Associate Director, IT Applications
Hikma

Manish Garg, PMP, PSM

Pharmaceutical serialization is evolving through the power of operational intelligence

Pharmaceutical serialization has reached a decisive inflection point.¹

³ With full enforcement of the U.S. Drug Supply Chain Security Act (DSCSA) now operational across manufacturers, distributors, and big dispensers and parallel advancements like alert management, digital link, Blockchain etc. accelerating in Europe and other global markets, the industry has largely completed the compliance build phase of traceability, what I refer to as the foundation phase. What remains unresolved is how the unprecedented volume of serialized supply chain data, often reaching billions of EPCIS events annually in large networks, will be operationalized.

This article argues that serialization infrastructure should no longer be viewed primarily as a regulatory reporting mechanism, but as a strategic operational intelligence asset.⁴ Drawing on real-world experience across large pharmaceutical trading networks, it examines persistent failure modes that remain hidden behind nominal compliance, including exception overload, certificate expiry, third‑party logistics (3PL) complexity, network fragility, and untested manual fallback procedures. Grounded in current regulatory expectations and increasing legislative scrutiny, what follows outlines a pragmatic path for transforming serialization from reactive compliance into proactive supply chain stewardship.

From Regulatory Obligation to Operational Control

The pharmaceutical industry in major countries worldwide has been focusing on getting its serialization and track and trace infrastructure and network build for more than a decade and it would be safe to say that in these ten to fifteen years, the industry has built one of the most granular product traceability infrastructures of any regulated sector. At the package level, prescription medicines are now traceable across manufacturing sites, logistics providers, wholesale distributors, and dispensers. This transformation was driven by a clear and enduring need because threats to the supply chain such as counterfeiting, diversion, theft and imports of falsified, unapproved, or otherwise unsafe drugs, could result in unsafe, ineffective drugs in U.S. distribution.

 And as per WHO, at least 1 in 10 medicines in low-and middle-income countries are substandard or falsified.

 Overall, this leads to believe that counterfeit and diverted medicines are continuing to pose a significant risks to patient safety and global supply chain integrity.

Despite this progress, a critical operational gap remains. In many organizations, serialization systems function adequately under stable conditions but degrade quickly under stress, such as during partner outages, data mismatches, system changes, or peak distribution periods. Transaction data are captured, transmitted, and archived yet rarely leveraged to improve resilience or reduce disruption when conditions deviate from the norm.

This creates a paradox increasingly visible across the industry: organizations are technically compliant, yet operationally fragile.

Investment in supply chain analytics and advanced automation continues to accelerate across industries.

 As policymakers intensify scrutiny of pharmaceutical supply chain transparency, resilience, and foreign dependency, the role of serialization is being redefined.

 The question is no longer whether traceability exists, but whether it performs reliably under real-world conditions.

Persistent Operational Challenges in Serialization Programs

Even in mature implementations, several systemic challenges continue to undermine serialization effectiveness at scale and few critical one are addressed below:-

Exception Overload and Non‑Linear Growth

Modern serialization environments routinely generate thousands and sometimes even tens of thousands of exceptions per month within large trading networks. These events are rarely isolated. Minor defects, such as aggregation mismatches, incomplete EPCIS events, or partner‑specific formatting differences can cascade across multiple downstream partners.

In practice, exception volume often grows disproportionately as networks scale. Resolution requires cross‑referencing ERP transactions and correlating them against the touch points from Manufacturing till the end customer, serialization repositories with tracking and tracing details, partner acknowledgments, and historical communications—activities that are inherently time‑consuming and difficult to standardize.

A minor aggregation discrepancy originating at a contract packaging site could propagate through multiple customers including distributor and dispensers’ nodes, triggering product quarantine across the organizations. When performing investigation, we generally find that the technical defect itself was simple; the effort required to identify ownership, align stakeholders, and coordinate corrective action was not.

Organizations that have successfully reduced exception backlogs follow a consistent pattern: they start not with AI, but with taxonomy. Before any automated triage can be effective, exception types must be formally classified—by root cause category (data quality, system connectivity, partner configuration, aggregation error, Operator/clerical errors or quality related issues), by trading partner tier, and by regulatory risk level. Build patterns and trends and rank them. This structured classification becomes the training foundation for machine learning based detection and prioritization. Specifically, teams should: (1) export six months of historical exceptions from their serialization platform and categorize them by type and resolution path; (2) Apply 80-20 rule where identify the 20% of exception types that drive 80% of resolution effort—these are the highest value targets for ML prioritization; and (3) build a standardized exception intake and possible responses form that enforces consistent data capture, which in turn should improve AI model accuracy over time.

 Once this foundation is in place, ML based triage can be piloted on high-frequency, well defined exception types, allowing analysts to focus human judgment on novel or ambiguous cases where it adds the most value.

Exception handling remains heavily dependent on human intervention. Resolution quality varies based on individual expertise, familiarity with specific partners, and access to historical context. During peak shipping periods or organizational transitions, this variability increases and documentation consistency declines, introducing both audit and continuity risk.

Many organizations discover after go‑live that scaling exception handling linearly with headcount is neither sustainable nor effective. The limiting factor is not capacity, but consistency (i.e., how reliably issues are identified, classified, and resolved regardless of who is on shift) and resource churn increase those limits further.

The most effective near term intervention for human variability is a RACI-anchored exception routing model combined with natural language processing (NLP) tools that translate technical system logs into plain language action summaries. Practically, this means defining which team owns each exception category: quality for product level discrepancies, Distribution for Inventory discrepancy, Claims/customer service for Overages, Damages and mis-pick/mis-ships, for IT for connectivity and system and data failures, operations for trading partner coordination. Once routing is explicit, NLP engines can parse incoming exception logs, classify the likely root cause, and surface the relevant owner with a pre-populated action summary, reducing the cognitive load on any individual analyst regardless of their experience level. Organizations that have implemented this pattern report that on-boarding time for new exception analysts drops significantly, and that shift change handoffs become structured data transfers rather than informal verbal briefings, reducing audit documentation gaps.

Another key error point noticed which causes DSCSA disruption between trading partners is digital certificate expiry which is fairly a preventable cause. In EPCIS based exchanges, expired certificates can silently block data transmission without generating clear system level alerts. Operationally, this often surfaces downstream as unexplained product quarantines. ²

When a certificate lapse at an intermediary node occurs, it disrupts EPCIS transmissions i.e. Interoperability across multiple trading partners. By the time the issue been investigated and root cause is being identified, product movement had already been impacted, requiring coordinated remediation across several organizations. Even though the certificate renewal could happen quickly; the operational recovery extends to length and across an entire business cycle.

Certificate failures are entirely preventable with a structured lifecycle management program. Organizations should maintain a centralized certificate inventory, whether a simple spreadsheet or an integrated monitoring dashboard tool whichever could capture the certificate name, associated trading partner, expiry date, responsible owner, and renewal lead time for every AS2, SFTP, and HTTPS certificate in their serialization network. Automated monitoring tools available within most middleware platforms (e.g., LsPedia, TraceLink, rfxcel, Antares Vision) can be configured to send 90-day, 60-day, and 30-day expiry alerts and reminders to designated owners, escalating to management if the 30-day threshold passes without renewal confirmation. IT teams should additionally implement automated connection health checks daily or periodically and schedule them outside of business hours. These checks confirm that certificate handshakes succeed and will catch post-renewal mismatches (such as a new certificate not yet trusted by the partner’s system) before they affect live shipments. A written certificate renewal SOP, including escalation contacts and a 24-hour emergency renewal procedure, should be tested at least annually. Industry guidance from GS1 US provides specific technical recommendations for certificate management within DSCSA EPCIS implementations.

System Changes and External partner like 3PL Dependencies

Serialization ecosystems are highly interdependent. Configuration changes, API updates, or platform upgrades at a single wholesaler or 3PL can affect dozens of connected partners simultaneously. When logistics providers act as agents for multiple manufacturers, accountability can become diffused.

A recurring operational challenge is that the point of failure is rarely the point of ownership. This misalignment delays resolution and amplifies downstream disruption, particularly under DSCSA timelines for which responsiveness is critical.

Planning the internal changes and change management communication, periodic touchpoints are essential along with system and data impact assessment. involving the external partners with clarity of scope, independence and language helps avoid day 1 disruptions. The most effective intervention for CMO or 3PL related serialization failures is ensuring that trading partner and 3PL service agreements include a formal Serialization Change Notification requirement for example a minimum 30 days advance notice before any platform configuration change, API update, or certificate modification that could affect EPCIS transmission. Beyond contracts, what helps organization is a shared Serialization Change Calendar across all key partners, updated monthly and distributed to IT, quality, and operations leads, so that risk windows are visible before they become incidents. When failures do occur, resolution speed depends entirely on whether a pre-agreed escalation matrix exists hence a simple document that defines who to contact at the 3PL, the expected response time, and the remediation documentation required for regulatory audit. Organizations that have notification clause, change calendar, and escalation matrix in place could resolve partner caused exceptions faster and with less downstream disruption than those managing the same failures reactively.¹⁴

System outages, whether caused by infrastructure failures, cyber incidents, or third‑party disruptions are inevitable. Regulatory obligations for traceability do not pause during downtime.³

In practice, fallback processes are often improvised rather than rehearsed. Risk arises not because systems fail, but because organizations are unprepared to operate effectively without them.

A resilient serialization program requires a written, tested fallback SOP that operations staff can execute without IT support during a system outage. The SOP should specify, at minimum: (1) the maximum permissible outage window before shipments must be held (typically defined by the organization’s DSCSA trading partner agreement); (2) a manual exception log template that captures all product movements with required DSCSA data fields during downtime; (3) the re-transmission procedure for backlogged EPCIS events once systems are restored, including the sequence in which events must be submitted to preserve chronological integrity; and (4) a partner notification template for informing downstream trading partners of the outage and estimated resolution time. Critically, this SOP must be rehearsed—not merely documented. Quarterly tabletop exercises, in which the operations and IT teams simulate a four hour outage scenario and walk through the manual process step by step, expose gaps and build the operational muscle memory that prevents improvisation under pressure. Manufacturers supported by 3PL, the SOP must also define who within the 3PL has authority to invoke fallback procedures and how that decision is communicated back to the manufacturer’s quality and IT teams in real time.

The regulatory environment surrounding pharmaceutical traceability has shifted from implementation guidance to enforcement and assurance.

In the United States, DSCSA enforcement deadlines now apply across manufacturers, repackagers, wholesalers, and dispensers, with remaining stabilization provisions scheduled to sunset for smaller dispensers in 2026.³ Serialized data exchange at scale is now an operational reality.

In parallel, European regulators are advancing beyond the Falsified Medicines Directive toward broader Digital Product Passport concepts under the Ecodesign for Sustainable Products Regulation.

 GS1 EPCIS 2.0 and Digital Link standards provide the technical foundation for this expanded transparency.

Governance expectations for advanced analytics and artificial intelligence are also maturing. The FDA has published draft guidance on AI credibility for regulatory decision‑making, along with clarification on predetermined change control plans for adaptive systems.

 The European Medicines Agency has articulated similar principles emphasizing risk based governance and human oversight.

 These expectations align closely with the NIST Artificial Intelligence Risk Management Framework, which is increasingly referenced by regulators on both sides of the Atlantic.

Layered onto regulatory oversight is growing Congressional attention. Multiple hearings and committee reports between 2025 and 2026 have explicitly linked supply chain transparency to patient safety, national security, and drug affordability.⁸ These inquiries make clear that traceability data will increasingly be used not only by regulators, but by policymakers evaluating foreign dependence, pricing practices, and system integrity.

https://www.fda.gov/drugs/drug-supply-chain-security-act-dscsa/title-ii-drug-quality-and-security-act

EPCIS & CBV: GS1 Standards for Event‑Based Visibility.

DSCSA Compliance: Milestones, More Deadlines—and the Road Ahead.

https://www.pharmaceuticalcommerce.com/view/dscsa-compliance-milestones-more-deadlines-and-the-road-ahead

Prescription Drug Supply Chain Transparency and Resilience Hearings.

https://www.aging.senate.gov/hearings/prescription-for-trouble-drug-safety-supply-chains-and-the-risk-to-aging-americans

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https://www.fda.gov/drugs/drug-safety-and-availability/drug-supply-chain-integrity

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https://www.who.int/news-room/fact-sheets/detail/substandard-and-falsified-medical-products

Mackey TK, Liang BA, York P, Kubic T. Counterfeit drug penetration into global legitimate medicine supply chains: a global assessment. 

https://pmc.ncbi.nlm.nih.gov/articles/PMC4455087/

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AI in Pharma & Biotech Market to Reach US$13.1 Bn by 2034.

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https://www.aging.senate.gov/imo/media/doc/senate_aging_american_drugs_report.pdf

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https://kezzler.com/blog/understanding-epcis-benefits-event-types-and-serialization/

https://ispe.org/publications/guidance-documents/gamp-guide-artificial-intelligence

https://www.gs1us.org/industries-and-insights/by-industry/healthcare/standards-in-use/pharmaceutical/dscsa-implementation-guidelines

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https://www.dummies.com/book/business-careers-money/business/operations/supply-chain-management-for-dummies-281875/

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https://green-forum.ec.europa.eu/implementing-ecodesign-sustainable-products-regulation_en

https://www.gs1.org/standards/gs1-digital-link

Considerations for the Use of Artificial Intelligence to Support Regulatory Decision‑Making.

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological

Predetermined Change Control Plans for AI‑Enabled Systems.

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/marketing-submission-recommendations-predetermined-change-control-plan-artificial-intelligence

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Articles

Pharmaceutical serialization has moved from build-and-comply to full-scale operational execution under DSCSA, with parallel global momentum via EPCIS 2.0, Digital Link, and emerging Digital Product Passport concepts. Despite technical compliance, many networks remain operationally brittle when stressed by outages, partner changes, data mismatches, and exception surges. High-volume EPCIS data should be treated as an operational intelligence layer to improve resilience, reduce quarantine events, and strengthen stewardship. Pragmatic controls include exception taxonomy, RACI-based routing, certificate lifecycle monitoring, change governance, and rehearsed manual fallback procedures.

Pharmaceutical serialization must evolve from regulatory compliance into operational intelligence, addressing hidden failures like exception overload, certificate expiry, and 3PL complexity.

Transforming Pharmaceutical Serialization from Compliance to Operational Intelligence

In 2026, the US pharmaceutical landscape is defined by a shift from “just-in-case” to “AI-synchronized” operations. For the chief manufacturing officer, the mandate has evolved: Onboarding and managing contract partners is no longer a months-long administrative hurdle but a high-speed digital integration aimed at offsetting global volatility.

As the pharmaceutical industry navigates Pharma 5.0,

 the traditional 18- to 24-month window for human-driven technology transfer and contract development and manufacturing organization (CDMO) onboarding has become a critical business liability. Driven by patent cliffs,

 personalized modalities, and the “China+1” diversification mandate accelerated by the BIOSECURE Act,

 chief manufacturing officers face immense pressure to balance automation with human expertise across global networks without sacrificing quality or speed.

This article proposes a new digital orchestration framework for 2026 that moves beyond manual AI onboarding toward “zero-lag” integration. Autonomous systems perform multithousand-page standard operating procedure (SOP) gap analyses and compatibility prescreening, delivering specifications and integration orchestration support in days rather than months. 

 illustrates the dramatic differences between traditional and AI-synchronized approaches.

This investigation employed a comprehensive multimethod research approach that combined a systematic literature review, industry case study analysis, and quantitative benchmarking of pilot programs conducted between 2020 and 2025. The methodology integrated 3 primary data collection streams to establish evidence-based recommendations for digital transformation in pharmaceutical manufacturing operations.

Literature Review and Framework Development

A systematic review of peer-reviewed publications, industry white papers, and regulatory guidance documents published between 2020 and 2025 was conducted across major databases, including PubMed, ScienceDirect, and industry-specific repositories. Search terms included 

. The review identified 47 relevant studies focusing on AI implementation in pharmaceutical operations, digital twin applications, and manufacturing process optimization.

From this foundation, the “digital orchestration framework” was developed through the synthesis of successful implementation patterns identified across leading pharmaceutical manufacturers and emerging biotech firms. The framework incorporates 5 key dimensions: partnership agreements, contract customization, continuous monitoring, risk-based manufacturing, and technical release protocols.

Detailed case studies were analyzed from 12 major pharmaceutical companies and 8 contract manufacturing organizations (CMOs) that implemented AI-driven manufacturing technologies between 2020 and 2025. Data sources included company press releases, regulatory filings, published implementation reports, and industry conference presentations. Quantitative performance metrics were extracted when publicly available, including timeline reductions, cost savings, quality improvements, and operational efficiency gains.

Case selection criteria prioritized organizations that (1) deployed AI or digital twin technology in good manufacturing practice environments, (2) reported measurable outcomes with sufficient detail for analysis, (3) operated under FDA, European Medicines Agency, or equivalent regulatory oversight, and (4) completed implementation phases allowing for outcome assessment. This approach enabled the identification of validated success patterns and common implementation challenges across diverse organizational contexts.

Performance metrics from validated pilot programs were systematically collected and analyzed to establish baseline comparisons between traditional and AI-synchronized approaches. Key performance indicators included technology transfer timeline duration, document review cycle times, batch failure rates, regulatory inspection outcomes, and partner onboarding costs. When available, year-over-year comparisons were analyzed to assess sustained performance improvements vs initial implementation effects.

Statistical analysis employed descriptive statistics to characterize central tendencies and variability across implementation outcomes. Percentage improvements were calculated using traditional manual approaches as baseline comparators. Data quality assessment included verification of measurement methodologies and evaluation of potential reporting biases in self-reported industry data.

Today’s manufacturing environment faces a complexity paradox. Although scientific breakthroughs offer unprecedented therapeutic potential, the operational burden of delivering them has increased exponentially.

 presents a comprehensive matrix of these challenges alongside their corresponding digital solutions.

Procedural Slowness and Regulatory Bottlenecks

Manual technology transfers historically take 18 to 24 months, now financially unsustainable given patent cliffs and rapid competition.

 Procedural slowness in securing regulatory approvals compounds these delays, creating cascading effects throughout product launch timelines with mounting costs from idle capacity and delayed revenue realization.

Nonstandardized Practices Between Trading Partners

Misalignment between marketing authorization holders and CMOs creates dangerous fragmentation. Nonstandardized practices in quality systems, documentation protocols, and compliance approaches introduce variability that regulators increasingly scrutinize, manifesting in divergent regulatory interpretations and incompatible data management systems.

Departmental divisions between research and development, information technology, and manufacturing lead to data fragmentation, resulting in critical insights being lost in translation. When manufacturing facilities, warehouses, and third-party logistics providers operate with disparate systems, technical release becomes a multimonth negotiation rather than a streamlined handoff.

Technical Complexity and Resource Constraints

Pharmaceutical manufacturing demands specialized expertise that remains in short supply.

 Organizations struggling with skilled resource shortages face alignment issues cascading through technology transfer, process validation, and commercial launch. These gaps become critical vulnerabilities when navigating evolving regulatory requirements and the geopolitical complexities posed by trade tensions.

Emerging Technology Trends: The 2026 Toolkit

Agentic AI and Intelligent Document Processing

AI has evolved to encompass agentic systems performing complex tasks autonomously.

 These agents prescreen thousands of SOP pages and batch records in days, identifying compatibility gaps and directly addressing nonstandardized practices by systematically mapping process differences.

Virtual manufacturing line replicas allow CMOs to simulate onboarding before filling a single vial, potentially reducing transfer time by 50%.

 Digital twins enable virtual validation, identifying and resolving technical complexity issues before physical production begins.

Blockchain ensures immutable records across borders, satisfying US Drug Supply Chain Security Act requirements,

 while providing real-time visibility into supply disruptions. This technology breaks down logistic silos by creating unified audit trails accessible to all stakeholders.

Strategic Solutions: The Digital Orchestration Framework

Detailed Agreements and Early SME Engagement

Successful 2026 partnerships demand comprehensive agreements during initial negotiations. Organizations must engage subject matter experts (SMEs) during contracting to map technical specifications, quality requirements, and regulatory pathways before finalizing commitments. This early engagement addresses nonstandardized practices plaguing relationships between traditional trading partners (contract manufacturing organizations and marketing authorization holders).

Embedding risk mitigation into contractual frameworks establishes clear protocols for handling procedural delays. Agreements specify escalation pathways, define acceptable timeline parameters, and allocate responsibilities for managing regulatory complexity.

AI transforms contracting from an administrative burden to a strategic advantage. AI systems can analyze business cases, assess partnership strengths and weaknesses, and recommend customized trade-offs tailored to each arrangement. These platforms evaluate thousands of contract clauses against regulatory databases and industry benchmarks. AI-driven customization tackles nonstandardized practices by identifying where partner protocols diverge and recommending harmonization strategies. When business cases require trade-offs between speed, cost, and quality, AI models simulate outcomes under various contractual scenarios, enabling data-driven decisions that are particularly valuable for geopolitical complexities.

Commercial Validation: Proving the AI Model Works

The theoretical promise of AI-synchronized operations has been decisively validated through real-world implementation across the pharmaceutical industry. Between 2020 and 2025, leading organizations deployed digital transformation initiatives that demonstrate measurable, sustained improvements in manufacturing efficiency, quality outcomes, and regulatory compliance. 

 summarizes key industry pilot programs with documented performance metrics.

Proposed digital integration implementation follows a structured 4-phase approach, as detailed in 

. Each phase builds upon previous achievements while establishing the foundation for subsequent advances. This is based on our observations and expert judgments.

By 2030, the $2.3 trillion global pharmaceutical market

 will be driven by the digital efficiencies noted in Table 4, entering an era of self-correcting supply chains. Organizations implementing comprehensive strategic approaches will operate predictive manufacturing networks in which AI systems autonomously detect and remediate emerging issues.

In the geopolitical minefield of 2026, the ability to digitally onboard and manage partners at speed through detailed agreements, AI-powered customization, continuous realignment, risk-based manufacturing, and streamlined technical release protocols is no longer an advantage—it is the baseline for survival. Organizations embracing this digital orchestration framework will collapse onboarding timelines from 18 months to weeks while improving quality outcomes and regulatory compliance, establishing the operational foundation for success in the decade ahead.

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In 2026, pharmaceutical manufacturing is shifting from “just-in-case” execution to AI-synchronized networks that compress CDMO onboarding and tech transfer from 18–24 months to weeks. Patent cliffs, personalized modalities, and China+1 imperatives amplified by the BIOSECURE Act make legacy, human-driven transfers a material commercial and compliance risk. A digital orchestration framework—spanning agreements, contract customization, monitoring, risk-based manufacturing, and technical release—uses agentic AI, digital twins, and blockchain to reduce friction, improve quality, and sustain inspection readiness amid geopolitical volatility.

A framework for 2026 that moves beyond manual AI onboarding toward "zero-lag" integration for pharmaceutical manufacturing.