How Does AI-based Supply Chain Optimization Help Pharma Companies Save Money?

The pharmaceutical supply chain is incredibly complex: From sourcing and supplying materials to manufacturing and distribution of incredibly sensitive products that could be rendered inactive without the right environmental conditions. 

This complexity – and the dependence of governments and populations on the pharmaceutical industry’s life-saving products – are why pharma supply chains are so important. 

But they’re also very fragile, which is a big reason why AI (and in particular machine learning, or ML) has become an important element in the pharmaceutical supply chain.

Challenges Inherent in the Pharma Supply Chain

Plenty of challenges exist within the process of sourcing materials for a drug, manufacturing it, getting it to market, and conducting postmarket surveillance.

Professional services firm Deloitte calls the pharma supply chain a “golden thread between the discovery of new therapies and patients receiving them.” Links in the pharmaceutical supply chain include research and development, clinical development, manufacturing, launch/commercialization, and postmarket surveillance.

Each link in the chain also includes smaller sub-activities, with the manufacturing step alone also including sourcing, manufacturing, distribution, delivery, and patient care.

But one of the most prevalent challenges for pharma companies is the reality of the cold supply chain, which describes the transportation of temperature-sensitive products. The cold supply chain requires extra considerations around refrigeration and thermal packaging, lest drug companies risk spoiling the fruits of their labor. Covid-19 vaccines, for example, must be kept at -94F at all times. Many anticancer drugs must be kept between +2 °C and +8 °C, or they could become ineffective or even toxic.

It’s a real issue for pharmaceutical companies, who lost more than $35B in 2021 from products spoiling within cold supply chains. 

Add to this several other issues plaguing pharmaceutical supply chains, including:

• An inability to manage unexpected peaks or troughs in demand, often leading to drug shortages or oversupply
• A lack of strong processes to ensure drug integrity
• A lack of transparency into several links in the supply chain
• No mechanisms to examine environmental footprints or medical waste
• No fall-back in case of natural or human-made disasters

There’s also the pressing issue of new zoonotic diseases such as Covid-19 and Ebola, a potentially devastating issue the UN predicts will rise exponentially thanks to climate change and loss of wildlife habitat, putting more pressure on the pharma supply chain.

Indeed, plenty of researchers and governments are already mildly concerned about the latest version of H5N1 making the rounds in birds – along with the worrisome fact that it has reportedly jumped to mammals relatively recently. 

All this is to say that pharmaceutical supply chains are one of humankind’s main lines of defense against epidemic and pandemic diseases, along with a score of other conditions.

Keeping these supply chains running smoothly is imperative for both governments and their populations. This is why the pharma supply chain’s digital transformation using AI and ML makes so much sense.

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