Orchestrating Complex Scientific Workflows: Why LIMS, ELN, and LES Aren’t Enough

The Coordination Gap Is Costing You More Than You Think

Pharmaceutical R&D organizations have invested heavily in laboratory informatics, including electronic lab notebooks (ELN), laboratory information management systems (LIMS), and laboratory execution systems (LES software) And yet, in bioprocess cell line development (as an example of one complex scientific workflow), work still stalls. Samples go missing between teams. Tech transfer timelines stretch into months. Scientists spend hours each week not doing science — but chasing approvals, updating spreadsheets, and sending status emails.

The tools aren't broken. The problem is the space between them.

A Familiar Problem with an Underappreciated Cause

In bioprocess development, a single cell line development campaign can span cell revival and culture, transfection, pool selection and screening, single-cell cloning, clonal expansion, clonal screening, and cell banking — each phase involving different teams, different systems, and different handoff protocols.

When those handoffs happen over email, the consequences compound quickly. A QC review that should take hours; waits days because the right scientist didn't see the notification. A sample moves to the next phase before results are formally approved. A manufacturing team inherits a process with undocumented parameters because the development team's institutional knowledge lived in a notebook entry no one thought to flag.

These aren't failures of individual scientists or managers. They are structural failures — the predictable result of asking domain-specific tools to do coordination work they were never designed to perform.

Why the Problem Is Getting Harder to Ignore

The pressure on pharmaceutical R&D operations has intensified. Biologics pipelines are growing in complexity. Regulatory expectations around data integrity, traceability, and GxP compliance are rising. And organizations are being asked to accelerate timelines — moving from discovery to IND-enabling studies faster, with fewer resources and less tolerance for rework.

At the same time, the informatics landscape has fragmented further. Most mid-to-large pharma organizations now operate a heterogeneous stack: one ELN for experiment capture a separate LIMS system (laboratory information management system) for sample management, and laboratory execution system for equipment. Each system does its job well. None of them were designed to orchestrate work across the others.

The result is a coordination layer that exists entirely in email inboxes, shared spreadsheets, and informal Slack threads — invisible to managers, unauditable by QA, and entirely dependent on individual scientists remembering to follow up.

A New Category: Workflow Orchestration for Scientific Operations

The emerging answer to this problem is not another domain-specific tool. It is a coordination layer — software designed specifically to sit above existing laboratory informatics systems and manage the flow of work, data, and decisions between them.

This is a meaningfully different category from LIMS, ELN, or LES. Those systems manage data within a domain. A workflow orchestration solution manages processes across domains — defining how work moves from one team to the next, what conditions must be met before a handoff occurs, and who is accountable at every step.

Signals LabGistics™, from Revvity Signals, is purpose-built for this role. Rather than replacing existing laboratory systems, it connects them — integrating with third-party LIMS, ELN, and LES solutions, and working natively with Signals Notebook™ for organizations already in the Revvity Signals ecosystem. The architecture is built on three tiers: Processes (strategic frameworks that define high-level scientific operations), Workflows (the operational execution layer that models team handoffs, decision points, and data movement), and Tasks (the detailed execution level, tied to SOPs (standard operating procedures), with assignments, deadlines, and automated triggers).

For a cell line development team, this means the entire campaign — from transfection through cell banking — can be modeled as a reusable, auditable workflow. When a transfection experiment completes in Signals Notebook, Signals LabGistics can automatically pull the results, evaluate them against a predefined viability threshold, route passing samples to the next phase, and notify the QC team — without a single email. If a sample falls below threshold, it routes to a rework path. Every decision is logged. Every assignee is recorded. The audit trail builds itself.

This is not automation for automation's sake. It is the difference between a scientific operation that is visible and one that is invisible — between a manager who can see in real time which experiments are blocked and one who finds out when a deadline is missed.

The AI Dimension: Reducing the Cost of Getting Started

One of the practical barriers to workflow standardization in pharma R&D has always been the cost of configuration. Building a workflow system that reflects the actual complexity of a bioprocess development campaign — with conditional branching, multi-team handoffs, and compliance requirements — has historically required months of IT involvement and significant consulting resources.

Signals LabGistics addresses this directly through AI-powered workflow generation. The system can parse standard operating procedure (SOP) content, extract relevant steps, and generate an initial workflow model — reducing the manual design effort required to get a new process into production. For organizations with mature SOP libraries, this represents a meaningful acceleration in time-to-value.

For existing Signals Notebook or Signals One customers, the value proposition is particularly direct. Signals LabGistics uses the templates already built in Signals Notebook — no migration, no schema changes, no reconfiguration. Scientists continue working in the same interface. The orchestration layer runs above it, turning one-off manual experiments into repeatable, automated workflows with decision logic and cross-team coordination built in.
 

Key Takeaways

• Fragmentation is structural, not accidental. LIMS, ELN, and LES systems were designed for domain-specific data management — not cross-functional workflow coordination. Expecting them to solve a coordination problem is asking the wrong tool to do the wrong job.
• The coordination layer is the missing piece. Workflow orchestration solutions sit above existing systems, managing the flow of work and data between them without requiring replacement of existing investments.
• Signals LabGistics is built for the discovery-to-manufacturing continuum. Its three-tier architecture — Processes, Workflows, Tasks — maps directly to how pharmaceutical R&D organizations actually operate, from early cell line development through tech transfer and scale-up.
• AI-powered workflow generation reduces time-to-value. Generating workflows from SOPs rather than building them from scratch lowers the barrier to standardization for teams with mature process documentation.
• For Signals Notebook or Signals One users, orchestration is the natural next step. Signals LabGistics extends the value of existing Signals investments without migration, reconfiguration, or disruption to scientist workflows.

The Path Forward

The pharmaceutical industry has spent a decade building out its laboratory informatics stack. The next challenge is not adding more systems — it is making the systems already in place work together. Workflow orchestration is how organizations move from a collection of capable tools to a coordinated scientific operation.

If your bioprocess development team is still managing handoffs by email, tracking sample status in spreadsheets, or discovering bottlenecks only after timelines have slipped, the coordination layer is the gap worth closing.
 

Learn more about how Signals LabGistics can orchestrate your scientific workflows 
on the Signals LabGistics product page.