Case Studies / Case Study 01
Process Simulation & Workflow Optimization
Using Process Simulation to Identify the Real Constraint in Laboratory Workflow
- Industry
- Healthcare Laboratory Operations
- Region
- Southeastern United States
- Focus
- Workflow Optimization | Process Simulation | Bottleneck Analysis
The challenge
What leadership needed to understand.
A large healthcare organization needed greater visibility into the flow of molecular specimens through a laboratory testing process. Operational delays were occurring, but it was unclear whether the primary constraint was instrument capacity, processing activity, or the way specimens accumulated before testing.
Traditional workflow observation alone could not adequately demonstrate how specimen volume, processing patterns, and batching requirements interacted throughout a full operating cycle.
Objective
Develop a data-informed representation of the existing workflow to identify where time was being consumed, distinguish necessary processing from waiting, and determine where operational improvement efforts should be focused.
Approach
A process simulation was developed to recreate specimen movement through key stages of the laboratory workflow.
Rather than relying on a single average processing rate, historical timestamp data was used to reproduce variation in specimen activity throughout the day.
This allowed the model to reflect periods of higher and lower activity and more accurately represent how specimens moved through the operating environment.
The analysis separated value-added activity from non-value-added waiting and examined the contribution of each workflow stage to the overall cycle.
Visualized workflow
- Specimen Arrival
- Accession
- Batch Accumulation
Primary constraint identified
- Sample Preparation
- Instrument
Key finding
The constraint was upstream.
The model identified a significant concentration of non-value-added time associated with specimens waiting to form required testing batches.
The analysis showed that this waiting was not simply the result of poor employee performance or insufficient instrument capacity. It was largely a structural consequence of the relationship between specimen volume and required batch size.
Instrument and sample-preparation activities represented relatively small portions of the modeled cycle, indicating available downstream capacity.
The question shifted from
"Do we need more instrument capacity?"
To
"How can specimens accumulate and flow into testing batches more effectively?"
Leadership insight
Simulation findings were translated into a bottleneck analysis for organizational leadership, providing a visual and quantitative explanation of where time accumulated within the process.
This allowed leadership to distinguish between structural waiting inherent to the batching model and process conditions that could potentially be redesigned or managed differently.
Result
The engagement produced a data-informed understanding of the laboratory's primary workflow constraint and demonstrated that additional instrument capacity was not the immediate operational priority.
The organization gained a clearer basis for directing improvement efforts toward specimen accumulation, batching strategy, and upstream flow.
Sustainability
The simulation established a repeatable analytical framework that could be used to evaluate potential workflow changes before altering the live laboratory environment.
Rather than relying solely on assumptions or averages, potential future-state scenarios could be evaluated against operating patterns represented within the model.
Confidentiality
The organization represented in this case study has been intentionally anonymized. Certain operational details have also been generalized or omitted to protect confidentiality.
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