Phorecaster365: A Human-Supervised Reference Architecture for Hybrid Pharmaceutical Sales Forecasting and Planning Decision Support

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Pharmaceutical sales forecasts inform planning across products, regions, and distribution channels, but their interpretation depends on inventory availability, transaction semantics, product lifecycle, and the information available when each forecast was issued. A raw model prediction loses this context. Phorecaster365 addresses the governance layer rather than forecasting accuracy. The central contribution is two formal data structures. A forecast context package preserves the source snapshot, forecast target, temporal cutoff, available covariates, data quality state, and hierarchy version—ensuring forecasts can be reproduced and audited after the fact. A forecast evidence package links predictions to model and calibration versions, exceptions, human adjustments, and publication history. The paper is unusually honest about its limitations: it is explicitly an implementation-neutral system design, validated on a synthetic panel of 10,950 daily records across 30 product-region series. It states directly that the report 'does not establish real-world forecasting accuracy, comparative superiority, or operational benefit.' This is valuable governance architecture for pharma planning teams but not a demonstrated forecasting advance.