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Black Belt training - truly facilitated


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If you think you know what a "Lean Six Sigma Black Belt Training" is - then think twice.
There's a lot of "Analytics". That needs to feed into "Story Telling" to drive engagement. And both need to feed into lasting improvement.
It's all known. Yet, the way you want to learn that is in a truly facilitated approach.
We get this done by blending online and classroom learning with references to articles and solid research so that in class we can focus on practicing real-life problem solving, getting the most out of our combined business experience and not last: have fun.
See how that works.

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Black Belt training - truly facilitated

  1. 1. 1 Define and Measure Flipchart protocol April 2014 Dr. Michael Ohler, Principal, BMGI BMGI GB to BB upgrade training
  2. 2. 2 …. …
  3. 3. 3 …. …
  4. 4. 4 Let‘s think with the end in mind
  5. 5. 5 What is so special about managing „improvement projects“?
  6. 6. 6 Tollgate checklists …
  7. 7. 7 How to define your project
  8. 8. 8 Are you improving what should be innovated? A company managing a portfolio of improvement projects needs a methodology to answer to that question. Concepts such as „Job to be Done“ and the „S-curve“ can help.
  9. 9. 9 Improvement projects spell change The „change curve“ is well known. We can transform the „harmful resource“ of the „valley of despair“ into a useful resource: „hey, we need to change things!“. At the same time, we need to watch out for people for who the change is not good: for them, the underlying assumption in the change curve is not fulfilled.
  10. 10. 10 Know thyself … We looked into our DiSC profiles and understood we can‘t communicate the same way to all people – while we may have our personal preferences. And that can stand in the way of success…
  11. 11. 11 Learn from „big transformations“ In his 1995 HBR article, J. Kotter established „Eight mistakes why transformations fail“. Also when our improvement project does not spell a true „transformation“, it still spells change – and we can learn from Kotter: what if people could be complacent with regards to our project? What if there were no leadership team to steer it? What if…
  12. 12. 12 How does your management spend their time? Understand their challenge: they need to „create“ tomorrow‘s business, you help to „improve“ today‘s business that other „operate“. Only shared resources spell the advantage of large corporations over start-ups. No easy task to balance it all…
  13. 13. 13 Data analysis is always the same: Y = f(x) …
  14. 14. 14 Get ALL insights out of your data binars/time-%E2%80%93-time- out-make-most-out-your-process- lead-time-data
  15. 15. 15 Taguchi‘s concept for the „Voice of the customer“ how-make-accurate-improvement-estimates-during-project- selectio
  16. 16. 16 Measurement system analysis: Get it ALL out of the data The „Minitab data-logic“ demands to have x1, x2, … Y in columns and individual events in rows. We want only parts to be significant in the ideal measurement system. We may want to study more than the „usual suspects“ of operator and trial# (like: who RECORDED the data). Then we need to collect additional data.
  17. 17. 17 Test it! When you don‘t remember: 1) Formulate your question 2) Simulate in Minitab.
  18. 18. 18 Multinomial attribute MSA: Can you recognize this smell? We can always ask „special questions“ to the data – which the „usual“ MSA package does not allow to answer.
  19. 19. 19 Identify special causes: where‘s that bump coming from? Every now and then we observe a „bump“ in a histogram. How do we know it is significant? 1) Identify the most likely distribution 2) Determine the number of observed counts in the bump 3) Determine the number of expected counts in the bump 4) Is the difference significant? 2-proportion test (total count).
  20. 20. 20 Value Stream Mapping Example of a real-life „door by door“ value stream map. A Process Cycle Efficiency of 0.3% is GREAT for our project: it means there are 99.7% of opportunity in the process. N.B.: This company went out of business.
  21. 21. 21 Non-normality is „normal“ – or indicates something note-worthy! Skewed distribution are often only coming from a natural limit such as: lead times can‘t be faster than zero. Distributions that are „too flat“ or „too pointed“ indicate the data are coming from different distributions. Always understand non-normality. If there are special causes, they spell quick-win opportunities!
  22. 22. 22 Re-visiting our expectations Full circle: fully met Empty circle: not met at all. We voted, averaged and then rounded to „full“, „¾ full“, „½ full“, „¼ full“, „empty“.
  23. 23. 23 Define and Measure Flipchart protocol BMGI GB to BB upgrade training Contact: