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. Which is why the pharma supply chain’s digital transformation using AI and ML makes so much sense.
How AI Can Help Improve the Pharma Supply Chain
AI can create efficiencies and better reliability across the pharma supply chain, from optimizing schedules to improving decision-making and removing manual busywork from supply chain workers in favor of more value-added tasks.
ML, in particular, is well-suited to improving pharma supply chains because ML models learn from data – and data is not in short supply in the pharma industry.
“Digital transformation is critical for us,” says Bertrand Bodson of global pharma company Novartis, which served nearly 700 billion patients in 2022, in BBC Storyworks. “We have around 60 manufacturing facilities across the world, and we need the ability to be flexible. How do you adjust in real-time and be agile enough to serve those almost 800 million patients every day?”
The answer lies with AI and ML technologies able to give companies such as Novartis better market intelligence and the flexibility to change course in real-time. “If our manufacturing team knows where ingredients are and how our supply facilities are behaving in real-time, we can adjust and proactively plan for issues instead of reacting to them,” says Bodson.
But how, exactly, can AI help pharma supply chains? Here are some of the most profound ways.
- Cold chain management: Combined with internet-of-things (IoT) sensors, ML models can track any changes in the temperature of shipped products, determine the risk, and (if necessary) alert the driver to the problem.
- Route optimization: Just like riders want the shortest and most efficient route from their Uber driver, sensitive pharmaceuticals can benefit from the power of ML models to select the most efficient route and even combine that data with other factors, such as weather or road closures. This can potentially trim hours or days from a drug’s journey.
- Demand planning: The pharma industry’s just-in-time model, similar to the food industry, is necessary because of the highly perishable nature of many drugs. But drug shortages are currently very common. The European Association of Hospital Pharmacists estimated in 2019 that approximately 95 percent of hospitals dealt with shortages over the past couple of years. To avoid over- or undersupply, drug companies need to be able to forecast and even predict surges or drops in demand more accurately.
ML models can be very good at predicting demand with the right data. Models applied to this problem can be fed with data around historical ordering patterns, market trends, consumer behavior, competitors, and epidemiological trends.
- Predictive maintenance: Losing a batch of expensive drugs because a refrigerator failed is not an option for pharma companies with millions of dollars invested. Based on equipment data, ML-fueled predictive models can tell maintenance crews which equipment is the most likely to fail – and when.
- Supply chain and inventory management: It has been noted that replenishment times from manufacturer to retailer takes more than twice as long for the pharma business compared to other industries. Intelligent supply chain management based on ML has the potential to make this process more efficient.
- Warehouse automation: ML can learn and predict which items need storing for longer and which will likely be ordered soon, allowing warehouses to speed up the pick-and-pack distribution method. According to Forbes, one cold-chain food supplier increased productivity by 20 percent using this approach.
- Identifying and eliminating counterfeit drugs: The World Health Organization (WHO) reported in 2017 that one in 10 drugs in developing countries are counterfeit. ML models combined with big data and IoT sensors can help identify these products in real-time.
Pharma Supply Chain Data Sources
ML models aren’t very effective without the right data to learn from – and luckily, the pharmaceutical industry is awash in various big data sources perfect for feeding ML models hungry for information.
- Product data, including information around drug composition, expiry, price, and ideal prescription conditions.
- Demand data, including sales history and trends, and demographics, cross-referenced with sales.
- Planning data, such as internal performance metrics, marketing metrics, and production plans.
- Manufacturing data, including production capacity and data generated by IoT devices.
- Inventory data, including available stock, which stock needs replenishment, stock in transit, and inventory policies.
- Logistics data such as warehousing information, transportation information, and returns data.
- Supplier data, such as information around specific suppliers.
- Customer data, including unstructured data such as medical histories, prescriptions, bills, and phone transcripts. It must be noted this kind of protected health information (PHI) may fall under HIPAA and must be kept private, although aggregated and depersonalized data can be used.
- Public data such as government websites, news articles, and social media posts can be invaluable, especially when performing postmarket surveillance.
Power Up Your Supply Chain With CapeStart
CapeStart’s teams of data scientists and ML experts work with hospitals, medical device makers, pharmaceutical companies, and other healthcare organizations every day to drive efficiencies and improve the bottom line.
From helping to scale pharmacovigilance and postmarket surveillance, to improving the efficiency and accuracy of systematic reviews and clinical evaluations, CapeStart can help you push innovation forward and scale your healthcare and pharmaceutical business.