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Software for Design of Experiments

In the Tutorial lessons on the Design of Experiments(DOE for short) I discussed the simplicity, logic and power of the D. E. P. L. O. Y. method for the Design of Experiments.

D. E. P. L. O. Y. method is simple, step-wise, easy to use and effective.

For industrial product development and improvement projects, it provides one of the fastest and most cost effective methods for finding the underlying cause and effect relationships and optimums.

However DEPLOY still has a weakness, it still is hard to use without powerful software. 

Here are some good popular packaged software solutions for the design of experiments.

  • JMP
  • Minitab
  • Design-Ease and Design-Expert
  • SAS
  • SPSS
  • StatGraphics
  • Statistica

and at least a half a dozen others.

All of these software packages are very good in the hands of an experienced professional and a heavy user. I started using SAS and Minitab in the early seventies on an IBM mainframe. I have personally used them all, at one time or the other, to help my customers. I still do. Each of these packages excels in one or more technique and occasionally you do need that specific technique. However, all these software packages have a few common problems. They assume: 

  • You are well versed in the powerful statistical techniques and terminology.
  • You use these techniques frequently enough and have mastered the software functionality, statistical jargon, and the computational details. 

These assumptions are FAULTY.

  • A typical scientist user works on three to four projects per year and uses Design of Experiments at most three to eight times a year. 
  • On average that is less than once a month.
  • The frequency of usage is just not enough to master the complex concepts in the design of experiments.

Imagine teaching a kid to ride a bike. Imagine that the kid is allowed to practice the bike for only half an hour a day, ten to twelve times per year! Do you think that kid would even learn to ride the bike?

More importantly, none of these packages do an adequate of of providing the most effective method of using the Design of Experiments, Sequential Assembly method or sequential design of experiments. I would venture to guess that in industrial projects, about 80% of proects can and must use SDOE if the primary goals is to solve the problem fast, and to solve it cost effectively. 

To address this problem, many years ago my associate and I developed a DOE package called FastR&D. It was specifically designed to leverage the full power of SDOE and make SDOE exceptionally easy to use by automating all of the statistical decisions.  

FastR&D software was well received and sold as an ultra high-end DOE software package. Here are some of our early FastR&D customers. BF Goosrich, Geon, Oxy Vinyl, Sherwin Williams, Teknor Apex, Equistar, Celanese. Ben Venue Pharmaceuticals, Eltech Research, Ferro Corporation, Castrol, DA Stuart, Deft, IOC-Canada, CRM- Canada, Estee Lauder Belgium, Coca-Cola Germany, NOCIL, Godrej and Pidilite in India. 

While SDOE and ease of use were the key reasons for the success of FastR&D, it does not have the intuitive elegance and simplicity of the DEPLOY method. My associates adn I are pelased to announce that we have tackled this problem and integrated DEPLOY methodology with the ease of use of the FastR&D in a re-revised new verion of the software package. 

We have a simple vision.

You, the practicing scientist/engineer must be able to perform all DOE calculations in one minute or less.

  • Build all needed linear models in one minute or less.
  • Draw all the needed graphs in one minute of less
  • Find all interesting optimums
  • Play "what if ..." and get your answers in one minute or less.

FastR&D software is  being launched April 5, 2022. The clock is ticking. Tick, Tock, Tick, Tock, Tick ...

And a pleasant surprise awaits the early bird at the FastR&D website.

Sincerely, 

Mukul Mehta
Founder, FastR&D Software

About Author

Mukul Mehta


Mukul Mehta has over 40 years of proven industrial experience in chemical , polymer, and plastics industry. Worked as a Sr. Manager, Statistics and Computer Aided Research for BF Goodrich Chemical, a Fortune 500 company, and then as a software entrepreneur, promoted "quantitative, predictive modeling in one minute or less as a mantra for R&D and New Product Development." Many multi-million dollar successes for dozens of Corporate R&D clients in chemical and pharma industry. Trained over 750 R&D chemists, engineers and managers to Speedup New Product Development through statistical design of experiments.

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