The Missing Coordination Layer for Laboratory Systems: What Modern Workflow Orchestration Makes Possible

The Coordination Gap No One Talks About

Pharmaceutical and biotech organizations have invested heavily in laboratory informatics. Electronic lab notebooks (ELNs) capture experimental data. Laboratory information management systems (LIMS) track samples. Manufacturing execution systems (MES) govern production. And yet, for all this investment, a persistent and costly problem remains: the work that happens between these systems is largely invisible.

When a cell line development campaign moves from transfection to pool selection, who coordinates that handoff? When a process development team completes a critical run and needs QC review, how does that request travel? In most organizations, the answer is email, spreadsheets, and institutional memory — none of which scale, and none of which leave an audit trail.
 

The Problem Is Coordination, Not Data Capture

The challenge facing bioprocess scientists, process development teams, and lab directors today is not a lack of data. It is a lack of connected process context.
Each domain system — ELN, LIMS, LES, MES — manages its own slice of the workflow exceptionally well. But the handoffs between them remain fragile. Samples move between teams without automated tracking. Decisions made in one system are invisible to the next. Tech transfer from development to manufacturing requires manual documentation, repeated clarification, and months of delay. The result is a coordination gap that inflates timelines, introduces compliance risk, and forces scientists to spend time managing logistics rather than doing science.

Why This Problem Is Growing More Urgent

The complexity of modern drug development is accelerating this challenge. Biologics pipelines, cell and gene therapies, and advanced modalities require tightly coordinated, multi-disciplinary teams operating across discovery, development, quality, and manufacturing — often simultaneously and across multiple sites.

At the same time, regulatory expectations are rising. Standards frameworks like ISA-95 (ANSI/ISA-95, also known as IEC 62264) — which defines how enterprise systems should communicate across manufacturing layers — and ISA-88, the batch process control standard that brings structure and consistency to pharmaceutical batch operations, reflect an industry-wide recognition that process integration is not optional. These frameworks exist precisely because the cost of disconnected systems is well understood. ISA-95 organizes technology and business processes into defined layers and outlines how an enterprise can set up interfaces to communicate among them. ISA-88 provides the blueprint for standardizing terminology, managing recipes across sites, and enabling smooth integration between systems. Together, they describe an ideal that most lab environments have not yet achieved in practice.

Lab workflow automation is no longer a "nice to have" capability. For organizations managing cell line development campaigns, pharma tech transfer timelines, or multi-site process scale-up, the absence of a coordination layer is a measurable competitive disadvantage.
 

A New Category: Workflow Orchestration for Scientific Operations

The response to this challenge is not another domain system. It is a layer that sits above existing systems — one that orchestrates the work between them without replacing them.

This is a meaningful distinction. A workflow orchestration approach does not ask organizations to migrate away from their LIMS or abandon their ELN investment. Instead, it connects those systems into a coherent process, preserving scientific data, decision logic, and ownership context as work moves from one team or phase to the next. The goal is to make the process visible — not just the data.

Modern cloud-native approaches to this problem support the kind of nonlinear, decision-driven workflows that characterize real scientific operations. Sequential steps, parallel branches, conditional routing based on experimental results, and human decision points can all be modeled and executed within a single coordinated environment. When a transfection experiment yields results below a viability threshold, the system routes the sample to a rework path automatically — no email required, no context lost.

Signals LabGistics™, a workflow orchestration solution from Revvity Signals is designed to address this coordination gap directly. Rather than replacing existing laboratory informatics investments, Signals LabGistics supports workflows that span ELNs, LIMS, LES, and MES systems — connecting them through a three-tier model of processes, workflows, and tasks. For organizations already using Signals Notebook™ as part of Signals One™, it extends those capabilities by turning individual experiments into coordinated, repeatable, and auditable processes. AI-powered workflow generation allows teams to configure workflows from natural language descriptions and standard operating procedures (SOPs), reducing the manual effort typically required to stand up new process models.

The result is a scientific environment where lab workflow automation supports the kind of structured, traceable operations that ISA-88 and ISA-95 frameworks describe — without requiring organizations to rebuild their informatics stack from scratch.
 

What This Looks Like in Practice

Consider a cell line development workflow. Without orchestration, a scientist manually creates a transfection experiment from a template, records results, and emails the QC team to request review. The QC scientist searches for the experiment, opens it, reviews the data, and emails back an approval. The original scientist then manually creates the next experiment — pool selection — and the cycle repeats. At each step, context can be lost, samples can be misplaced, and delays compound.

With a workflow orchestration approach, that same process becomes a coordinated sequence. The transfection experiment is created automatically from an approved template. Results are pulled into the workflow and evaluated against predefined criteria. The QC assignee is notified automatically, with the relevant data already surfaced. On approval, the next experiment is created with properties auto-populated from the transfection results. Every step is tracked, every decision is logged, and the complete audit trail is available without manual reconstruction.

This is not a theoretical improvement. It is the difference between a process that depends on individual memory and one that is encoded, repeatable, and compliance-ready.
 

Key Takeaways

• The coordination gap is the real bottleneck. ELNs, LIMS, and MES systems manage their domains well — but the handoffs between them remain manual, fragile, and invisible to managers.
• Standards frameworks like ISA-95 and ISA-88 define the goal. They describe integrated, structured, traceable operations. Workflow orchestration is how organizations move toward that goal in practice.
• Lab workflow automation requires more than task lists. Effective orchestration supports conditional branching, decision logic, parallel execution, and cross-team coordination — not just linear checklists.
• Tech transfer and cell line development are high-stakes use cases. These workflows involve multiple teams, long timelines, and significant compliance requirements — making coordination failures especially costly.
• Orchestration works alongside existing systems. The right approach does not require replacing LIMS or ELN investments. It connects them.
   

What to Do Next

The coordination gap in pharmaceutical and biotech R&D is well understood.  However, solutions purpose-built to address it at the workflow level are still emerging, until now! Signals LabGistics has launched and early access information is available now.

If your organization is navigating the challenges of disconnected systems, manual handoffs, or tech transfer delays, this is a development worth following closely.

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