如何预测下一起事故将在何时何地发生？do u know when& where your next injury occur？

“Will I have an incident tomorrow?”

How employs advanced and predictive analytics to keep you ahead of disaster. By recording data from inspections and on-site observations, How creates a matrix of leading indicators and predictions on future risk in real time.

 Many companies whooperate near the base of thispyramid are drowning in data andhaveno answers beyond backward-looking, lagging-indicator reports. Progressivecompanies, on the other hand, areable to turn their raw data intoactionable information and thenoptimize their preventive efforts toreduce injury rates. These leadersaredriving competitive advantage for their companies by moving up the advanced analytics pyramid.

One example of predictive analytics in safetyis the “Red Flag” model. This modeldelivers an automated report that flags sites, operations, or teamswithin a company that have a high likelihoodofinjury.Inplainterms,ifyoursite,operation,orteamisflagged,youhaveanearly70%probability ofexperiencing a two to threetimes increase in safety incidents atthat site, within that operation, or acrossthat team. Conversely, if your site is not flagged, there is, on average,a 90%probability that no increase in incident risk willoccur.

With the results ofthe Red Flag model, a safety professional can take immediate action atthose flagged sites to prevent any predicted injuries from occurring. This risk management activity isdirectedbythefindingsofthemodel,aswellasadeeperanalysisoftheunderlyingsafetyinspectiondata used to trigger theflag.

Rather than rely on the safety observations of one inspector, the model aggregates data from many inspectors. Further, this aggregation does not just occur within one company, but across many companies using similar, but not exact, data sets. This provides not just more accurate models,
but models that are more applicable across diverse companies and industries. This aggregation is especially powerful for small companies who simply cannot collect a statistically significant data set on their own – they can rely on and benefit from similar data from other, larger companies to help them scale the advanced analytics pyramid.