{"title":"Findings From Production Management Research","description":null,"products":[{"product_id":"machine-learning-based-prediction-of-missing-parts-for-assembly-book-fabian-steinberg-9783658450328","title":"Machine Learning-based Prediction of Missing Parts for Assembly","description":"Manufacturing companies face challenges in managing increasing process complexity while meeting demands for on-time delivery, particularly evident during critical processes like assembly. The early identification of potential missing parts at the beginning assembly emerges as a crucial strategy to uphold delivery commitments. This book embarks on developing machine learning-based prediction models to tackle this challenge. Through a systemic literature review, deficiencies in current predictive methodologies are highlighted, notably the underutilization of material data and a late prediction capability within the procurement process. Through case studies within the machine industry a significant influence of material data on the quality of models predicting missing parts from in-house production was verified. Further, a model for predicting delivery delays in the purchasing process was implemented, which makes it possible to predict potential missing parts from suppliers at the time of ordering. These advancements serve as indispensable tools for production planners and procurement professionals, empowering them to proactively address material availability challenges for assembly operations.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51123329171729,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51123333103889,"sku":"NIN9783658450328","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52593693720849,"sku":"NLS9783658450328","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3658450320.jpg?v=1770374416"},{"product_id":"optimisation-and-control-of-engineering-change-schedules-in-the-automotive-indus-book-ognjen-radii-aberger-9783658510732","title":"Optimisation and Control of Engineering Change Schedules in the Automotive Industry with Metaheuristics and Machine Learning","description":"Adaptation and change are imperative for products and companies to remain competitive. Managing these changes, however, is increasingly difficult and requires thorough planning and management. Especially in complex production systems, the efficient handling of these engineering changes becomes a competitive edge. This book embarks upon the task to manage the increasingly difficult optimisation and control of engineering changes through artificial intelligence. Based on a knowledge base gained from a systematic literature review, it is shown how AI methods can be applied to resolve challenges faced in production environments. Based on metaheuristic algorithms, optimal EC effectivity dates are determined, which are then validated and controlled by machine learning based business process monitoring. These advances provide significant support for change coordinators and material planners by reducing administrative effort end ensuring complexity control.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":53264990634257,"sku":"NGR9783658510732","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":53352576876817,"sku":"NLS9783658510732","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783658510732.jpg?v=1773876189"},{"product_id":"human-ai-collaboration-in-production-management-book-carl-ren-sauer-9783658510046","title":"Human-AI Collaboration in Production Management","description":"The advent of artificial intelligence (AI) and machine learning (ML) has significantly transformed production management in industrial manufacturing by enabling data-driven decision-making. While human decision-making is valued for its adaptability and contextual understanding, AI-driven systems offer the advantage of faster, data-driven decisions. The concept of hybrid intelligence combines these two poles by utilizing the potential of AI without neglecting the necessary integration of human expertise. However, a structured method for determining the appropriate degree of automation – defining the division of tasks between humans and AI for each decision – is still lacking. Thus, this thesis develops a framework to determine the optimal level of Human-AI collaboration for production management use cases. This framework enables organizations to leverage AI effectively across various scenarios, complementing human expertise to enhance operational efficiency and decision-making processes. By offering a systematic method, the framework helps avoid suboptimal AI\/ML deployments and supports organizations in harnessing hybrid intelligence for innovative and future-ready production management.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":53264991256849,"sku":"NGR9783658510046","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":53352576909585,"sku":"NLS9783658510046","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783658510046.jpg?v=1773876191"},{"product_id":"reinforcement-learning-based-planning-of-factory-layouts-book-benjamin-heinbach-9783658515539","title":"Reinforcement Learning-Based Planning of Factory Layouts","description":"Facility layout planning is a core discipline in production management, directly shaping operational efficiency, material flow, and cost structures. Despite its criticality, facility layout planning presents a complex combinatorial problem, often approached through heuristics or metaheuristics that lack scalability and adaptability. This book investigates the use of (Deep) Reinforcement Learning (DRL) to automate and enhance layout planning by conceptualising facility layout planning as a Markov Decision Process (MDP). The author found that DRL agents – trained solely through interaction feedback without domain-specific input – can autonomously generate layout configurations that significantly reduce material handling costs and generalise across varying problem instances, thus demonstrating DRL's viability as a scalable and adaptive resolution technique for facility layout planning. Building on the conceptual parallel between human iterative layout adjustment and Reinforcement Learning processes, this research follows a Design Science Research paradigm of experimental artefact design. It unfolds over four peer-reviewed publications. Beyond the experimental contributions, this work opens a path toward AI-driven factory planning tools that can potentially reduce planning effort, improve layout quality, and ultimately enable more responsive and data-driven production system design in dynamic industrial environments.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":53475973169425,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":53475973497105,"sku":"NGR9783658515539","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783658515539.jpg?v=1778752800"}],"url":"https:\/\/www.worldofbooks.com\/en-gb\/collections\/findings-from-production-management-research-book-series.oembed","provider":"World of Books ","version":"1.0","type":"link"}