[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127133-en":3,"doc-seo-127133-105":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},127133,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Quality in Production Planning - Definition, Quantification and a Machine Learning Based Improvement Method","Production plans lose reliability rapidly within days after creation, driven by uncertainties, insufficient or low-quality planning data, unsuitable planning/control models, and unforeseeable events. These deviations increase control effort to meet logistical KPIs such as due-date reliability and lead time. The paper proposes a machine-learning-based methodological approach to measure and improve planning quality. It also clarifies missing standards for a definition of planning quality and distinguishes it from robust planning using an industrial steel-manufacturing case.","[Available online at](Available online at www.sciencedirect.com)[ www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia Computer Science 217 (2023) 358–365  \n4th International Conference on Industry 4.0 and Smart Manufacturing Quality in production planning: Definition, quantification and a  \nmachine learning based improvement method  \nLukas Lingitza , Viola Gallinaa,∗, Johannes Breitschopfa , Luana Finamorea , Wilfried  \nSihna,b  \na Fraunhofer Austria Research GmbH, Theresianumgasse 7, A-1040 Wien,Austria  \nb Vienna University of Technology, Research Unit of Industrial-, Systems Engineering and Facility Management, Theresianumgasse 27, A-1040  \nWien, Austria  \nAbstract  \nThe reliability of production plans drops drastically within several days after plan creation. The reasons for the deviation between planning and execution are manifold. Causes can be e.g., uncertainties, inaccurate or insufficient planning data (e.g., data quality and availability), inappropriate planning and control models and systems or unforeseeable events, leading to high control effort in order to reach the desired logistical KPI’s, such as due date reliability or lead time. The paper addresses this problem with machine learning and states a methodological approach to measure and improve the quality in production planning. Although the term “planning quality”(PQ) has been used several times by the scientific community, a standard definition of PQ in the field of production planning and a clear distinction to other similar concepts like “robust planning” is still missing. Furthermore, PQ and its application within the production planning process are evaluated with a real industrial use case from the steel manufacturing industry.  \n© 2022 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 4th International Conference on Industry 4.0 and Smart Manufacturing  \nKeywords: production planning and scheduling; resilience; machine learning; planning quality; prediction  \n1. Introduction  \nThe importance of logistics performance, quantified by delivery time, inventory levels, capacity utilization or lead time, is becoming increasingly important for sustainable business success [1]. Production planning and control (PPC) coordinates all relevant logistics activities along the entire order processing chain and therefore contributes significantly to ensuring the economic realization of the production program reaching the desired logistics performance [2]  \n[1] . Moreover, complexity in planning is driven on the one hand externally due to fluctuations in demand and supply  \n∗ Corresponding author. Tel.: +43-676-888-61-646  \n[E-mail address:](E-mail address: viola.gallina@fraunhofer.at)[ viola.gallina@fraunhofer.at](E-mail address: viola.gallina@fraunhofer.at)  \n1877-0509 © 2022 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 4th International Conference on Industry 4.0 and Smart Manufacturing  \n10.1016/j.procs.2022.12.231  \nLukas Lingitz et al. / Procedia Computer Science 217 (2023) 358–365 359  \nwithin the past two years caused by the Covid-19 pandemic and the resulting crisis [3] . However, this trend cannot be considered as new, but it evolved in the past decades and is likely to continue in the future. Furthermore, complexity within PPC is driven by the interaction between the production system, the increasing number of digital artefacts and the social aspects named as “cyber-physical sozio-system”(CPSS) [4] . This results in increasing complexity and anon-linear behaviour of th","cbCaiuYL4A9voxen","https://ap.wps.com/l/cbCaiuYL4A9voxen","pdf",547649,1,"English","en",105,"# Introduction\n## Production plan reliability and logistics performance\n## Drivers of planning complexity in Industry 4.0\n## Objective and methodological approach","[{\"question\":\"Why does production plan reliability drop after plan creation?\",\"answer\":\"Reliability decreases within days due to uncertainties, inaccurate or insufficient planning data, inadequate planning/control models, and unforeseeable events.\"},{\"question\":\"What does the paper propose to address the planning quality problem?\",\"answer\":\"It presents a methodological approach that uses machine learning to measure and improve the quality of production planning.\"},{\"question\":\"How is planning quality (PQ) positioned compared with similar concepts?\",\"answer\":\"The paper notes that while “planning quality” has been used, a standard definition and a clear distinction from concepts like “robust planning” are still missing, which it aims to address.\"}]","Quality in Production Planning - 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