| Approaches for the prediction of lead times in an engineer to order environment—A systematic review P Burggräf, J Wagner, B Koke, F Steinberg IEEE Access 8, 142434-142445, 2020 | 31 | 2020 |
| Trust in artificial intelligence within production management–an exploration of antecedents T Saßmannshausen, P Burggräf, J Wagner, M Hassenzahl, T Heupel, ... Ergonomics 64 (10), 1333-1350, 2021 | 25 | 2021 |
| Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell P Burggräf, J Wagner, B Heinbach, F Steinberg, L Schmallenbach, ... Procedia CIRP 104, 641-646, 2021 | 11 | 2021 |
| Machine learning-based prediction of missing components for assembly–a case study at an engineer-to-order manufacturer P Burggräf, J Wagner, B Heinbach, F Steinberg IEEE Access, 2021 | 10 | 2021 |
| A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry F Steinberg, P Burggräf, J Wagner, B Heinbach, T Saßmannshausen, ... Supply Chain Analytics 1, 100003, 2023 | 6 | 2023 |
| Impact of material data in assembly delay prediction—a machine learning-based case study in machinery industry F Steinberg, P Burggaef, J Wagner, B Heinbach The International Journal of Advanced Manufacturing Technology 120 (1), 1333 …, 2022 | 4 | 2022 |
| Reinforcement learning for process time optimization in an assembly process utilizing an industry 4.0 demonstration cell P Burggräf, F Steinberg, B Heinbach, M Bamberg Procedia CIRP 107, 1095-1100, 2022 | 4 | 2022 |
| Life cycle assessment for adaptive remanufacturing: incorporating ecological considerations into the planning of maintenance activities–a case study in the German heavy … P Burggräf, J Wagner, F Steinberg, B Heinbach, M Wigger, ... Procedia CIRP 105, 320-325, 2022 | 4 | 2022 |
| Smart Containers—Enabler for More Sustainability in Food Industries? P Burggräf, F Steinberg, T Adlon, P Nettesheim, H Kahmann, L Wu Congress of the German Academic Association for Production Technology, 416-426, 2022 | 2 | 2022 |
| Machine learning implementation in small and medium-sized enterprises: insights and recommendations from a quantitative study P Burggräf, F Steinberg, CR Sauer, P Nettesheim Production Engineering, 1-14, 2024 | 1 | 2024 |
| Bridging data gaps in the food industry–sensor-equipped metal food containers as an enabler for sustainability P Burggräf, F Steinberg, T Adlon, P Nettesheim, J Salzwedel ESSN: 2701-6277, 687-697, 2023 | 1 | 2023 |
| Publication II: Impact of Material Data in Assembly Delay Prediction—a Machine Learning-based Case Study in Machinery Industry F Steinberg Machine Learning-based Prediction of Missing Parts for Assembly, 51-74, 2024 | | 2024 |
| Theoretical Background for the Prediction of Missing Parts for Assembly F Steinberg Machine Learning-based Prediction of Missing Parts for Assembly, 9-25, 2024 | | 2024 |
| Publication I: Approaches for the Prediction of Lead Times in an Engineer to Order Environment—a Systematic Review F Steinberg Machine Learning-based Prediction of Missing Parts for Assembly, 27-49, 2024 | | 2024 |
| Publication IV: Predicting Supplier Delays Utilizing Machine Learning—a Case Study in German Manufacturing Industry F Steinberg Machine Learning-based Prediction of Missing Parts for Assembly, 105-136, 2024 | | 2024 |
| Critical Refection and Future Perspective F Steinberg Machine Learning-based Prediction of Missing Parts for Assembly, 137-138, 2024 | | 2024 |
| Publication III: Machine Learning-based Prediction of Missing Components for Assembly—a Case Study at an Engineer-to-order Manufacturer F Steinberg Machine Learning-based Prediction of Missing Parts for Assembly, 75-103, 2024 | | 2024 |
| Deciding on when to change–a benchmark of metaheuristic algorithms for timing engineering changes P Burggräf, F Steinberg, T Weißer, O Radisic-Aberger International Journal of Production Research 62 (9), 3230-3250, 2024 | | 2024 |
| Enhancing Resource Efficiency And Monetization In Metal Recycling Through Supply Chain-Wide Digitalization: An Approach For Single-Variety Metal Stream Optimization And CO2 … P Burggräf, F Steinberg, M Diebel, A Becher, CR Sauer, M Wigger, ... ESSN: 2701-6277, 729-740, 2024 | | 2024 |
| Machine Learning-based Prediction of Missing Parts for Assembly F Steinberg Springer Nature, 2024 | | 2024 |
Prof. Dr.-Ing. Peter BurggräfSiegen University; Aachen UniversityVerified email at wzl.rwth-aachen.de
