Optimizing Intelligent Forklift Operations in Biotechnology Manufacturing through Advanced Machine Vision Technologies

Abstract

With the advent of advanced information technologies, the manufacturing industry is undergoing significant transformations towards heightened intelligence. This evolution is crucial as the maturity of manufacturing technologies is intricately linked to effective cost management strategies. This paper delves into the application of an embedded machine vision system within intelligent forklift operations, which are pivotal in biotechnological manufacturing processes. The maturity of these systems is assessed using edge detection algorithms, specifically focusing on edge operators that highlight the operational readiness of the intelligent forklifts. Additionally, this study employs the Particle Swarm Optimization (PSO) algorithm to correlate the maturity levels of intelligent forklift systems with key financial metrics: direct material costs, direct labor costs, and overall manufacturing costs. Through empirical testing, the proposed method demonstrates its efficacy in not only precisely evaluating the intelligence maturity of manufacturing operations but also in facilitating targeted cost management strategies. Results indicate that this approach allows for a robust analysis and subsequent optimization of manufacturing costs, grounded in the maturity assessments of intelligent forklifts equipped with machine vision technologies. This research provides a scalable model for integrating advanced machine vision in intelligent forklifts, significantly contributing to the strategic management of manufacturing costs in the biotechnology sector.