The Hidden Cost Of Poor Data In Global Supply Chains

By Laura Hay, Senior Director, Global Program Management at Trax Group | 2025 Winner, everywoman Customer/Passenger (Leader) Award

When people think about poor data, they often picture reporting issues, administrative inefficiencies, or the occasional spreadsheet error. While those challenges certainly exist, they only scratch the surface of the real problem.

The true cost of poor data in clinical trial supply chains is much larger than reporting or administrative error. It disrupts the operational flow between RTSM/IRT systems, ERP inventory records, and depot execution systems long before any human review occurs. It affects decision-making, operational performance, customer satisfaction, and ultimately an organization's ability to compete in an increasingly complex and fast-moving environment.

At its core, every supply chain decision depends on data. Whether organizations are forecasting demand in RTSM, reconciling inventory in ERP, or releasing product from a depot, they are operating with multiple systems that do not update at the same speed. When that information is incomplete, inaccurate, or inconsistent, the consequences can ripple throughout the entire business.

One of the first places this breakdown appears in clinical trial supply is the misalignment between RTSM forecasts, ERP inventory records, and depot stock positions. These systems do not simply “fail.” They operate on different update cycles, which creates temporal drift in what each system believes is true inventory. A common example is inventory management. If RTSM reflects updated randomization demand but ERP has not yet posted depot receipts or shipments, the result is three different versions of inventory truth across the network. Teams lose visibility into what is available, where it is located, and when it can be delivered.

In clinical trial supply chains, this often shows up as misaligned visibility between RTSM/IRT forecasts, depot stock levels, and actual site-level accountability, where systems indicate product availability that does not match physical stock position.

This misalignment creates a decision gap where supply planners must choose whether to trust RTSM demand signals or ERP inventory records, often without a clear system of priority.

As uncertainty grows, organizations often respond by carrying additional inventory as a safety net. This buffer is not random; it is a structural response to system uncertainty between forecasting and execution layers. While this may seem like a practical solution, it comes at a significant cost. Excess inventory ties up working capital, increases storage expenses, and reduces operational agility.  

What starts as a data accuracy issue quickly becomes a supply design behavioral response, where buffer stocks replace system confidence. In clinical trial operations, this often results in depot-level overallocation and early packaging decisions that accelerate expiry exposure before patient demand materializes.

It also becomes a financial challenge. In clinical trial programs, this often translates into overallocation to depots and early packaging decisions that compress remaining shelf life before patient demand materializes.

A typical RTSM-ERP–depot misalignment event unfolds as follows:

• RTSM updates forecast demand following a shift in enrollment or randomization patterns at site level.
• ERP systems receive this update later through batch processing or manual intervention, creating a temporal lag between demand signal and inventory reconciliation.
• Depot stock reports still reflect physical inventory without context of updated demand curves.
• Supply planners identify a mismatch between projected patient need and available ship-ready stock.
• To protect dosing windows, the planner may override standard allocation rules or initiate expedited replenishment orders.
• This can result in split shipments, over-shipment to certain regions, or reprioritization of pack lots with higher remaining shelf life.

This is the point where data misalignment becomes an operational decision rather than an analytical issue.

The same pattern emerges across transportation and logistics operations. When shipment data cannot be trusted, teams create buffers and contingency plans to compensate for uncertainty. Delivery schedules become less predictable, resources are used less efficiently, and customer expectations become more difficult to meet.

If shipping and temperature data are incomplete or late, clinical supply teams may delay release decisions or initiate duplicate shipments to protect dosing windows and avoid protocol disruption. Over time, these inefficiencies become embedded within the organization. Processes are built around compensating for poor visibility rather than improving performance.

Perhaps even more concerning is the impact poor data has on people.

Throughout my career, I have seen talented teams spend countless hours reconciling information between systems instead of focusing on strategic decision-making. Rather than analyzing opportunities, improving customer experiences, or driving innovation, employees are forced to validate reports, investigate discrepancies, and determine which version of the data is correct.

This manifests as a shift from analytical work to reconciliation work, where supply planners spend more time matching RTSM outputs to ERP records than optimizing supply strategy. This creates a workflow where analytical decision-making is replaced by constant data verification across RTSM, ERP, and clinical operations systems.

This not only slows down operations but also creates frustration across teams. When employees cannot confidently access reliable information, collaboration becomes more difficult and decision-making becomes slower. For clinical trial supply chains, the critical failure point occurs when teams no longer use systems as the primary source of truth and instead rely on local spreadsheets or email confirmations to validate inventory and shipment status.

For organizations operating in highly regulated industries, including healthcare and life sciences, the stakes can be even higher. Delays caused by inaccurate data can impact critical products, customer commitments, and patient outcomes. In these environments, data accuracy is not simply an operational requirement — it is a business imperative.

When trial supply data is not aligned across RTSM, ERP, depots, and site management systems, it can force manual workarounds between clinical operations, supply planning, and site coordination, increasing the risk of missed dosing windows or resupply delays.

However, the most significant hidden cost of poor data may be the loss of trust. Once teams stop trusting the data, they stop using it. Instead of relying on systems and analytics, people begin making decisions based on assumptions, personal experience, or isolated information sources. Different departments create their own reports and workarounds, resulting in multiple versions of the truth across the organization.

This is most evident when RTSM forecasts, ERP inventory records, and depot system data diverge to the point where operational teams default to manual tracking files to reconcile discrepancies. At that point, even the most advanced technology investments struggle to deliver value because the foundation — trusted, accurate data — is missing.

As supply chains continue to become more connected, digital, and data-driven, organizations must view data quality as a strategic priority rather than a technical issue. Investments in technology, automation, and artificial intelligence can only be as effective as the information that powers them.

Strong data governance, clear ownership, and consistent data management practices are no longer optional. They are essential capabilities for organizations seeking to improve resilience, increase efficiency, and deliver better outcomes for patients and sites.

The most successful clinical supply chains are not necessarily those with the most technology. They are the ones that have built a culture of trust in their data, because when data is reliable, organizations can move faster, make better decisions, and respond more effectively to change. And in today's global supply chain environment, that confidence can become a significant competitive advantage.

About The Author:

Laura Hay is a global supply chain leader specializing in program management, customer success, and account strategy. She has a proven track record of leading cross-functional teams to deliver complex, high-impact initiatives on time and within budget. Laura is known for building strong stakeholder relationships, driving operational excellence, and managing multimillion-dollar programs. She is passionate about connecting people, processes, and technology to build scalable, resilient supply chain solutions that deliver measurable business impact.