Tuesday, November 18, 2008

Why Product Development Takes Too Long--and How To Accelerate It Effectively!

While new product development in some industries (such as computers and consumer electronics) occurs at dizzying speed, other industries seem to move at a snail’s pace. Developing products such as new drugs, new energy sources, engines, transmissions, aircraft, and military equipment takes an exceedingly long time.

Lengthy development processes not only reduce companies’ profitability, they deprive us access to life-saving cures and energy independence! Most R&D and Product Development processes do not utilize the many optimization methods that exist – methods that not only speed up the process drastically, but also result in optimal solutions (safe, reliable and robust products).

Typical Product Development efforts often include:

• Haphazard and wasteful “trial and error” approaches to evaluating design alternatives

• Inappropriate comparison methods (not statistically based) when evaluating alternatives—inadequate analysis

• A significant amount of guessing or “judgment” is used for decisions regarding materials, dimensions, processing steps, and other important product details

• Inadequate reliability testing is done to ensure products will perform over time in the conditions that products will encounter

• In some cases, products are over-designed (at a higher cost) due to the uncertainty from lack of knowledge

• Testing or experimentation which does NOT generate predictive models to identify optimal solutions

All of the above points contribute to lengthy and often unsuccessful development efforts. Additional outcomes often include products that are not reliable, competitive, desired, effective, or safe.

Optimal solutions (meeting all product requirements at the lowest overall cost) are rarely stumbled upon by chance—that is, trial and error. The fact that products must simultaneously satisfy multiple requirements (what good is a rechargeable battery that goes 500 miles between charges but occasionally catches fire?) makes finding optimal solutions even more difficult.

What Can Be Done

Fortunately, help is available. Academics have produced a plethora of mathematical and statistical methods to quickly evaluate an infinite set of combinations and find optimums. While you may be thinking of computer simulations that may or may not capture reality, we are not. Instead, we refer to acquiring actual data (physical data) from the judicious and strategic testing of products.

Most industry professionals are unaware of these methods. This is extremely disturbing—especially given challenges such as cancer, the quest for alternative energy sources, and the survival of some of our economically important industries.

Researchers may be highly trained in subjects such as chemistry, physics, biology, medicine, and engineering, but they are typically not armed with strong quantitative and optimization backgrounds. As a result, they do not apply advanced mathematical methods to aid them in their research. However, by pairing scientists and engineers with talented applied mathematicians, critical discoveries and solutions could be achieved quickly.

Design of Experiments (DOE)

Applied mathematicians with specialties in optimization can significantly reduce product development time via numerous quantitative techniques. One of the many useful techniques is Design of Experiments (often referred to as “DOE”).

Unfortunately, most DOE approaches (and efforts) are unsuccessful, and most do not produce effective predictive models. However, when applied correctly, DOE results in powerful predictive models that predict product performance as a function of relevant design choices. The predictive models also empower researchers to determine how changes in inputs (e.g. specifications) impact product performance.

A critical feature of these models is that they describe the effect of interactions between various inputs (where the effect of one factor depends on yet another factor). The functional forms can be non-linear—which is important since much physical behavior is not linear. Ultimately, mathematical models can be used to identify optimal solutions, and these may be validated with strategic follow-up testing.

Future blogs will delve into the use (and misuse) of DOE in practice as well as other techniques for accelerating product development to produce superior products and new technologies. Pharmaceutical R&D (and other formulation problems) have unique challenges—and while traditional DOE does not apply (due to many constraints on formulation problems), Mixture Experiments definitely apply—and offer tremendous benefits.

The question is whether scientists and engineers will welcome mathematicians in their quest for crucial innovations. Einstein did…but will today’s researchers?

By: Steven Wachs & Allise Wachs, http://www.integral-concepts.com/


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