S&OP Gap in Scaling Biotech: Why End-to-End Planning Breaks Down Without a Unified Foundation

Biotech is moving capital faster than it is moving its operating muscle. In 2025, announced global biotech deals totaled $228.4 billion, up from $132.3 billion in 2024, signaling a sector willing to scale through dealmaking even as macro pressure persists. The operational reality has not kept pace. The global life sciences supply chain management market sits at roughly $12.5 billion and is projected to grow to $25.8 billion by 2030 at a 9.5% compound annual rate, evidence that life sciences leaders know the gap exists and are funding it. What that funding has yet to fix is the structural problem at the planning layer, where companies still run demand, supply, inventory, and production on disconnected spreadsheets that cannot scale with a commercial portfolio.

Shiv Kumar Lodha has spent more than 16 years inside that gap. As an Associate Director Supply Chain working with biotech organizations transitioning from clinical to commercial operations, he leads end-to-end planning implementations that replace fragmented forecasting with a governed, integrated foundation. He is also a member of the IEEE Engineering in Medicine and Biology Society. His position is direct: the S&OP gap in scaling biotech is not a technology gap. It is a structural absence of integration, governance, and adoption discipline.

The Compounding Cost of Disconnected Planning

Disconnected planning is the default state in life sciences, not the exception. Overall integrated business planning was described as "integrated" by only 23% of life sciences respondents, while 32% cited this process as operating in "silos". Life sciences companies were also most likely to still use spreadsheets alone for their planning needs and least likely to use an integrated supply chain suite. The result is predictable. Demand, supply, inventory, and production each run on different worksheets, each refreshed on a different cadence, with no binding consensus process tying them together.

That fragmentation is exactly what Lodha encountered when he led an end-to-end planning implementation for a U.S. biotech transitioning from clinical to commercial. As the lead business analyst representing the business, he defined comprehensive requirements across all four planning functions, partnered with IT and implementation partners to shape the solution, and ran the validation cycles that confirmed the system reflected real planning needs rather than idealized demos. Before implementation, demand, supply, inventory, and master production schedules each existed as their own Excel artifact. After it, planners worked from a common record with version control, audit trails, and integrated source-to-target data flows.

"Disconnected planning is not a technology problem; it is a governance problem that the technology will eventually expose," Lodha says. "When demand, supply, and inventory each live in their own spreadsheet, the organization is not running one plan with disagreements. It is running four plans that happen to share a name."

What a Unified Planning Foundation Actually Replaces

The fix is not the platform alone. By 2028, 60% of supply chain digital adoption efforts will fail to deliver promised value due to insufficient investment in learning and development, according to Gartner. The pattern is consistent: leaders fund the tool but underfund the operating model that has to use it. A separate Gartner finding is sharper. Ninety-five percent of supply chains must quickly react to change, but only 7% can execute decisions in real time. The bottleneck is not data availability. It is the absence of a single, governed record everyone trusts.

What a unified planning foundation actually replaces is the patchwork of localized truths. In Lodha's implementation, the project displaced manual Excel-based methods with a centralized platform supporting commercial and clinical demand, supply, inventory, and master production planning together. He worked closely with IT to make sure business requirements were met, supported change management efforts including user training, and provided post-launch hypercare to reinforce adoption. The implementation displaced the legacy spreadsheet routines and condensed the full demand-and-supply cycle into a single monthly process where one had not formally existed before.

"A platform replaces tools, not behavior," Lodha notes. "If you migrate four spreadsheets into a unified system without changing how decisions get made, you have built a more expensive spreadsheet. The point of the foundation is to make decision rights explicit and reviewable."

Data Integration Is the Layer Most Implementations Underbuild

The most consistently underbuilt layer in planning implementations is the one that sits below the planning workflow itself. Life sciences organizations face a fundamental data challenge: products, materials, and suppliers are coded under different schemes across regulatory submissions, manufacturing, quality, commercial, and supply chain systems, and reconciling those schemes is rarely treated as a precondition for planning. Empirical research found that while 58.33% of pharmaceutical participants assess product master data quality as satisfactory, all organizations surveyed remained dependent on manual data entry methods. Manual entry is where governed planning quietly fails.

Lodha built the data layer first. End-to-end integration with source and target systems removed the manual import-and-arrange work that previously consumed the planning team, and centralization gave one common archive of historical and current data rather than locally maintained copies. He is also a program committee member for the SPHERA Workshop at ETRA 2026, the ACM Symposium on Eye Tracking Research and Applications track on sensors, processing, and hardware for real-time pipelines.

"Planning is downstream of data," Lodha observes. "If the system pulls dirty inputs from disconnected sources and runs them through governed workflow, the output is governed-looking noise. The integration layer is where credibility is earned or lost long before anyone runs a forecast."

Change Management Decides Whether the New System Becomes Real

The most predictable failure mode of a planning implementation is not technical. It is human. A Gartner survey of 306 logistics professionals found that 76% of logistics transformations fail to meet critical budget, timeline, or KPI metrics, with internal resistance identified as a more significant obstacle than external pressures. Leaders who actively engaged with that resistance and acted on team feedback improved transformation success rates by 62%, while a directive "get with the programme" leadership style decreased the odds of success by 47%. The data is direct: adoption is engineered, not assumed.

Lodha treats change management as core delivery work rather than a closing checkpoint. On the implementation, he led multiple training programs, ran Train-the-Trainer sessions, managed communications across global teams to support adoption, and stayed in hypercare after go-live to resolve issues and reinforce user confidence in the new process. He is also a program committee member for ACM CUI 2026, the SIGCHI Conference on Conversational User Interfaces.

"You cannot push a planning system into a regulated organization and expect adoption to follow," Lodha reflects. "Adoption is a sequence of small commitments people make to use the system in real cycles, and each commitment has to be earned through repeated training and the post-launch evidence that the system holds up when it counts."

From Workarounds to Measurable Forecast Accuracy

The proof that a planning foundation has taken hold is whether forecast accuracy becomes measurable at all. One-third of supply chain planning leaders cite the lack of effective decision making in the S&OP meeting process as the most critical problem to solve for their function's overall performance, and the recommended response is structured scenario planning. Yet that depends on a foundation many organizations do not have: stable master data, integrated source-to-target flows, a defined cadence, and a single record of forecast versions. Without those, scenario planning is a slide. With them, it becomes a decision the business can defend.

The shift Lodha leads is from a planning environment where forecast accuracy was not even tracked to one where it can be reported, compared, and improved. Manual effort that previously consumed planners on data assembly was systematically displaced by integration. The demand-and-supply cycle was condensed into a monthly governed process. Forecast accuracy moved from an unmeasured variable to a tracked operating metric. The deeper change is that S&OP stops being a meeting where workarounds are reconciled and starts being a process where decisions are made, recorded, and reviewable next month.

"The end state of an end-to-end planning implementation is not the platform going live," Lodha concludes. "It is the moment forecast accuracy becomes a number the organization argues about with evidence rather than instinct. That is when you know the foundation is real, and that is when scaling stops being a threat to operational discipline."