首页|Investigators at Leuphana University Luneburg Detail Findings in Machine Learning (Case Study On Delivery Time Determination Using a Machine Learning Approach In Small Batch Production Companies)
Investigators at Leuphana University Luneburg Detail Findings in Machine Learning (Case Study On Delivery Time Determination Using a Machine Learning Approach In Small Batch Production Companies)
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NETL
NSTL
Springer Nature
Investigators discuss new findings in Machine Learning. According to news reporting out of Luneburg, Germany, by NewsRx editors, research stated, “Delivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades.” Financial supporters for this research include Lower Saxony Ministry of Science and Culture, Lower Saxony “Vorab” of the Volkswagen Foundation, Center for Digital Innovations (ZDIN), German Federal Ministry of Economics and Technology (BMWi), through the Working Group of Industrial Research Associations (AIF). Our news journalists obtained a quote from the research from Leuphana University Luneburg, “In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions.”
LuneburgGermanyEuropeCyborgsEmerging TechnologiesMachine LearningLeuphana University Luneburg
NETL
NSTL
Springer Nature
