Articles

Improvement for Warehouse Activity Processes PT. Pos Logistik Indonesia Branch Office Makassar, Sidenreng Rappang’s Area by Failure Mode and Effect Analysis (FMEA) & Fault Tree Analysis (FTA) Methods

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PT Pos Logistik Indonesia Branch Office Makassar is a company engaged in services or 3PL (Third-Party Logistics). This research aims to identify the factors causing product damage and the corrective actions that will be taken. This research uses the Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) methods with the 5W+1H tools. The first stage of this research involves identifying the causes of damage using FMEA and determining the potential causes. The second stage details the potential causes identified in the first stage using FTA. The third stage involves proposing improvements using the 5W+1H method. Based on the analysis conducted, it was found that the factors cauing product damage include frequent dropping of items during receipt and transfer to the storage area, rodent infestation, use of rough pallets, incorrect input of incoming and outgoing product quantities into the system, frequent dropping of items during storage and overly high stacking of goods.

Predictive Analysis for Inventory Management of Coconut Warehouse (Case Study: Banio Lahewa)

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Inventory management plays a pivotal role in the coconut farming business, directly influencing sales and income. An essential component of this management is warehousing, which not only affect revenue but also involves suppliers in the coconut storage process. Warehousing management and technology are two elements that can help companies operate more effectively and efficiently. This research focuses on efforts to improve warehouse management efficiency in the agricultural sector, particularly at Banio Lahewa, a company that operates as a coconut supplier in a small village with limited resources. Currently, the company still records data manually and lacks a real-time system to monitor demand patterns, stock rotation, and restocking frequency in the warehouse. This situation is caused by uncertainty about the products entering the warehouse, leading to the company’s focus being more limited to daily operational issues rather than future planning. To address this challenge, this research uses future event prediction methods, specifically forecasting by applying two neural network models: the Feed Forward Neural Network and the Long Short Term Memory. The implementation of this system is expected to provide new insights to the company, enabling them to be more adaptive in efficiently managing warehouse systems. With an understanding of patterns and predictions of future events, it is expected that the company can be more prepared and responsive to changes in customer demand and able to expand products more quickly. The results of this research are expected to make a positive contribution to the company, helping them optimize warehouse management and become more adaptive to market dynamics.