By: Megha Sinha, Founder and CEO, Kamet Consulting Group
The Operational Crisis Hiding in Plain Sight
Every drug on the market is a moving target. From the day of approval, a product enters a continuous cycle of change - sites relocate, suppliers rotate, formulations evolve, labeling updates. According to publicly-available benchmarking data, a large pharmaceutical company will assess roughly 6,000 post-approval changes annually, around 3,300 of which require regulatory submissions, adding up to some 90,000 country-level filings across more than 140 health authorities.1,2
A single site transfer could involve 10+ functions - regulatory, supply chain, quality, manufacturing, labeling, legal, pharmacovigilance, IT - each with its own timelines and deliverables. This might then be multiplied across 50-100 countries with their own respective submission categories, review timelines, and grace periods for implementation. Achieving global approval for a single change can take as long as 3-5years, if the entire process is being coordinated through spreadsheets, email chains, and weekly calls (still typically the case today).
Invisible Exposure at the Top
What makes this situation particularly insidious is how seldom inefficient lifecycle change management registers as a crisis. Post-approval change management tends to be seen as a friction rather than a failure. Deadline slippage is absorbed, for instance. Budget overruns are rationalized. Supply gaps are patched via expedited shipments and the gargantuan efforts of in-country teams already juggling excessive and competing workloads.
For CQOs and COOs, this creates a specific blind spot in operational exposure. Although each respective function is highly competent, gaps often appear between those points, with the result that a GMP certificate has been deemed ready prematurely; artwork plates have been ordered before regulatory has confirmed the final text; or an implementation grace period has lapsed unnoticed after old stock has entered distribution. Such scenarios are far from exceptional and are expensive to resolve.
When the Costs Surface: Real-World Examples
In one large-scale API site transfer spanning 50-plus markets, a missed grace period deadline in a strict jurisdiction created a supply gap lasting several weeks, with revenue at risk running into the millions. The submission had been approved on time, yet there had been a coordination breakdown between the supply chain team managing the manufacturing cutover and the regulatory team tracking grace period trigger dates. Each was working to its own planning timeline, and no one had joined the dots.
In the case of a post-merger rebrand, an artwork pipeline misaligned with the regulatory submission sequence resulted in a 4-month delay to market entry and around $200,000 in packaging write-offs.
A divestiture requiring transfer of MAH status across 80-plus countries, meanwhile, generated inconsistent filings and rejected submissions, resulting in a remediation program that took over two years at a cost of several million dollars. The common thread connecting these avoidable events was that no single person or system had the full picture.
A Worsening Baseline
Change workloads are rising sharply, too. M&A and divestiture activity over the last half decade has been substantial, and much of its operational aftermath is still occupying regulatory teams today. Supply chain restructuring is accelerating, meanwhile, as companies nearshore and reshore in response to geopolitical pressure, the US BIOSECURE Act, and COVID-era lessons. Each CDMO switch or facility move triggers a cascade of post-approval changes across every registered market.
At the same time, regulatory expectations across 156-plus countries continue to diverge rather than harmonize. Change implementation grace periods vary from zero to 365 days, with different trigger events per market. The experienced practitioners who historically held this complexity in institutional memory are steadily retiring now, so that knowledge is leaving without being replaced by systems. All the while, same-sized teams are left to manage 2-3 times the change volume with the manual tools they have always relied on.
The Business Decision That Isn’t Being Made
All of this inefficiency is unnecessarily costly, not least because many lifecycle changes are so often initiated without any clear understanding of whether they actually make financial sense. Manufacturing might propose a cost-savings initiative — a supplier switch, site consolidation, process optimization — with projected savings that look appealing on paper. But once regulatory fees across 50-plus countries, artwork updates, dual production runs, packaging write-offs, resource hours, and 2-3 years of execution time have been factored in, the actual cost equals or even exceeds the promised savings. Because planning is so fragmented, the crucial details are slipping between the cracks. Instead of being treated as a business decision (Should we do this?), the proposed change is viewed immediately through a compliance lens (How do we implement this?).
Why Inertia Persists
The as-yet unresolved problem of inefficient post-marketing lifecycle change management persists because no single individual is accountable for it, end to end. Meanwhile subject matter expertise has historically lived in practitioners’ heads: the professionals who know that Brazil blocks sequential filings; that Japan requires three submissions where Europe bundles into one; that South Korea’s manufacturing grace period is 90 days, for instance. In most companies that knowledge has never been formally captured; when those individuals leave, it goes with them.
But this standpoint is indefensible now that AI tools have emerged which are capable of encoding this expertise, computing cross-functional dependencies, flagging risks before they materialize, and providing the aggregate portfolio view no human team can sustain manually. In reality, the probability of AI delivering genuine near-term operational return in lifecycle change management is meaningfully higher than in many areas currently attracting far greater industry investment. The bottleneck is not technological readiness, but rather a lack of appreciation of (a) the strategic cost of avoiding progress; and (b) how easy it could be now to solve the problem once and for all.
Planning a Systematic Response
Transforming lifecycle change management requires movement on three fronts: organizational structure, process, and technology. The organizational part requires a designated program lead for each lifecycle change with cross-functional coordinating authority. The process element demands standardized planning frameworks that map regulatory pathways by country, identify dependencies, and sequence execution with defined escalation paths. The technology element involves a structured regulatory intelligence layer — a validated database capturing not just what to submit, but what happens after submission, and what constraints apply to manufacturing and supply chain transitions while approval is pending.
To tackle transformation in a manageable way, companies should ideally start with one change type for one product portfolio — typically a manufacturing site transfer. The aim should be to prove the approach, measure improvement in timeline accuracy and cost predictability, then scale.
Doing Nothing is Not an Option
Lifecycle change management may be a dry topic that rarely features in investor presentations, yet it underpins the commercial life of every marketed product. As change volumes rise, regulatory environments continue to diverge, and experienced practitioners exit the workforce, the opening to address this before it becomes a board-level risk is narrowing.
Companies that do the work to build structured regulatory intelligence, establish cross-functional governance, and connect planning to execution in a single unified view should find that they are able to execute change more swiftly, spend less in the process, and protect supply continuity more reliably than those still reliant on spreadsheets and institutional memory. The tools to enable this exist today; all that’s really lacking is senior recognition that lifecycle change management in pharma is a costly and risk-laden friction that could now so easily be alleviated.
About the Author
Megha Sinha is founder and CEO of Kamet Consulting Group, which advises global pharmaceutical and life sciences companies on regulatory operations, quality, and large-scale transformation. With more than 17 years’ experience across the industry, including senior leadership roles at global consulting firm PwC, Sinha has worked with many of the world’s largest pharma organizations, solving their most complex regulatory, supply chain, and lifecycle management challenges.