A Machine Learning Model for Forecasting Inventory Requirements in Small-Scale Retail Logistics Systems

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@article{1709616,
      author = {Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho, Patience Okpeke Paul},
      title = {A Machine Learning Model for Forecasting Inventory Requirements in Small-Scale Retail Logistics Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {11},
      pages = {447-461},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709616.pdf},
      abstract = {Inventory forecasting in small-scale retail logistics systems presents a persistent challenge due to resource constraints, unpredictable consumer behavior, and limited access to advanced planning tools. Traditional forecasting methods often fall short in handling the non-linearities and variability characteristic of retail demand, especially in small-scale operations. This paper proposes a conceptual machine learning-based inventory forecasting model tailored to small-scale retail environments, focusing on optimizing stock levels, reducing holding and stock-out costs, and improving decision-making accuracy. Through a comprehensive literature review of over 100 scholarly and industry sources, this paper identifies relevant forecasting challenges, evaluates current inventory prediction models, and consolidates best practices in machine learning implementation. The proposed framework integrates supervised learning techniques, such as Random Forest and Gradient Boosting, with time-series data preprocessing and feature engineering strategies. Key factors considered include sales trends, promotional events, seasonal effects, and supplier lead times. The model's applicability is discussed in the context of resource-limited settings, with a focus on scalability, interpretability, and minimal data preprocessing. The study contributes to the field by offering a roadmap for data-driven inventory optimization and guiding future research in machine learning applications in low-resource retail logistics systems.},
      keywords = {machine learning inventory forecasting model, small-scale retail logistics systems, demand prediction algorithm efficiency, data-driven supply chain optimization, supervised learning inventory models, retail stock-out risk management},
      month = {May},
  }