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Palm yağı fiyatlarını etkileyen parametreler ve fiyat tahmin modeli

Parameters affecting palm oil prices and price forecasting model

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  1. Tez No: 948972
  2. Yazar: HASAN ÖZMAY
  3. Danışmanlar: DOÇ. DR. ESRA BAŞ
  4. Tez Türü: Yüksek Lisans
  5. Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2025
  8. Dil: Türkçe
  9. Üniversite: İstanbul Teknik Üniversitesi
  10. Enstitü: Lisansüstü Eğitim Enstitüsü
  11. Ana Bilim Dalı: Endüstri Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Endüstri Mühendisliği Bilim Dalı
  13. Sayfa Sayısı: 79

Özet

Bitkisel yağ piyasalarında oluşan fiyat hareketlerinden hareketle, özellikle palm yağındaki fiyat dalgalanmaları üzerine bir tahminleme modeli oluşturulması için literatür taraması yapıldı. Yapılan araştırmalar ve taranan makaleler sonucunda emtia piyasalarındaki fiyat hareketlerinden başlayarak fiyat dalgalanmalarına sebep olabilecek parametreler belirlendi. Bu parametreler oluşturulan etki-frekans analiziyle incelendi. Tümden gelimle başlanan çalışmada etkisi ve gerçekleşme olasılığı yüksek olan parametreler arasında bir ilişki kurmak üzerine yoğunlaşıldı. Tedarik zincirindeki arz talep dengesinden, stok tutma maliyetlerine kadar pek çok teknik terimden faydalanılarak emtia piyasalarındaki fiyat hareketlerinin şirketlerin karlılık hedefleri için ne kadar etkili olabileceği tartışıldı. Araştırmada ilk önce bitkisel yağlar ve palm yağı ile ilgili bilgi verilmiş, ardından bu piyasadaki fiyat hareketlerine yönelik parametreler ve tarihsel olaylar incelenmiştir. Palm yağının fiyat dalgalanmaları ile ilgili etkili olabilecek politik istikrarsızlıklar, son on yılda yaşanan ülkeler arası çatışmalar, palm yağı sektöründe yürütülen projeler, ASEAN ülkelerindeki yabancı işçi oranları ile tamamlayıcı ve ikame ürünler gibi parametrelerin incelendiği makaleler taranmış, incelenen tüm parametreler bir tablo haline getirilmiştir. Bu parametrelerin birbiriyle olan ilişkisini anlamlandırabilmek için kullanılacak olan ana metodoloji sistem dinamiği olarak belirlenmiştir. Fakat sistem dinamiğinden farklı olarak sistemin tamamına odaklanmak yerine, küçük sistemlerin ilişkisine odaklanılmış ve bütündeki payı anlaşılmaya çalışılmıştır. Bu sebeple de incelenen tüm parametrelerden etkisinin ve gerçekleşme olasılığı yüksek olan parametreler belirlenmiştir. Odaklanılan parametrelerle metodolojinin çıktısı olarak bir palm fiyatı tahmin modeli ortaya konulmuştur. Parametrelerin oluşturduğu modeldeki değişkenliklere bağlı olarak palm yağı fiyatlarında değişimler simüle edilmiştir. Bu palm fiyatı tahmin modelinin bir tedarik zinciri modelinde nasıl uygulanabileceğine dair tartışmalar yapılmış ve sonuçlar paylaşılmıştır. Uygulamanın fizibilite çalışmasında tedarik zinciri açısından değerlendirmesini yapabilmek için gerekli olabilecek terimlerinin bulunması için bir kaynak taraması yapılmıştır. Literatür taramalarında seçilen arz talep dengesi, stok tutma maliyeti, teslim süresi gibi terimlere ağırlık verilerek uygulamaya devam edilmiştir. Tedarik zinciri modellemelerinde palm yağı fiyatlarında oluşabilecek kırılganlıklarına göre hareket edebilmesi için dinamik bir karar verme modeli kurulmaya çalışılmıştır.

Özet (Çeviri)

A comprehensive literature review was carried out to develop a forecasting model that integrates both economic and supply chain perspectives, specifically targeting the price fluctuations observed in the global vegetable oil markets, with a particular emphasis on palm oil. This review aimed to bridge the gap between macroeconomic indicators, political influcers, and operational supply chain variables, offering a holistic understanding of the forces driving price volatility. By systematically analyzing existing academic literature and industrial knowledge, the study identified a wide array of parameters that influence commodity price movements. The study, which adopted a deductive approach, concentrated on establishing relationships among parameters that had both high impact and high probability of occurrence. Through the use of various technical supply chain terms ranging from supply-demand balance to inventory holding costs, the impact of price movements in commodity markets on companies' sustanability on profitability targets was discussed. The research commenced with a foundational overview of commodity markets and general supply chain strategies, which emphasizing the dynamic nature of supply chains and the necessity for adaptable network models that respond to changing market conditions. The literature review then narrowed its focus to vegetable oils, with palm oil receiving particular attention due to its widespread use across multiple industries, including food, cosmetics, and biofuel. As one of the most consumed vegetable oils globally, palm oil's market behavior is of critical importance to stakeholders across the value chain. Global palm oil production is geographically concentrated in Southeast Asia, and palm oil prices highly sensitive to regional developments such as climate conditions, labor policies, and trade regulations. The research followed by an examination of parameters influencing price movements in this market, as well as relevant historical events. Academic articles investigating the parameters that could potentially affect price fluctuations in palm oil were reviewed, and all examined parameters were represented into a table. Political instability, conflicts between countries over the past decade, projects that conducted in palm oil sectors, the ratios of foreign labor in ASEAN countries, complementary products and substitute products can be example of these parameters. In addition to the core analyses conducted in this study, further investigations were carried out on parameters such as boycott, freight rates and trend shifts, which have the potential to contribute to the existing literature. These variables, often underexplored in conventional models, were examined for their relevance and impact on palm oil price dynamics. System dynamics was determined as the primary methodology to understand the interrelationships among these parameters. However, unlike traditional system dynamics approaches that focus on the entire system, this study concentrated on the interactions within smaller subsystems in an effort to comprehend their contribution to the whole. Therefore, among all examined parameters, those with high impact and high likelihood of occurrence were selected for further analysis. To ensure a comprehensive identification of focused parameters, a stepwise methodological framework was designed and implemented. Quantitative data spanning a 25-year period were collected for the selected parameters. This dataset included production and export volumes of palm, soybean, and coconut oils; global and regional price indices; inflation and GDP figures; and transportation and logistics costs. These data points formed the foundation for the forecasting model. Results of regression model was used as an input for system dynamics. Focusing on these selected parameters, a palm oil price forecasting model was proposed as the outcome of the methodology. A regression model was developed using historical data to estimate the annual directional movement of palm oil prices. The model simulated price changes based on historical variations in the selected parameters, capturing both direct and indirect effects. Given that changes in certain parameters influence others, system simulation was employed to better capture and illustrate these interdependencies. The model's predictive capability was further validated through scenario analysis and simulation using a stock-flow structure, allowing for the testing of various assumptions such as changes in land use, production efficiency, and port capacities. Furthermore, discussions were held on how this palm oil price forecasting model could be applied within a supply chain framework, and the results were presented. Results of regression showed that parameters, which have normally positive relationship with palm oil price, caused a price decrease in palm oil prices. The forecasting model has several practical applications. In supply chain management, it enables firms to anticipate price fluctuations and adjust stock management strategies accordingly. This is particularly important given the long lead times and logistical constraints associated with sourcing from Southeast Asia. An analytical equation is proposed for companies seeking to implement the model in order to optimize their inventory strategies. This formulation enables firms to evaluate the trade-off between anticipated price fluctuations and inventory holding costs, thereby supporting data-driven decision-making within their supply chain operations. In financial planning, the model supports budgeting, hedging, and cost control, enhancing profitability and competitiveness. For agricultural planning, including farmers and cooperatives, the model provides valuable insights for crop planning and land use optimization. Moreover, the model can be integrated into sustainability initiatives by helping companies prioritize sourcing from certified sustainable producers. To assess the feasibility of the model's application from a supply chain perspective, a literature review was conducted to identify relevant terminology. Particular emphasis was placed on concepts such as supply-demand balance, inventory holding cost, and lead time. These terms were used to guide the implementation phase. To account for potential volatilities in palm oil prices, a system dynamics model was developed within the scope of supply chain modeling. The research also highlighted the importance of continuously updating the regression model with real-time data to maintain its relevance and effectiveness in a rapidly changing global market with using established methodology. In conclusion, this study presents a robust, data-driven framework for understanding and forecasting palm oil prices. By focusing on high-impact parameters and leveraging system dynamics, it provides actionable insights for stakeholders across the palm oil supply chain with a regression model that achieved 75% directional accuracy. The model's adaptability and predictive power make it a valuable asset for navigating the complexities of the global commodity market. Future research could enhance the model by incorporating real-time data and machine learning algorithms, broadening its applicability to other commodities and improving its predictive accuracy.

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