@inproceedings{BenderBretschneiderFattahWeil2024,
  author    = {Bender, Benedict and Bretschneider, Sina and Fattah-Weil, Jasmin},
  title     = {Advances in demand forecasting},
  series = {AMCIS Proceedings 2024},
  booktitle = {AMCIS Proceedings 2024},
  publisher = {AIS},
  address   = {Atlanta},
  pages     = {1 -- 11},
  year      = {2024},
  abstract  = {This systematic literature review highlights the gap in demand forecasting in the manufacturing sector, which is challenged by complex supply chains and rapid market change. Traditional methods fall short in this dynamic environment, highlighting the need for an approach that combines advanced forecasting techniques, high-quality data, and industry-specific insights. Our research contributes by evaluating advanced forecasting methods, the effectiveness of AI and data strategies to improve accuracy. Our analysis reveals a shift towards machine learning and deep learning to improve accuracy and highlights the untapped potential of external data sources. Key findings provide both researchers and practitioners with guidance on effective forecasting strategies and key data types and offer an integrated framework for improving forecasting accuracy and strategic decision-making in manufacturing. This work fills a critical research gap and provides stakeholders with actionable insights to manage the complexity of modern manufacturing, representing a significant advance in forecasting practice.},
  language  = {en}
}