Research labs need to be thoughtful and efficient with their resources. That’s why researchers use tools like Design of Experiments; to systematically investigate process or product variables that affect the desired outcome of the experiment. What if research labs were just as thoughtful about their operations as they were about their experiments?
Discrete Event Simulation (DES) is becoming increasingly popular for research labs to model, analyze, and optimize complex processes involving variable sequences of discrete events. It enables researchers to visualize the flow of activities in the entire laboratory, identify bottlenecks, and improve resource allocation.
Different types of research labs have different purposes: from handling clinical trials to running biological experiments. DES is used to model workflows for tasks such as specimen processing, data collection, and analysis. By simulating these processes, labs can identify inefficiencies, optimize throughput, and minimize wait times for equipment and personnel.
For Diagnostic labs, DES can be used to improve throughput by simulating and optimizing processing of swabs and diagnostic assays. In addition to balancing workload, the simulation can help predict testing capacity and appropriately allocate resources like testing kits, equipment, and technicians.
Labs often deal with limited resources like specialized equipment or research personnel. DES models help simulate demand for these resources based on experiment schedules, predicting future bottlenecks and allowing for better planning and allocation. Specifically, by simulating the sequencing and scheduling of experiments, and modeling the interaction of experiments that require shared resources, researchers can optimize experiment timing to prevent delays caused by equipment or personnel constraints.
Research labs, especially those dealing with consumables (e.g., reagents, pipettes, glassware), often face supply chain issues. DES models can simulate inventory levels and reordering strategies to ensure critical supplies are available when needed, while also minimizing excess stock.
Before building or redesigning a lab space, use DES to evaluate different laboratory layouts, considering how researchers move between workstations and equipment. By simulating researcher movement and task.
Unfortunately, not all experiments prove to be successful. Research labs often deal with experiments that may fail or need to be repeated. DES can model the probability of failures and their impact on lab schedules and resources. By simulating various scenarios, researchers can assess the risk of failures and prepare contingency plans.
In large-scale research projects that require collaboration between different labs or institutions, DES helps in coordinating activities across multiple locations. Simulations can help predict delays and optimize the flow of experiments across labs, ensuring that resources and data are shared efficiently. This improved collaboration leads to more efficient research workflows and quicker achievement of research objectives.
As we’ve written before, DES can be a powerful tool for sustainability initiatives. Labs often use significant amounts of energy, especially those dealing with cold storage, equipment that operates continuously, or environmentally controlled environments. DES models energy consumption patterns, enabling labs to optimize their processes to reduce energy usage and improve sustainability efforts.
Want to learn more? Request a demo to see how DES can support your research lab.