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CONTROLLED DESIGN MANAGEMENT – Part 4

By Moises J Goldman & John Jonelis

Today’s business culture is more strongly creative and entrepreneurial than at any time in history, posing new organizational opportunities and challenges.  That calls for a new way to think about and implement design management.  This is the final installment of a four-part series introducing the Controlled Design Management Model.  Using the language of the digital age, this model applies a radically different technique to managing the creative process.  The history and theory was discussed in Parts 1 through 3.  Now let’s set up a working model. 

Figure 6 – Controlled Design Management Model

Practical Example

Let’s optimize an organization using a Controlled Design management Model.  For clarity, this particular enterprise is engaged in the deployment of a product and has just three primary departments:

  • MARKETING DEPARTMENT – Produces market research, and marketing planning. The input to this department is idea generation from its founders, its research wing, or its own internal analysis. Its output is the product concept, complete with all the required features and characteristics that the market requires in tandem with a marketing plan for a successful launch.  Marketing’s output serves as the input for Systems Engineering.
  • SYSTEMS ENGINEERING DEPARTMENT: Translates the marketing requirements into engineering concepts, tests their feasibility (simulation), and produces the required technical maps and schematics to be able to create a working prototype. Its input is the output from Marketing. Its output is the technical representation of the product, including mathematical and simulation results, schematics, and mock-ups.  This becomes the input for Applied Engineering.
  • APPLIED ENGINEERING DEPARTMENT: Creates and tests a physical prototype until it is ready to deploy. (We’re not taking into consideration production or logistics in this example.). Applied Engineering starts with the output from System Engineering. Its output is the finished prototype ready for testing and then deployment by Sales.

We have three departments.  Each is solely responsible for the optimization and efficiency of its own particular function within the organization.  Each is in a dependent, sequential relationship with two other departments.  Now, we link the individual department’s optimizing flow chart (from Figure 6) into one companywide Controlled Management Model.  (See Figure 7.)

Figure 7 – Optimized Departments

Let’s look at application.  Based on Figure 7, it’s clear that to achieve optimum productivity, a department must minimize internal disturbances.  Examples of such disturbances include underperforming employees, faulty data, equipment malfunctions, changes in existing regulations, policy changes induced by government, budgetary restrictions, new competition, company restructuring.  All of these are down-to-earth practical matters, as are the corrections, which are ordinary responses and decisions.  What is new is the simple structure of the decision-making process and the ability to map it and to know exactly where, in the larger picture, you are at any given time.  That helps eliminate bottlenecks and confusion, and helps address a problem early—before a weakness becomes magnified down the line.

Conclusion

Does the Controlled Design Management Model meet the goals stated earlier in the paper?

  • Intuitive – The management system is readily understood and implemented using visual tools in the language of the digital age. It entirely bypasses complex mathematics as well as the sequential categorization of past models.
  • Adaptable – It does not impose a particular organizational structure but rather adapts to any.
  • Focused – Departments do not involve themselves in the optimization of other departments—each is concerned only with what is under its direct control.
  • Practical – It provides a roadmap for effectively optimizing and controlling the release of any new product. Each department’s optimization is a benefit to the workflow of the entire organization.
  • Measurable – Because the sum of independent optimized departments adds up to the optimized organization, upper management can easily map and manage the progress of each department and the entire organization. Even in a complex organization, it is a simple matter to identify the bottlenecks in the process.

The Controlled Design Management Model works with the same basic material as all previous models—people, ideas, and structure—but does so from an entirely different perspective, using different thinking and tools—the very same principles as electronic control system design.  It provides a practical digital approach in a digital age.

Go back to Part 1

Download full paper (PDF)

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References

  1. Deming, W. Edwards (1964) [1943].  Statistical Adjustment of Data. Dover. ISBN 0-486-64685-8. LCCN 64-24416. (1966) [1950].  Some Theory of Sampling. Dover. ISBN 0-486-64684-X. LCCN 66-30538.
  2. William Ouchi: “Theory Z” How American Business can meet the Japanese Challenge.  Addison-Wesley Publishing Company, 1981
  3. Lean was originated by Eiji Toyoda and Taichi Ohno of Toyota Motors.   Ohno, Taiichi (1988), Toyota Production System: Beyond Large-Scale Production, Productivity Press, ISBN 0-915299-14-3
  4. C. F., L. S. Shieh, Joint Automatic Control Conference, Michigan, p 454
  5. Shieh, L.S. and Goldman, M. J., 1974 I.E.E.E. Trans. Circuit Syst., 21, 341
  6. The Hollow Corporation, Anita Campbell, Small Business TRENDS (2012)  https://smallbiztrends.com/2004/04/hollow-corporation.html

Graphics

Flow-charts by Moises Goldman and John Jonelis.

Graphics from MS Office.

About the Authors

Dr. Moises J Goldman holds an MSEE and a PhD in Engineering Systems from UCLA, specializing in large-scale systems, process optimization, and product innovation. MBA from MIT Sloan, specializing in strategic planning and business development.  His focus is on periods of challenge and change, including startup, growth and restructuring.  Goldman served as CEO, COO, and CTO in diverse industries and developed business across the USA, Germany, Spain, Mexico, Dominican Republic, Jamaica, and Brazil, working with small firms as well as branded giants such as Lockheed, Rockwell, ATT, America Movil, GM, Ford, Scotia Bank, and HSBC. Sits on several boards where entrepreneurship and innovation are the primary goals.  Consults to merging companies during the integration phase as well as startups, helping them become going concerns. Member of several advisory boards at MIT.  Founding member of the TALENT program at IMSA.  Dr. Goldman can be reached at Moises.Goldman@outlook.com

John Jonelis patented seven products and developed dozens more in the field of air pollution control.  Created the Revelation suite of trading algorithms.  Private equity investor.  Artist.  Writer, and publisher of Chicago Venture Magazine and News From Heartland—the Journal of the Heartland Angels.  Author of the novel, The Gamemaker’s Father.  Illinois Wesleyan BFA, 1974.  Kellogg MBA 1989.

 

Copyright © 2019 Moises Goldman & John Jonelis. All rights reserved. Quotation with attribution is permitted for educational purposes.

Chicago Venture Magazine is a publication of Nathaniel Press www.ChicagoVentureMagazine.com Comments and re-posts in full or in part are welcomed and encouraged if accompanied by attribution and a web link. This is not investment advice. We do not guarantee accuracy. Please perform your own due diligence. It’s not our fault if you lose money..
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ALTERNATE HISTORIES

Monte Carlo Simulation

ALTERNATE HISTORIES – John Jonelis – White Paper (pdf)

ALTERNATE HISTORIES – John Jonelis – Journal of the MTA (pdf)

The Back Story

Mr. X offers a seductive and pricey stock trading system and a poor dupe buys it.  What if it were you?  That may sound funny (and it is) but the real question before us is the way in which we view and understand data.  We all use data in one way or another. But how predictive is it? How can we find out?

As it turns out there’s an easy way. It’s called a Monte Carlo simulation and it’s been hidden in the obscurity of academia for too long. Now anybody can measure the nature of a data set, be it trading results, survey results, demographic data, or what have you. With modern software, the analysis is visual and intuitive. And the software can be free.

Reviews

ALTERNATE HISTORIES is a white paper published as the lead article in the Journal of Technical Analysis – Issue 63, Market Technicians Association. Here are some reviews from people you might find familiar:

“Wow! I just picked up the 2005 issue of the Journal of Technical Analysis and the first article by John Jonelis has the right stuff. It describes a visual approach to statistical analysis of trading systems that any numerically-phobic technician ought to find comfortable/intuitive. Stats don’t have to be a gauntlet, the basic ideas are common sense, an idea that Mr. Jonelis demos nicely. Kudos to our great editor Charlie Kirkpatrick and his reviewers.”
John Bollinger, CFA, CMT http://www.bollingerbands.com/

“ Not having that much education about the topic, I was able to follow what you were saying. It was very clearly and logically written, easy to follow. I particularly enjoyed the not so hypothetical examples you set up, which made it much more real” Gregory Leifel, Chairman BAAC and author of “The Day I Met Walt Whitman” ISBN 0-9679997-0-7

“Your article was of the caliber that the MTA would like to see published in the Journal of Technical Analysis.” Charles Kirkpatrick, Editor

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Read it on PDF

ALTERNATE HISTORIES – John Jonelis – White Paper (pdf)

ALTERNATE HISTORIES – John Jonelis – Journal of the MTA (pdf)

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Find Chicago Venture Magazine at www.ChicagoVentureMagazine.com Comments and re-posts are welcomed and encouraged. This is not investment advice – do your own due diligence. I cannot guarantee accuracy but I give you my best.

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