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All Journal Jurnal Manajemen dan Teknologi Rekayasa
Eka Rachmadi Endarta Putra
Institut Teknologi Sumatera
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Job Safety Analysis as a Sustainable Solution for Hazard Identification and Control in the Sugarcane Klentek Frame Fabrication Process at PT. XYZ Achmad Samudra Dewantara; Sherin Ramadhania; Nia Sastra Permata; Muhammad Suryo Panatagomo Abi Suroso; Eka Rachmadi Endarta Putra
JUMANTARA: Jurnal Manajemen dan Teknologi Rekayasa Vol 5, No 2 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/jumantara.v5i2.3613

Abstract

This study applies Job Safety Analysis (JSA) as a qualitative method to manage occupational safety and health (OSH) risks in the production of the Frame Klentek Tebu component at PT. XYZ, an agricultural machinery company. Despite having existing OSH programs, the company continues to face high-frequency incidents of near-misses and Lost Time Injuries, mainly due to heavy machinery use, welding, and painting. The initial analysis suggests that these accidents are caused by procedural lapses and inadequate hazard identification at the task level. The JSA process involved analyzing key tasks Cutting, Fit-Up, Welding, Primer Painting, and Finishing Painting to identify hazards such as falling materials, electric shock and burns during welding, and chemical inhalation during painting. A significant finding was the non-compliance with Personal Protective Equipment (PPE), largely due to operator habits. The research proposed a control hierarchy, prioritizing Elimination (e.g., chamfering sharp edges, replacing forklifts with automated conveyors) and Engineering Controls (e.g., proper grounding systems, installing barriers). This JSA serves as an essential tool for embedding safety into Standard Operating Procedures (SOPs), ensuring business continuity, and creating a safer work environment at PT. XYZ.
Design Of Artificial Neural Network (ANN) Based Analytics Model for Safety Stock and Reorder Point Prediction at Galeri 1 ITERA Guido Immanuel Simanungkalit; Hersa Dwi Yanuarso; Frieska Ariesta Syafnijal; Eka Rachmadi Endarta Putra; Sherin Ramadhania
JUMANTARA: Jurnal Manajemen dan Teknologi Rekayasa Vol 5, No 2 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/jumantara.v5i2.3992

Abstract

Inventory management in retail environments such as Galeri 1 ITERA faces significant challenges due to fluctuating and irregular demand patterns, which often lead to stockout and overstock conditions. This study proposes an integrated inventory analytics system that combines ABC classification, demand forecasting using Artificial Neural Network (ANN), safety stock (SS) and reorder point (ROP) calculation, as well as interactive dashboard visualization to support data-driven inventory decision-making. The research methodology consists of: (1) ABC analysis to determine inventory control priorities for 56 products; (2) demand forecasting using ANN models trained on historical sales data; (3) calculation of safety stock and reorder point based on forecasting results and lead time variability; and (4) development of an analytics dashboard using Microsoft Power BI. The proposed ANN model utilizes a multilayer feedforward architecture capable of capturing nonlinear demand patterns. Forecasting performance was evaluated using WAPE, RMSE, and MAE metrics. The results indicate that 24 category-A items (42.9%) contribute to 79.45% of the total inventory consumption value, highlighting the need for tighter inventory control. The ANN forecasting model achieved an average accuracy of 88.72% with an average WAPE of 11.28%, while most products showed stable MAE and RMSE values below 2.5 units. Forecasting outputs were subsequently integrated into safety stock and reorder point calculations, producing dynamic inventory control parameters ranging from 3–209 units for safety stock and 3–296 units for reorder points. The main scientific contribution of this study lies in the integration of ABC classification, ANN-based forecasting, and dashboard-based predictive analytics into a unified inventory management framework for retail operations. The resulting dashboard enables interactive monitoring of inventory conditions, stock alerts, demand predictions, safety stock, and reorder point values, thereby improving the effectiveness and responsiveness of inventory control decisions.
Co-Authors Achmad Samudra Dewantara Frieska Ariesta Syafnijal Guido Immanuel Simanungkalit Hersa Dwi Yanuarso Muhammad Suryo Panatagomo Abi Suroso Nia Sastra Permata Sherin Ramadhania