Real-time Demand Forecasting

Real-Time Demand Forecasting is a demand sensing method that continuously assimilates high-frequency, near-instantaneous data streams to dynamically update demand predictions at minimal latency, distinguishing it from traditional forecasting by its emphasis on immediacy, responsiveness, and operational integration in supply chain decision-making.

137

Publications

7.7K

Citations

426

Authors

225

Institutions

Publications per year

2017–2026

69

Authors

426

  1. MF
    Marco Franchini

    University of Ferrara, Italy

    4 publications · 300 citations

    4

    h-index

  2. SA
    Stefano Alvisi

    University of Ferrara, Italy

    4 publications · 300 citations

    4

    h-index

  3. ZK
    Zoran Kapelan

    University of Exeter, United Kingdom

    4 publications · 303 citations

    4

    h-index

  4. RL
    Renzhi Lu

    Huazhong University of Science and Technology, China

    2 publications · 185 citations

    2

    h-index

  5. RF
    Robert Fildes

    Lancaster University, United Kingdom

    2 publications · 85 citations

    2

    h-index

1–5 of 426

Venues

  1. International Journal of Production Economics

    12 publications · 786 citations

    12

    h-index

  2. Applied Energy

    7 publications · 484 citations

    7

    h-index

  3. Expert Systems with Applications

    4 publications · 352 citations

    4

    h-index

  4. Procedia Engineering

    4 publications · 105 citations

    4

    h-index

  5. 4

    h-index

1–5