About this Presentation
In this keynote presentation, Dr. Alan Barnard, CEO of Goldratt Research Labs, will share, a number of key lessons we can learn from the way individuals, organizations and countries responded to the Covid19 pandemic. In many cases, there was initially an under-reaction by decision makers. The number of cases and deaths were low. But as soon as decision makers realized it was growing exponentially, they reacted. In some cases, it triggered over-reactions, trying to secure as many of resources as possible, driving up prices and overloading an already constrained supply. When there are so many unknowns and uncertainties, how can decision makers ensure they do not under-react or over-react? In this presentation, Dr. Barnard will share his insights on this question, and specifically, how the principles and applications of theory of constraints can help decision makers, not only decide when and how best to respond to cases where we have the lethal combination of exponential growth and finite resources, but also how to be better prepared for the next pandemic.
What Will You Learn
To help you get the most value from this session, we’ve highlighted a few key points. These takeaways capture the main ideas and practical insights from the presentation, making it easier for you to review, reflect, and apply what you’ve learned.
Understanding system constraints is crucial for managing and improving organizations. A constraint is any resource that does not have enough capacity to meet the demand that's placed on it to achieve the committed system goal.
System complexity is determined by the number of constraints and their interdependencies. In chaotic systems, small changes in the right area can have a dramatic impact on the system's output and reliability.
During recovery from a crisis like COVID-19, it's important to consider possible recovery scenarios and plan accordingly. This involves identifying and protecting your organization from big downside risks, reducing commitment in chaotic systems to achieve more stability, and identifying decisions or assumptions causing bottlenecks or too small buffers and challenging them.
Instructor(s)
