We Can Track How a Rocket is Built. Why Can't We Track Our Life Science Samples?

In engineering, design changes, supplier inputs, manufacturing tolerances, and quality control are fully visible and optimized through integrated digital systems. NASA, for example, can map every step of rocket manufacturing with extraordinary precision.

In contrast, life science labs worldwide still struggle to answer a basic question: What happened to those samples from last Tuesday?

This gap persists despite the availability of highly advanced laboratory instruments. While modern labs excel at measurement and automation, workflow management often relies on spreadsheets, static SOPs, disconnected systems, and manual handoffs. Most solutions still force complex, iterative science into linear documentation models that fail to reflect real operations.

In the blog that follows, we explore why sample tracking and other aspects of research slow progress and how orchestrating the full research lifecycle across samples, processes, people, and data can enable better science.

The Hidden Challenges That Slow the Life Sciences

Challenge 1: Science Isn’t Linear

Scientific discovery is inherently complex and non-linear, shaped by iterations, decision points, quality checks, and parallel processes. Yet most digital documentation systems still assume fixed, step-by-step workflows.

A cell revival workflow illustrates this gap. While it may look like a simple 3-day process on paper, in reality, it requires viability assessments, manual inspections, go/no-go decisions, and potential restarts, with critical logic residing in the scientist’s head rather than in the documentation. This pattern repeats across industries, from flavor formulation to materials testing.

When digital systems force non-linear science into rigid templates, scientists spend more time managing documentation than doing research. “What” is captured, but the “why” is lost, leaving institutional knowledge trapped with individuals, driving inefficiency, higher costs, and reduced reproducibility.

Challenge 2: Manual Workflow Generation Creates Blind Spots

Another challenge arises before experiments even begin: Workflow creation is manual and time-intensive, and each new process requires significant planning and setup. At the same time, senior management lacks end-to-end visibility, leaving bottlenecks hidden until timelines slip or costs rise.

The result is high operational overhead, poor resource allocation, and delayed time-to-market. Across drug development and materials R&D alike, limited real-time visibility slows innovation long before scientific hurdles emerge.

Challenge 3: Sample Tracking Can Become a Black Hole

Despite decades of digital tools, “What happened to this sample?” remains one of the most common questions in laboratories. Embedded in this question are a lot of more specific mini-questions, including:

• Where is the sample?
• Who handled it last?
• When was it handled?
• Why was it used?

Answering these questions often depends on manual sample plans, static templates, or spreadsheets that fail to reflect real experimental complexity.

As conditions, time points, and replicates increase, plans must be rebuilt each time, introducing errors and limiting scalability. The result is wasted effort, higher risk, and lost samples that drive compliance issues, delayed outcomes, and missed insights.

Challenge 4: Unfindable, Inaccessible Historical Data

Years of data siloed in paper notebooks and disconnected instruments or software systems are another major barrier. In this situation, extracting insights across experiments is difficult, if not impossible.

There is no systematic way to learn from past work at scale.

When scientists leave, institutional knowledge goes with them, leaving data without structure or context for analytics or AI. The result is repeated mistakes, missed optimization opportunities, and growing compliance burdens.

Rethinking the Research Lifecycle and Giving Scientists What They Need

Modern research demands systems that manage samples, processes, and data holistically, supporting a seamless flow from R&D to scale-up and manufacturing.

This evolution looks different across industries, but the underlying needs are the same:

• Visual, real-time control to see workflows as they actually happen, including branches, loops, and decision points.
• Know where processes stand in real time and identify bottlenecks before they impact downstream steps.
• Intelligent automation that can generate workflows from existing SOPs, dynamically create sample plans based on experimental design, and automatically route tasks to the right people.
• Built-in intelligence that supports decision-making at critical quality gates, keeps humans in the loop where expertise matters, and automates what doesn’t.
• Data to be treated as a living pipeline with every step generating reusable, queryable information that flows seamlessly across stages, forming the foundation for analytics, AI, and continuous improvement.

The Path Forward: 3 Pillars of Modern Laboratory Operations

1. Efficiency: Reducing Operational Costs

   Modern orchestration reduces operational costs by automating routine tasks, eliminating manual sample plan creation, and optimizing resource allocation through intelligent task routing. As a result, scientists spend more time on discovery, less on administration, and time-to-market accelerates.

    

2. Integrity: Data Quality and Compliance by Design

   Data integrity cannot be bolted on at the end. Modern systems embed compliance from the start, with complete audit trails, timestamped actions, and traceable decisions. Data is stored in queryable, reusable formats aligned with FAIR principles.

    

3. Collaboration: Breaking Down Silos 

   Task-based personas can enable seamless collaboration. Systems that can integrate with MES, LIMS, and other platforms create a unified data model across the lifecycle and enable teams to hand off work smoothly across departments. Managers gain real-time visibility without micromanagement. This collaboration model is essential across all scientific domains.

Signals LabGistics: Orchestrating the Future of Research

Research complexity is increasing, not decreasing. Organizations that can execute with precision while preserving scientific creativity have the greatest chance of success. With Signals LabGistics, we remove operational friction and enable intelligent orchestration, freeing scientists to focus on what they do best: Asking bold questions and finding innovative answers. The solution streamlines scientific operations by enabling teams to plan, execute, and monitor complex scientific workflows across research, development, and manufacturing, ensuring seamless data flow between stages and eliminating manual handoffs. It creates a structured, real-time environment for workflow coordination, automated process generation, and compliance-ready documentation, accelerating timelines, improving collaboration, and enabling more efficient scaling of scientific work.

See how Signals LabGistics can streamline your scientific workflows across the entire drug development lifecycle. Request early updates.!