Minimize uncertainty and risk in quality control and assurance through quality by design
and risk analysis.

World Class Quality Practices
Fit for Intended Use
Understand Data and Its Use
Know and Control the Risk
Controlled Analytical Testing

Navigating through new regulatory guidelines

New regulatory guidelines talk about the lifecycle, quality by design, decision rules, target measurement uncertainty, measurement uncertainty, risk analysis, analytical target profile, and more. What do these mean? What do they mean to you and your company?

World class quality practices ensure you know, understand, and control your business. They focus on quality assurance, quality control and manufacturing working together seamlessly to support your company goals. Quality practices ensure you are doing exactly what you need to do – no more and no less. Zinata brings extensive knowledge and the ability to understand the whole picture, including how Quality fits into the mix. We understand how silos are linked and how to best bridge the gaps that often exist between them. We understand and can show you how quality assurance applies to the laboratory and how the laboratory assists production.

Don’t waste your time, money and resources

Manufacturing requires some amount of testing in an analytical laboratory. The challenge is to ensure the results of that testing are suitable for the decisions made using them.

Decision rules bring people together from all parts of the organization to define how a result is used. This process involves a great deal of collaboration, breaking down silos by providing a mechanism and language to clearly communicate across teams. The decision rule outlines the acceptable probability of being wrong, which drives risk analysis.

Use your data wisely

Once the decision rule is created, the data can be evaluated against the requirements of the decision rule. Changes and decisions are made against these requirements.

Risky business

The world class quality process provides the tools to identify, evaluate, and control the risks. A formal structured process using risk analysis tools facilitates a thorough evaluation of the analytical procedure. The control points are identified and adequate controls implemented. The behavior of the process is monitored to provide evidence of good performance and to identify poor performance quickly.

The balance between control and change

The continued verification of the performance of an analytical procedure ensures the data has adequate, known quality. The measurement uncertainty of the analytical procedure is known and continually confirmed. It can be separated from the variability of manufacturing or of the product. This knowledge is used to ensure the entire process, manufacturing and testing, remain in a state of control. If a change occurs, it is possible to know if the change is caused by the laboratory or in manufacturing.

Pain Points

Do you know if your analytical testing is adequate or too much, and whether your specifications make sense?Do you know how to prevent method transfers between laboratories from failing?Are you having trouble determining if the problem is in the laboratory or in production?

Master the balancing act

Knowing the measurement uncertainty and the probability a product will fail or pass allows you to know exactly how much testing is required. We can show you an approach to evaluating measurement uncertainty that is logical and straightforward. We can help you implement and understand the lifecycle approach to ensure you are doing the right amount of testing.

Let’s be crystal clear

It is imperative that the use of the specification in a decision rule be clearly defined, forcing the specification to make sense. This sense can then be rolled out to production and Quality. Zinata can teach you how to create the decision rules, understand the probability of success and failure, and set a sensible specification.

The lifecycle approach

With the lifecycle approach to analytical procedures comes understanding of the procedure and knowledge on how to make it work – anywhere! We teach you how to use the control strategy for the procedure to set realistic, achievable acceptance criteria for transfer of methods between laboratories.

Incorporate the control strategy

The control strategy for an analytical procedure includes knowing the variability in your testing and how well the testing is performing. Production can use this knowledge to determine its variability and performance. You can trust the laboratory results. We can teach you how to design the control strategy, understand the variability in testing, and know whether the laboratory is performing to the level you need.

Do you know if your analytical testing is adequate or too much, and whether your specifications make sense?
Do you know how to prevent method transfers between laboratories from failing?
Are you having trouble determining if the problem is in the laboratory or in production?