AI Is Redefining Pharma Forecasting, and Eliminating the Old Speed-Versus-Compliance Trade-Off

Global trade tensions and shifting tariff policies are creating significant challenges for manufacturers as they work to maintain product availability on retail shelves. According to Christy Christian, senior industry principal with Kinaxis, these disruptions are not entirely new, but the pace and unpredictability of change have intensified. Manufacturers now face a rapidly evolving landscape where tariffs can shift overnight—from metals to paper to other key materials—making it difficult to maintain stable, long-term supply chain strategies.

A key issue lies in the extended and globally distributed nature of modern supply chains. Many companies source components or raw materials from regions far from where final products are assembled, which introduces delays and reduces flexibility in responding to sudden tariff changes. The cascading effects of such disruptions extend across tier one, tier two, and tier three suppliers, amplifying bottlenecks and vulnerabilities.

Traditionally, companies have mitigated supply chain risk by building inventory buffers. However, in today’s financial climate, organizations are less willing or able to tie up capital in excess inventory. Maintaining large stockpiles directly impacts free cash flow, which remains a top priority for many manufacturers. This constraint leaves supply chains with limited slack and little room for error when disruptions occur.

As a result, firms must find ways to optimize and adapt their networks within increasingly complex conditions. Agility—both in planning and execution—has become essential. Companies need to rapidly update sourcing strategies, reallocate inventory, and pivot operations in response to tariff shifts or supplier closures. Yet, the challenge is ensuring that adjustments happen quickly enough to prevent excess raw materials from arriving after market conditions have changed. In today’s volatile global environment, the ability to dynamically balance inventory, cash flow, and sourcing flexibility defines supply chain resilience.

Christian also comments on the hurdles that typically come with moving from manual spreadsheets to AI-powered orchestration in the healthcare supply chain, the reliability of AI-driven demand forecasts in the face of unpredictable events; how supply chain modernization directly affects the affordability and accessibility of OTC medicines; and more.

Further, in traditional pharmaceutical manufacturing, organizations often had to compromise between speed and compliance. As production scaled, maintaining consistent quality and audit readiness became increasingly challenging, leaving many companies unprepared when regulatory inspections arose. Hari Kiran Chereddi, CEO of HRV Pharma, explains that its new operating model fundamentally changes this dynamic by digitally orchestrating processes across all partner facilities. Each site connects to a unified digital backbone that enforces standard operating procedures, quality metrics, and documentation standards in real time. This centralized system automatically tracks and validates every process deviation and corrective or preventive action, ensuring continuous alignment with regulatory expectations.

The result is a dramatically more efficient regulatory pathway, as organizations using this model have achieved regulatory review cycles that are 30% faster. Submission packages—such as drug master files for active ingredients—are pre-validated by an AI engine, allowing companies to enter regulatory review with confidence and significantly reduced risk of deficiencies.

A transcript of their conversation with PC can be found below.

PC: How reliable are AI-driven demand forecasts in the face of unpredictable events, and furthermore, what role does AI play in transforming supplier management, compliance validation, and production forecasting within a global pharma network?

Christian: I truly think it depends on the stability of the product, because if it's forecastable and it's pretty stable, that data is going to be pretty clean. You're going to have the anomalies of those disruptions, which you can pull out. I think where you are going to have the challenge leveraging it is something that's sporadic or project-based where it truly is that sawtooth type of demand, it's going to have a challenge in that, because is that the pattern of the product, or is that a disruption?

Even leveraging that on products that are fairly stable, that would take a significant amount of their portfolio, so they could actually leverage that, apply the human creativity and subjectivity to it, and be much faster in decision-making. Again, getting people out of the mindset that we have to have everything or we can't have anything. It's a crawl, walk, run. What can we do today to free up some time and make faster decisions while we work on the other pieces that don't necessarily fit the model today.

Kiran Chereddi: Speed and compliance were always trade-offs. The more you scale production, the harder it became to maintain quality, or even audit readiness. Typically, a lot of people would look at it and say, oh, you know what FDA is on my door, and I have something happening tomorrow morning. I don't know how ready I am.

What we have seen is that our model is actually able to break that, where we actually orchestrate some of these things. Each partner facility plugs into our digital backbone, which enforces the standard operating procedures, quality metrics, document templates, in real time. This means that every process deviation, or every corrective and preventive action is tracked and validated through this one. So what happens then?

What we’ve seen is that 30% faster regulatory review cycle. What happens is that from the time we actually have the drug master file ready—especially with the active ingredient—to the time the submissions are there, this is all pre-validated by the AI engine that's there.

We have seen quicker supplier onboarding. We have seen anywhere between 15% to 20% quicker supplier onboarding. That's something that we are mapping out and actually making sure the standard operating procedures around that are more robust at this point in time, and getting to a point where there are absolutely no compliance deviations at all. And because of that, the quality control is something that happens continuously and not in retrospect. And then with that, for us today, the virtual model has actually given us the global reach, but the AI layer is what is giving us the precision if I may, and also the regulatory precision panel that is there. A lot of people have seen this panel in the formulation space, but a lot of people have not seen it in the active ingredient space.