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Measuring Supply Chain Performance on Residential Construction Faqih Ramadhan; Muhamad Rizki; Nazrudin Fadilah; Aryati Indah Kusumastuti
Interdiciplinary Journal and Hummanity (INJURITY) Vol. 5 No. 2 (2026): Injurity: Interdiciplinary Journal and Humanity
Publisher : Pusat Publikasi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58631/injurity.v5i2.1517

Abstract

Supply chain management in residential construction projects requires a performance measurement system that supports effective evaluation, control, and decision-making. This study aims to analyze the effect of supply chain performance measurement on the performance of the housing development supply chain. The research was designed using an explanatory quantitative approach with a structured, questionnaire-based survey method. Data were collected cross-sectionally from 50 respondents who are the main actors in the housing supply chain, including project managers, contractors, subcontractors, procurement staff, and material suppliers. Supply chain performance measurement is operationalized through the dimensions of cost efficiency, timeliness, quality, flexibility, collaboration, risk management, and sustainability. Data were analyzed using descriptive statistics and linear regression analysis with the help of SPSS. The results show that supply chain performance measurement has a positive and significant effect on the performance of the housing development supply chain (? = 0.791; p < 0.001), with a coefficient of determination (R²) value of 0.651. These findings indicate that approximately 65.1% of the variations in housing supply chain performance can be explained by supply chain performance measurement. This research makes a theoretical contribution by positioning supply chain performance measurement as a strategic construct tested inferentially, rather than merely an evaluative tool. Practically, the results confirm the importance of implementing a structured supply chain performance measurement system to improve the operational performance of housing projects. All data presented are processed survey data, allowing replication and retesting in different research contexts.