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Quantifying, tracking, and anticipating risk at a manufacturing facility, taking deviation type and staffing conditions into account

  • US 9,671,776 B1
  • Filed: 08/20/2015
  • Issued: 06/06/2017
  • Est. Priority Date: 08/20/2015
  • Status: Active Grant
First Claim
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1. A method comprising:

  • identifying correlations between staffing conditions of a manufacturing facility and one or more deviations from existing manufacturing procedures based on an analysis of deviation data and historical operational data, the deviation data comprising one or more preexisting deviation reports, each of the one or more deviation reports comprising a textual description of a deviation from existing manufacturing procedures, the historical operational data including information related to historical staffing conditions of the manufacturing facility during a previous period of time, the historical staffing conditions including a total number of employees of the manufacturing facility during the previous period of time;

    generating a risk data model based at least in part on the identified correlations between the staffing conditions and the one or more deviations from existing manufacturing procedures, the risk data model including a plurality of risk metrics related to the identified correlations;

    obtaining, from a computing system of the manufacturing facility, operational data associated with the manufacturing facility, the operational data including information related to staffing conditions of the manufacturing facility;

    accessing, from a machine-readable storage medium, the risk data model corresponding to the manufacturing facility;

    calculating, using the risk data model, a risk score based on the operational data, the risk score providing a measure of risk associated with the manufacturing facility, the calculating of the risk score being performed by one or more processors of a machine;

    determining a deviation type of each of the one or more deviations from existing manufacturing procedures based on an analysis of the textual description included in each of the one or more deviation reports; and

    causing presentation of a user interface on a client device, the user interface including a display of the risk score and a breakdown of the one or more deviations from existing manufacturing procedures by deviation type.

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