COVID-19 lessons

According to drastic change around the world, I learned a new lesson from COVID-19: We need a new revolution approach like metaheuristics methods which are useful for NP-hard problems, for leadership as well. Now we know how huge changes can happen quickly and we need much more agility to make a sustainable management system and how the business environment can be changed and affected by some causes and create NP-hard problems for business.

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5 Replies
Duke Okes
196 Posts

Biomimicry and TRIZ are perhaps non-quantitative metaheuristic methods.

Jay Arthur
5 Posts

Instead of looking for more complicated problem-solving methods, we might consider solving all of the uncomplicated (but not simple) problems in the world using data, control charts, Pareto charts, histograms and Ishikawa diagrams. Agile approaches to quality improvement could make massive headway on these kinds of problems in 18-24 months. Maybe after we've taken all of the “easy shots” there won't be so many complex ones left.

The countermeasures for COVID-19 are simple and well known: wear a mask, wash your hands, stay out of crowds and get vaccinated. We don't need complicated analysis to solve this problem. But like all quality improvements, compliance with countermeasures is rarely uniform.

https://sites.google.com/site/fieldreliability/corona-virus-survival-analysis/

Thank you for your consideration and reply. I do agree with you we should focus on simple methods instead of more complicated. The concept of all meta-heuristics methods are very simple like ant colony optimization and AI or genetic algorithm, but a huge different in results. Some simple aspects can make a huge changes on business.

Anish Shah
1 Posts
Lowell Arthur, I agree and support your comments about solving Simple ( and not so complex ) problems first. My observation from the field of manufacturing and manufacturing quality is that we often collect too much data and then wonder what to do with it when it is TOO late. Also, the terms AI and ML have taken software analysis to a new ( and often unpredictable ) level with over reliance on computers doing the DATA tasks that we humans should be able to comprehend and accept first, without throwing it blindly at software that we believe will always give us the CORRECT answer. Your ( and others ) comments welcome.