable communities. Therefore, this study employs a novel hybrid multi-criteria decision-making (MCDM) framework, integrating the interval-valued Pythagorean fuzzy (IVPF) theory with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze the challenges to mitigate TC in the densely populated urban areas of an emerging economy like Bangladesh. The challenges were identified through a review of existing literature, which was later validated by a panel of experts. The findings of the study suggest that the three most significant challenges to TC mitigation are “Lack of efficient coordination and management of traffic signals,” “Insufficient choices for public transportation,” and “Lack of integration of advanced data analytics and IoT-based technologies.” The anticipated impact of this study lies in its substantial contribution to future innovation and development in urban planning and management. This study aims to alleviate TC in densely populated cities and promote urban sustainability by aiding policymakers, urban planners, and stakeholders in formulating long-term strategies.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100558"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The art and science of business analytics: A journey from data to action","authors":"Madjid Tavana , Prof. Dr.","doi":"10.1016/j.dajour.2025.100554","DOIUrl":"10.1016/j.dajour.2025.100554","url":null,"abstract":"<div><div>Business analytics is a balanced mix of art and science, where methods form the structure, and visualization breathes meaning and creativity into it. The key to success in problem-solving lies in the art of asking the right questions. Successful problem-solving combines curiosity and courage to challenge assumptions, pushing beyond the obvious to uncover hidden insights. The transition from investigation to creation in problem-solving follows the path of scientific inquiry, where a clear purpose guides every step. Models are like maps; even the most detailed and complete maps are pointless without a clear destination. Just as an artist requires the right brush to bring their ideas to life, a model requires quality data to uncover transformative insights. Model-building is an art of precision and humility, where knowing the boundaries sparks innovation, and failure becomes our most influential teacher. It is a dynamic process of experimentation, where each repetition refines our approach, and continuous iteration leads us to a broader perspective and deeper understanding. Business analytics is a powerful blend of imagination and rigor, where creativity and precision join to drive change. Models are more than equations and algorithms; they are dynamic and evolving reflections of real-world complexities. When the art and science of problem-solving successfully converge, they illuminate the journey from data to insightful actions.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100554"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An expert system for modeling skill levels for corporate power relations in an entropy-based environment using SPIRIT","authors":"Maximilian Schröer ,&nbsp;Elmar Reucher","doi":"10.1016/j.dajour.2025.100556","DOIUrl":"10.1016/j.dajour.2025.100556","url":null,"abstract":"<div><div>Collaboration and organizational structures in companies are changing, especially with the ‘New Way of Working’ post-COVID-19. From both a theoretical and practical perspective, a quantitative analysis of how skill levels evolve within the ‘New Way of Working’ provides valuable insights into power structures and potential changes. The expert system Shell-SPIRIT can be used to conduct these quantitative analyses. This system has already been applied to several articles to support faithful knowledge processing and to model power structures while measuring their power potentials. This article discusses the theoretical view and a case study about power structures modeled with an entropy-based approach using Shell-SPIRIT. Compared to previous studies, this article presents a novel approach that enhances realism by incorporating more variables. This means that participants’ skill levels are reflected directly in the quantitative model of quantitative power structures.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100556"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced outlier detection methods for enhancing beta regression robustness","authors":"Oktsa Dwika Rahmashari,&nbsp;Wuttichai Srisodaphol","doi":"10.1016/j.dajour.2025.100557","DOIUrl":"10.1016/j.dajour.2025.100557","url":null,"abstract":"<div><div>Beta regression is a valuable statistical technique for modeling response variables within the standard unit interval (0, 1), where values represent rates, proportions, or probabilities. However, outliers in beta regression can severely impact parameter estimates and model performance, leading to predicted values that deviate significantly from actual observations. Detecting and managing these outliers is essential to ensure model reliability and accuracy. In this study, we propose three novel outlier detection methods: Tukey-Pearson Residual (TPR), Iterative Tukey-Pearson Residual (ITPR), and Iterative Tukey-MinMax Pearson Residual (ITMPR). These methods integrate the principles of Tukey’s boxplot with Pearson residuals, providing robust frameworks for detecting outliers in beta regression models. Extensive simulation studies and real-world data applications were conducted to evaluate their performance against existing outlier detection techniques in the literature. The results indicate that the ITPR method achieves the highest levels of precision and reliability, making it the most effective among the proposed methods. The TPR and ITMPR methods also exhibit strong performance, closely aligning with existing techniques. These findings highlight the potential of the proposed methods to enhance the robustness of beta regression analysis and its practical applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100557"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An extended simple additive weighting decision support system with application in the food industry","authors":"Peyman Zandi ,&nbsp;Mehdi Ajalli ,&nbsp;Narges Soleiman Ekhtiyati","doi":"10.1016/j.dajour.2025.100553","DOIUrl":"10.1016/j.dajour.2025.100553","url":null,"abstract":"<div><div>This study aims to expand the application of the multi-criteria decision-making (MCDM) technique based on expanded information on the alternatives from sub-alternatives. For this purpose, some initial information is collected at the sub-alternative level. Then, based on the scores obtained for the sub-alternative level, the main alternatives are ranked using the simple additive weighting (SAW) method. The goal is to analyze decision alternatives and sub-alternatives, rank the alternatives according to criteria and sub-criteria, and analyze sensitivity based on their criteria and weights. A program is developed in MS Excel to dynamically explore a large amount of information. The results confirm the designed model’s ability to rank all alternatives and sub-alternatives. The model has been used to rank 220 products and 12 product portfolios in a food industry company. Five categories of decision criteria, including production, procurement, finance, product and sales, and competitors, were selected with 36 quantitative and qualitative sub-criteria. The results show that the market indicators and competitors directly impact the product portfolio’s priority. Some of the contributions of this research can be considered as a method for ranking alternatives based on the expanded information from sub-alternatives. As a management tool, the proposed model can be used in other fields and with different techniques to manage the portfolio of alternatives and sub-alternatives.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100553"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}