These are good points. thanks everyone. However, I am very keen on detailing these - I just do not want to brush anything with a broad brush - for eg., high receivables is bad, low CFO/EBITDA is bad etc. unless we can overlay it with an understanding of the business, where cash is used and how it is earned.
I am looking for specific examples, case studies rather than stereotypes as I want to minimize both alpha (not capturing an issue) and beta errors (assuming an issue exists whereas in reality it does not).
For eg., in opto circuits even though acquisition led sales growth was high, CFO’s were consistently negative and receivables were going through the roof, resulting in a cash crunch that ultimately turned debt repayments and investments into a tight squeeze.
Triangulation is about using at least 3-4 data points together to make a judgement to minimize systemic error - not just stereotyping using one single data point.