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Forecasting Tractor Demand in Two Major Agricultural Crop-Producing Provinces of Iran

Vakili, Sepideh Sadat | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58265 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Rezapour Niari, Maryam
  7. Abstract:
  8. Today, demand function forecasting is one of the fundamental and critical challenges in organizational decision-making at both strategic and operational levels. Key decisions that significantly impact the success or failure of organizations -such as pricing, production planning, resource allocation, and market development- are directly influenced by the accuracy of demand forecasting. Since the demand function is typically affected by multiple factors including price, quality, economic conditions, social factors, and other variables shaping customer behavior, precise estimation requires employing diverse and accurate methods. Various approaches have been proposed in the literature for demand function forecasting, which can be broadly categorized into three main groups: survey-based methods, statistical methods, and machine learning techniques. Each of these approaches has its own advantages and limitations, and their effectiveness varies depending on the industry type and availability of data. The choice of forecasting method plays a significant role in the accuracy of demand predictions. This study aims to integrate these three categories through a hybrid approach applied at different stages to improve demand function forecasting. The case study focuses on forecasting tractor demand based on agricultural needs in the two major crop-producing provinces of Fars and Khuzestan in Iran -an area that, despite its importance for food security, sustainable development, agricultural product pricing, and economic considerations, has been relatively underexplored in domestic research. The findings of this research provide valuable insights for policymakers and stakeholders in this field to gain a clearer understanding of demand behavior. This understanding can help not only in mitigating challenges caused by demand uncertainty but also in formulating more effective pricing policies
  9. Keywords:
  10. Demand Function ; Demand Forecasting ; Statistical Methods ; Machine Learning ; Survey ; Customer Choice Behavior ; Factors Affecting Tractor Demand ; Data-Driven Forecasting ; Tractor Demand Forecasting

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