Abstract

The life sciences sector is wrestling with rising complexity in global supply chain oversight. Stricter regulations, uneven demand, and the pressing need for ready product availability in patient care intensify the challenge. Classic forecasting and inventory practices grind to a halt under the weight of siloed and unreliable master data, incurring inefficiencies, compliance exposure, and subpar strategic choices. In response, this paper introduces a cohesive framework that harnesses Artificial Intelligence to elevate predictive supply chain optimization, underpinned by high-fidelity, rigorously governed master data across life sciences organizations. The methodology kicks off with the aggregation, cleansing, and enrichment of product, supplier, and location data through modern Master Data Management platforms. AI-powered predictive models—deploying machine-learning approaches such as Long Short-Term Memory networks and gradient boosting—then create demand forecasts, supplier reliability scores, and lead-time estimates of striking accuracy. The derived insights are fluidly recirculated into enterprise platforms, prompting anticipatory inventory distribution, risk abatement, and compliance synchronization. Tests within a simulated life sciences distribution network reveal substantial gains in forecast precision, a decrease in stockouts, and heightened overall supply chain resilience. The results highlight AI-augmented master data as a vital strategic lever for nurturing agile, compliant, and patient-focused supply chain operations in the life sciences arena.

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