ASSESSMENT OF THE IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN PRODUCTION PROCESSES: A COMPOSITE INDEX AND OPTIMIZATION FRAMEWORK
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https://doi.org/10.5281/zenodo.21241253Kalit so‘zlar:
artificial intelligence, production processes, economic efficiency, composite index, AHP, ART model, multi-objective optimization, SCADA integration, industrial digitalization, industry 4.0.Abstrak
The accelerating diffusion of artificial intelligence (AI) technologies across manufacturing has created a pressing need for robust, replicable instruments for evaluating the depth of AI integration and its effect on economic efficiency. This paper proposes a two-part methodological framework designed for general application to production processes. First, the Smart Integration Index (SI-index) - a composite indicator built from thirteen sub-indicators grouped into five thematic blocks (digital infrastructure, data analytics, intelligent automation, human capital, and organizational readiness), aggregated through Min–Max normalization and weighted via the Analytic Hierarchy Process (AHP). Second, the AI-based Resource Targeting (ART) model - a multi-objective optimization formulation for the intelligent redistribution of four key production resources (raw materials, energy, labor, and equipment capacity). The framework integrates with SCADA/MES data streams through a four-layer architecture and is calibrated by a five-level maturity scale. A demonstrative evaluation shows that the proposed instruments are statistically stable, interpretable by management, and transferable across industrial sub-sectors. The study contributes a methodologically grounded, decision-oriented tool for policy-makers, industrial managers, and researchers seeking to quantify and improve the economic efficiency of AI deployment in production.
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