Developing AI-enhanced metrics and KPI-based analytics tools for comparative process optimization in manufacturing

Abstract

The transformation that the current generation of manufacturers is witnessing requires efficiency, flexibility as well as timely and accurate decisions to sustain competitions in the current highly dynamic market environment. This paper describes the KPI-based analytics tool that will be used for process improvement in the manufacturing industry. The tool links KPIs with the help of AR and 3D visualization to transform ordinary numbers and operational data into powerful visual patterns. It involves data collection from Hiava Oy’s Lean process planning (LeaPP) system and Ainak’s Visual Planner to create a strong online data analysis system permitting actual time comparison and assessment of the manufacturing processes.

Where current process optimization lacks effectiveness in areas like static display and analysis, the tool allows detailed simulation and future hypothesis analysis. Based on the case of Hiava Oy, it proves the tool works well by showing positive increases in the lead time of the process, production activity, and Overall Equipment Effectiveness (OEE). Moreover, components for future development that deal with the integration of AI are included in the thesis. On the other hand, it has line plans for adopting machine learning-based predictive analytics solutions, real-time analysis for anomaly detection, and a parameter recommendation engine that is also based on AI. These future integrations are used to improve more configurations of procedures and the quality of the decision being made.

In this respect, it brings both practical value to Industry 4.0 projects and lays a foundation for the continuous advancement of AI-driven innovations in the manufacturing process, combined with the ideas of the digital twin. This multi-faceted approach is well oriented to sustainable industrial development.