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

• Current Assignee: Palantir Technologies Incorporated
• Original Assignee: Palantir Technologies Incorporated
• Inventors: Beard, Mitchell, Shen, Kai, Hu, Roger, Shah, Sachi, Danka, Miklos, Pliuskys, Laurynas, Pearson, Thomas
• Primary Examiner(s): VON BUHR, MARIA N
• Application Number:
  US14/831,733
• Time in Patent Office: 656 Days
• Field of Search: 700108, 705 728
• CPC Class Codes: G05B 19/4063 Monitoring general control system G05B19/4062 takes precedence
• CPC Class Codes: G05B 19/41885 characterised by modeling, simulation of the manufacturing system
• CPC Class Codes: G05B 2219/31461 Use risk analysis to identify process parts that should be specially monitored
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

A system comprising a computer-readable storage medium storing at least one program and a method for determining, tracking, and anticipating risk in a manufacturing facility are presented. In example embodiments, the method includes obtaining operational data associated with a manufacturing facility. The method further includes accessing a risk data model corresponding to the manufacturing facility, and calculating a risk score based on the current operational data using the risk data model. The method further includes causing presentation of a user interface that includes a display of the risk score to provide a user with a measure of risk at the manufacturing facility.

Claims

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.

2. The method of claim 1, wherein the user interface further includes a graph of historical risk scores of the manufacturing facility.

3. The method of claim 1, wherein the generating of the risk data model comprises:

obtaining the deviation data associated with the manufacturing facility; and

obtaining the historical operational data associated with the manufacturing facility.

4. The method of claim 1, wherein the user interface further includes an additional breakdown of the one of more deviations from existing manufacturing procedures by at least one of subtype, root cause, location, product, or criticality.

5. The method of claim 1, wherein the staffing conditions include at least one of a total number of temporary employees of the manufacturing facility, a total cost corresponding to the employees of the manufacturing facility, an amount of goods created by the employees of the manufacturing facility, a total number of hours worked by the employees of the manufacturing facility, or a total number of overtime hours worked by the employees of the manufacturing facility.

6. The method of claim 1, wherein the calculating of the risk score comprises:

determining a risk metric value for each risk metric of the plurality of risk metrics based on the operational data;

applying a weight to each of the risk metric values based on the risk data model to generate weighted risk metric values; and

calculating an average of the weighted risk metric values;

wherein the risk score includes the average of the weighted risk metric values.

7. The method of claim 6, wherein the plurality of risk metrics includes a headcount change, a productivity measure, overtime hours as a percentage of all worked hours, and a percentage of employees who are temporary employees.

8. The method of claim 1, further comprising:

receiving user input entered via the user interface, the user input including projected operational data; and

recalculating the risk score based on the projected operational data.

9. The method of claim 1, further comprising:

receiving production target data including a production target of the manufacturing facility; and

determining a risk level corresponding to the production target.

10. The method of claim 1, further comprising identifying a repeat deviation based on analysis of the one or more deviations reports, the repeat deviation corresponding to multiple occurrences of an identical deviation occurring within a predefined time range.

11. The method of claim 1, further comprising determining a risk level of the manufacturing facility based on the risk score, wherein the user interface includes a display of the risk level.

12. A system comprising:

a risk modeling engine configured to identify 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, the risk modeling module further configured to generate 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;

an interface module configured to obtain operational data associated with a manufacturing facility, the operational data including information related to staffing conditions of the manufacturing facility, the interface module further configured to cause presentation of a user interface on a client device;

a risk scoring module, comprising one or more processors, configured to access the risk data model corresponding to the manufacturing facility, the risk scoring module further configured to calculate, 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 user interface including a display of the risk score; and

a classifier module configured to determine 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, the user interface further including a breakdown of the one or more deviations from existing manufacturing procedures by deviation type.

13. The system of claim 12, wherein the interface module is further configured to receive projected operational data associated with the manufacturing facility, the projected operational data including information related to projected staffing conditions;

wherein the risk scoring module is further configured to recalculate the risk score based on the projected operational data.

14. The system of claim 12, wherein the risk modeling engine is further configured to perform operations comprising:

obtaining the deviation data associated with the manufacturing facility; and

obtaining the historical operational data associated with the manufacturing facility.

15. The system of claim 12, wherein the risk scoring module is further configured to calculate the risk score by performing operations comprising:

determining a risk metric value for each risk metric of the plurality of risk metrics based on the operational data;

applying a weight to each of the risk metric values based on the risk data model to generate weighted risk metric values; and

calculating an average of the weighted risk metric values to produce the risk score.

16. The system of claim 15, wherein the risk scoring module is further configured to determine a risk level of each of the risk metrics based on the corresponding risk metric value, and wherein the user interface includes a display of the risk level of each of the risk metrics.

17. The system of claim 14, further comprising a data analysis module configured to identify a repeat deviation based on analysis of the one or more deviations reports, the repeat deviation corresponding to multiple occurrences of an identical deviation occurring within a predefined time range.

18. The system of claim 12, wherein the historical staffing conditions include at least one of a total number of temporary employees of the manufacturing facility, a total cost corresponding to the employees of the manufacturing facility, an amount of goods created by the employees of the manufacturing facility, a total number of hours worked by the employees of the manufacturing facility, or a total number of overtime hours worked by the employees of the manufacturing facility.

19. The system of claim 12, wherein the risk score indicates a likelihood of future deviations occurring at the manufacturing facility.

20. A machine-readable storage medium embodying instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:

identifying, by one or more processors of a machine, 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 deviation reports, each of the one or more preexisting 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 operational data associated with a manufacturing facility, the operational data including information related to staffing conditions of the manufacturing facility;

accessing 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;

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, 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.