From Excel to Intelligence: Why Clinical Supply Planning Needs an SAP IBP Governance Layer

In clinical operations, precision is non-negotiable. Yet, the systems designed to forecast, plan and distribute trial materials often depend on spreadsheets stitched together by anxious planners racing against regulatory timelines. A misplaced formula or version error can delay study launches, inflate inventory or distort demand signals across global sites. Despite billions invested in research and logistics, the weakest link in many life-science supply chains remains the one that cannot scale—Excel.

Clinical supply chains were historically insulated from digital transformation. Their unpredictability—variable patient enrollment, stringent controls and multi-phase dependencies—made standardisation difficult. But as clinical portfolios expand and therapies become more targeted, organisations can no longer afford planning processes that lack traceability, auditability and speed.

“Technology alone cannot stabilise a supply chain. Governance is the multiplier that turns systems into intelligence,” says Shiv Kumar Lodha, the Associate Director of Global Supply Chain at Gilead Sciences. With more than fifteen years in end-to-end planning, Shiv has witnessed how the absence of structured governance—not technology gaps—creates operational drag. His work reframes supply chain transformation as a leadership discipline rooted in design rather than reaction.

When Governance Becomes the Missing System

For most clinical programs, planning errors are caused by fragmented ownership rather than inadequate tools. Each function—clinical operations, manufacturing, quality and distribution—interprets demand differently. Without a common governance model, even advanced tools degrade into data silos.

Manual forecasting extends this divide. Uncoordinated spreadsheets offer local control but erase global alignment. Trial supply forecasts get updated in isolation, validation rules remain inconsistent and demand visibility collapses once data crosses departments.

Industry assessments indicate that nearly 60 percent of mid-sized biotech companies continue to rely on manual methods for clinical planning, exposing them to double-digit waste and missed milestones. The absence of governance amplifies these risks. Governance, in Shiv’s words, “is far from bureaucracy—it is choreography.” It synchronises timing, accountability and feedback loops across diverse functions.

Without it, even the best digital tools fail to deliver the control and confidence that regulated environments demand. The question is no longer whether to modernise but how to institutionalise the structure before automation.

From Forecast to Framework: SAP IBP as a Turning Point

In clinical operations, the move to SAP Integrated Business Planning (IBP) began as a response to a quiet but costly inefficiency—forecasts scattered across countless spreadsheets, each offering a different version of truth. The goal was never just to replace Excel. It was to redefine how clinical demand is captured, validated and aligned across trials, bringing structure to a process that had relied on fragmented, manual routines.

Within that transition, Shiv played a central role in translating planning behaviours into governed system logic, shaping a framework that introduced rhythm, ownership and accountability. The new IBP environment enabled version control, forecast simulation and structured approvals—capabilities essential in a regulated biotech context—but the real innovation went deeper. Governance was no longer an external checkpoint; it became a built-in layer of the workflow itself, ensuring that every update carried traceability and every decision path was auditable.

“Governance was not an afterthought—it had to be designed into every planning layer,” he explains. The transformation compressed reconciliation cycles from weeks to hours, standardised reporting across global functions and gave clinical operations a new kind of reliability—one where visibility and discipline replaced manual patchwork with measurable control.

Designing Intelligence: Governance as Architecture

In most organisations, digital transformation begins with tool selection and ends with adoption metrics. Shiv reverses that sequence. His principle: governance precedes tooling. Systems must reflect the logic of decision-making before they can scale it.

Governance, in his framework, functions as an architecture of clarity. It defines who owns each forecast, how exceptions escalate, when alignment occurs and where accountability rests. This triad—People, Process and Tool—forms a controlled ecosystem where every planning decision leaves a verifiable trail.

Data governance ensures master data integrity and consistency across modules. Process governance secures cross-functional synchronisation through a defined S&OP cadence. People governance establishes clear ownership, preventing “too many editors” syndrome that haunts spreadsheet cultures. Together, they transform planning from reactive coordination to guided orchestration.

A recent industry analysis found that supply chains with formal governance frameworks stabilise operations up to 30 percent faster after technology rollouts. The reason is structural rather than procedural: governance eliminates ambiguity, enabling faster trust calibration across business and IT.

“In the long run, system governance outpaces system speed,” Shiv notes. “Organisations mature when they codify decisions rather than just data.”

The Next Frontier: Intelligent Governance for AI-Driven Planning

The supply chain of the near future will be measured by governance resilience, contrary to automation depth. Artificial intelligence, now entering clinical operations, can generate adaptive forecasts and detect anomalies, yet it depends entirely on structured governance to remain credible. AI without governed data pipelines risks amplifying inconsistency instead of reducing it.

The emerging frontier is a composite ecosystem—AI plus IBP governed by defined accountability frameworks—where predictive algorithms learn from validated data and respond within approved parameters. For life-science supply chains, this model delivers measurable benefits: fewer planning cycles, optimised inventory and transparent compliance audits.

Shiv’s forward view centres on sustainable intelligence: building planning ecosystems that evolve through traceability, discipline and cross-functional trust. “Intelligence is contrary to automation—it is alignment,” he concludes. “The future of supply chain belongs to organisations that can govern information with the same rigor as they produce it.”