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Predict business startups success example How to do it

Predict business startups success example How to do it ORE applied to Startups development came up when we read examples of “archival analyses” on startups. Researchers used data mining to show that: board “age”, “gender”, “capital”, “IP” and finally “race” have insignificant effects (after 5 years) on either merger/acquisition rate OR survival of operations of startups. Those results left the authors of the research (Kaufmann Foundation) with the feeling that “something was missing” in this type of analyses. Those results…

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Solomon Islands gold mine contaminated water spill disaster

The Solomon Islands gold mine contaminated water spill disaster constitutes a perfect case for discussing the concept of failure criteria. Failure criteria is the mirror image of the success criteria. Failures are the events that generate risks studied in risk assessments. Tailings dams are built with the purpose (goal) of storing byproducts of mining operations, i.e. the tailings. Tailings consist of finely ground ore (the mineral) and process effluents generated by mechanical and chemical processes.  In fact, those processes goal…

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Business startups success show stoppers and tolerable risks

Business startups success show stoppers and tolerable risks This is a follow up on last week’s post showing how ORE can evaluate business startups success, show stoppers and tolerable risks. Indeed, ORE showed quantitatively where the a priori: “party-breakers”, “underlying assumptions” as well as “key success elements” were in each case. Thus it allows to avoid those pitfalls. The success and failure probabilities were in good agreement with a Kaufmann Foundation study. For the portfolio analysis we have now to look at…

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ORE predicts business startups 3 financing rounds success

Startups investors and money lenders asked us two questions. Based on our risk assessment and management experience, is ORE capable of: predicting business startups 3 financing rounds success? finding ways to reduce the risks from inception? We derived a subset from ORE (Optimum Risk Estimates, ©Riskope) and deployed it on 7 companies. The companies are at different maturity stages. They are active in different spaces in three countries (Canada, Italy, Switzerland), namely: Company  Country Space Startup #1 Canada Networking hardware…

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