How To Reduce Development Process With Quantisweb Technologies Services?


If you are working to develop a pharmaceutical product or a new manufacturing process, from scratch, you know that there are only a handful of proven systems, strategies, and attitudes that will get you to success.  Unfortunately, it is more often than not that these processes take a long time to complete as you analyze one variable at a time or track one set of changes after another in a limited testing space.

Quantisweb Technologies, though, hopes to help more people change the way they approach these processes.  They have developed new ways to solve these issues by expanding on the proven models to offer better and more concise analysis and more efficiently, too.


Process Optimization with Quantisweb

Quantisweb process optimization software is a “multi-objective” and multi-variable, patented Stochastic Approximation Optimation methodology which can render more innovation than traditional DOE methodologies.  Quantisweb combines the efficacy of Expert Systems (Optimization, Simulation and Statistical) software with the efficiency of Data Mining and a little bit of randomization to simultaneously formulate and optimize a product recipe or a manufacturing process.

Three Pronged Process

Quantisweb focuses on three principles in this process:

  • Determine the model or the behavioral law of a random system through the lowest possible number of experiments
  • Generate an objective function or goal utilizing the product’s specific properties
  • Generate optimal design or recipe that optimizes both product function of the product and the model of said product.

How It Works?

Quantisweb optimization software can analyze a specific set of variables in order to calculate how to approach product development solutions. This variable set is: value proposition, product uniqueness, outcome assumptions, and system innovation.

Value proposition, of course, ensures user knowledge via an optimization process instead of trying to predict the outcomes by analyzing previous statistics. This helps to notably cut down on the number of experiments needed to perform in order to determine the best outcome of a complicated problem.

Product Uniqueness looks at how rare the product will be once it is completed. Basically, it starts with the end product in mind, leading the developer to analyze potential obstacles along the path to completion; addressing, understanding, and repairing variables as necessary before they become problems.

Outcome Assumptions further assist to predict how certain statistical errors may affect the outcome.

Finally, System Innovation attempts to determine how each variable will influence each other by shifting experiment parameters and conducting several tests.

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