[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117173-en":3,"doc-seo-117173-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117173,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine Learning in Predictive Quality Management - Master’s Thesis","This master’s thesis examines how machine learning can outperform traditional Statistical Process Control (SPC) and Anomaly Detection (AD) for monitoring and controlling process parameters to keep systems stable and meet quality objectives. Predictive Quality Management is framed through predictive analytics, integrating forecasting of future process patterns with machine learning tools. A fishing-industry case study uses AIS, catch, and environmental data to derive correlations, apply data cleaning and preprocessing, and evaluate multiple regression models to forecast fish species locations.","Department of Industrial Engineering  \nMachine Learning in Predictive Quality Management  \nSimbarashe Keith Chikwekwe  \nMaster’s thesis in Industrial Engineering-INE 3900 – May 2024  \nAbstract  \nThis thesis investigates how machine learning techniques has greater advantages over traditional Statistical Process Control (SPC) methods and Anomaly Detection (AD) in monitoring and controlling process parameters so that the process remains stable and in control to meet required quality satisfactions. The broader term of Predictive Quality Management introduces an important subject of predictive analytics which can be collaborated with machine learning tools to predict or forecast future process patterns. This thesis employs a case study, of fishing industries that amass large marine data in form of Automatic Identification Systems data, catch data and environmental but fail to draw meaningful correlations between data variables towards sustainable and efficient use of fishing vessel resources. Different machine learning models were implemented, techniques for data cleaning and preprocessing provided a leeway to draw patterns and trends in our dataset. and a performance evaluation using suitable metrics was conducted to determine which regressor algorithm predicts and generate forecasts for location of fish species. This thesis contributes to a deep understanding of data analysis and offers recommendations to decision makers.  \nKeywords: statistical process control, predictive analytics, anomaly detection, random forest, machine learning, metrics, algorithm.  \nTable of Contents  \n1 Introduction ........................................................................................................................ 1  \n1.1 Background.................................................................................................................. 1  \n1.1.1 Case: Fishing Industries ....................................................................................... 3  \n1.2 Problem Statement....................................................................................................... 4  \n1.3 Project benefits ............................................................................................................ 4  \n1.4 Hypothesis ................................................................................................................... 5  \n1.5 Assumption .................................................................................................................. 5  \n1.6 Objectives .................................................................................................................... 5  \n1.7 Scope ........................................................................................................................... 5  \n1.7.1 A review on the use of data in SPC and machine learning .................................. 5  \n1.7.2 Data availability ................................................................................................... 6  \n1.7.3 References/Links .................................................................................................. 6  \n1.8 Organization ................................................................................................................ 6  \n2 Literature Review ............................................................................................................... 8  \n2.1 Data Analytics in Fisheries .......................................................................................... 8  \n2.1.1 Machine Learning in fisheries .............................................................................. 9  \n2.2 Empirical Studies Review ........................................................................................... 9  \n2.2.1 Statistical Process Control (SPC) ......................................................................... 9  \n2.2.2 Successful application of SPC.................................................","cbCaisedH0oyxR3J","https://ap.wps.com/l/cbCaisedH0oyxR3J","pdf",2798347,1,101,"English","en",105,"# Introduction\n## Background\n## Case: Fishing Industries\n## Problem Statement\n## Project benefits\n## Hypothesis\n## Assumption\n## Objectives\n## Scope\n## Organization\n# Literature Review\n## Data Analytics in Fisheries\n## Empirical Studies Review\n## Anomaly Detection in Manufacturing\n## Machine Learning in Anomaly Detection\n## Challenges of Anomaly Detection\n## Link between SPC and ADM\n## Research Gaps and Trends\n## Summary of Literature Review\n# Methodology\n## Research Philosophy","[{\"question\":\"How does the thesis compare machine learning with traditional SPC and anomaly detection approaches?\",\"answer\":\"It investigates the advantages of machine learning over traditional Statistical Process Control (SPC) methods and Anomaly Detection (AD) for monitoring and controlling process parameters to keep processes stable and in control.\"},{\"question\":\"What predictive task does the thesis focus on in the fishing-industry case study?\",\"answer\":\"It uses AIS, catch, and environmental data to forecast future patterns, implementing regression models to predict and generate forecasts for the locations of fish species.\"},{\"question\":\"How are the machine learning models and data processing steps evaluated?\",\"answer\":\"The thesis applies data cleaning and preprocessing to enable pattern discovery, then conducts performance evaluation using suitable metrics to identify which regressor algorithm best produces the forecasts.\"}]","Machine Learning in Predictive Quality Management - Master’s Thesis | PDF",1785674219,255,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-in-predictive-quality-management-masters-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-in-predictive-quality-management-masters-thesis/117173/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis compare machine learning with traditional SPC and anomaly detection approaches?","Question",{"text":75,"@type":76},"It investigates the advantages of machine learning over traditional Statistical Process Control (SPC) methods and Anomaly Detection (AD) for monitoring and controlling process parameters to keep processes stable and in control.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What predictive task does the thesis focus on in the fishing-industry case study?",{"text":80,"@type":76},"It uses AIS, catch, and environmental data to forecast future patterns, implementing regression models to predict and generate forecasts for the locations of fish species.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the machine learning models and data processing steps evaluated?",{"text":84,"@type":76},"The thesis applies data cleaning and preprocessing to enable pattern discovery, then conducts performance evaluation using suitable metrics to identify which regressor algorithm best produces the forecasts.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]