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AI systems can use past trends and market signals to forecast demand.
In the pharmaceutical industry, supply chain management (SCM) aims to ensure that products are delivered effectively and efficiently, but challenges remain in achieving this goal. Failures in temperature control result in significant financial losses, estimated at $35 billion annually (1)(2). Many pharmaceutical manufacturers, around 90%, report limited visibility into their supply chains, affecting trust in data about their product journey (3). Additionally, the industry's significant energy costs, amounting to $1 billion a year, especially considering that its emissions exceed those of the automotive industry by 55% (4).
Inefficiencies in SCM, such as time-consuming manual paperwork, can result in a loss of 6,500 hours per year for companies, impeding productivity (5). This inefficiency also extends to inventory management, where poor practices have resulted in approximately $700 million worth of expired medications in Latin American warehouses due to excess stock (6). These figures outline the need to improve SCM practices in the pharmaceutical sector to reduce waste and improve operational efficiency.
"DeliverEase AI" is a predictive analytics tool designed to increase on-time delivery in pharmaceutical supply chains by evaluating factors such as inventory and logistics, thereby helping to prevent costly delays.
Variables such as warehouse temperature, market signals, product type, and historical sales volume are extracted from a synthetic data set modeled under real industry conditions. The research by Moosivand et al. (1), IQVIA white papers (2), Tive analysis (3), reports on energy consumption in pharmaceutical supply chains (4) and studies by the Economic Commission for Latin America and the Caribbean (6) gave the guidelines for creating the database, ensuring a realistic simulation of the dynamics of the supply chain.
In the pharmaceutical industry, supply chain management (SCM) aims to ensure that products are delivered effectively and efficiently, but challenges remain in achieving this goal. Failures in temperature control result in significant financial losses, estimated at $35 billion annually (1)(2). Many pharmaceutical manufacturers, around 90%, report limited visibility into their supply chains, affecting trust in data about their product journey (3). Additionally, the industry's significant energy costs, amounting to $1 billion a year, especially considering that its emissions exceed those of the automotive industry by 55% (4).
Inefficiencies in SCM, such as time-consuming manual paperwork, can result in a loss of 6,500 hours per year for companies, impeding productivity (5). This inefficiency also extends to inventory management, where poor practices have resulted in approximately $700 million worth of expired medications in Latin American warehouses due to excess stock (6). These figures outline the need to improve SCM practices in the pharmaceutical sector to reduce waste and improve operational efficiency.
"DeliverEase AI" is a predictive analytics tool designed to increase on-time delivery in pharmaceutical supply chains by evaluating factors such as inventory and logistics, thereby helping to prevent costly delays.
Variables such as warehouse temperature, market signals, product type, and historical sales volume are extracted from a synthetic data set modeled under real industry conditions. The research by Moosivand et al. (1), IQVIA white papers (2), Tive analysis (3), reports on energy consumption in pharmaceutical supply chains (4) and studies by the Economic Commission for Latin America and the Caribbean (6) gave the guidelines for creating the database, ensuring a realistic simulation of the dynamics of the supply chain.
