Data-Driven Business Process Improvement untuk Mendukung Optimasi Proses Bisnis pada UMKM Menggunakan Process Mining
-
Novia Andini
*
SATU University, Bandung,
Indonesia
- Haerul Haerul SATU University, Bandung, Indonesia (*) Corresponding Author
Abstract
In this digital era, technology has become an essential requirement for Micro, Small, and Medium Enterprises (MSMEs) to support business competitiveness. However, limitations in information systems often result in data being stored in non-standardized spreadsheet formats, causing the data to remain "dirty" and unprepared for process analysis. This poses a significant technical challenge in transforming raw data into valid event logs, particularly regarding timestamp synchronization, accurate Case ID mapping from manual records, and process flow adjustments. Many MSMEs still lack such standardization and rely on conventional approaches to Business Process Improvement (BPI), where decision-making is based on subjective and less accurate interviews or observations. Therefore, this study aims to develop a data-driven BPI approach by incorporating process mining to assist in the optimization of MSME business processes. The methodology of this research includes collecting MSME business process activity data in the form of digital event logs using spreadsheets, analyzing data and processes through mining techniques, identifying bottlenecks and inefficiencies, and designing business process improvements. The data used consists of operational activity records from a traditional herbal medicine (Jamu) MSME in Cimahi, which has been simply digitized using spreadsheets. The novelty of this research lies in the application of data-driven process mining for BPI using MSME spreadsheet event logs. The results of this study, processed from 6,225 events and 1,027 cases, reveal two process variants: a normal variant (93.87%) with an average duration of 5 hours 6 minutes, and a variant involving rework (6.13%) with an average duration of 6 hours 41 minutes. Performance analysis identified bottlenecks in the transition from Production to Packaging (1 hour 55 minutes) and from Shipping to Completed Order (1 hour 53 minutes). A new process model is proposed to eliminate "Rework," which is projected to reduce the overall average duration to 4 hours 42 minutes. The contribution of this research is the development of a data-driven BPI approach based on process mining that can be applied to MSMEs with limited information systems through the utilization of spreadsheet-based event logs. This approach enables the identification of process variations and bottlenecks based on actual operational data and provides a measurable basis for designing improved business processes. The results of the study indicate that this approach can be used to support business process optimization in MSMEs without relying on complex information systems.
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