Machine Learning Forecast-based Replenishment

Machine Learning Forecast-Based Replenishment is a replenishment methodology that leverages machine learning algorithms to generate demand forecasts and dynamically optimize inventory restocking decisions in response to predicted future demand patterns.

10

Publications

375

Citations

38

Authors

24

Institutions

Publications per year

2017–2026

7

Authors

38

  1. VK
    Vinod Kumar Mishra

    Madan Mohan Malaviya University of Technology, India

    2 publications · 44 citations

    2

    h-index

  2. RS
    Ranu Singh

    Madan Mohan Malaviya University of Technology, India

    2 publications · 44 citations

    2

    h-index

  3. FC
    Fei Chiang

    McMaster University, Canada

    1 publications · 40 citations

    1

    h-index

  4. JT
    John T. Blake

    Dalhousie University, Canada

    1 publications · 40 citations

    1

    h-index

  5. CK
    C.K. Kwong

    Hong Kong Polytechnic University, Hong Kong SAR China

    1 publications · 31 citations

    1

    h-index

1–5 of 38

Venues

  1. Materials Today Proceedings

    1 publications · 11 citations

    1

    h-index

  2. Neurocomputing

    1 publications · 31 citations

    1

    h-index

  3. Annals of Operations Research

    1 publications · 26 citations

    1

    h-index

  4. Journal of the Operational Research Society

    OR · 1 publications · 18 citations

    1

    h-index

  5. International Journal of Production Research

    1 publications · 105 citations

    1

    h-index

1–5