ACCURATE DEMAND FORECASTING FOR ARABICA COFFEE EXPORTS: A CV. GAYO COFFEE ORO PERSPECTIVE

Abstract

Demand forecasting is a crucial aspect for every business, as it involves predicting future events using specific techniques to make informed decisions. The decomposition method is a popular forecasting technique that employs four components, namely trend, seasonal, cycle, and random, to predict future events based on the repeating patterns observed in historical data. The coffee industry in Indonesia serves as a pertinent case study due to its significant demand, both domestically and through exports. This study focuses on CV. Oro Kopi Gayo, an Indonesian coffee processing company, which primarily produces Arabica coffee beans for export. However, the company faces challenges in meeting export demands during non-coffee season periods, resulting in production inefficiencies and strained business relationships. Consequently, accurate demand forecasting becomes crucial to optimize production and maintain positive stakeholder interactions. This research aims to develop an effective demand forecasting model for Arabica coffee exports, addressing the company's challenges. The study involves analyzing historical export data, identifying patterns, and applying the decomposition method to predict future demand accurately. By incorporating the trend, seasonal, cycle, and random components, the forecasting model can provide valuable insights for decision-makers. The primary data sources include export data from the International Coffee Organization (ICO) and the Ministry of Trade, providing a comprehensive understanding of the coffee export market in Indonesia. Additionally, qualitative data from interviews with key personnel within CV. Oro Kopi Gayo will complement the quantitative analysis, incorporating subjective judgments to enhance the forecasting model's accuracy. The anticipated research findings will not only assist CV. Oro Kopi Gayo in improving their production planning but also contribute to the wider body of knowledge in demand forecasting. The study's significance lies in its potential to provide valuable insights and practical implications for companies facing similar challenges in other agricultural sub-sectors. Moreover, by optimizing production planning, businesses can reduce production costs and enhance their competitiveness in the global market