Phorecaster365 connects human input to pharma sales forecasts
Phorecaster365: A Human-Supervised Reference Architecture for Hybrid Pharmaceutical Sales Forecasting and Planning Decision Support
Computation and LanguageArtificial Intelligence
Summary
Predicting pharmaceutical sales is complicated because many factors affect accuracy, like product lifecycle and available data. The authors designed Phorecaster365, a system that links sales forecasts with the data they depend on and lets planners review and adjust them. It organizes data and forecasts in a way that shows when and how predictions were made, helping people understand if the forecasts are reliable for planning. The system was tested using synthetic data but does not claim to improve forecast accuracy in real settings yet.
What this means in practice
- •For pharmaceutical planning teams: Provide a reviewable and adjustable forecasting workflow that links ERP data to sales predictions to improve transparency during planning.
- •For supply chain analysts: Enable analysts to track forecast versions, exceptions, and data quality to support better inventory and distribution decisions.
Tested on simulated data.
Authors
Houman Kazemzadeh, Kamyar Naderi
Abstract
Pharmaceutical sales forecasts inform planning across products, regions, and distribution channels, yet their interpretation depends on inventory availability, transaction semantics, product lifecycle, and the information available when each forecast is issued. A model prediction alone does not preserve these conditions or establish whether a forecast is suitable for operational use. We present Phorecaster365, a human-supervised reference architecture that connects enterprise resource planning data to reviewable pharmaceutical sales forecasts. The architecture separates source ingestion, product-region series construction, temporally valid feature generation, statistical and machine-learning modeling, ensemble formation, uncertainty assessment, planner review, and lifecycle governance. Its central intermediate representation is a forecast context package that preserves the source snapshot, forecast target, temporal cutoff, available covariates, data-quality state, and hierarchy version. A corresponding forecast evidence package links predictions to model and calibration versions, exceptions, human adjustments, and publication history. Development experience with a synthetic panel of 10,950 daily records across 30 product-region series informs the design. Historical experiment summaries are retained only as descriptive evidence of development because their evaluation does not establish independent predictive validity. We specify a rolling-origin evaluation protocol, baseline and ablation requirements, uncertainty and robustness assessments, and a staged pathway from synthetic testing to use in governed planning. The contribution is an implementation-neutral system design and validation framework; the report does not establish real-world forecasting accuracy, comparative superiority, or operational benefit.