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Analysis of Green Supply Chain Management Factors for Improving Firm Performance in Food Industry
Muhammad Asrol, Muchammad Arief, Hendra Gunawan,
Volume 34, Issue 3 (IJIEPR 2023)
Abstract
The food industry's supply chain primarily relies on materials that are not environmentally friendly. To address this issue and improve overall performance, the implementation of Green Supply Chain Management (GSCM) becomes crucial. The objective of this research is to analyze the factors influencing the adoption of GSCM and its impact on the performance of the food industry, particularly in Indonesia where there is a high potential for waste production and environmental impact. The study targeted 83 food industry companies as respondents, achieving a response rate of 76.82%. The research employed a Partial Least Squares (PLS) and statistical analysis approach to test hypotheses regarding food industry performance. The findings indicate that GSCM does not directly affect food industry performance. However, GSCM has a positive influence on Green Innovation, which in turn has a positive impact on Company Performance. Green Innovation acts as a mediator between GSCM and Corporate Performance. The implementation of a GSCM at the food industry not only enhances environmental performance but also to improved economic performance. It is emphasized that renewable company innovations should be integrated alongside the adoption of green supply chains. The study highlights that the positive effects of the GSCM  are more significant when mediated by green innovation.
 
Agglomeration Agricultural Zone Analysis Based on the Hybrid Hierarchical Clustering Approach in Madura, Indonesia
Iffan Maflahah, Dian Farida Asfan, Selamet Joko Utomo, Fathor As, Raden Arief Firmansyah,
Volume 35, Issue 4 (IJIEPR 2024)
Abstract
Madura Island, comprising four regencies, exhibits a diverse array of agricultural resource potential, particularly in paddy, maize, cassava, and soybeans. Althought the Gross Regional Domestic Product assesses economic progress. it inadequately reflects the whole spectrum of potential within each region. A comprehensive observation of this diversity is required to facilitate a more focused development approach. This study aims to employ a hybrid hierarchical clustering method to delineate and classify the geographical regions of Madura Island according to their agricultural potential. K-means clustering, that part of hybrid hierarchical clustering approach was used to achieve aims of research. Number of farmers, land area, and commodities production were variable that used to classify regional based on its potentials. First, hierarchical method was performed to determine the appropriate number of clusters then K-means clustering was applied to classify the regions based on agricultural commodities. The results show effectively determined Madura Island's agricultural potential using the hybrid hierarchical clustering method, which categorizes locations based on characteristics of agricultural production. The research reveals six clusters, each characterized by a unique profile of primary commodity production, including paddy, corn, soybeans, and cassava. Implication of this result is offering insights into regional development of Madura based on agricultural potential.

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