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Technology and Innovation Leeds School of Business University of Colorado Boulder, CO 80309-0419

Technology and Innovation Leeds School of Business University of Colorado Boulder, CO 80309-0419. Professor Stephen Lawrence. Introduction to OM Productivity Linear programming Quality SPC tools Timeliness Queueing Theory Flexibility Inventory Theory. Technology and Innovation

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Technology and Innovation Leeds School of Business University of Colorado Boulder, CO 80309-0419

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  1. Technology and Innovation Leeds School of Business University of Colorado Boulder, CO 80309-0419 Professor Stephen Lawrence

  2. Introduction to OM Productivity Linear programming Quality SPC tools Timeliness Queueing Theory Flexibility Inventory Theory Technology and Innovation Project Management Operations Strategy Business Performance Excellence Course Outline Charles Sheeler Suspended Power 1939

  3. Agenda • Competing with technology • S-Curve analysis • Disruptive technologies • Innovation in history • Normal vs. revolutionary innovation • Paradigms • Managing Technology • Technological Forecasting

  4. The Value Equation

  5. Competing with Process Innovation Competing with Marketing Capabilities Competing with Operational Capabilities Competing with Technological Capabilities Process Enhancements Product Enhancements

  6. Evolution of Product/Process Innovation Product Innovation high Process Innovation Rate of Innovation low early late Stage of Product Life-cycle

  7. Trajectory of Tech Innovation Physical limit of technology Performance Effort (funds) Technological performance often follows an S-shaped curve Foster, Innovation: The Attackers Advantage, Summit Books, 1986

  8. Successive Tech Innovations Performance Physical limit of technology Effort (funds) Foster, Innovation: The Attackers Advantage, Summit Books, 1986

  9. Patterns in History • Unorderly • not an orderly process of research and development; few elements of planning or cost-benefit analysis. • Breaks constraints. • Technological change involves an attack by an individual on a constraint that is taken as a given by everyone else. • Unexplained timing. • Often no good reason why an invention was made at a particular time and not centuries earlier (e.g. wheelbarrow and stirrup in Medieval times). Moykr, The Lever of Riches: Technological Creativity and Economic Progress, Oxford University Press (NY), 1990.

  10. Stages of Technical Evolution • Invention: Acquisition of new knowledge • Innovation: Application of new knowledge • Diffusion: Acceptance and adoption of new knowledge • Analogy to Evolution • Invention = Mutation • Innovation = Adaptation to Environment • Diffusion = Evolutionary Success • Lesson: dead ends have value

  11. Normal vs. Revolutionary Innovation • Normal Innovation • innovation with an accepted “paradigm” • incremental in nature • increasing specialization required • Revolutionary Innovation • often a response to “intellectual crisis” • often proceeded by competing theories and ideas • changes the world view of a discipline • establishes a new paradigm Kuhn, T.S., The Structure of Scientific Revolutions, Univ of Chicago Press, 1962.

  12. Paradigms • Paradigm – a set of rules and regulations (written or unwritten) that does two things: • Establishes or defines boundaries • Governs how to behave inside the boundaries in order to be successful

  13. Examples of Paradigms • Everything that can be invented has been invented • Charles H. Duell, Commissioner, U.S. Office of Patents, 1899. • Louis Pasteur's theory of germs is ridiculous fiction. • Pierre Pachet, Professor of Physiology at Toulouse, 1872 • Airplanes are interesting toys but of no military value. • Marechal Ferdinand Foch, Professor of Strategy, Ecole Superieure de Guerre. • There is no reason anyone would want a computer in their home. • Ken Olson, president, founder of Digital Equipment Corp., 1977 • 640K ought to be enough for anybody. • Bill Gates, 1981

  14. Innovation as Chaos • Path dependent and self-reinforcing • Initial conditions are critical • Small perturbations in initial environment can have a large subsequent in eventual technological evolution • Inherent randomness (unpredictable) • Positive feedback reinforces an evolutionary path • Example: Beta vs. VHS video tapes • Example: PC operating system

  15. The Future? What disruptive technologies are currently evolving that are fundamentally changing the way we produce and deliver goods and services?What paradigms are being broken?

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