Digitalization in Chemical Development
85% of laboratory data is lost because it is not transferred from instruments or is not recorded at all. Learn how to ensure data capture with this free white paper.
The pressure is on to accelerate new product introductions. In order to do this, scientists multi-task across project teams and deliver on demanding objectives. In this environment, the personal productivity of every scientist counts. When people are empowered to do their most productive work, they can use resources more wisely and still achieve outstanding results. The references and resources below describe how innovative companies are identifying and addressing key bottlenecks in scientists' workflows.
White Papers and Webinars
COVID-19 Recovery in Chemical R&D
This white paper discusses the implications of the COVID-19 global pandemic, as well as how scientists who invested in automation in chemical R&D were able to remain agile and productive during site restrictions and closures.
Optimizing Solvent Swap Distillation
This white paper outlines important design aspects of the solvent swap operation and presents a workflow with key modeling and experimental activities to achieve an efficient and robust process.
Automation at AbbVie
This presentation describes how AbbVie adopted and leveraged new automation technologies to advance the pipeline.
AstraZeneca's Journey to Lab Digitalization
This webinar describes how Mettler-Toledo instruments have been integrated with AstraZeneca's ELN and some of the challenges that arose during the project.
Organizational Productivity Challenges
- Organizations typically operate with intensity during the day but slow or shut down during the night
- Experiments are scheduled inefficiently because the scientist must be present to take a sample or change a process variable
- Unreliable results are obtained from non-standard experimental methods
- Scientists must be present to record information and any data that is collected to be transcribed manually to a notebook
- Scientists waste time preparing data from multiple sources for analyzing and reporting
- Valuable data is routinely lost and scientists cannot leverage institutional knowledge to minimize the hands-on work needed to complete projects