[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118423-en":3,"doc-seo-118423-105":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},118423,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Advancing Organizational Analytics - A Strategic Roadmap for Implementing Machine Learning in Warehouse Management System","The thesis examines how advanced analytics, with a focus on machine learning, can classify spare parts to support better space allocation and packing in warehouse operations. The work targets a logistics organization facing missing dimensional data in its enterprise resource planning system and replaces the missing information through a classification approach. A comprehensive literature review covers warehouse management systems, machine learning in supply chains, and analytical maturity, while data is gathered through employee interviews and analyzed via a design science methodology.","Advancing Organizational Analytics: A Strategic Roadmap for Implementing Machine Learning in Warehouse Management System  \nUniversity of Oulu Information Processing Science Master’s Thesis  \nNándor Hajdu  \nAbstract  \nThis thesis explores how advanced analytics, specifically machine learning, can be utilized to classify spare parts, aiding the case company in optimizing space allocation and packing in warehousing. The study focuses on an organizational branch involved in logistics operations, aiming to address the issue of missing dimensional data in the company's enterprise resource planning system. By substituting this data with a classification system, the logistics stakeholders can enhance and streamline existing processes for better customer service.  \nA comprehensive literature review covers warehouse management systems, machine learning applications in supply chains, and analytical maturity. The research employs design science methodology, involving the creation of an innovative artifact to tackle the company's current challenges. Data collection was conducted through interviews with various employees to gather insights into their perspectives on analytical maturity, attitudes toward artificial intelligence and machine learning, and the lack of dimensional data. This qualitative data provided an understanding of the current analytical environment and resources to accommodate an advanced analytical solution.  \nThe resulting artifact is a roadmap with a high-level machine learning model to assist with dimensional data. The findings suggest that this artifact can serve as a valuable guideline for advancing the company's analytical capabilities. It highlights key areas for supporting machine learning development and deployment. However, the study encountered two significant limitations: the abstract nature of the artifact necessitates implementation for proper evaluation, and the human resources aspect of analytical maturity was not considered in the roadmap due to insufficient data.  \nKeywords  \nOperation Management , Machine Learning , Warehouse, Optimization  \nSupervisor  \nMsc, University Teacher, Sami Pohjolainen PhD, University Lecturer, Elina Annanperä  \nForeword  \nI am deeply grateful for the opportunity to collaborate with an unnamed case company on this master's thesis during the academic year 2023-2024, and this experience has been both challenging and enriching. I would like to express my sincere appreciation to Antti and Otto, whose unwavering support and insightful input were crucial to the successful completion of this thesis. I also extend my gratitude to all the company members who participated in the interviews and provided valuable feedback, greatly enhancing the quality and depth of this research.  \nFurthermore, I am incredibly thankful for the expert guidance and engaging discussions provided by my academic mentors at the University of Oulu, Elina , and Sami. Their wisdom and academic rigor have significantly influenced the direction and quality of this thesis, and I am grateful for their contributions.  \nFinally, I would like to express my deepest gratitude to my family and friends. Their constant support, empathy, and encouragement have been the bedrock of my inspiration and resilience throughout this challenging academic pursuit.  \nAbbreviations  \n3PL – Third– Party Logistics AI – Artificial Intelligence  \nANN – Artificial Neural Networks  \nBA– Business Analytics BDA – Big Data Analytics BI – Business Intelligence DS – Design Science  \nDSR – Design Science Research  \nDSRM – Design Science Research Methodology  \nERP – Enterprise Resource Planning ETL – Extract, Transform, Load HU – Handling Unit  \nHUM– Handling Unit Management IS – Information Systems  \nIT – Information Technology JIT – Just in Time  \nKNN – K– nearest neighbor KPI – Key Performance Indicator MAE – Mean Absolute Error  \nML – Machine Learning NN – Neural Networks  \nPoC – Proof-of-Concept RF – Random Forests  \nRMSE – Root Mean Squared Erro","cbCairYqLI6WRqPF","https://ap.wps.com/l/cbCairYqLI6WRqPF","pdf",1994472,1,77,"English","en",105,"# Contents\n## Abstract\n## Foreword\n## Abbreviations\n## 1. Introduction\n## 2. Background","[{\"question\":\"How does the thesis use machine learning in warehouse management?\",\"answer\":\"It applies machine learning to classify spare parts, replacing missing dimensional data so logistics processes can be improved for packing and space allocation.\"},{\"question\":\"What problem in the enterprise resource planning system does the study address?\",\"answer\":\"The study addresses the absence of dimensional data in the company’s ERP, which affects warehouse planning and execution.\"},{\"question\":\"What research approach is used to develop the solution?\",\"answer\":\"The study uses design science methodology, creating an artifact—a roadmap with a high-level machine learning model—to guide implementation.\"}]","Advancing Organizational Analytics - A Strategic Roadmap for Implementing Machine Learning in Warehouse Management System | PDF",1785683542,194,{"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},"advancing-organizational-analytics-a-strategic-roadmap-for-implementing-machine-learning-in-warehouse-management-system","",{"@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/advancing-organizational-analytics-a-strategic-roadmap-for-implementing-machine-learning-in-warehouse-management-system/118423/",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 use machine learning in warehouse management?","Question",{"text":75,"@type":76},"It applies machine learning to classify spare parts, replacing missing dimensional data so logistics processes can be improved for packing and space allocation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem in the enterprise resource planning system does the study address?",{"text":80,"@type":76},"The study addresses the absence of dimensional data in the company’s ERP, which affects warehouse planning and execution.",{"name":82,"@type":73,"acceptedAnswer":83},"What research approach is used to develop the solution?",{"text":84,"@type":76},"The study uses design science methodology, creating an artifact—a roadmap with a high-level machine learning model—to guide implementation.","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"]