How to Handle Impossible Projects: A Real-Life Integration Case Study

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  • Опубліковано 17 лис 2024

КОМЕНТАРІ • 16

  • @AllenRamsey-b5z
    @AllenRamsey-b5z 2 місяці тому +3

    It’s always refreshing to see beyond the 'smoke and mirrors' of integration failures and get to the real, no-nonsense solutions.

  • @ericleake6934
    @ericleake6934 2 місяці тому +3

    “Water Walker” Reynolds. I trademarked it. Love these videos

  • @marc_akoto
    @marc_akoto 2 місяці тому +2

    Saying YES is tempting, for all integrators that have bills to pay. Most integrators would sign for the work no matter what, and change order afterwards, or not even deliver on the expected technical result. But TRANSFORMATIVE LEADERSHIP is not just about technology or budget. Being value-driven also requires to say NO sometimes, even though it is frightening for most mainstream manufacturers.

  • @jonbikaku6133
    @jonbikaku6133 2 місяці тому +3

    Reaaly insightful, thanks for sharing!! When is the new bootcamp batch starting? :)

    • @ZackScriven
      @ZackScriven 2 місяці тому +1

      The next 16-Week Accelerator is Starting Q1 2025, and for the next MES Bootcamp I will be leading a study group kicking off in October.

  • @dura2595
    @dura2595 2 місяці тому +3

    Isn't it 24*60*6

  • @VastCNC
    @VastCNC 2 місяці тому +3

    Data always trumps algorithm.

  • @ravis2381
    @ravis2381 2 місяці тому +1

    Sorry i haven't understood, the client had the all data which clearly specified the down time due to lack of raw material , what am i missing ? what normalization and contextualization had to be done on this information ? What more information was required ?

    • @4.0Solutions
      @4.0Solutions  Місяць тому

      Every data point, whether directly or indirectly related to the downtime event caused by a lack of raw materials, needs to be considered. The accuracy of predictions is often low unless the situation closely matches other similar instances. With the limited data available, especially when it's low-resolution (e.g., recorded once per day), identifying patterns becomes challenging. That's where machine learning comes in-it can uncover trends that aren't obvious to the human eye. If the issue were simply a matter of burn rate exceeding available inventory, they wouldn't need AI for such a straightforward scenario.

    • @ravis2381
      @ravis2381 Місяць тому +1

      @4.0Solutions
      what you say is still confusing but i thought the video talked only about lack of raw material for SCM may be i will replay and listen to it carefully again ! having and Logging all the events and tracking their status just prior to lack of raw material would give you what information ? Logging of all events is understood ( for any other downtime reasons) but the relationship with lack of material is still confusing to me ... sorry ...

  • @NickNiculita-nicks-software
    @NickNiculita-nicks-software Місяць тому +1

    haha...not everybody has f**k you money...

    • @walkerreynolds973
      @walkerreynolds973 Місяць тому +1

      No question this is true... the lesson here is that doing the right thing in the short term pays off in the long-term, for both the client and the consultant.