Chain reactions to smarter supply strategies for life sciences

How mature are your supply chain capabilities?

It’s a question that has come up a lot in client conversations over the past year. And after a bit of a pause and a healthy dose of self-congratulation, the clients we speak to are always surprised to hear that average overall supply chain maturity in life sciences companies is currently only 34% according to our latest research, “Next stop, next-gen. Tap into new value with advanced supply chain capabilities”.

Before we explore supply chains… What exactly do we mean by maturity and why does it matter?

Our report defines it as the extent to which a company is willing to embrace evolving technologies. These technologies, for example, digital, artificial intelligence, machine learning capabilities, allow for continual optimization in real-time. Investing in intelligent tech like these is beneficial in the long term and allows for quick smart actions in the present.

The research shows that while the global maturity across all industries has progressed quickly over the past years, most life sciences companies are primarily concerned with what we consider to be “now” capabilities. They can provide companies with quick wins, but they lack perspective. Companies could be getting themselves into a situation where they run out of breath after the first few steps and pull down the maturity average for the whole industry.

What are the most pressing challenges facing the industry?

As we navigate through constant disruption and ongoing change, we need to recognize the supply chain's unique and critical role in the life sciences industry. Apart from the maturity issues and delay in implementing digital capabilities I mentioned earlier, the industry is facing other pressing challenges. One common issue is the acceleration in drug discovery and clinical trials which results in a quicker turnaround for drugs being introduced into the market, leaving less time to ensure that the supply chain is ready.

Strategic decisions in supply chain design: Moving out of the boardroom to the frontline

Another challenge comes from within the company. Traditionally, companies designed their supply chain and operations in silos long before the product entered the commercial supply chain. Decisions made during development—regarding manufacturing recipes (bill of materials, equipment), formulation, packaging, release, shipping methods, chemistry, manufacturing, and controls (CMC) filing strategy, and choice of supply nodes—can have extreme long-term impacts on commercial supply chain agility, sustainability, and resilience. Breaking down existing silos and cultivating collaboration between departments should be top of mind for those looking to lead the way.

Manufacturing Site Mayhem

Imagine a world where every manufacturing site is a kingdom of its own, complete with its own set of rules, technology, politics, even languages…now translate that into the real world. Manufacturing complexity increases depending on how many sites are involved in the process and how well they stay harmonized with each other and their colleagues in R&D who are developing new drugs.

Each site manages its own production process, equipment, asset management, quality control, operations technology system, and is continuously implementing improvement programs at individual steps. Local site-level continuous improvements or corrective and preventative actions can lead to divergent evolutions of the recipes for a product. This in turn can lead to complex post-approval change controls that must be dealt with by regulatory affairs for all markets as well as product variants being managed by supply chain planners.

Data and AI: The Dynamic Duo

Enter the heroes of our story: coordinated data and artificial intelligence (AI) tools.

Data and AI aren't your average superheroes; they're the ones that get stuff done behind the scenes. With their powers combined, they turn a fragmented supply chain into a well-oiled machine. Think of them as the ultimate party planners, making sure everyone knows where they need to be and what they need to do, all while keeping the supply chain running smoothly.

Incorporating data and AI, companies can design a full end-to-end supply chain based on collaboration, resiliency, agility, autonomy, and sustainability in operations. By adopting a full, or even a partially AI based supply chain, companies are enabling simpler workflows between manufacturing and R&D teams, real-time communication between sites, and this approach puts the patient first by accelerating the delivery of treatments to those in need.

In our latest report “Reinventing life sciences in the age of generative AI”, we provide real-life examples of how companies that focus on creating a strong digital core can use it as one of their biggest strengths on the market. It supports their sustainability efforts, helps with accelerating operations, and allows for efficient innovation and growth.

How would an AI supply chain work? Let’s take a closer look

Teams responsible for developing recipes, supplying clinical trials, supporting CMC filing, managing tech transfers, and running commercial manufacturing, QC, QA and supply planning are good examples of the persona groups that could benefit from AI. A cross network data fabric, knowledge management tools, control towers and decision support tools can predict risks (e.g. in n-tier supplier), support improved decision making on next best action (e.g. in helping to identify the optimal corrective and preventative action after a deviation) and catalyse faster, data driven optimisation (e.g. to drive down cycle time and COGS while ensuring continual quality).

Digital technologies can also influence planning accuracy and efficiency by facilitating collaboration between sales, financial, and supply chains, with real time control over unexpected shifts (risks or opportunities) in the demand/supply equilibrium. With increased maturity, a company can use AI-based tools for internal and external data that shows the product lifecycle for constant forecasts. This data allows for a more accurate prediction to meet patient needs (and create greater revenue).

How to include people into the mix

Typically, teams that develop recipes, supply clinical trials and support CMC filings, and their counterparts that run commercial manufacturing, quality and supply chain resided within the previously mentioned silos. They were focused inwards and have limited periods of collaboration over the end-to-end drug lifecycle. Clients regularly express frustration that these periods of collaboration are often characterised by stress, overburden, repeat work and inordinate time looking for or regenerating new data to support decisions or solve problems and mitigate risks With AI tools, we can break down these barriers and optimize the process. One of the examples presented in our “Reinventing life sciences in the age of generative AI” report is how there’s an opportunity to consolidate 100 roles representing typical product development, manufacturing, quality and supply chain organization to 70 positions. The 30 employees who can move away from routine repetitive work with interspersed periods of stress and overburden, to invest their time to work on more strategic, value-add activity such as proactive data driven improvements and collaboratively preparing for/adopting new modalities and technologies into the supply chain. This leaves a lot more space for those employees to work on strategic priorities.

Change comes from the top

I spoke about our AI & data heroes, and I mentioned employees and changes to their structure. The only thing that’s left to mention is the captains of this ship - CEOs. We already know that supply chain is moving from being part of the discussions around the boardroom table, to the frontline of companies. What it means for CEOs is that they need to adopt and implement intelligent technologies at the very top of the organization. Imagine a company that has implemented digital tools and is actively finetuning its processes with a CEO board that works only with (virtual) pen and paper – it wouldn’t make sense, right?

That’s why we prepared to 5 imperatives CEOs must consider reaping the benefits of intelligent tech adoption:

1. Lead with value
2. Reinvent talent and ways of working
3. Understand and develop an AI-enabled secure digital core
4. Close the gap on responsible AI
5. Drive and support continuous reinvention

I encourage you to check our research to find out more what they could mean for your company.

In a nutshell

It’s clear that embracing evolving technologies in life sciences isn't just smart—it's essential. By powering up with digital tools, we're enabling real-time tweaks, swift decisions, and major gains for companies, R&D teams, Regulatory departments and of course, the patients. For more details, visit our supply chain website or contact me directly.