[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-450430-105":59,"doc-detail-450430-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","optimizing-energy-downtime-and-throughput-in-footwear-production-through-machine-learning","Optimizing energy, downtime, and throughput in footwear production through machine learning","","A study evaluates how optimizing machine learning models can improve production efficiency in footwear manufacturing. By systematically refining a logistic regression model, predictive accuracy rises from 94.12% to 97.06% with complete specificity (100%). Compared with other supervised algorithms such as SVM, Naive Bayes, and Multinomial classifiers, the tuned model achieves stronger sensitivity, F1-score, and balanced accuracy. These gains yield higher throughput (+7.2%), reduced equipment downtime (-9%), and lower energy consumption (-5.3%), supporting sustainable intelligent manufacturing decisions.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/optimizing-energy-downtime-and-throughput-in-footwear-production-through-machine-learning/450430/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/optimizing-energy-downtime-and-throughput-in-footwear-production-through-machine-learning/450430.png","ImageObject",300,407,{"name":92,"@type":93},"Kyle","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-30",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What model optimization approach improves footwear production efficiency in the study?","Question",{"text":111,"@type":112},"The study refines a logistic regression model through systematic parameter and model optimization to improve predictive performance.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"How does the optimized model perform compared with other supervised learning algorithms?",{"text":116,"@type":112},"It delivers superior sensitivity, F1-score, and balanced accuracy versus Support Vector Machines, Naive Bayes, and Multinomial classifiers.",{"name":118,"@type":109,"acceptedAnswer":119},"What measurable industrial improvements are reported from the predictive gains?",{"text":120,"@type":112},"The results show a 7.2% throughput enhancement, a 9% reduction in equipment downtime, and a 5.3% decrease in overall energy consumption.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},450430,1790733199,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":36},3985741905716,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nOptimizing energy, downtime, and throughput in footwear production through machine learning  \nP. K. Sudhakar􀀍 & R. Muthucumaraswamy  \nThis study examines the influence of machine learning model optimization on improving production efficiency within the footwear manufacturing domain. Through systematic refinement of the logistic regression model, predictive accuracy increased from 94.12 to 97.06%, while achieving complete specificity (100%), indicating a stronger capability to correctly classify defect free outputs. When benchmarked against other supervised learning algorithms, including Support Vector Machines, Naïve Bayes, and Multinomial classifiers, the optimized model exhibited superior sensitivity, F1-score, and balanced accuracy, demonstrating its robustness across diverse operational conditions. From an industrial performance perspective, these predictive gains translated into measurable process improvements a 7.2% enhancement in production throughput, a 9% reduction in equipment downtime, and a 5.3% decrease in overall energy consumption. Such improvements emphasize the practical relevance of integrating tuned classification models with real-time manufacturing analytics. The results collectively underscore the potential of advanced data driven optimization frameworks to enhance productivity, energy efficiency, and sustainability within intelligent footwear production shop floor.  \nKeywords Process planning, Flow shop scheduling, Downtime, Throughput, Performance evaluation, Sustainable optimization  \nThe rapid evolution of intelligent manufacturing has increased the demand for predictive models that can optimize real time decision making in complex production environments1. Automation and cyber physical integration have enabled continuous data acquisition, requiring analytical methods that support operational adaptability and energy efficient scheduling2. Regression based methods such as multivariate adaptive regression splines (MARS) are widely used due to their capability to capture nonlinear interactions while maintaining interpretability, making them effective for production reliability and process optimization3. These models have demonstrated strong performance in improving precision driven manufacturing tasks and reducing operational uncertainties4. In parallel, machine learning techniques such as support vector machines are applied for classification and fault detection due to their robustness in handling non-linear boundaries5. Logistic regression has been adopted for predictive quality analytics in categorical systems6, while Naive Bayes is utilized for fast probabilistic decision making in high variability production conditions7.  \nOptimization of takt time has emerged as a key strategy for aligning production capacity with demand while controlling cost and variability8. Studies demonstrate the use of MARS models to enhance precision machining performance and reduce energy fluctuations in process intensive applications9. Empirical investigations further show how regression-based forecasting improves schedule adherence in industries with dynamic takt time requirements10. MARS driven prediction models have also been effective in optimizing production cycle time under varying operational conditions11. Regression based hybrid methodologies continue to gain prominence in modelling nanofluid processing and predicting precision manufacturing behaviour12. Moreover, genetic algorithms have proven effective in reducing energy consumption by optimizing machine allocation sequences13. Other studies have successfully applied hybrid metaheuristics to minimize job tardiness and energy waste inflow shop scheduling14. Regression fuzzy integrated models have also been used for energy forecasting and environmental impact prediction in industrial operations15.  \nMachine learning has contributed significantly to throughput optimization through predict","cbCaigo6ED54HdVk","https://ap.wps.com/l/cbCaigo6ED54HdVk","pdf",2212713,16,"English","# Introduction\n## Machine learning for intelligent manufacturing\n## Predictive modeling approaches\n# Model optimization and benchmarking\n## Logistic regression refinement\n## Comparison with other supervised algorithms\n# Industrial performance impact\n## Throughput enhancement\n## Downtime reduction\n## Energy consumption reduction\n# Sustainability and practical relevance","[{\"question\":\"What model optimization approach improves footwear production efficiency in the study?\",\"answer\":\"The study refines a logistic regression model through systematic parameter and model optimization to improve predictive performance.\"},{\"question\":\"How does the optimized model perform compared with other supervised learning algorithms?\",\"answer\":\"It delivers superior sensitivity, F1-score, and balanced accuracy versus Support Vector Machines, Naive Bayes, and Multinomial classifiers.\"},{\"question\":\"What measurable industrial improvements are reported from the predictive gains?\",\"answer\":\"The results show a 7.2% throughput enhancement, a 9% reduction in equipment downtime, and a 5.3% decrease in overall energy consumption.\"}]","Optimizing energy, downtime, and throughput in footwear production through machine learning | PDF"]