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All Journal JURMATIS : Jurnal Manajemen Teknologi dan Teknik Industri Eduvest - Journal of Universal Studies Jurnal Logic: Logistics & Supply Chain Center
Dewy, Cyndy Kresna
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Health Professions Industrial & Manufacturing Engineering Neuroscience Social Sciences Transportation

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PENERAPAN METODE FORECASTING DOUBLE MOVING AVERAGE DAN DOUBLE EXPONENTIAL SMOOTHING SATU PARAMETER PADA UMKM CILOK CEU OEY Nadira, Fadiya; Dewy, Cyndy Kresna; Tiojay, Sabila Utami Syifa; Anwar, Asep
Jurnal Logic: Logistics & Supply Chain Center Vol 2 No 1 (2023): Jurnal Logic: Logistics & Supply Chain Center
Publisher : Widyatama University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jlscc.v2i1.999

Abstract

Peramalan diperlukan untuk memperoleh informasi tentang perubahan di masa depan dimana informasi ini dapat mempengaruhi suatu aktivitas penjualan. Manfaat dari forecasting adalah untuk memprediksi masa depan sebuah bisnis dan dengan adanya forecasting dapat memenuhi kepuasan pelanggan. Pada penelitian ini, kami melakukan penelitian terhadap Usaha Mikro, Kecil dan Menengah (UMKM) di Cilok Kuah Ceu Oey kemudian melakukan forecasting penjualan Cilok Kuah dengan menggunakan metode Double Moving Average (DMA) dan metode Double Exponential Smoothing One Parameter (BROWN) serta membuat Master Production Schedule (MPS) untuk mengetahui berapa produk yang harus diproduksi. Dengan demikian, hasil forecasting menggunakan metode Double Exponential Smoothing One Parameter (BROWN) memiliki hasil error terkecil dan berdasarkan hasil MPS terdapat 160 pack Cilok Kuah Ceu Oey yang harus diproduksi setiap bulannya.
Desain Rute dan Optimasi Biaya Transportasi Pengisian Tabung Oksigen menggunakan Metode Complete Enumeration pada CV. Tasman Gases Az-Zahra, Vira Luthfiati; Dewy, Cyndy Kresna; Fikri, Muhammad Albi; Fauzi, Muchammad
JURMATIS (Jurnal Manajemen Teknologi dan Teknik Industri) Vol. 5 No. 1 (2023): January
Publisher : Universitas Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/jurmatis.v5i1.2207

Abstract

The increasing demand for filling oxygen cylinders makes CV. Tasman Gases has always been consistent in planning transportation cost optimization strategies. The filling of oxygen cylinders in one distribution can transport up to 45 tubes sent to the concerned partner. The purpose of this study is to determine the shortest route and optimal cost in distributing. The method used is Complete Enumeration. The samples used include distribution locations and mileage. The procedure used begins with determining the number of routes, estimated mileage and fuel costs. There is the most optimal route selected from the 12 modeled routes. The selected route has the least transportation costs. This research makes CV. Tasman Gases has the best route, A-D-C-E-F with a distance of 10.9 km worth Rp. 10,423.13.  This value is more effective than the existing A-E-C-D-F condition with a distance of 12.8 km obtaining fuel costs of Rp. 12,240.00. Thus, the distribution route and costs that have been optimal make CV. Tasman Gases can implement and identify future transportation constraints.
Design of Inventory Information System Model on Smart Warehouse Management System (WMS) Based on Artificial Intelligence (AI) with Integration of Waterfall Method and Design Thinking to Optimize Inventory Accuracy Dewy, Cyndy Kresna; Prambudiab, Yudha; Kumalasarib, Iphov
Eduvest - Journal of Universal Studies Vol. 5 No. 10 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i10.51297

Abstract

Modern warehouse operations face significant challenges with manual inventory management processes, resulting in accuracy rates as low as 65% and substantial operational inefficiencies that directly impact customer satisfaction and profitability. This study presents the design and implementation of an innovative Inventory Information System Model for Smart Warehouse Management Systems (WMS) based on Artificial Intelligence technology, specifically developed to address these critical inventory management deficiencies. The research objectives focus on developing an automated system that minimizes human errors, provides real-time data analytics, and enhances overall operational efficiency through intelligent decision-making capabilities. The methodology integrates the structured Waterfall development approach with user-centered Design Thinking principles, ensuring both systematic development and optimal user experience. The AI-powered system incorporates machine learning algorithms for demand forecasting, computer vision for automated stock counting, natural language processing through integrated chatbots for enhanced user interaction, and predictive analytics for optimized inventory levels. Implementation and testing within the Geoff Max Group demonstrated significant improvements, achieving 95% inventory accuracy compared to the previous 70% manual accuracy rate, reducing stock-out incidents by 60%, and decreasing inventory carrying costs by 25%. The system successfully processes real-time data from multiple warehouse locations, providing managers with comprehensive dashboards and automated alerts for critical inventory thresholds. The implications of this research extend beyond operational improvements, offering a scalable solution for modern supply chain management that can be adapted across various industries. This integrated approach represents a significant advancement in warehouse automation, demonstrating how AI-driven systems can transform traditional inventory management practices while providing economic benefits.
Co-Authors Anwar, Asep Az-Zahra, Vira Luthfiati Fikri, Muhammad Albi Kumalasarib, Iphov Muchammad Fauzi Nadira, Fadiya Prambudia, Yudha Tiojay, Sabila Utami Syifa