TY  - CHAP
A1  - Bender, Benedict
A1  - Bretschneider, Sina
A1  - Fattah-Weil, Jasmin
T1  - Advances in demand forecasting
BT  - a systematic review of methods, the role of AI, and data strategies in manufacturing
T2  - AMCIS Proceedings 2024
N2  - 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.
KW  - demand forecasting
KW  - sales forecasting
KW  - forecasting methods
KW  - manufacturing industry
KW  - forecasting data
KW  - systematic literature review
Y1  - 2024
UR  - https://aisel.aisnet.org/amcis2024/stratcompis/stratcompis/7/
SP  - 1
EP  - 11
PB  - AIS
CY  - Atlanta
ER  -