Statistical Engineering Techniques (SET) for Product Development
Advantages and Disadvantages of SET
Advantages of SET
- Usually the time involved in problem solving by SET is short. It is not unusual to solve a production problem in that may be baffling the company for long time in a week's time with SET.
- Quick problem diagnosis leading to a quick cure cuts the expenses of the high cost operation. Typically the time and effort are saved by 33% - 50% and often more. Bigger the problem 'the better are the savings. This also means that the company can solve more problems in a given year and realize a lot more savings from improved operations.
- SET uses the company's professional expertise to the maximum advantage but does not unduly rely on it. If the professional judgment is sound, a lot of time and effort is further saved; if the judgment is faulty, no time or effort is lost.
- SET has a built-in forecasting technique that tells under what conditions you would get good results; under what conditions you would get bad results. This helps in getting better understanding of the operations involved and results in trouble-free operations and more savings.
- SET also generates a lot of secondary information at no extra cost. This is coupled with a sensitivity analysis of the economics involved and gives rise to several decision alternatives. This serves as a powerful aid to the executive.
- Staff morale and productivity shoot up because of high efficiency of SET in solving problem and by its very nature SET is guaranteed never to fail.
Disadvantages of SET
- SET requires that the performance criteria should be measurable. This is a very minimal requirement, for if you do not know how good the product is or how bad it is, a comparison between products cannot be made objectively, and no improvements can be made either.
- SET uses several methods to solve problems. It is necessary therefore to know how to use these methods and which method to use for a given problem. The staff can be trained to use these methods and learn to apply them to the problems under consultant supervision. In due course they would also develop the necessary judgment as to which method is likely to be the most effective. This would result in a permanent improvement in company operations.
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.
Mukul is bilingual. He speaks Chemical Engineering and Applied Statistics.
As a Senior R&D Manager, Statistics and Computer-Aided Research at BF Goodrich Chemical, he championed the use of Design of Experiments (DOE) for predictive modeling, performance optimization, scale-up, and quality control.
Currently, he is the Founder and President of FastR&D, LLC, based in Cleveland, Ohio.
Over his career, he has trained nearly 1,000 R&D scientists, engineers, and senior executives. He has led 750 DOE studies across industries including chemicals, food, polymers, plastics, pharmaceuticals, and medical devices. His projects range from scaling up a one-inch fluid bed reactor to an 18-foot production reactor, to optimizing the design of a tiny angioplasty device for renal artery denervation and blood pressure control.
Mukul has advised numerous Fortune 1000 chemical firms on innovation, rapid new product development, and managing NPD as a structured business process.
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