Laboratory Workflow Management

Laboratories receive a plethora of work requests every day. These requests typically consist of one or more activities executed in a specific order and with dependencies. To ensure smooth laboratory operations it is critical to manage the lab tasks effectively from submission to fulfillment and to respond to new requirements in an agile way. Transparency into the status of lab work will provide an overview of tasks, queues and due dates and help avoid bottlenecks.

BIOVIA ONE Lab Tasks > Dassault Systemes

Powerful Task Planning

BIOVIA ONE Lab enables scientists to easily plan, request, assign and execute laboratory processes. It supports the way scientists work in the lab. Flexible activity templates enable users to configure and manage activities that map to critical laboratory workflows for analytical and mechanical tests, chemical synthesis processes, biological testing, protein puri-fication processes, polymerization, formulations optimization, and more. Business processes such as approvals, notifications, and routing can all be defined in accordance with organizational needs.

Digitalizing Lab Processes

BIOVIA ONE Lab supports the typical processes in the lab like the preparation and the testing of solutions and materials, the calibration and verification of laboratory equipment and the data acquisition from instruments. Additionally, scientists can monitor task progress by reviewing laboratory planning, schedules and performance, and collate task results for review, analysis, and reporting.

BIOVIA Task Management > Dassault Systemes

Capabilities

  • Define lab activities and create tasks from available activities
  • Assign tasks to samples and lab personnel
  • Prioritize tasks and assign due dates
  • View lists per person with tasks to be executed
  • Manage the submission, routing, receiving, tracking and reporting of results

Benefits

  • Improve collaboration between scientists with shared workflows and appropriate task assignments
  • Increase lab efficiency through better work distribution and resource allocation
  • Reduce inconsistencies in assigning and tracking of experimental work across different labs
  • Simplify the transition from process and method development to execution
  • Improve the tracking, defining and labeling of samples associated with experimental tasks

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