.png){kind=logo}
Automated Inventory Management, order routing, demand forecasting and Supply Chain Optimization with AI.
Order management in the pharmacy context refers to receiving, processing, and fulfilling medication orders, involving various complex tasks such as inventory management, order tracking, and logistical coordination. Despite technological advancements, many pharmaceutical companies still rely on manual and outdated systems to handle these processes, leading to inefficiencies, delays, and errors. The global order management market in the pharmaceutical sector is valued at $10 billion and is expected to grow at a compound annual growth rate of 11.5% between 2022 and 2028 (1). Seventy percent of pharmaceutical companies consider order management a critical challenge (2), with an average revenue loss of 2% due to errors in the process (3). Additionally, 85% of pharmaceutical companies prioritize automation of order management to address these issues (4).
To counter these challenges, a prediction model, “PharmaPredict AI”, has been devised that uses synthetic data to improve drug demand predictions. While the tool currently categorically determines demand levels, development efforts are directed toward a regression-based model for accurate numerical forecasts.
The current predictive model is based on a variety of variables, such as healthcare trends, market analysis from sources such as Grand View Research (1), and information from sector-specific reports from Deloitte (3) and McKinsey & Company (4). However, to achieve a regression model that predicts accurate demand figures, a larger and more detailed data set is required. This would potentially include more detailed aspects of sales data, deeper market penetration analysis, and real-time supply chain variables.
Order management in the pharmacy context refers to receiving, processing, and fulfilling medication orders, involving various complex tasks such as inventory management, order tracking, and logistical coordination. Despite technological advancements, many pharmaceutical companies still rely on manual and outdated systems to handle these processes, leading to inefficiencies, delays, and errors. The global order management market in the pharmaceutical sector is valued at $10 billion and is expected to grow at a compound annual growth rate of 11.5% between 2022 and 2028 (1). Seventy percent of pharmaceutical companies consider order management a critical challenge (2), with an average revenue loss of 2% due to errors in the process (3). Additionally, 85% of pharmaceutical companies prioritize automation of order management to address these issues (4).
To counter these challenges, a prediction model, “PharmaPredict AI”, has been devised that uses synthetic data to improve drug demand predictions. While the tool currently categorically determines demand levels, development efforts are directed toward a regression-based model for accurate numerical forecasts.
The current predictive model is based on a variety of variables, such as healthcare trends, market analysis from sources such as Grand View Research (1), and information from sector-specific reports from Deloitte (3) and McKinsey & Company (4). However, to achieve a regression model that predicts accurate demand figures, a larger and more detailed data set is required. This would potentially include more detailed aspects of sales data, deeper market penetration analysis, and real-time supply chain variables.
