Dampak Digitalisasi Sistem Manajemen Pabrik Garmen Skala Menengah Terhadap Kinerja Keuangan: Evaluasi Akurasi Pembukuan Dan Ketepatan RAB (Rencana Anggaran Belanja) - Rizky Muflih; Julio Arky Aquino Reyaan; Erwan Mulyanto; Vitri Tundjungsari (Jurnal Kajian Ilmiah, 2026)
Medium-scale garment factories often rely on paper records that are prone to errors, slow processes, and hinder accurate project cost tracking. This affects the accuracy of financial record-keeping and the precision of the Bill of Quantities (BOQ). This study assesses the impact of digitalisation through an integrated application in the garment industry on these key metrics. Using a pre-and-post approach on a single case (action research), the study examines a factory transitioning from a manual to a digital system. The intervention includes: linking attendance and payroll with projects; tracking monthly and project-specific expenses; managing stock (in/out) with automatic cost recording; calculating BOQ per project; comparing BOQ with actuals based on proportional overhead allocation; and calculating profit per project. The main indicators include: (i) financial record accuracy—ensuring consistency across modules (Attendance–Payroll–Expenses–Stock), accurate material cost recording, and easy export for summaries; (ii) BOQ accuracy—the difference between planned and actual costs in categories such as Materials, Labour, Overheads, and total project costs. Data from application logs and reports show that after implementation, errors and duplicate payments decreased; labour cost transparency increased; stock discrepancies and material cost variances reduced due to linking outgoing transactions with project costs; and the gap between BOQ and actuals narrowed. The digital system integrating BOQ, Production, Stock, Expenses, Attendance/Payroll, and Profit improves data quality and cost management, thereby enhancing the accuracy of financial records and BOQ in medium-scale garment factories. These findings support the development of standardised processes and data-driven decision-making for cost control.