<![CDATA[This research was motivated by the company's inability to meet modern retail market demand of 21%. This research was conducted to analyze demand data patterns for leaf vegetables, predict future market demand, and develop production planning strategies to obtain optimal profits. The research analysis methods used are moving average, single exponential smoothing, ARIMA. The selection of the appropriate forecasting method is determined based on the smallest error value produced. Production planning to determine the right production strategy. Based on the research results, it is clear that the resulting request data pattern is a random pattern. The chosen forecasting method is Single exponential smoothing for romain and ARMA for coriander, curly lettuce, horenzo. The results of the research show that forecasting demand for leaf vegetables for the next six months obtained a constant level of demand forecasting for romain and varied with a decreasing trend for coriander, curly lettuce, and horenzo. Production planning strategies to increase profits in this research can be fulfilled with two alternatives, namely achieving land area using the economy of scale principle and increasing supply from partners with a comparison of the benefits of increasing land area with a more optimal economy of scale principle, namely 300% for romain, 450% for coriander, 237% for curly lettuce, and 185% for horenzo rather than increasing supply from partners. The advantage of this research is discussing four types of commodities while comparing three quantitative analysis methods. The contribution to the company from this research is being able to analyze up to the comparison stage the profits that will be received by the company. This research provides a solution so that companies can prepare production in an effective and efficient manner and achieve optimal profits.]]>
Copyrights © 2025
