Efficient laboratory operations depend heavily on how samples are processed, data is handled, and results are delivered. Laboratories across industries, including pharmaceuticals, food safety, environmental analysis, and clinical diagnostics, must decide between two primary workflow models: batch processing and continuous workflow. Selecting the optimal approach directly impacts laboratory throughput, resource allocation, equipment utilization, and data handling efficiency.

This article explores the advantages, challenges, and operational considerations of both batch processing and continuous workflow systems. By understanding how these two models affect staffing requirements, turnaround times, and overall productivity, laboratories can optimize their workflows to match their unique testing demands.

Get training in Metrics and Productivity and earn CEUs.

One of over 25 IACET-accredited courses in the Academy.

Metrics and Productivity course

What is Batch Processing?

Batch processing is a workflow method where samples are accumulated and processed together in scheduled batches. Laboratories using this approach typically hold samples until a predefined batch size is reached, at which point all samples are processed simultaneously.

Advantages of Batch Processing:

• Efficient Equipment Utilization: Instruments can be operated at full capacity during batch runs, improving cost-per-sample efficiency.
• Standardized Processes: Processing all samples together reduces variability and ensures uniform treatment.
• Streamlined Resource Allocation: Staff can focus on preparing and processing batches at designated times, reducing the need for constant oversight.

Challenges of Batch Processing:

• Longer Turnaround Times: Samples may sit idle until a full batch accumulates, delaying results.
• Uneven Workloads: Peaks and troughs in processing demand may lead to inefficient staffing patterns.
• Data Handling Delays:Data generation and review occur after batch processing completes, slowing real-time data analysis.

What is Continuous Workflow?

Continuous workflow (or continuous processing) is a sample processing method where samples are processed individually or in small groups as they arrive, without waiting for batch accumulation.

Advantages of Continuous Workflow:

• Faster Turnaround Time: Samples are processed immediately upon arrival, reducing delays.
• Smooth Resource Allocation: Staffing levels can remain consistent with steady processing needs.
• Real-Time Data Handling: Data is generated and reviewed as each sample completes, enabling quicker decision-making.

Challenges of Continuous Workflow:

• Higher Equipment Downtime: Instruments may operate below full capacity if sample arrivals are sporadic.
• Increased Staffing Needs: Continuous monitoring and processing require more consistent staffing across shifts.
• Inconsistent Processing Conditions: Sample variability could increase if conditions fluctuate between individual runs.

Resource Allocation: Optimizing Staff and Equipment Utilization

In batch processing, staff schedules are typically concentrated around batch preparation and processing times, allowing for efficient staffing during peak periods. Equipment usage is also optimized during these batch runs, with instruments running at full capacity. However, batch processing is less adaptable to fluctuating sample volumes, as labs must wait for enough samples to accumulate before processing.

In contrast, continuous workflow requires steady staffing across shifts to ensure samples are processed as they arrive. While this allows for greater flexibility in handling fluctuating sample volumes, equipment may be underutilized if sample arrivals are inconsistent.

⚖️ Verdict: Batch processing allows for concentrated resource use, while continuous workflow offers flexibility at the cost of higher ongoing labor.

Turnaround Time: Balancing Speed and Efficiency

Batch processing inherently delays sample processing until a full batch is ready, meaning results are only delivered after the entire batch has completed. This can slow down responsiveness, especially for urgent samples.

Continuous workflow, on the other hand, processes samples immediately upon arrival and delivers results as each sample completes. This responsiveness makes continuous workflow better suited for time-sensitive testing.

⚖️ Verdict: Continuous workflow significantly reduces turnaround time, making it better suited for time-sensitive testing.

Equipment Usage: Maximizing Utilization and Longevity

Batch processing maximizes equipment utilization during batch runs, as instruments are used at full capacity. However, between batches, equipment may experience extended downtime. The concentrated use of equipment can also increase wear and tear.

With continuous workflow, equipment usage is spread more evenly over time, especially if sample flow is consistent. This helps balance equipment wear, though underutilization may occur if sample arrivals are irregular.

⚖️ Verdict: Batch processing maximizes short-term equipment efficiency, while continuous workflow offers more balanced instrument use.

Staffing Requirements: Scheduling and Labor Flexibility

Batch processing requires concentrated staffing efforts during batch preparation and processing times, allowing for efficient staffing during these periods. Labor efficiency is higher on a per-sample basis when processing large batches. However, cross-training is often limited to batch-specific tasks.

Continuous workflow requires consistent staffing across all shifts to handle incoming samples, which can increase overall labor costs. This approach also demands more cross-training, as staff must be capable of handling varied tasks throughout the workflow.

⚖️ Verdict: Continuous workflow demands more flexible staffing but better aligns with steady sample inflows.

Data Handling Efficiency: Real-Time Data Management and Review

Batch processing defers data processing and review until the entire batch is complete. This results in large volumes of data being reviewed at once, which can overwhelm data management systems and delay decisions.

Continuous workflow, by contrast, allows for real-time data generation and review, enabling faster decision-making and more responsive quality control. Data review is spread evenly over time, easing the workload on data management staff.

⚖️ Verdict: Continuous workflow supports real-time data review, enhancing decision-making for labs handling time-sensitive analyses.

Summary Table: Batch Processing vs. Continuous Workflow

Conclusion: Choosing the Best Workflow Model for Your Lab

The choice between batch processing and continuous workflow depends on the laboratory’s sample volume, testing urgency, staffing capacity, and equipment availability. Laboratories with high sample volumes and standardized testing may favor batch processing for its efficiency and resource concentration. In contrast, labs handling time-sensitive or unpredictable sample inflows benefit from the speed and flexibility of continuous workflow.

Some laboratories adopt hybrid processing strategies, combining batch runs for routine analyses with continuous processing for urgent or high-priority samples. Regardless of approach, aligning workflow design with laboratory goals ensures optimal throughput, cost control, and data handling efficiency.

This content includes text that has been generated with the assistance of AI. Lab Manager’s AI policy can be found here.