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Advances in demand forecasting

  • 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,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.show moreshow less

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Metadaten
Author details:Benedict BenderORCiDGND, Sina Bretschneider, Jasmin Fattah-WeilORCiDGND
URL:https://aisel.aisnet.org/amcis2024/stratcompis/stratcompis/7/
Title of parent work (English):AMCIS Proceedings 2024
Subtitle (English):a systematic review of methods, the role of AI, and data strategies in manufacturing
Publisher:AIS
Place of publishing:Atlanta
Publication type:Conference Proceeding
Language:English
Year of first publication:2024
Publication year:2024
Release date:2024/08/07
Tag:demand forecasting; forecasting data; forecasting methods; manufacturing industry; sales forecasting; systematic literature review
Number of pages:11
First page:1
Last Page:11
Organizational units:Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre
DDC classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Peer review:Nicht referiert
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