Leveraging AI in the Pharmaceutical Supply Chain

Artificial Intelligence (AI) is a game-changing technology for all industries, not just pharmaceuticals.

Over the past few years, we’ve already seen how AI can benefit the healthcare sector. It’s redefining how scientists develop new drugs and find cures, how doctors diagnose, treat and make sense of clinical data, and how drug adherence is managed.

However, the promise of AI in the pharma supply chain has largely been overlooked despite huge technological and commercial opportunities in this area.

It’s time for more investment in AI solutions specifically targeting longstanding problems with the pharma supply chain if we are to enjoy the full benefits of AI development in other areas such as drug discovery, clinical trials, diagnosis and treatment.

 

The technological arms race 

Big pharma is well aware of the technological arms race taking place in the global healthcare industry.

In recent years we’ve seen an acquisition spree with some of the largest pharmaceutical companies snapping up biotech start-ups, investing in state-of-the-art AI solutions and hiring in-house data scientists to work alongside, well, scientists.

Yet technology in the pharma supply chain that connects the lab to the marketplace is lagging, with investment instead being prioritised towards the discovery, development and marketing of products.

As the pharma industry becomes increasingly globalised and demand grows for new product types, supply chains need to become smarter. And AI may be the silver bullet.

 

A supply chain fit for purpose today and tomorrow

A supply chain that’s fit for purpose today and tomorrow is one that’s not just reactive, but proactive. It will anticipate and accommodate current and future trends, driving forces and challenges.

There are many stress factors (both positive and negative) forcing the pharma industry to adapt while continuing to develop new and quality medicines at affordable prices.

These stress factors are intensifying every year and combining to present a real challenge, particularly for supply chain management. Here is a brief overview of just some of them:

  • Environmental pressures: regulators are imposing stricter environmental controls across the design, manufacture and transportation of pharma products to help curb carbon emissions and reduce plastic and water waste.
  • A new wave of medicines: complex biologic drugs and gene therapies are becoming increasingly popular but throw up huge challenges for manufacturing and distribution networks due to their sensitivity and short life cycle.
  • Demographic shifts: populations around the world are ageing and so is the prevalence of associated chronic diseases associated such as diabetes, cardiovascular disease, cancer and dementia.
  • Falsification: the criminal market for falsified medicine is worth over $200 billion per year, making the protection of medicine quality and safety a priority, including the development of tamper-proof packaging technologies.
  • Demand from emerging markets: to unlock the potential of developing regions such as the BRIC economies (Brazil, Russia, India and China), pharma needs to invest in and implement truly global supply chains.

Combined, these forces represent a huge challenge for modern pharma. As the frontier of drug innovation evolves, so must manufacturing and distribution systems.

AI could hold the key to overcoming these pressing challenges and help keep the industry be one step ahead of future ones too.

 

Leveraging AI in the pharma supply chain

There are two distinct types of AI technology: Augmentation and Automation.

The first, augmentation, is an assisting technology to the workforce, boosting efficiency and reducing human error. The latter, automation, is when AI technology has no human intervention and works completely autonomously on a task.

There’s no shortage of AI solutions (of both kinds) in the pharmaceutical market. Looking at the supply chain specifically, we’re seeing a growing number of AI-led technologies offering answers to many of the current problems faced by the industry.

For example, programmes can independently monitor market signals and accurately predict risks related to medicine shortages; others are using machine learning to control and reduce pharmaceutical costs, using real-time signals to direct when to buy and recommend formulator strategies.

AI can even pick up that a large number of people in a city are complaining of flu-like symptoms on social media and use this analysis to predict an imminent large-scale outbreak, giving local authorities and healthcare systems more time to react.

When it comes to transportation, AI is making it possible to predict and manage transportation capacity at a highly granular level, while virtually eliminating manual work and best-guess decisions.

 

Overcoming the challenges to AI adoption

The promise of AI in streamlining the pharma supply chain and delivering the next generation of medicines is clear.

However, as is the case with most new technologies, outdated IT infrastructure and skill sets are limiting its uptake on the ground.

Over the coming years, money and resources need to be ring-fenced for updating legacy IT infrastructure, digital skills training and the on-boarding of specialist data scientists to make sure any AI solutions are being leveraged to the max.

Unfortunately, throwing money at IT doesn’t automatically equate to success. A long-term perspective with the flexibility to adapt is crucial because re-designing and implementing changes can be a lengthy process.

There is no doubt that AI is the future of operating exponentially more efficient and more intelligent supply chain systems but today, the industry is at a crossroads. And it’s up to us which direction we take.

For too long many in the pharmaceutical industry have relied on supply chain networks which are neither flexible nor cost-effective. A radical overhaul of pharma supply chains is long overdue and if left in their current state, we risk slowing the delivery of innovative new products and increasing poor health outcomes.

Pharma companies need to think about what digital functions they will need to compete in two, five, or even ten years’ time, not just today. Collaborating with biotech and AI start-ups more closely can help keep big pharma ahead, or at least on top, of the curve.

These cost-saving and efficiency gains will simultaneously help the industry fulfil its social responsibilities, including the need to both pioneer more sustainable manufacturing processes and produce more effective and safer medicines the entire world can afford.

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