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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 was triggered when reading examples of “archival analyses” (performed through data mining) showing that board “age”, “gender”, “capital”, “IP” and “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 and stimulated Riskope to look for solutions.…

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Solomon Islands gold mine contaminated water spill disaster

Solomon Islands gold mine contaminated water spill disaster constitutes a perfect case for discussing the concept of failure criteria (or its mirror image, success criteria) in relation to 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 used to extract the desired product from the ore. It is unfortunately impossible to reclaim all…

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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. ORE showed quantitatively where the a priori “party-breakers” “underlying assumptions” or “key success elements” were in each studied case, thus avoiding the pitfalls described above (1). The success and failure probabilities were in good agreement with a Kaufmann Foundation study. For the portfolio analysis we have now to look at Figure below…

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ORE predicts business startups 3 financing rounds success

Startups investors and money lenders asked us, based on our risk assessment and management experience, if it would be possible that ORE predicts business startups 3 financing rounds success and find ways to reduce the risks. The model, derived from ORE (Optimum Risk Estimates, ©Riskope), has been deployed to date on 7 companies at different maturity stages, active in different spaces in three countries (Canada, Italy, Switzerland), namely: Company  Country Space Startup #1 Canada Networking hardware products Startup #2 Canada Agricultural waste recycling…

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