use cases

Improve Supply Chain Management

AI systems can use past trends and market signals to forecast demand.


The crucial aim of Supply Chain Management (SCM) within the pharma industry is to make the right product, for the right customer, in the right amount, at the right time (1). And that process carries out many challenges in the operational part regarding real-time data collection and developing sustainable methodologies to optimize the use of resources and avoid waste of materials and products.

Size of the Problem

  • According to the IQVIA Institute for Human Data Science, the biopharma industry loses $35 billion USD annually due to temperature-control failures across supply chains (2).
  • 90% pharma manufacturers reported that they didn’t have full visibility into their supply chains and didn’t trust the in-transit data they were receiving (3).
  • The pharmaceutical industry spends about $1 billion per year on energy expenses and produces 55% more emissions than the automotive industry (4).

Why it matters

The SCM has a lot of data-heavy and monotonous work that implies money and time for the pharmaceutical and medical device industry. As an example, filing paperwork manually can cost businesses 6,500 hours a year, a substantial time loss that affects productivity. AI can take care of these administrative jobs, freeing human employees to work on other projects at the same time (5). Not only that, that inefficiency provokes the waste of resources and materials, where only in Latam there is a loss of 700 Million USD in drugs that expired out of the overstock in the warehouses (6).


AI systems can use past trends and market signals to forecast demand. Warehouse managers can use them to see what they need to store more or less of. They could then avoid surplus and deficit, maintaining a consistently prepared operation. Thus, pharma manufacturers will have a full report of the dynamics of demand, storage and production of their products that will allow a better operation and decision-making process. Moreover, AI predictions about customer demands will help to fill orders faster, prioritize shipments, optimize route planning and inventory management (5,7).

It has been demonstrated that early adopters of AI in supply chain management saw a decrease in logistics costs of 15%, an increase in inventory levels of 35%, and a boost in service levels of 65% (7).


  • Inventory Records: information about the products that come into the warehouse, leave the warehouse and remain within the warehouse.
  • Demand records: historical data of the regional sales where the warehouse is located.
  • Production data: self-reported data of the amount of product that comes from fabric.


  1. Moosivand A, Rajabzadeh Ghatari A, Rasekh HR. Supply Chain Challenges in Pharmaceutical Manufacturing Companies: Using Qualitative System Dynamics Methodology. Iran J Pharm Res. 2019 Spring;18(2):1103-1116. doi: 10.22037/ijpr.2019.2389. PMID: 31531092; PMCID: PMC6706717.
  2. IQVIA. (2023, January). Pharma’s Frozen Assets: Cold chain medicines. IQVIA, White Paper. Retrieved February 10, 2023, from
  3. Tive, S. B. (2022, March 30). Five Critical Challenges Facing Pharma Supply Chains. 2022-03-30 | SupplyChainBrain.
  4. Pharma Companies Cutting Energy Consumption To Gain A Competitive Advantage. (n.d.). Centrica Business Solutions.
  5. Nichols, M. R. (2021, December 15). 10 Ways AI Improves Distribution and the Supply Chain. Medium.
  6. División de Desarrollo Económico de la Comisión Económica para América Latina y el Caribe (CEPAL. (2018). Estudio Económico de América Latina y el Caribe. Retrieved February 10, 2023, from
  7. Fletcher, C. (2023, January 6). How AI can mitigate supply chain issues. VentureBeat.

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