[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121435-en":3,"doc-seo-121435-105":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},121435,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Organizational, Sociological and Procedural Uncertainties in Statistical and Machine Learning - A Systematic Literature Review","Digitalization has increased the use of statistical learning and machine learning for scheduling and forecasting in supply chains, yet trust is weakened by uncertainties in data quality, data sharing platforms, and data processing. Decision-relevant outputs become vulnerable through problems in training data, learned models, and the embedded infrastructure. Given the high stakes of supply-chain disruptions, the work asks how far such systems should be trusted for strategic, operational, and tactical decisions. A systematic literature review is conducted and yields a concept matrix synthesizing findings across organizational, sociological, and procedural perspectives.","CONFERENCE ON PRODUCTION SYSTEMS AND LOGISTICS  \nCPSL 2023􀀐2  \n5th Conference on Production Systems and Logistics Organizational, Sociological and Procedural Uncertainties in  \nStatistical and Machine Learning: A Systematic Literature Review  \nNick Große 1, Maximiliane Wilkesmann2, Andrea Bommert3  \n1Chair of Enterprise Logistics, TU Dortmund University, Germany  \n2Sociology of Work and Organization, TU Dortmund University, Germany  \n3Department of Statistics, TU Dortmund University, Germany  \nAbstract  \nDriven by the potential of digitalization, statistical learning and machine learning methods are commonly used for scheduling complex processes or forecasting in supply chain domains. However, trust in such methods is hampered by uncertainties in data quality, data exchange platforms, and data processing, affecting its traceability and reliability. Decision-relevant output provided by such methods is prone to trust issues in the data used for training, in the resulting model, and in the infrastructure in which the model is embedded. Considering the vulnerability of supply chains, wrong decisions have far-reaching consequences, raising the question of to what extent systems alone should be trusted for strategic, operational, and tactical decisionmaking. In this paper, we take a multidisciplinary perspective with the intention to analyze trust in statistical learning and machine learning methods from an organizational, sociological, and procedural perspective. The information base for this article is gathered through a systematic literature review. The central results of our research are a concept matrix comparing papers based on relevant criteria derived from literature and subsequent findings derived from this matrix. We encourage researchers in the fields of supply chain management, sociology, and statistics or machine learning to open up for interdisciplinary research and to build upon our findings.  \nKeywords  \nTrust; Uncertainty; Statistical Learning; Machine Learning; Supply Chain Management; Literature Review  \n1. Introduction  \nSupply chain actors are subject to vulnerability and interdependence of partners within a value network, in which trust becomes increasingly relevant [1] . In the era of digitalization and Industry 4.0, the omnipresent efforts of data collection and utilization provide companies with a decision basis for estimating the recent status and upcoming changes of asset conditions and processes [2] . As part of so-called decision support systems, companies use statistical learning and machine learning methods exemplarily for forecasting or classification [2] . Statistical learning and machine learning methods belong to the ubiquitous field ‘artificial intelligence ’ (AI) [3] . Because most decision-makers in supply chain management are less experienced in statistics and computer science, ambiguity in their use is considerable [4] . Knowing when and why to trust such methods is mandatory to enable decision-makers to make resilient decisions under remaining uncertainty in the data and its further processing [4] .  \nOur research deals with trust and uncertainty issues evoked by statistical learning and machine learning methods resulting from a lack of transparency about the interplay of the data, model, and the infrastructure in which it is embedded. However, considering only an organizational lens neglects a profound  \nDOI: [https://doi.org/10.15488/15255](https://doi.org/10.15488/15255)  \nISSN: 2701-6277  \n527  \nunderstanding of how and why uncertainties arise in statistical learning and machine learning methods at different levels (micro, meso, macro) . Therefore, a complementary view from an organizational, sociological, and statistical or machine learning perspective (which we call a procedural perspective) is necessary. The research question of our article is: “To what extent do recent research activities investigate uncertainties from organizational, sociological, and procedural perspectives, conside","cbCairzfk3VSgZAa","https://ap.wps.com/l/cbCairzfk3VSgZAa","pdf",431923,1,11,"English","en",105,"# Introduction\n## Trust and uncertainty in supply chains and decision support\n# Criteria on Trust and Uncertainty in Statistical Learning and Machine Learning\n## Organizational perspective\n# Methodology and literature review\n## Concept matrix and results visualization\n# Conclusion and future research outlook","[{\"question\":\"Why does trust in statistical learning and machine learning for supply chains become a concern?\",\"answer\":\"Trust is hampered by uncertainties in data quality, data exchange platforms, and data processing, which reduce traceability and reliability across training, modeling, and infrastructure.\"},{\"question\":\"What multidisciplinary perspective does the paper use to study uncertainties?\",\"answer\":\"It combines organizational, sociological, and procedural perspectives to analyze how uncertainties arise at different levels and how they affect trust in statistical and machine learning methods.\"},{\"question\":\"How are the paper’s results structured and summarized?\",\"answer\":\"The study conducts a systematic literature review and derives a concept matrix that compares papers using criteria from the literature and synthesizes subsequent findings across perspectives.\"}]","Organizational, Sociological and Procedural Uncertainties in Statistical and Machine Learning - A Systematic Literature Review | PDF",1785735650,28,{"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},"organizational-sociological-and-procedural-uncertainties-in-statistical-and-machine-learning-a-systematic-literature-review","",{"@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/organizational-sociological-and-procedural-uncertainties-in-statistical-and-machine-learning-a-systematic-literature-review/121435/",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","2026-08-03",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},"Why does trust in statistical learning and machine learning for supply chains become a concern?","Question",{"text":75,"@type":76},"Trust is hampered by uncertainties in data quality, data exchange platforms, and data processing, which reduce traceability and reliability across training, modeling, and infrastructure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What multidisciplinary perspective does the paper use to study uncertainties?",{"text":80,"@type":76},"It combines organizational, sociological, and procedural perspectives to analyze how uncertainties arise at different levels and how they affect trust in statistical and machine learning methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the paper’s results structured and summarized?",{"text":84,"@type":76},"The study conducts a systematic literature review and derives a concept matrix that compares papers using criteria from the literature and synthesizes subsequent findings across perspectives.","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"]