[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119612-en":3,"doc-seo-119612-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119612,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","An ensemble Machine Learning algorithm for Lead Time Prediction","A research project presents a real case study using an ensemble machine learning approach to predict product lead time. The workflow applies a clustering method as preprocessing, then compares multiple supervised ML models to identify the most suitable predictor for the target industry. The study specifically evaluates how fuzzy clustering affects predictive quality. Results show Random Forest achieves the highest accuracy, and fuzzy clustering as a preprocessing step further improves predictive performance, supporting more reliable production planning.","An ensemble Machine Learning algorithm for Lead  \nTime Prediction  \nElena Barzizza1, Nicolò Biasetton1, Marta Disegna1, Alberto Molena1, Luigi Salmaso1  \n1University of Padova, Department of Management and Engineering, Stradella San Nicola, 3, 36100 Vicenza, Italy [elena.barzizza@phd.unipd.it](elena.barzizza@phd.unipd.it), [nicolo.biasetton@phd.unipd.it](nicolo.biasetton@phd.unipd.it), [marta.disegna@unipd.it](marta.disegna@unipd.it), [alberto.molena.1@phd.unipd.it](alberto.molena.1@phd.unipd.it), [luigi.salmaso@unipd.it](luigi.salmaso@unipd.it)  \nAbstract-In this research project a real case-study based on an ensemble Machine Learning algorithm aims to predict the lead time of a product is presented. Specifically, the prediction has been achieved by employing a clustering algorithm as a preprocessing method and comparing several supervised Machine Learning algorithms to determine which one is most suitable for the industry under analysis. The primary aim of this article is to assess the effectiveness of the fuzzy clustering algorithm in enhancing the performance of the prediction algorithm. Our analysis reveals that the Random Forest yields more accurate prediction. Furthermore, the application of a fuzzy clustering algorithm as pre-processing method proves to be advantageous in terms of predictive accuracy.  \nKeywords: Lead time, Fuzzy clustering, Predictive models, Ensemble Machine Learning algorithm.  \n© Copyright 2024 Authors - This is an Open Access article published under the Creative Commons Attribution License terms ([http://creativecommons.org/licenses/by/3.0](http://creativecommons.org/licenses/by/3.0)). Unrestricted use, distribution, and reproduction in any medium are permitted, provided the original work is properly cited.  \n1. Introduction  \nIn today’s business landscape, companies compete in a market that demands high-quality products delivered as quickly as possible and at competitive prices. To be efficient in these terms, it is crucial for companies to have a well-structured and reliable production planning and scheduling function. One of the most important Key Performance Indicators (KPIs) that companies consider in this area is undoubtedly the Production Lead Time (PLT), as an accurate forecast of this indicator enables more precise and efficient  \nproduction planning, allowing for a reliable prediction of the customer’s waiting time.  \nMoreover, having a reliable forecast of PLT is fundamental nowadays: indeed, the issue of an inaccurate prediction could have highly adverse effects on the company. Indeed, for example, if the PLT is underestimated, the inaccurate prediction leads to actual delays that might propagate throughout the entire supply chain, all the way to the end customer, who could feel disappointed or neglected, resulting in a decreased brand loyalty. However, underestimating PLT can also pose serious consequences for the company. For instance, if the time required to produce an item is less than initially predicted, the costs of warehousing the work in progress would significantly escalate.  \nWith the advent of Industry 4.0, advanced artificial intelligence technologies are becoming increasingly relevant and utilized within the planning and production scheduling process, as they allow accurate and precise estimation of the PLT.  \nIn this work, we aim to introduce a novel prediction process that combines supervised and unsupervised Machine Learning (ML) algorithms. Specifically, fuzzy C-medoids clustering algorithm is used as pre-processing method before the adoption of a predictive model to enhance predictive accuracy.  \nClustering is an unsupervised ML model, used to partition data into homogeneous groups based on a set of segmentation variables. Among the various clustering variants, two primary approaches emerge: crisp clustering and fuzzy clustering. These two approaches differ in their ability to handle uncertainty and flexibility in point-to-cluster assignments: in crisp clustering, ea","cbCaiuU8n8hYCgfJ","https://ap.wps.com/l/cbCaiuU8n8hYCgfJ","pdf",843192,1,"English","en",105,"# Introduction\n## Lead time prediction and Production Lead Time (PLT)\n## Role of Industry 4.0 and ML in planning\n# Methodology\n## Fuzzy C-medoids clustering as preprocessing\n## Crisp vs fuzzy clustering\n## C-means vs C-medoids\n# Literature review\n## Lead time prediction using machine learning algorithms\n# Case study structure\n## Related works\n## Proposed methodology\n## Case study formal statement\n## Findings and conclusions","[{\"question\":\"What is the main goal of the study on lead time prediction?\",\"answer\":\"The study aims to predict product lead time using an ensemble machine learning pipeline and to evaluate whether fuzzy clustering improves prediction quality.\"},{\"question\":\"How does fuzzy clustering contribute to the predictive model?\",\"answer\":\"Fuzzy clustering is used as a preprocessing step, creating a representation that helps the downstream supervised learning model achieve better predictive accuracy.\"},{\"question\":\"Which supervised learning approach delivers the most accurate predictions?\",\"answer\":\"The Random Forest model yields the most accurate lead time predictions in the analysis.\"}]","An ensemble Machine Learning algorithm for Lead Time Prediction | 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is the main goal of the study on lead time prediction?","Question",{"text":74,"@type":75},"The study aims to predict product lead time using an ensemble machine learning pipeline and to evaluate whether fuzzy clustering improves prediction quality.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does fuzzy clustering contribute to the predictive model?",{"text":79,"@type":75},"Fuzzy clustering is used as a preprocessing step, creating a representation that helps the downstream supervised learning model achieve better predictive accuracy.",{"name":81,"@type":72,"acceptedAnswer":82},"Which supervised learning approach delivers the most accurate predictions?",{"text":83,"@type":75},"The Random Forest model yields the most accurate lead time predictions in the 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