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Analisis Penerapan Praktik Green Supply Chain Management di PT. Basundari Inovasi Muda Mulyani, Enci; Lestari, Sofiana Tri; Rahmah, Hasna Nur; Safa'at, Muhammad Alim
Jurnal TRINISTIK: Jurnal Teknik Industri, Bisnis Digital, dan Teknik Logistik Vol 3 No 1 (2024): Maret 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/trinistik.v3i1.1415

Abstract

A comprehensive analysis of the implementation of Green Supply Chain Management at PT Basundari Innovation Muda highlights a strategic approach to sustainable practices in the supply chain. The goal is to reduce impacts and increase resource efficiency. The research evaluative method identifies gaps between current practices and the ideal GSCM system through observations, interviews, and literature studies, focusing on three supply chain components: upstream, internal, and downstream. Even though the company has implemented several GSCM practices, there are still gaps, especially in using non-environmentally friendly materials and consistency in implementing practices throughout the supply chain. Recommendations include selecting environmentally friendly suppliers and improving managerial training regarding GSCM.
Data-Driven Product Segmentation for Shallot Commodities using PCA and K-Means Clustering Approach Winati, Famila Dwi; Arifin, Miftahol; Faturohman, Muhammad Iqbal; Mulyani, Enci
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1307

Abstract

The shallot industry plays a strategic role in the Indonesian economy, especially in the Brebes Regency as the largest production center. However, challenges in the form of price fluctuations and low value-added products still burden farmers. Previous research tends to focus on individual products without considering a holistic product clustering strategy. This study aims to address the gap by applying the K-Means clustering method combined with Principal Component Analysis (PCA) to identify patterns in shallot and processed product sales data. The research data includes sales of 308 products from 2022-2024. The variables analyzed include product type, size, number of sold, and turnover. The results of the analysis formed three main groups, which are group 0 (small products with low performance), group 1 (large products with superior performance) and group 2 (medium products with stable performance). The findings indicate the importance of more targeted marketing strategies and product diversification. The implications of this study include optimizing superior products, revitalizing low-performing products, and developing stable products to expand the market. A customized e-Commerce-based strategy per cluster can improve the financial performance of the organization and the welfare of shallot farmers in a sustainable manner.