Quantisweb Optimization Software as a Tool to Bridge Creativity and Standardization

By William Blasius
“Voice of the Customer” Advisor to Quantisweb Technologies
A Quantisweb Technologies White Paper

January 2017

Executive Summary

There is a deep tension between creativity and standardization, with extremes dominating the conversation in industry.  Pro-creative groups are convinced that any attempts to apply a standard practice will kill creativity and pro-standardizing groups professing that creativity is out of control and needs to follow a uniform template.  The middle ground of a little free creativity and some standardization is an option, but only useful for technical service and incrementalism.  Quantisweb Technology’s optimization software helps creatives to focus and standardizers to evolve.  With this bridging tool, managers can allow dreamers and mechanics to do what they are best at, while collaborating efficiently to create empirically modeled and optimized new products.

Creativity and standardization are typically cast as irreconcilable opposites.  Attempts to standardize creativity to ISO standards (TC279, AWI 50501), LeanSigma, Stage-Gate controls and any number of prescribed best practices have repeatedly led to less innovation and more incrementalism (1).  This includes copying past successes without modification in a world that is constantly changing.  It is difficult to quantify the level of frustration and employee turn-over inspired by misapplication of standardization concepts.  Of course, creativity without some discipline is at worst; chaos and at best; inefficient.

New product and process development (NP&PD) is a complex business process requiring both creativity (divergent thinking) and standardization (convergent thinking) but at different times and with different people.  Everyone has a certain capacity or comfort level for effectively shifting between divergent and convergent thinking (2).  Workers happiest with divergence are the dreamers and visionaries.  Those who are strongest in convergence are the mechanics.  Finally, the rare (2.5% of the general population) group (3) who can competently shift between convergence and divergence are the innovators.  We do not need to worry about the innovators other than to avoid creating an environment that demotivates them.  The key NP&PD question can be framed as, “How can a manager bridge divergence (creativity) and convergence (standardization) efficiently without losing the best of either?”

There are good techniques available to help at the very front end of development, where maximum divergence is encouraged, such as mind-mapping and consumer behavioral observation (4).  On the convergent back end, automated data acquisition and process capability software suites are routinely deployed (5).  The zone between imagination and final execution is less well served.  This helps to explain the proliferation of opinions and commensurate frustrations on how to best shepherd a new product/process development.

Quantisweb Technologies has created software that combines updated mathematical strategies for designed experiments with an analytical hierarchy process and modern decision theory in order to help firms model and optimize their complex business processes.  Given the inherent variables and unknowns intrinsic in every new product and process development, NP&PD qualifies among the most complex business processes a firm can pursue.

Quantisweb provides new product and process developers the ability to investigate up to 200 variables (divergence) while optimizing for 100 outputs (convergence) in one to several iterations of the number of variables plus one (Np+1) experiments per iteration.  Interacting with Quantisweb requires enough discipline on the part of dreamers (setting goal priorities, constraint identification, variable definition, anticipated interactions) to keep them focused and on task while being efficient enough that intuitions can be followed without creating exponentially higher development costs as can be the case with traditional design of experiments.  The output will be a set of behavioral laws that the mechanics can apply to formulations or processes and combinations of the two.  As data is added, the behavioral laws evolve into empirical models which may be applied to pilot and production scale operations.  These models will be based on optima, which by definition, should be treated as standardized formulations and processes.

By utilizing Quantisweb methodologies, managers have a catalytic tool that can help keep dreamers on task and focused without eliminating serendipity while allowing mechanics to spend more time on development and less on fire fighting.  The tool allows the manager to take advantage of divergent and convergent thinkers to supplement the few divergent-convergent innovators to attack big opportunities and react quickly to market changes.  By utilizing the large variable capacity of Quantisweb, highly complex innovative products with both formulation and process variables can be naturally optimized through the NP&PD process to a standard.  Products optimized from launch without a cost or time penalty is much more efficient than a “satisficed” launch to be revisited repeatedly and tweaked towards optimum.  Should conditions change, a Quantisweb enabled product/process will come with behavioral laws that will allow simulations to be run before taking any development or production time.

Quantisweb complex business process optimization software is the bridging tool to reconcile the tension between creativity and standardization without compromising either, while improving product and process development efficiency.

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  • Weisman, Robert. Understanding Innovation in Problem Solving, Science, invention and the Arts.  Page 465.  John Wiley & Sons, Hoboken, New Jersey, 2006.
  • Rogers, Everett. Diffusion of Innovations, 3rd ed.  Page 246.  The Free Press, New York, New York, 1983.
  • Gassmann, Oliver and Schweitzer, Fiona, Editors. Management of the Fuzzy Front End of Innovation.  Page 166.  Springer International Publishing, Switzerland, 2014.
  • Lengyel, Lubomir. “Production Data Acquisition and Analysis Management System:  An Example Based on a Study of Automotive Supplier Solution”.  Page 103.  Quality Innovation Prosperity, XVII/2 – 2013