The AI-Driven Blue Ocean: Reimagining Innovation




Abstract:
Lean concept and Total Productive Maintenance (TPM) are modern strategies for managing industrial processes. These strategies largely rely on reliable and consistent data records, which are essential for monitoring and managing Overall Equipment Effectiveness (OEE). Support for TPM principles can be considered through the potential application of blockchain technology in the digital record-keeping domain. We used Python to simulate the maintenance activities in the production of sintered parts. The created model integrates daily aggregated TPM losses across all six standard categories into a structure of linked records supported by blockchain. Each record contains key maintenance indicators and process performance metrics. The simulation results indicate a statistically significant improvement in OEE performance with a reduction in process variability and convergence toward World Class Manufacturing (WCM) reference values. In this way, we preserved the data integrity within the simulation model. Recording of TPM activities supported by blockchain provides a single, reliable source of truth (SSOT) for multifunctional teams, while enabling systematic tracking of TPM losses. In this paper, we highlighted challenges related to integration with enterprise resource planning (ERP), as well as practical implementations of blockchain for the TPM records in the transformation of Lean systems within the Industry 4.0 framework.

CITATION:

IEEE format

A. Đokić, D. Dudić, I. Mačužić, M. Čabarkapa, H. Stefanović, “The AI-Driven Blue Ocean: Reimagining Innovation,” in Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2026, pp. 400-406. doi:10.15308/Sinteza-2026-400-406

APA format

Đokić, A., Dudić, D., Mačužić, I., Čabarkapa, M., Stefanović, H. (2026). The AI-Driven Blue Ocean: Reimagining Innovation. Paper presented at Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2026-400-406

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