Manage Your Product Life Cycle With Quantisweb

Quantisweb Technologies is dedicated to improving the formulation process of pharmaceutical drugs as well as the processes of other products like polymers and paints.

And with Quantisweb Technologies it could be easier to manage your product life cycle through its various stages.


During the Exploration stage of a product life cycle, you will need to develop adaptive models based on patterns.  By observing and analyzing patterns you will begin to know which avenues are available to you and, more importantly, the best strategy and approach.

Once you have identified the core components, you can begin to build proprietary recipes and then, after more study and analyze, begin to implement radical and incremental innovation. This final aspect is crucial as the best results often depend on taking calculated—and necessary—risks.



After the initial product is developed, it is time to formulate something for simple market distribution to get an initial response. This allows for function integration and process integration too. These are important, of course, to get a better handle on early response and also potential side effects or unforeseen consequences that can then be addressed.

This, then, leads to recipe modification to finally develop a product ready for further formulation and development.  Of course, this prepares the product for more widespread distribution.


Obviously, this process will also yield some obstacles, some problems.  At this stage, then, you need robust solution mapping to explore the possible reasons for such problems and then, of course, any possible solutions. This, then, leads to improving the utility of recipes—how easy they can be made, replicated, duplicated, produced.  This ensures that the product can be consistently manufactured with the smallest possible margin of error.


And that, of course, leads to the product manufacturing optimization stage which ensures the lowest possible cost for mass distribution to both keep prices lower but equally maximize profit.  It also leads to yet more reliable data mining about the quality of the product, which also informs on the success of the process.

This part of the process is, perhaps, the most important because it collects information over a long period of time to attempt to predict changes that might need to be made as well as market shifts that may require reanalysis of the initial product.

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