In the intricate web of global supply chains, few industries operate under the same level of scrutiny and stakes as consumer healthcare. In healthcare supply chain, data quality isn’t just a matter of operational efficiency; it’s a matter of public trust, regulatory compliance, and, in some cases, patient well-being.
In consumer healthcare, poor data doesn’t just cause delays and financial setbacks; it leads to far more critical consequences: stockouts of essential medicines, misaligned production plans, and compliance gaps that can trigger regulatory action, resulting in delayed care and eroded consumer trust.
The supply chain and manufacturing systems of global consumer healthcare companies are riddled with data quality issues, amplified by the layered complexities of the ecosystem. Despite the presence of robust ERP and supply chain management (SCM) platforms for centralized planning, data inconsistencies continue to surface. And not just occasionally—they’re systemic.
The underlying issue? A persistent disconnect between how business processes are designed, how systems are configured, and how people interact with both, causing data inconsistencies and errors.
Critical Dimensions of Consumer Healthcare Supply Chain
Root Causes of Poor Data Quality in Healthcare Supply Chains
The erosion of data quality in healthcare supply chains is rarely a result of negligence. It’s a structural issue - rooted in the intersection of disparate systems, conflicting business logics, and poorly aligned user processes.
Here’s a breakdown of why data quality fails and what organizations can do about it.
Systemic Fragmentation and Misaligned Integration
Across global operations, it’s common for each regional manufacturing site to run its own third-party ERP instance, customized to meet local regulatory, tax, and business requirements. These localized systems feed into a centralized supply chain management (SCM) environment, which is responsible for orchestrating global demand and supply planning. However, the integration between these systems is often anything but seamless.
SCM platform expects clean, harmonized inputs - standardized units of measure, consistent lead times, minimum order quantities, and unified sourcing logic. But in reality, each ERP instance defines these attributes differently. Over time, the middleware scripts and mapping layers that attempt to reconcile these differences become brittle, opaque, and prone to silent failures.
Take, for example, a product manufactured in both Germany and Brazil. In Germany, the lead time is defined as eight working days; in Brazil, it’s eight calendar days. SCM platform, unaware of the semantic difference, treats them as equivalent. The result? Inaccurate supply plans, inventory imbalances, and service level disruptions.
To address this, organizations should introduce validation layers in the middleware i.e., custom ETL pipelines, to catch discrepancies before they reach the SCM platform. Establishing canonical data models that translate local formats into globally accepted templates is also critical. And regular interface audits can help detect schema drifts and attribute-level anomalies before they cause downstream chaos.
Lack of Unified Governance
One of the most persistent challenges in healthcare supply chains is the absence of clear data ownership. Master data is touched by many - manufacturing, supply chain, regulatory - but governed by none. This leads to a host of inconsistencies that quietly undermine planning accuracy.
For instance, the definition of a “sourcing location” might vary depending on who you ask. Manufacturing may define it as the physical plant, while the supply chain team sees it as the distribution hub. Conflicting definitions like this result in misaligned assumptions and planning errors.
Duplicate SKUs across systems cause chaos, one active in the ERP but retired in SCM platform, or carrying different lead times and sourcing rules by region. The result: confused planning, inflated forecasts, and fragmented supply coverage that drives overproduction or stockouts.
Worse, master data changes are often made in isolation. A sourcing rule updated in the ERP may not reflect appropriately in the SCM platform, or regulatory updates may alter specifications without planners knowing. Without structured impact analysis, these silent errors that surface will result in missed shipments, compliance violations, or costly write-offs.
To mitigate these risks, organizations must assign clear data domain ownership. Planners should own planning attributes, regulatory teams should manage formulation specs, and manufacturing should oversee production parameters. Establishing metadata catalogs can also help ensure consistent interpretation of key fields across functions and systems.
Legacy Technical Debt
Many ERP systems in use today were implemented over a decade ago, often with business-specific logic hardcoded into custom Z-tables and scripts. These configurations, while once useful, are now outdated and poorly documented. As a result, critical planning logic is buried in legacy code, invisible to most users and resistant to change.
For example, one plant had its MOQ hardcoded into a legacy planning script in the ERP system. This script overrode the configuration in the SCM platform, causing the system to consistently over-order. The issue went unnoticed until excess inventory triggered a deeper investigation.
Addressing this requires a deliberate effort to uncover and unwind technical debt. A data lineage audit can help trace how data flows through the system and identify hidden transformations. Critical business logic should be migrated into configurable, governed platforms like Master Data Governance (MDG) or a modern Master Data Management (MDM) solutions. And technical debt repayment should be prioritized in the IT roadmap—not as a side project, but as a foundational enabler of supply chain resilience.
Human Error and Insufficient Training
Despite the sophistication of modern systems, much of the master data in healthcare supply chains is still entered manually - often through spreadsheets, forms, or unvalidated ERP screens. This introduces a high risk of human error.
Simple mistakes, like transposing digits in a lead time field or misusing a field for an unintended purpose, can have cascading effects. In one case, a planner in Asia modified the “source location” field for a SKU during a stock transfer scenario. This change caused the SCM platform to reroute all demand from an incorrect plant, leading to service level failures across EMEA.
To reduce these risks, organizations should invest in role-based training programs tailored to different user groups—planners, schedulers, MDM analysts, and so on. Field-level tooltips and validations in the ERP GUI or modern UX applications can guide users toward correct entries. In-system nudges or alerts can flag out-of-policy values before they cause damage.
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For Healthcare & Life Sciences to Improve Outcomes
From Triage to Transformation: Building Resilient Data Foundations
Consumer healthcare enterprises need a data partner who can ensure data is accurate, reliable, and actionable - enabling seamless supply chain operations, strengthening trust, and supporting their mission to deliver better healthcare. An effective healthcare supply chain strategy demands a balanced approach—stabilizing the present, designing for long-term resilience, and embedding the right culture across teams.
Short-Term Stabilization: Tactical Fixes
Organizations can begin by implementing tactical measures that stabilize operations and build momentum for long-term change. This includes regular data profiling and health dashboards to identify duplicates, nulls, and misaligned records. Cross-functional “war rooms” can be used to run focused sprints on high-impact SKUs or regions. And validation layers in integration pipelines can serve as checkpoints between ERP and SCM platforms, catching issues before they escalate.
Long-Term Strategy: Resilience by Design
True transformation requires a strategic shift. A unified data governance model combining global policy with local accountability can provide the structure needed for sustainable quality. Master data platforms like MDG or MDM can serve as systems of record, while AI-augmented engines can flag anomalies such as sudden shifts in lead time or MOQ. Feedback loops and data contracts between source systems and planning tools ensure that expectations are clearly defined and consistently met.
The Human Element: Training as Infrastructure
Ultimately, fixing data isn’t just a technical challenge - it’s a cultural one. Training should be treated as infrastructure, not an afterthought. Persona-based learning paths can help each user group understand their role in maintaining data integrity. Gamification and real-time examples can illustrate how a single data error can trigger downstream fire drills. And open feedback channels can empower users to suggest improvements and report inconsistencies.
Conclusion: In Healthcare, Precision Is Not Optional
In the world of consumer health, data is more than a digital record - it’s the backbone of product availability, regulatory adherence, and brand credibility. It determines whether a mother finds the right pediatric medicine on the shelf, whether a plant meets its GMP obligations, and whether a planner can trust the forecast they’re acting on.
Fixing data quality isn’t about throwing more cleansing tools at the problem. It’s about building alignment—between systems that speak different dialects, between teams that operate in silos, and between local realities and global expectations.
The path forward lies in creating shared definitions, embedding accountability, and designing systems that are resilient to change. Because in healthcare, the margin for error is razor-thin and the cost of inaction is far too high.
AUTHOR - FOLLOW
Panchanan Mishra
Senior Manager, Tredence Inc
