ANALYSIS OF RAW MATERIAL INVENTORY PREDICTION FOR PLASTIC ORE USING A COMBINATION OF CAUSALITY AND TIME SERIES METHODS: A CASE STUDY IN A TEXTILE INDUSTRY COMPANY

Frangky Rawung; Agus Budi Raharjo; Diana Purwitasari

doi:10.52436/1.jutif.2024.5.1.1809

Frangky Rawung Informatics, Electrical Technology Faculty, Institut Teknologi Sepuluh Nopember, Indonesia
Agus Budi Raharjo Informatics, Electrical Technology Faculty, Institut Teknologi Sepuluh Nopember, Indonesia
Diana Purwitasari Informatics, Electrical Technology Faculty, Institut Teknologi Sepuluh Nopember, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.1.1809

Keywords: bilstm, causality, inventory, mlr, prediction, time series

Abstract

Raw material inventory is a valuable company asset in production activities. Inadequate or excessive availability can lead to production failures or cost wastage. This research aims to predict raw material inventory based on factors such as initial stock, receipts, usage, final stock, and differences in usage. A causality-based approach with Multiple Linear Regression (MLR) is used as the basis, complemented by a time series data approach that processes data trends using the Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The prediction results from both models are then combined using the harmonic mean. This research utilizes a dataset of raw material inventory and applies the Root Mean Squared Error (RMSE) and R-squared (R²) performance parameters for model evaluation. The research is expected to provide useful information for companies in managing their raw material inventory and improving the efficiency of their production processes. Results show that, in the BiLSTM deep learning model, Polyethylene Terephthalate (PET) raw materials yielded an RMSE of 6.53 and an R² of 0.93. These results indicate that PET raw materials have a higher predictive value than other materials.

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