Services Provided by The Zlota Company

The key differentiator in the services that we provide is our capability to contribute to both the process chemistry and the chemical engineering aspects of a project, while understanding complex business objectives and organizational dynamics.

We Provide Project Support and Training in the Following Areas:

As part of our project support, in the area of Risk Analysis, The Zlota Co. provides clients with the following:

  • Recommendations for the formation of a suitable team to conduct risk analysis, avoiding the selection of too large or too small groups, with possible background bias.
  • Guidance for building an effective cause-and-effect diagram, in particular to avoid possible confusion ensuring the inclusion of meaningful process parameters during the brainstorming stage.
  • Advice in defining ranking scales for semi-quantitative risk assessment, to be consistently used by the team*.
  • A semi-quantitative method for risk analysis suitable for the project stage: one method for early, and another for late development, or for prior to technology transfer to a plant
    • For early development projects we enable rapid risk analysis, avoiding “busy work”, and spending too much time for such analysis, producing short, manageable documents summarizing the risk assessment.
  • Advising in designing effective pre-DoE (statistical design of experiments) as one-variable-at-a-time (OVAT) experiments, using the fundamental science available, an important factor in determining the success of a DoE.

*Ranking scales, together with other general recommendations for a particular company are often captured in an internal guideline for QbD implementation, a suitably short document, updated as needed, which we help compile. Such internal guidelines lead to recommendations for best practices for QbD implementation in that specific organization.

For additional information please contact us.

Courses Offered by The Zlota Company

  1. Introductory concepts
  • Review of key DoE concepts
  • DoE feasibility
  1. Case Study #1
  • Early development DoE
  • Pre-DoE experimentation
  • Challenges with categorical factors
  • Meaningful numerical factor ranges
  • Design discussion
  • Fit-for-purpose data analysis
  • Machine Learning tool
  • Statistical vs. practical significance
  • Chemical knowledge vs. statistical analysis
  • Follow-up DoEs
  1. Case Study #2
  • DoE for reaction and work-up
  • Responses
  • Design Discussion
  • Replication, blocking
  • Fit-for-purpose data analysis
  1. Case Study #3
  • Design Augmentation strategies
  • Design quality
  • Scale-independent and scale-dependent factors
  • Design discussion
  • Fit-for-purpose data analysis
  1. Case Study #4
  • Optimization (RSM) designs
  • Meaningful PAT use
  • Design discussion
  • Fit-for-purpose data analysis
  • Model editing strategies
  • Verification experiments
  1. Case Study #5
  • Process Robustness
  • Design discussion
  • Fit-for-purpose data analysis
  • Probabilistic risk calculations
  • Design Space, PARs/NORs
  • Critical Process Parameters
  • Visualization tools
  • Technology transfer
  • Principal Component Analysis for historical batch data analysis