ANALISIS PENGUKURAN KINERJA SUSTAINABLE SUPPLY CHAIN MANAGEMENT (SSCM) BERBASIS SUPPLY CHAIN OPERATION REFERENCE (SCOR) 14.0 DAN ANALYTICAL HIERARCHY PROCESS (AHP) (STUDI KASUS: LURIK PRASOJO)

Fisalma Fadhia Triya Wardaya, NIM.: 21106060034 (2025) ANALISIS PENGUKURAN KINERJA SUSTAINABLE SUPPLY CHAIN MANAGEMENT (SSCM) BERBASIS SUPPLY CHAIN OPERATION REFERENCE (SCOR) 14.0 DAN ANALYTICAL HIERARCHY PROCESS (AHP) (STUDI KASUS: LURIK PRASOJO). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Sustainability issues in the industrial sector are receiving increasing global attention. This has encouraged companies to not only focus on financial productivity (profit) but also on environmental (planet) and social responsibility (people). Lurik Prasojo is a company that produces lurik woven fabrics in Klaten Regency. Based on observations, several problems were identified, including a lack of attention to supplier sustainability, high rates of machine breakdowns and defective products, and overproduction that led to production halts and temporary employee layoffs, ultimately affecting profitability and worker welfare. This study aims to measure the performance of Sustainable Supply Chain Management (SSCM) using the Supply Chain Operation Reference (SCOR) model 14.0 and the Analytical Hierarchy Process (AHP) method. The study also applies the Snorm De Boer method for normalizing actual performance values and the Traffic Light System (TLS) for categorizing performance levels. Based on the measurement results, the SSCM performance score in January 2025 was 87.44 and in February was 87.83. According to supply chain performance standards, the scores for both months fall into the 'good' category, indicating that performance levels are approaching the company’s targets. In addition, based on the TLS assessment, there are several indicators in January and February 2025 that require improvement, including ISO 14001-certified suppliers (S.RL-1), percentage of defective products (T.RL-1), number of malfunctioning machines (T.RL-2), and machine utilization optimization (T.AM-1). Therefore, this study provides improvement suggestions for indicators with low performance, which can serve as a reference for making informed decisions to address these issues

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