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Implementing continuous improvement using genetic algorithms

Implementing continuous improvement using genetic algorithms. Petter Øgland, Department of Informatics, University of Oslo QMOD/ICQSS Conference, Verona, Aug 28th 2009. Structure of presentation. Introduction Literature review of CQI methods The new CQI method

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Implementing continuous improvement using genetic algorithms

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  1. Implementing continuous improvement using genetic algorithms Petter Øgland, Department of Informatics, University of Oslo QMOD/ICQSS Conference, Verona, Aug 28th 2009

  2. Structure of presentation • Introduction • Literature review of CQI methods • The new CQI method • Example of new method in practical use • Discussion • Conclusion

  3. Classical QMOD: Deming & Lewin Juran (1986): Plan, Control, Improve Deming (1986): Plan, Do, Check, Act Lewin (1950): Unfreeze, change, freeze

  4. Unpredictable organizations where project-by-project approaches fail

  5. Genetic Algorithms: Cultivate the flock rather than the individuals

  6. Research questions • RQ1: Is it possible to use the GA approach for effective QMS design? • RQ2: If it is possible, why is it not used?

  7. Structure of presentation • Introduction • Literature review of CQI methods • The new CQI method • Example of new method in practical use • Discussion • Conclusion

  8. Genetic Algorithms (GA) has been suggested for QM as a part of a more general Complex Adaptive Systems (CAS) approach (Dooley et al., 1995; Dooley, 2000) GA on a metaphorical level (Goldstein, 1993; Nelson & Winter, 1982) Simulation models based on GA (Bruderer & Singh, 1996) GA as integrated part of decision support systems (Greer & Ruhe, 2003) GA for understanding OD

  9. GA for implementing TQM

  10. Structure of presentation • Introduction • Literature review of CQI methods • The new CQI method • Example of new method in practical use • Discussion • Conclusion

  11. Genetic Algorithm (Wikipedia, 2009) • Choose initial population • Evaluate the fitness of each individual in the population • Repeat until termination: (time limit or sufficient fitness achieved) • Select best-ranking individuals to reproduce • Breed new generation through crossover and/or mutation (genetic operations) and give birth to offsping • Evaluate the individual fitnesses of the offspring • Replace worst ranked part of population with offspring

  12. Structure of presentation • Introduction • Literature review of CQI methods • The new CQI method • Example of new method in use • Discussion • Conclusion

  13. Example: The KLIBAS system • 1991-95 • Formal development project • High prestige, management commitment • Project “completed”, but nothing worked • 1996-99 • Informal maintenance cycle • Low prestige, little management commitment • Problems, complaints requests fixed as reported • A practical and useful system develop through many small iterations

  14. Process maturity in KLIBAS due to managing knowledge/power

  15. SYNOP AWS: Automatic weather stations PRECIP: Manual precipitation stations e-mail e-mail e-mail UASS: upper air sounding stations METAR: Airport weather stations e-mail Pareto analysis e-mail e-mail HIRLAM: quality control by use of forecast data e-mail e-mail Monitoring of system outputs and users (customer satisfaction) System monitoring QMS as CAS with automated Pareto analysis at the nexus

  16. Enter office on the morning of day i. Evaluate population: Real-time and nightly automatic data collection for total system by use of e-mail. Select solutions for next population: Run a Pareto analysis for setting the agenda for the day. This defines the population of processes to be improved. Perform crossover and mutation: Read, write, discuss; design and implement etc.; the daily practical work of process improvement. Exit office in the afternoon of day i. i: = i + 1 GA implementation of daily maintenance & development

  17. Productivity indicator

  18. Structure of presentation • Introduction • Literature review of CQI methods • The new CQI method • Example of new method in practical use • Discussion • Conclusion

  19. Is GA the same as kaizen?

  20. GA is a SPECIAL type of kaizen • It is strictly mathematical (an algorithm), not dependent on intuitive or cultural skills • It is ”stupid” in the sense that each ant in a colony has a lesser brain than an elephant • It is ”unfocused” as it aims for many improvements at the same time • It is ”inefficient” as it progresses by trial and error

  21. But it works!

  22. Why others do not use this approach • People are unwilling to be run by computer • The GA approach generates complexity • It is “common knowledge” that the unfreeze-change-freeze approach is the “one best way” • TQM personnel lack technical skills for understanding GA • GA makes TQM invisible and thus a poor choice when wanting work acknowledgement

  23. Structure of presentation • Motivation • Overview of current CQI methods • The new CQI method • Example of method in use • Discussion • Conclusion

  24. Conclusion • There are sociological reasons why people might reject the GA approach to TQM, although it WORKS and it is SIMPLE to implement • The GA approach seems well-suited for designing QMS bottom-up in complex organizations or as a TQM method for people who enjoy living in chaos

  25. Thank you

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