An emerging disruption in drug traceability for India and beyond

Avi Chaudhuri, PhD, Founder, The Kulinda Consortium reviews the challenges of drug traceability and new approaches being developed in India, with the emphasis on enabling advanced supply chain traceability intelligence with minimal incremental operational burden

Over the past two decades, I have worked closely on serialisation and traceability deployments in multiple markets, transitioning recently to architectural design of large-scale national programmes. As an independent advisor and commentator, I have lately written extensively about why India’s domestic drug traceability effort did not (and could not) succeed under the prevailing structural and policy framework. I claimed that the operational characteristics of India’s pharmaceutical supply chain — its scale, dispersion, multi-tiered distribution and uneven digital maturity — rendered a comprehensive end-to-end traceability regime impractical in its conventional form.

There is however good news on the horizon, not because the complexity has diminished or because policy flaws have been resolved but because the technological landscape itself has materially shifted within a short period. Here, I first revisit the context in which first-generation traceability systems were created so as to understand why the unfolding technological disruption is so pivotal.

The way we were

Many programmes took shape in the early 2010s when enterprise software was largely on-premise, integration was bespoke, hardware requirements were capital-intensive and packaging operations were only partially digitised. Serialisation was layered onto existing legacy infrastructure, with aggregation hardware retrofitted into operations that in emerging markets relied heavily on manual processes. In that context, designing traceability around hierarchical aggregation models and tightly coupled data exchanges was not merely logical, it was in practice unavoidable.
Embedded within that architecture were implicit expectations that digital continuity could be maintained across highly heterogeneous supply chains, that downstream scanning discipline would scale uniformly and that regulatory standardisation would evolve in parallel with programme maturity. Although these assumptions were appropriate at the time, it is now clear that those expectations no longer apply to the current reality of the Indian traceability landscape.

What followed over the first half of the next decade was rapid acceleration. Pharmaceutical manufacturing expanded globally, supply chains became more distributed and outsourcing models introduced new layers of operational interdependence. At the same time, regulatory pronouncements multiplied across geographies, data volumes increased exponentially and yet the constraints that plagued adoption in emerging markets remained in place. The field was ready and eager for innovation.

The future’s so bright …

A quiet disruption is now taking place, not in policy revision but in enterprise technology itself. Modular SaaS architectures have recently become the norm even within regulated industries. API-driven integration has reduced the friction of coordinating across heterogeneous systems. Cloud-scale computing and storage economics have materially lowered the cost of processing and analysing high-volume supply chain data. At the same time, companies have grown more cautious about long-term vendor lock-ins, escalating software costs and losing control over their own data.

Advances in artificial intelligence (AI) and machine learning have further altered the equation. Pattern recognition, anomaly detection and reconciliation that were once dependent on rigid rule sets and manual oversight can now be continuously refined through adaptive models. These developments do not eliminate supply chain complexity. They do however expand architectural flexibility and user autonomy. Traceability no longer needs to be conceived solely as a monolithic network construct. It can function as a modular intelligence layer that is capable of evolving without coercing organisations into inflexible long-term vendor lock-ins.

Technological inflection also introduces advantages in ways that are not immediately obvious. Early adopters absorb the cost and friction of immature technology, whereas those who build later inherit advances in infrastructure, implementation and learnings. In the context of drug traceability, that dynamic is becoming increasingly relevant. Systems designed by a new generation of purveyors today are no longer bound by the constraints that defined first-generation deployments. Advances in modular SaaS architectures, AI-assisted analytics and product identity technologies have revised architectural assumptions that once seemed inflexible. In short, the tumult of the past is paving way for the reality of tomorrow where second-mover advantage will prevail in emerging markets.

Homeward bound

Several countries that attempted comprehensive traceability a decade ago were operating within design limitations and technological boundaries of that era. The hurdles that ensued created some painful failures, such as the Indian drug traceability programme that was recently repealed after a decade of effort and investment. But now, India stands at a different moment. Its pharmaceutical industry has dramatically expanded, its supply chains are more complex and the prior failure has exposed operational realities that any serious system must now address. That combination of scale, lived experience and advent of a more mature technological landscape creates a distinctly different starting point.

It is within this altered context that the pressing requirements of Indian drug makers warrant renewed consideration by way of both out-of-the-box thinking and execution. What if, for example, an effective drug traceability programme could be enabled without involving each and every supply chain participant? That would get around the traceability bottleneck that I have recently written about. And what if the market can capitalise on a markedly different digital environment — one shaped by interoperable platforms, cloud-scale infrastructure and materially lower infrastructure and operational overhead for enterprise software deployment. That is the emerging new reality where machine learning, AI-driven analytics and modular system designs are no longer experimental but are becoming increasingly embedded within mainstream enterprise system architecture.

In that setting, it is becoming increasingly apparent that solutions to India’s domestic drug traceability challenges may be closer to home than previously assumed. In my view, traditional solutions providers were suitable for a different technological era — one defined by higher infrastructure costs, limited automation and restricted integration flexibility. By contrast, newer entrants are building solutions under materially different technological conditions. For example, I have been observing AltiusHub, a Hyderabad-based traceability platform develop an AI-native architecture designed to integrate seamlessly with existing enterprise systems while balancing automation with regulatory compliance.

The emerging emphasis is on enabling advanced supply chain traceability intelligence with minimal incremental operational burden. Drug makers may now find that the business and societal benefits long associated with comprehensive traceability are no longer beyond reach, provided they are pursued through platforms aligned with today’s technological capabilities. The opportunity lies not merely in scaling operations across India’s vast supply chain landscape, but in doing so under economic and deployment models that were previously unattainable.

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