Automated forecasting and replenishment planning system for pharmacy inventories using AI-based time series analysis

Abstract

Automated forecasting and replenishment planning system for pharmacy stocks, comprising a computer-aided infrastructure for recording dispensing data and stock levels, a computer-based time series processing unit for generating demand forecasts, and a replenishment control unit for automatically triggering replenishment measures based on the forecasts.

Description

Technical field

[0001] The present invention relates to the technical field of computer-aided inventory management, artificial intelligence, and data-driven logistics systems in healthcare. In particular, the invention relates to an automated, cloud- or server-based system for forecasting demand and planning the replenishment of pharmaceutical stocks in pharmacies using AI-based time series analysis. State of the art

[0002] Pharmacies operate in environments characterized by fluctuating demand patterns, regulatory constraints, therapeutic substitution options, expiration sensitivity, and high demands on drug availability. Conventional inventory management systems are typically based on historical sales averages, static reorder thresholds, or manually configured forecasting rules. Such approaches are unable to effectively respond to sudden changes in demand caused, for example, by seasonal illnesses, prescription variability, supply disruptions, or emergency situations.

[0003] Existing automated systems often treat pharmaceutical products as isolated storage units and fail to consider cross-drug substitution relationships, prescription volatility, or regulatory and clinical constraints in forecasting and replenishment planning. Furthermore, conventional forecast results are often static and not linked to actual replenishment performance, leading to recurring shortages, overstocking, or increased spoilage losses.

[0004] Therefore, there is a need for a technical system that continuously learns from ongoing pharmacy operational data, dynamically adapts its forecasting behavior, and automatically recalibrates replenishment decisions based on actual dispensing results. The present invention addresses these limitations by providing a closed, AI-driven forecasting and replenishment planning system specifically designed for pharmacy inventory environments. Objective of the invention

[0005] The aim of the present invention is to provide an automated technical system that enables adaptive, data-driven forecasting of drug demand and automatic replenishment planning for pharmacies, dynamically adapts to changing demand patterns and increases inventory resilience through continuous self-correction. Summary of the invention

[0006] The invention provides an automated forecasting and replenishment planning system for pharmacy inventories that captures real-time dispensing data, inventory levels, and context-related pharmacy parameters and analyzes them using AI-based time series processing. Dynamic demand states are generated for each drug, taking into account historical dispensing data, prescription volatility, substitution relationships, seasonal effects, and regulatory restrictions.

[0007] An intelligent forecasting module automatically detects changes in demand patterns and adjusts the forecasting strategy accordingly. The forecast results are directly transferred to automated replenishment planning.

[0008] Deviations between forecasted and actual consumption are continuously evaluated and used to automatically adjust forecast parameters and replenishment thresholds. This creates a closed, self-correcting control loop for inventory planning. Detailed description of the invention

[0009] The system according to the invention is designed as a computer-based platform and comprises a data acquisition unit for the continuous recording of dispensing transactions, stock levels, expiration dates, and delivery times from pharmacy systems. The acquired data is fed to an AI-based time-series processing unit, which calculates a dynamic demand state for each drug.

[0010] The time series processing unit considers not only historical consumption data but also prescription volatility, seasonal disease patterns, substitution possibilities between therapeutically comparable drugs, and regulatory supply restrictions. The system also includes a demand regime detection unit that automatically identifies changes in demand behavior based on variance changes, anomalies, or correlation shifts and adjusts the forecasting behavior accordingly.

[0011] A replenishment control unit translates the forecast results into automated replenishment decisions, taking into account drug criticality, expiration risks, storage capacities, and substitution rules. Additionally, a feedback mechanism is provided that compares forecasted consumption with actual dispensing data and automatically adjusts forecast models, safety stocks, and replenishment parameters.

[0012] This architecture enables adaptive, self-learning and fault-tolerant inventory planning, which reduces supply bottlenecks, minimizes spoilage losses and improves supply security in pharmacies.

Claims

[1] Automated forecasting and replenishment planning system for pharmacy stocks, comprising a computer-based infrastructure for recording dispensing data and stock levels, a computer-based time series processing unit for generating demand forecasts, and a replenishment control unit for automatically triggering replenishment measures based on the forecasts. [2] System according to claim 1, wherein the time series processing unit generates a dynamic demand state for each drug, which includes historical dispensing data, prescribing volatility, therapeutic substitution relationships, seasonal demand effects and regulatory constraints. [3] System according to claim 1, further comprising a demand regime detection unit that detects changes in demand behavior based on statistical variance changes, anomalies or correlation shifts and automatically adjusts the forecasting behavior. [4] System according to claim 1, further comprising a self-correcting feedback mechanism that compares forecasted consumption with actual delivery data and automatically readjusts forecast parameters, replenishment thresholds and safety stocks. [5] System according to claim 1, wherein the replenishment control unit takes into account pharmacy-specific boundary conditions including drug criticality, expiry risk, storage capacity and substitution permissibility in order to enable automated, supply- and regulation-compliant replenishment planning.