Laboratory automation projects rarely fail loudly. They fail quietly: a line is commissioned against average daily volume, respiratory season arrives, turnaround-time targets slip for months, and send-out costs quietly erode the business case that justified the capital. Robust throughput planning works backwards from demand — sample counts, peak behaviour, and turnaround commitments — before any instrument shortlist is drawn up. This guide sets out the sequence that procurement and laboratory teams should follow when sizing an automated line for five-year demand.
Model demand at the percentile, not the average
Start with average daily tests by test family, gathered over at least one full annual cycle so seasonal amplitude is visible. Averages hide exactly the peaks that break a workflow, so convert them into a peak-day model: for mid-size clinical laboratories, peak factors between roughly 1.3 and 1.8 are common planning assumptions, and the target is the 95th-percentile day rather than the single worst day on record. Layer on the STAT and add-on fraction, because it determines how often routine batches must be interrupted. Size the line against that peak day plus an explicit annual growth assumption, and the maths will discipline the shortlist.
- Average daily volume by test family over a full annual cycle, including seasonal amplitude
- A peak factor calibrated to the 95th-percentile day, not the historic worst day
- STAT and add-on rates, which set how frequently routine batches must yield
- Projected annual volume growth across the full five-year planning horizon
Match equipment classes to workload bands
Equipment classes overlap less than catalogues suggest. Benchtop 96-well extraction and amplification systems suit laboratories processing from the low hundreds of samples per day; a full plate run, extraction through amplification and read-out, typically occupies several hours including set-up, which caps how many cycles a shift can absorb. Mid-range configurations that add liquid handlers and plate hotels extend that band into the several-hundreds range. Track-based total laboratory automation becomes defensible when daily volumes reach the high hundreds to thousands and when pre-analytical sorting, centrifugation, and aliquoting dominate the labor bill. Buy for the workload band you will actually occupy, not the headline processing rate on the specification sheet.
Let turnaround targets arbitrate
Name a turnaround target per test category and hold the design to it. For routine molecular panels, 90th-percentile targets expressed in hours from receipt to report are more defensible than average turnaround, because averages flatter batch systems that occasionally strand low-volume requests. Batch-based platforms deliver excellent throughput per technician but degrade when a request must wait for a full batch to close. Random-access and sample-to-answer systems cost more per reportable result yet protect urgent categories from queueing behind routine work. Many laboratories resolve the tension with a two-tier configuration, reserving a smaller rapid platform for urgent categories and running the automated line as the volume engine.
Cost the operating model, not the instrument
The final sizing check is staffing and overhead. Count FTE hours for sample preparation, reagent preparation, maintenance windows, lot changeover, and repeat testing — a rerun allowance of five to ten percent is a prudent planning figure for molecular workflows — and confirm the line still meets turnaround targets during preventative maintenance windows. As a rule of thumb, commissioning at roughly sixty to seventy percent of nameplate capacity on the peak-day model leaves headroom for growth before the next capital cycle. A defensible automation plan fits on a few pages: the demand model, the peak-day definition, turnaround tiers by test category, and the staffing floor. Laboratories that write that document before meeting vendors keep the negotiation anchored to their own workload rather than to the catalogue.