Capacity Planning for High-Demand Sequencing Platforms: Best Practices for Scheduling

By Gabriella Ryan, M.S. Senior Application Scientist, Omni International (a Revvity company)

It’s 4:30 p.m. on a Thursday.

Your sequencer is booked solid, there’s a half-finished plate of libraries on the bench, and a PI just appeared in the doorway with an “urgent” project that “really needs data by Monday.”

Meanwhile, your inbox is full of people asking why their samples aren’t done yet.

If that feels familiar, you don’t have a sequencing problem.

You have a capacity planning problem.

And it usually starts well before the samples ever touch the sequencer.

In this post, I’ll walk through how to think about capacity planning for high-demand sequencing platforms—from mapping your real workflow to building a schedule that doesn’t depend on heroics. We’ll look at:

• Why bottlenecks appear where you least expect them
• How to quantify true capacity (not fantasy-land capacity)
• How to build a realistic scheduling model
• Where high-throughput sample prep and automation fit into the picture

This isn’t about buying more boxes. It’s about getting honest about how your lab actually runs, and then designing around that reality.

The Real Problem: Sequencer Demand vs. Lab Reality

On paper, capacity looks simple:

“Our sequencer can run X samples per week, so we should be able to support Y projects per month.”

In practice, it looks more like this:

• Three different PIs decide their projects are “top priority”
• Staff are split between prep, QC, and putting out fires
• Samples arrive late, incomplete, or in the wrong format
• Manual prep steps eat entire days of your most experienced scientists

The sequencer is rarely the true bottleneck.

The constraints usually live in:

• Sample prep (especially lysis, extraction, and library prep)
• Staffing (only 1–2 people truly trusted with critical steps)
• Reagents and consumables (stockouts and last-minute substitutions)
• Scheduling chaos (no clear rules or visibility)

The result:

• Constant rescheduling and “just this once” exceptions
• Rushed prep to meet a run time
• Inconsistent turnaround times
• Burnout, frustration, and a reputation for being “slow” or “unpredictable”

If that’s your day-to-day, you don’t need a hero. You need a system.

Step 1: Treat Capacity Planning Like a Scientific Problem

Most labs treat scheduling like calendar Tetris.

You’ll get better results if you treat it like an experiment.

Every workflow has:

• Inputs: samples, staff, instruments, reagents
• Constraints: run times, SOPs, QC steps, regulatory requirements
• Outputs: runs per week, reads per run, projects completed per month

Capacity planning is about:

1. Mapping your real workflow
2. Measuring the constraints at each step
3. Designing a schedule that respects those constraints
4. Optimizing it using real data over time

The good news: you already think this way about your experiments. You just need to apply the same discipline to your operations.

Step 2: Map Your End-to-End Workflow (Reality, Not the SOP Fantasy)

Start by documenting what actually happens from “sample arrives” to “data delivered.”

A simple outline might look like:

1. Sample intake and logging
2. Sample QC (concentration, purity, integrity)
3. Lysis / homogenization
4. Nucleic acid extraction
5. Quantification and normalization
6. Library prep
7. Library QC
8. Pooling and final normalization
9. Sequencing run
10. Initial QC and data delivery

For each step, capture:

• Hands-on time: How long someone is actually at the bench?
• Instrument time: How long does the sample occupy equipment?
• Staff dependency: Can anyone do this, or is it “only Alex can run that”?
• Batch size: Do you run this step 1 sample at a time, 8 at a time, 24, 96?
• Failure points: Where do delays or repeat steps most often occur?

This is usually where the first uncomfortable truth shows up:

The workflow you think you run and the workflow you actually run are not the same thing.

You might discover:

• “Quick checks” that aren’t in any SOP but always happen
• Waiting time between steps while someone finishes another task
• Informal rules (“we never start library prep after 2 p.m.”)
• Bottlenecks in sample prep because it’s all manual and highly variable

This isn’t about blame. It’s about getting a clear picture of reality, so you can design around it.

Step 3: Quantify True Capacity at Each Step

Once you see the full workflow, the next step is to measure capacity step by step, not just at the sequencer.

A simple way to start is with a table:

You’re looking for:

• Steps with high utilization: consistently near their max capacity
• Steps with low utilization but long cycle times: where automation could help
• Mismatch between upstream and downstream capacity: e.g., library prep can’t feed the sequencer consistently

This is often when labs realize:
The expensive sequencer is idle not because demand is low, but because prep can’t keep up in a predictable way.

That’s your first major capacity planning insight.

Step 4: Build a Scheduling Model That Matches Reality

Now that you understand capacity, you can design a schedule that works with your constraints instead of fighting them.

4.1 Choose a Scheduling Rhythm

You don’t have to run everything every day.

Common patterns that work well:

• Prep days vs. run days:

• Example: High-throughput sample prep on Monday/Wednesday, sequencing runs on Tuesday/Thursday.

• Fixed batch windows:

• “Samples received and QC’d by noon Wednesday are eligible for the Thursday run.”

• Dedicated application blocks:

• Mornings for high-complexity projects, afternoons for standard panels.

The goal is to:

• Reduce randomness
• Increase predictability for staff and stakeholders
• Make it clear when samples can realistically be turned around

4.2 Prioritization Rules (So Everything Isn’t “Urgent”)

Without rules, every request becomes an exception.

Define categories and put numbers around them:

• Tier 1 – Critical / clinical / time-sensitive
• Tier 2 – High-priority research (grant deadlines, collaborations)
• Tier 3 – Routine / backlog / exploratory work

Then decide:

• What percentage of your weekly capacity you reserve for each tier
• How much “emergency” capacity you hold back (for true emergencies, not “I forgot the deadline”)
• Minimum sample counts for efficient runs (e.g., no runs under X% of lane capacity unless Tier 1)

When people ask for exceptions, you’re no longer negotiating from emotion. You’re referring back to the model.

4.3 Make the Schedule Visible

Even a simple capacity calendar can change behavior:

• Planned run days per platform
• Cut-off times for sample submission
• Slots available per run (or whether the run is full)

If stakeholders can see:

“If I get my samples in by Tuesday at noon, I’m likely to be on the Thursday run,”

you’ve aligned expectations with reality. That alone will save you headaches.

Step 5: Use Tools and Automation to Protect Capacity

This is where instrumentation choices come in—not as magic bullets, but as levers.

The question isn’t “What instrument is shiny?”

The question is:

“Where in my workflow can automation have the biggest impact on capacity and predictability?”

In most high-demand sequencing environments, that answer is:
sample prep, especially at the lysis/homogenization and extraction/library prep stages.

5.1 Automate High-Volume, Repetitive Steps

If your scientists are still doing:

• Tube-by-tube homogenization
• Manually intensive lysis with inconsistent timing
• Prep workflows that can’t scale past 8–12 samples at a time

…then your schedule is at the mercy of human fatigue and variability.

High-throughput platforms (for example, 96-well bead mill systems paired with compatible bead kits) help you:

• Move from 1–12 samples to dozens or 96 at a time
• Standardize timing and intensity of lysis across all wells
• Reduce repeat preps due to inconsistent lysis or partial disruption
• Free up highly trained staff from “pipetting marathons” to higher-value work

You don’t automate because it’s trendy.
You automate because it removes a major source of unpredictability from your capacity plan.

5.2 Standardize Methods and Kits

Even without fully automated robotics, you can make huge gains by standardizing:

• Validated workflows for your most common sample types (e.g., fecal, tissue, blood, swabs, environmental)
• Pre-optimized bead kits and buffer combinations for those matrices
• Clear batch sizes with known cycle times

When you know:

“Running 2× 96-well plates of stool samples with this protocol will take X minutes of hands-on time and Y minutes of instrument time,”

you can confidently plug those numbers into your schedule and trust them.

5.3 Track the Right Metrics

To refine capacity planning over time, measure:

• Samples processed per week (by application)
• Turnaround time per project type
• Fail or repeat rates at key steps (pre-prep, library prep, sequencing)
• Utilization of major instruments (prep and sequencing)

If you can’t measure it, you can’t optimize it.

Over a few months, you’ll start to see patterns:

• Which days are overloaded
• Which applications consume the most staff time
• Where a modest investment (an additional prep platform, different consumables, or another trained staff member) would have outsized impact

Step 6: Build Flexibility Into the System

Even the best schedule won’t survive contact with reality if it has zero flex.

You need intentional slack.

6.1 Time and Capacity Buffers

• Don’t schedule your sequencers at 100% of theoretical utilization. That’s a recipe for disaster.
• Build buffer time between runs for:

• Maintenance and cleaning
• Unexpected troubleshooting
• Overruns on upstream prep steps

Similarly, don’t schedule every minute of your staff’s time.
You want them thinking and solving problems—not sprinting from 8 a.m. to 6 p.m. with no room to breathe.

6.2 “What If?” Scenarios

Plan for:

• Instrument downtime:

• If one sequencer goes offline, how do you triage runs?
• Can you temporarily shift to smaller runs or different platforms?

• Spike in demand:

• What if a large study or clinical program doubles your volume for 6–8 weeks?
• Do you know how much extra work you can absorb before turnaround time breaks?

Writing this down ahead of time turns full-blown crises into controlled annoyances.

6.3 Cross-Training Staff

One of the biggest silent bottlenecks is “only one person knows how to do that.”

Whenever possible:

• Cross-train at least two people on each critical step
• Build sanity-checked SOPs that a trained scientist can follow without tribal knowledge
• Rotate responsibilities to avoid burnout and single-point failures

It’s not just good operations—it’s good science culture.

Quick Capacity Planning Checklist

Here’s a simple checklist you can use as a starting point:

•  End-to-end sequencing workflow documented (actual, not idealized)
•  True capacity per step quantified (samples/day, runs/week, etc.)
•  Major bottlenecks identified (especially in sample prep and library prep)
•  Clear scheduling rhythm defined (prep vs. run days, cut-off times)
•  Prioritization rules established and communicated (Tier 1/2/3 work)
•  High-volume, repetitive steps evaluated for automation
•  Standardized methods and consumables for common sample types
•  Capacity calendar visible to stakeholders
•  Basic KPIs tracked (turnaround time, fail/repeat rate, utilization)
•  “What if?” scenarios defined (downtime, sudden volume increases)
•  Cross-training plan in place for key workflows

If you can honestly check most of these, you’re already ahead of many high-demand sequencing labs.

Where Omni Fits In

At Omni, I spend a lot of my time working with labs that are stuck in a very specific place:

The sequencer is powerful. The demand is high.
But sample prep and scheduling are holding everything back.

Sometimes that means:

• They’re still using improvised tools (yes, I’ve seen T-shirt presses for plate lysis)
• Their lysis is inconsistent across wells and matrices
• Their staff are losing entire days to manual homogenization or extraction

What we’ve seen, across a lot of different labs, is that when you:

• Move to batch-based, high-throughput homogenization (e.g., 96-well bead milling instead of one-tube-at-a-time), and
• Pair it with validated bead kits (get your free bead kit) and protocols for your most common sample types,

you don’t just save time.

You gain:

• More predictable workflows
• Lower repeat rates
• A schedule you can actually trust

The underlying technology—whether it’s a high-throughput homogenizer like the Bead Ruptor 96+, or an automated sample prep system like the LH 96—works best when it’s embedded in a thoughtful capacity plan, not bolted on as an afterthought.

That’s really the point of this whole post:

You don’t fix capacity planning with one purchase.
You fix it by designing your workflow intentionally, then choosing tools that support that design.

Final Thoughts: Start With One Walkthrough

If all of this feels overwhelming, don’t try to rebuild your entire operation in a week.

Start small:

1. Pick one high-demand application (for example, a common metagenomics or RNA-seq workflow).
2. Walk the entire process from sample arrival to data delivery.
3. Time each step. Note who’s involved and where they wait.
4. Identify the one bottleneck that creates the most chaos.
5. Decide what change would have the biggest impact on that bottleneck—better batching, clearer rules, or a smarter tool.

Do that a few times, and capacity planning stops being an abstract management task and becomes just another optimization problem—something you already know how to solve.

And if you want a second set of eyes on your sample prep bottlenecks or want to sanity-check where automation could help, that’s literally my day job.