Fresh Fruit Bunch (FFB) grading analysis for palm oil industry based on descriptive and predictive analytics

Mohamad Fadzli, Nurul Hafika Hafina (2023) Fresh Fruit Bunch (FFB) grading analysis for palm oil industry based on descriptive and predictive analytics. Project Report. Melaka, Malaysia, Universiti Teknikal Malaysia Melaka. (Submitted)

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Abstract

Fresh Fruit Bunches (FFB) grading is an important step in palm oil production as it allows producers to optimize oil extraction and ensure consistent quality. The detection of the quality of FFB is done based on Malaysian Standard (MS) grade specifications. The grading process usually involves the evaluation of various FFB, including ripe bunches, “Mengkal” bunches, young bunches, empty bunches, rotten bunches, fermented bunches, and old bunches. The dataset obtained is from one of the palm oil companies. This project involves the evaluation and prediction of outcome through a numerical value. Therefore, the developer decided to test out data with a few time series models. The models that are used are autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt-Winters (HOLTS), Naïve forecasting and Trigonometric seasonality Box-Cox transformation ARIMA errors Trend Seasonal components (TBATS). To ensure that each mill delivers good quality of palm oil, meets the set percentage, and can make it easier for the staff to monitor the grading of palm oil, the developer proposed index scoring for FBB grading based on mathematical calculation. This dashboard was developed with the aim of improving quality grading and business performance. This system is produced not only to monitor the grading but can also see the prediction for the index scoring produced according to the mill in the coming week. This can improve the quality of the fruit delivered and can improve the quality of the oil produced easily.

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