Source: https://patents.google.com/patent/US9671776B1/en
Timestamp: 2020-05-28 06:05:30
Document Index: 304646007

Matched Legal Cases: ['art 1108', 'art 1108', 'art 1108', 'arts 1308', 'arts 1308', 'arts 1308', 'art 1308', 'art 1309', 'art 1310', 'art 1311', 'art 1312', 'art 1312', 'arts 1602', 'art 1602', 'art 1603', 'art 1604', 'art 1605', 'art 1606', 'arts 1602', 'Application No. 14187996']

US9671776B1 - Quantifying, tracking, and anticipating risk at a manufacturing facility, taking deviation type and staffing conditions into account - Google Patents
Quantifying, tracking, and anticipating risk at a manufacturing facility, taking deviation type and staffing conditions into account Download PDF
US9671776B1
US9671776B1 US14/831,733 US201514831733A US9671776B1 US 9671776 B1 US9671776 B1 US 9671776B1 US 201514831733 A US201514831733 A US 201514831733A US 9671776 B1 US9671776 B1 US 9671776B1
US14/831,733
2015-08-20 Application filed by Palantir Technologies Inc filed Critical Palantir Technologies Inc
2015-08-20 Priority to US14/831,733 priority Critical patent/US9671776B1/en
2015-08-20 Assigned to Palantir Technologies Inc. reassignment Palantir Technologies Inc. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HU, ROGER, SHAH, SACHI, PLIUSKYS, LAUREN, PEARSON, THOMAS, DANKA, MIKLOS, BEARD, MITCHELL
2015-12-17 Assigned to Palantir Technologies Inc. reassignment Palantir Technologies Inc. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SHEN, Kai
2017-04-12 Assigned to Palantir Technologies Inc. reassignment Palantir Technologies Inc. CORRECTIVE ASSIGNMENT TO CORRECT THE CORRECT THE SPELLINGOF LAURYNAS PLIUSKYS NAME PREVIOUSLY RECORDED ON REEL 036408 FRAME 0366. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Assignors: HU, ROGER, SHAH, SACHI, PLIUSKYS, LAURYNAS, PEARSON, THOMAS, DANKA, MIKLOS, BEARD, MITCHELL
2017-06-06 Publication of US9671776B1 publication Critical patent/US9671776B1/en
G05B2219/31461—Use risk analysis to identify process parts that should be specially monitored
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.
The subject matter disclosed herein relates to data processing. In particular, example embodiments may relate to techniques for quantifying, tracking, and anticipating risk at a manufacturing facility.
Product manufacturing, especially pharmaceutical drug manufacturing necessarily entails some amount of risk. That is to say that in many manufacturing procedures that may be used in making products, there always exists some probability that an issue may occur. In some instances, deviations in an established manufacturing procedure may lead to unrecoverable product losses, and thus, unrecoverable profit loss. Even worse, if undetected, a deviation in the manufacturing process of a product has potential to cause injury to an end consumer of the product.
It is therefore important for manufacturing facility stakeholders to be able to understand what aspects of their manufacturing facility's operations contribute to risk so that corrective action can be taken to avoid or mitigate such risk in the future. However, achieving a common and accurate understanding of the risk is difficult because individual stakeholders may recognize different risk factors, and may attribute different severities to each factor. Further, heuristic methods used in traditional industry practice, which are devoid of data-driven metrics and analysis, often fail to identify correct risk factors and further fail to accurately determine a probability that such factors will actually lead to an issue occurring.
FIG. 2 is a block diagram illustrating various modules comprising a manufacturing risk analytics service, which is provided as part of the data processing platform, consistent with some embodiments.
FIG. 3 is a flowchart illustrating a method for generating a risk data model associated with a manufacturing facility, consistent with some embodiments.
FIG. 4 is a block diagram illustrating a risk data model, consistent with some embodiments.
FIG. 5 is a flowchart illustrating a method for determining risk associated with a manufacturing facility, according to some embodiments.
FIG. 6 is a flowchart illustrating a method for calculating a risk score associated with a manufacturing facility, according to some embodiments.
FIG. 7 is an interface diagram illustrating a user interface for providing an overview of risk associated with a manufacturing facility, according to example embodiments.
FIG. 8 is an interface diagram illustrating a user interface for providing an overview of risk associated with a manufacturing facility, according to alternative example embodiments.
FIG. 9 is an interface diagram illustrating a user interface for providing an overview of deviations occurring at a manufacturing facility, according to example embodiments.
FIG. 10 is an interface diagram illustrating a user interface for providing a detailed breakdown of deviations occurring at a manufacturing facility, according to example embodiments.
FIG. 11 is an interface diagram illustrating a user interface for presenting information related to chronic deviations in a manufacturing facility, according to example embodiments.
FIG. 12 is an interface diagram illustrating a user interface for presenting deviation timelines, according to example embodiments.
FIG. 13 is an interface diagram illustrating a user interface for presenting risk analytics related to staffing conditions, according to example embodiments.
FIG. 14 is an interface diagram illustrating a user interface for presenting risk analytics associated with future projections, according to example embodiments.
FIG. 15 is an interface diagram illustrating a user interface for presenting an overview of tasks associated with a manufacturing facility, according to example embodiments.
FIG. 16 is an interface diagram illustrating a user interface for presenting a detailed breakdown of tasks associated with a manufacturing facility, according to example embodiments.
FIG. 17 is a diagrammatic representation of a machine in the example form of a computer system within which a set of instructions for causing the machine to perform any one or more of the methodologies discussed herein may be executed.
Aspects of the present disclosure relate to systems and methods for quantifying, tracking, and anticipating risk in a manufacturing facility. Example embodiments involve systems and methods for generating various risk analytics associated with the manufacturing facility. The risk analytics are generated by analyzing data related to the operations of the manufacturing facility. For example, the method may include analyzing deviation reports that describe deviations from existing manufacturing procedures (referred to hereinafter simply as “deviations”). These deviations provide a quantifiable representation of the risk associated with the manufacturing facility. In example embodiments, the deviation reports are analyzed to provide an overview of the deviations that occur over time as well as a breakdown of deviations by type, subtype, root cause, work center, line-operations, product, and criticality, for example. Further, classification logic may be employed in the analysis of deviations reports to classify deviations into groups to enable the identification of repeat deviations.
Additional example embodiments involve systems and methods to generate a risk data model for the manufacturing facility. The risk data model may be used to determine the level of risk associated with the manufacturing facility. Accordingly, the risk data model includes factors that contribute to the risk in the manufacturing facility and indicators of the relative contribution each factor makes to the overall risk. The risk data model may be developed through an analysis of deviations that have occurred in the manufacturing facility and staffing conditions during such deviations. More particularly, at least some of the risk factors included in the risk data model are based on correlations between the deviations and the staffing conditions. Additional risk factors included in the risk data model may, for example, relate to financial risks (e.g., risk of financial issues affecting the manufacturing site), manufacturing risk (e.g., risk of manufacturing problems such as those caused by legacy systems), risks associated with excipients (e.g., risk of input materials and chemicals causing a problem), risks associated with change control (e.g., risk associated with change control events such as new processes or procedures, or failure to adhere to existing ones), and risk associated with mother nature (e.g., lightning storms, high humidity, high or low temperatures, or the like).
Additional example embodiments involve systems and methods to determine a risk level associated with the manufacturing facility. The determination of the risk level includes calculating a risk score for the manufacturing facility using a risk data model and information related to current or projected staffing conditions of the manufacturing facility. The risk score is then used to determine the overall risk level of the facility as well as risk levels associated with individual risk factors. Risk analytics, risk scores, and risk levels are presented to users such as stakeholders of the manufacturing facility so as to assist those stakeholders in making informed decisions about actions to take to avoid, or at least mitigate, further risk.
Turning specifically to the data processing platform 104, a web server 110 is coupled to (e.g., via wired or wireless interfaces), and provides web interfaces to, an application server 112. The application server 112 hosts one or more applications (e.g., web applications) that allow users to use various functions and services of the data processing platform 104. For example, the application server 112 may host a manufacturing risk analytics service 114 that provides a number of analytics related to risk associated with product manufacturing (e.g., pharmaceutical drug manufacturing). In some embodiments, the manufacturing risk analytics service 114 runs and executes on the application server 112, while in other embodiments, the application server 112 provides the client device 102 with a set of instructions (e.g., computer-readable code) that causes the web client 108 of the client device 102 to execute and run the manufacturing risk analytics service 114. The manufacturing risk analytics service 114 analyzes various data to quantify the risk associated with a manufacturing facility and to provide stakeholders with a number of risk analytics to assist the stakeholders in tracking risk at their facilities. Further, the manufacturing risk analytics service 114 makes use of the determined risk analytics to assist stakeholders in anticipating risk based on projected changes in employment and manufacturing output.
The data analyzed by the manufacturing risk analytics service 114 may, for example, include operational data and deviation report data. The deviation report data includes deviation reports that describe a deviation from an existing manufacturing procedure (e.g., an approved instruction or an established standard). Each deviation report may include a timestamp (e.g., a time and date of the deviation), a textual description of the deviation, and a criticality category (e.g., incident, minor deviation, major deviation, critical deviation).
The operational data includes information related to staffing conditions of the manufacturing facility. For example, the operational data may include a total number of employees of the manufacturing facility, a total number of employees of the manufacturing facility who are temporary employees, a total cost to employ the employees of the manufacturing facility, a total number of hours worked by the employees of the manufacturing facility, and a total number of overtime hours worked by the employees of the manufacturing facility. The operational data also includes output data including an amount of goods produced by the employees of the manufacturing facility (e.g., represented as a dollar amount). The operational data also includes information related to customer complaints associated with the manufacturing facility, scheduled audits, previously performed audits, and task data including overdue, scheduled, and upcoming tasks. The operational data analyzed by the manufacturing risk analytics service 114 includes historical operational data (e.g., information related to staffing conditions over a previous period of time), current operational data (e.g., information related to current staffing conditions), and projected operational data (e.g., information related to anticipated staffing conditions).
The data analyzed by the manufacturing risk analytics service 114 is obtained from a third-party computing system 118 (e.g., corresponding to a manufacturing facility), and in particular, a third-party database 120 communicatively coupled to the third-party computing system 118. The data may be routinely automatically retrieved (e.g., nightly) by the manufacturing risk analytics service 114, or manually provided by a user of the third-party computing system 118 or the client device 102 for subsequent processing and analysis by the manufacturing risk analytics service 114.
The data obtained from the third-party computing system 118 is stored in a database 116 that is communicatively coupled to the application server 112 (e.g., via wired or wireless interfaces). The data processing platform 104 may further include a database server (not shown) that facilitates access to the database 116. The database 116 may include multiple databases that may be internal or external to the data processing platform 104. Data representative of the various risk analytics and other pertinent data generated by the manufacturing risk analytics service 114 are also stored on the database 116.
FIG. 2 is a block diagram illustrating various modules comprising the manufacturing risk analytics service 114, which is provided as part of the data processing platform 104, consistent with some embodiments. As is understood by skilled artisans in the relevant computer and Internet-related arts, the modules and engines illustrated in FIG. 2 represent a set of executable software instructions and the corresponding hardware (e.g., memory and processor) for executing the instructions. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components (e.g., modules and engines) that are not germane to conveying an understanding of the inventive subject matter have been omitted from FIG. 2. However, a skilled artisan will readily recognize that various additional functional components may be supported by the manufacturing risk analytics service 114 to facilitate additional functionality that is not specifically described herein. Furthermore, the various functional modules and engines depicted in FIG. 2 may reside on a single computer (e.g., a client device), or may be distributed across several computers in various arrangements such as cloud-based architectures.
The manufacturing risk analytics service 114 is shown as including an interface module 200, a data retrieval module 205, a data analysis module 210, a classifier module 215, a risk modeling engine 220, and a risk scoring module 225, all configured to communicate with each other (e.g., via a bus, shared memory, a switch, or application programming interfaces (APIs)). The aforementioned modules of the manufacturing risk analytics service 114 may, furthermore, access one or more databases that are part of the data processing platform 104 (e.g., database 116), and each of the modules may access one or more computer-readable storage media of the client device 102.
The interface module 200 receives requests from various client computing devices, and communicates appropriate responses to the requesting client devices. The interface module 200 may receive requests from client devices in the form of Hypertext Transfer Protocol (HTTP) requests, or other web-based, application programming interface (API) requests. For example, the interface module 200 provides a number of interfaces (e.g., APIs or user interfaces that are presented by the client device 102) that allow data to be received by the manufacturing risk analytics service 114.
The interface module 200 also provides user interfaces that include graphical representations of the various analytics produced by the manufacturing risk analytics service 114. These user interfaces may include various graphs, charts, and other graphics used, for example, to represent and analyze risk associated with a manufacturing facility or a set of manufacturing facilities. The interface module 200 also receives and processes user input received through such user interfaces. Examples of the user interfaces provided by the interface module 200 are discussed below in reference to FIGS. 7-16.
The data retrieval module 205 is configured to retrieve data for processing and analysis. For example, the data retrieval module 205 obtains deviation report data and operational data associated with manufacturing facilities. In some embodiments, the data retrieval module 205 retrieves such data from the third-party database 120 of the third-party computing system 118 through appropriate requests (e.g., API requests or calls) transmitted over the network 106. The data may be retrieved by the data retrieval module 205 on a periodic basis (e.g., nightly). In some embodiments, the data retrieval module 205 obtains data from a location specified by a user (e.g., via a user interface provided by the interface module 200).
The data analysis module 210 is configured to analyze data to develop various analytics related to risk in a manufacturing facility. For example, the data analysis module 210 analyzes deviation report data to identify patterns in occurrences of deviations (e.g., departures from an existing manufacturing procedure). Given a set of deviation report data, the data analysis module 210 determines a number of analytics including a breakdown of the number of deviations that occur by type, subtype, root cause, work center, line-operations, product, and criticality, for example.
The data analysis module 210 also tracks the occurrences of deviations over time to identify chronic deviations. For example, the data analysis module 210 analyzes deviation report data to identify repeat deviations, which are multiple occurrences of the same deviation occurring within a predefined time range of one another. The data analysis module 210 includes classification logic that is used to classify deviations into groups so as to enable the data analysis module 210 to identify repeat deviations.
In some instances, the workload of employees at a manufacturing facility (e.g., a number of tasks assigned to each employee) contributes to the amount of risk at the manufacturing facility. Accordingly, the data analysis module 210 also analyzes task data (e.g., included as part of the operational data) obtained from a manufacturing facility to develop analytics to assist manufacturing facility stakeholders with task management and workload balancing. In some embodiments, the analysis of the task data may also be used in the determination of the amount of risk associated with manufacturing facilities.
The task data includes information related to planned and upcoming tasks associated with the manufacturing facility. The task data may include a scheduled date or date range, a due date, a responsible party (e.g., an employee of the manufacturing facility to whom the task is assigned), and a description of the task. The data analysis module 210 analyzes the task data to generate an overview of the tasks performed over a certain time period, and determine a number of overdue tasks, an amount of time each task is overdue, and a number of tasks scheduled for a given time period. The data analysis module 210 also generates a breakdown of tasks due by days overdue, due date, status, and criticality. The data analysis module 210 also generates a breakdown of the tasks assigned to each employee of the manufacturing facility and the status of each task (e.g., overdue, scheduled, and upcoming).
The classifier module 215 is configured to analyze the textual descriptions of deviations included in deviation report data to classify each deviation into one or more of several deviation types and subtypes. The deviation types and subtypes assigned to each deviation may be stored in a record associated with the corresponding deviation. In this manner, the data analysis module 210 can use the determined type and subtype to produce more informed analytics (e.g., a breakdown of deviations by type and subtype).
The risk modeling engine 220 is configured to generate risk data models for manufacturing facilities. A risk data model is used to quantify risk associated with the manufacturing facility. The risk data model includes one or more risk metrics, which are factors that contribute to risk. The one or more risk metrics may, for example, correspond to correlations between occurrences of deviations in a manufacturing facility and staffing conditions of the manufacturing facility. Accordingly, in generating the risk data model, the risk modeling engine 220 analyzes deviation report data and historical operational data to identify correlations between occurrences of deviations and staffing conditions, which are used as the risk metrics in the risk data model. For example, the risk metrics may include a headcount change (e.g., a number of employees hired over a period of time), a percentage of hours worked by employees that are overtime hours, a percentage of the total workforce who are temporary employees, and productivity (e.g., amount of goods created relative to an amount of people creating the goods). The risk data model also includes a weight of each risk metric to indicate an amount of risk attributable to that risk metric.
In some embodiments, the risk data model generated by the risk modeling engine 220 takes into account the output of (e.g., amount of goods produced by) the manufacturing facility. To this end, the risk modeling engine 220 analyzes deviation report data along with operational data to identify trends in occurrences of deviations relative to the output of the manufacturing facility. In this manner, the risk data model may be used to identify risk associated with an anticipated output of the manufacturing facility.
The risk scoring module 225 generates a risk score for a manufacturing facility using a risk data model generated by the risk modeling engine 220. The risk score provides a quantified measure of risk associated with the manufacturing facility. Specifically, in some instances, the risk score provides an indication of the likelihood that a future deviation will occur in the manufacturing facility.
In generating the risk score for a manufacturing facility, the risk scoring module 225 accesses a risk data model corresponding to the manufacturing facility, and operational data associated with the manufacturing facility. The operational data may include either current operational data (e.g., information related to current staffing conditions of the manufacturing facility) or projected operational data (e.g., information related to anticipated staffing conditions of the manufacturing facility). In instances in which the risk score is based on current operational data, the risk score provides an indication of the current risk associated with the manufacturing facility. In instances in which the risk score is based on projected operational data, the risk score provides an indication of the risk associated with the anticipated staffing conditions.
Once the risk scoring module 225 has accessed the risk data model and the operational data, the risk scoring module 225 determines a value for each risk metric (referred to herein as “risk metric values”) included in the risk data model. The risk scoring module 225 determines the risk metric values based on the operational data. Following the example from above, the risk scoring module 225 determines values corresponding to headcount change, overtime hour percentage, temporary employee percentage, and productivity. The risk scoring module 225 then weights each determined risk metric value based on the respective risk metric weight indicated in the risk data model. The weighted risk metric values are used to provide an indication of the risk associated with each individual risk metric; accordingly, the risk scoring module 225 may work in conjunction with the interface module 200 to provide a display of the weighted risk metric values. To generate the overall risk score associated with the manufacturing facility, the risk scoring module 225 aggregates the weighted risk metric values. For example, the risk scoring module 225 calculates an average of the weighted risk metric values to generate the overall risk score.
The risk scoring module 225 also determines risk levels associated with manufacturing facilities based on the determined risk score. The risk level may, for example, be high, medium, or low. Each risk level corresponds to a range of risk scores, and accordingly, the determination of the risk level of a manufacturing facility includes determining which range of values the determined risk score falls into.
In some embodiments, the risk scoring module 225 also calculates a risk score specifically associated with a production target of the manufacturing facility. The production target includes a recovery amount (e.g., an anticipated amount of goods to be produced or output). Production target data representative of the production target may be included in the operational data associated with the manufacturing facility, or provided by a user through an interface provided by the interface module 200. The risk scoring module 225 uses the risk data model along with operational data associated with the manufacturing facility to calculate the risk level associated with the production target.
FIG. 3 is a flowchart illustrating a method 300 for generating a risk data model for a manufacturing facility, consistent with some embodiments. The method 300 may be embodied in computer-readable instructions for execution by one or more processors such that the operations of the method 300 may be performed in part or in whole by the application server 112. In particular, the operations of the method 300 may be performed in part or in whole by the manufacturing risk analytics service 114; accordingly, the method 300 is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method 300 may be deployed on various other hardware configurations and the method 300 is not intended to be limited to the application server 112 or the manufacturing risk analytics service 114.
At operation 305, the risk modeling engine 220 obtains deviation report data associated with a manufacturing facility. The deviation report data includes one or more deviation reports identifying a deviation from a manufacturing procedure (e.g., an established standard or approved instruction). Each deviation report includes a timestamp, a criticality category, and a textual description of the deviation. An example of a deviation is placing an incorrect label on a product.
At operation 310, the risk modeling engine 220 obtains historical operational data associated with the manufacturing facility. The historical operational data includes information related to staffing conditions of the manufacturing facility over a previous period of time. The historical operational data may, for example, include a total number of employees of the manufacturing facility, a total number of employees of the manufacturing facility who are temporary employees, a total cost to employ 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, and a total number of overtime hours worked by the employees of the manufacturing facility.
At operation 315, the risk modeling engine 220 identifies correlations between the deviation report data and the historical operational data. Specifically, the risk modeling engine 220 identifies correlations between occurrences of deviations and staffing conditions. In some instances, the risk modeling engine 220 may identify a series of non-linear exponential correlated risk factors. The identification of the correlations between the deviation report data and the historical operational data results in the identification of factors that contribute to risk in the manufacturing facility, and the degree to which each factor contributes to the risk.
At operation 320, the risk modeling engine 220 generates a risk data model corresponding to the manufacturing facility using the identified correlations. In particular, the risk data model includes one or more risk metrics (e.g., factors contributing to risk) corresponding to the identified correlations. As an example, FIG. 4 is a block diagram illustrating an example risk data model 400, consistent with some embodiments. As shown, the risk data model 400 includes risk metrics 401-40 n, each of which is a factor that contributes to the overall risk of the manufacturing facility. In some embodiments, the risk metrics 401-40 n correspond to correlations between deviation report data and operational data associated with the manufacturing facility. The risk metrics 401-40 n may, in some embodiments, also relate to financial risks (e.g., risk of financial issues affecting the manufacturing site), manufacturing risk (e.g., risk of manufacturing problems such as those caused by legacy systems), risks associated with excipients (e.g., risk of input materials and chemicals causing a problem), risks associated with change control (e.g., risk associated with change control events such as new processes or procedures, or failure to adhere to existing ones), and risk associated with mother nature (e.g., lightning storms, high humidity, high or low temperatures, or the like), for example. As shown, the risk data model 400 also includes weights 411-41 n, each of which corresponds to a respective risk metric 401-40 n, and indicates a degree to which the respective risk metric 401-40 n contributes to the overall risk of the manufacturing facility.
Referring back to FIG. 3, at operation 325, the risk modeling engine 220 stores the risk data model in the database 116 for subsequent retrieval. The risk data model may be indexed according to, or otherwise associated with, an identifier of the manufacturing facility such that the risk data model may be subsequently retrieved for use in calculating risk scores specifically associated with the manufacturing facility.
FIG. 5 is a flowchart illustrating a method 500 for determining risk associated with a manufacturing facility, according to some embodiments. The method 500 may be embodied in computer-readable instructions for execution by one or more processors such that the operations of the method 500 may be performed in part or in whole by the application server 112. In particular, the operations of the method 500 may be performed in part or in whole by the manufacturing risk analytics service 114; accordingly, the method 500 is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method 500 may be deployed on various other hardware configurations and the method 500 is not intended to be limited to the application server 112 or the manufacturing risk analytics service 114.
At operation 505, the interface module 200 obtains operational data associated with a manufacturing facility. The operational data includes information related to staffing conditions of the manufacturing facility. The operational data may relate to current or projected staffing conditions. The interface module 200 may receive the operational data from a user via one or more user interfaces, or the data retrieval module 205 may provision the interface module 200 with the operational data after retrieving it from one or more third party sources.
At operation 510, the risk scoring module 225 accesses a risk data model corresponding to the manufacturing facility (e.g., from the database 116). The risk data model includes risk metrics that contribute to the overall risk of the manufacturing facility.
At operation 515, the risk scoring module 225 calculates, using the risk data model, a risk score for the manufacturing facility based on the operational data received at operation 505. As an example, FIG. 6 is a flowchart illustrating a method 600 for calculating a risk score associated with a manufacturing facility, according to some embodiments. Consistent with some embodiments, the method 600 may correspond to operation 515 of the method 500.
The method 600 may be embodied in computer-readable instructions for execution by one or more processors such that the operations of the method 600 may be performed in part or in whole by the application server 112. In particular, the operations of the method 600 may be performed in part or in whole by the functional components of the manufacturing risk analytics service 114; accordingly, the method 600 is described below by way of example with reference thereto. However, it shall be appreciated that at least some of the operations of the method 600 may be deployed on various other hardware configurations and the method 600 is not intended to be limited to the application server 112.
At operation 605, the risk scoring module 225 determines risk metric values for each of the risk metrics included in the risk data model of the manufacturing facility based on the operational data. At operation 610, the risk scoring module 225 weights each risk metric value according to the respective weight of each risk metric specified by the risk data model. At operation 615, the risk scoring module 225 aggregates the weighted risk metric values to generate the risk score for the manufacturing facility. For example, the risk scoring module 225 may calculate the average of the weighted risk metric values to determine the overall risk score for the manufacturing facility.
Referring back to FIG. 5, at operation 520, the risk scoring module 225 determines a risk level of the manufacturing facility based on the risk score. The risk scoring module 225 determines the risk level based on the range of risk scores the risk score determined in operation 515 falls into. The risk level may, for example, be high, medium, or low.
At operation 525, which is optional in some embodiments, the risk scoring module 225 accesses production target data including a production target (e.g., an anticipated recovery). The production target data may be included in the operational data associated with the manufacturing facility or it may be received directly from a user via a user interface provided by the interface module 200.
At operation 530, which is optional in some embodiments, the risk scoring module 225 determines a risk level associated with the production target using the risk data model and the determined risk metric values. The risk scoring module 225 may determine the risk level associated with the production target based on a comparison of risk scores associated with the manufacturing facility at previous production (e.g., recovery) levels.
At operation 535, the interface module 200 causes presentation of a user interface on the client device 102. The user interface includes the risk score and the risk level associated with the manufacturing facility along with other information such as the risk level associated with a production target. As an example of the foregoing user interface, FIG. 7 is an interface diagram illustrating a user interface 700 for providing an overview of the risk associated with a manufacturing facility, according to example embodiments.
As shown, the user interface 700 includes a graph 702 that illustrates risk scores associated with a manufacturing facility over a period of time, which in this example relate to deviations occurring over the period of time. The graph 702 includes the risk scores for both a previous and a current year. It shall be appreciated that a year is simply an example time period over which risk scores may be graphed, although, in other embodiments, a user may specific other time period for the graph 702.
In some instances, the method 500 may initially be performed for current operational data and then repeated for projected operational data to produce a current risk score and a projected risk score. That is, a risk score is initially calculated based on the current operational data, and then the risk score is recalculated based on the projected operational data. An example of the current and projected risk scores is included in the user interface 700. In particular, a window 704 includes a current risk score, and a window 706 includes a projected risk score for the next month.
Further examples of interfaces provided by the interface module 200 are discussed below in reference to FIGS. 8-16. FIG. 8, for example, is an interface diagram illustrating a user interface 800 for providing an overview of the risk associated with a manufacturing facility, according to an alternative example. As shown, the user interface 800 includes a number of risk analytics associated with the manufacturing facility. The risk analytics represented in the user interface 800 are developed by the data analysis module 210 through an analysis of deviation report data and operational data associated with the manufacturing facility.
In particular, the user interface 800 includes risk analytics associated with deviations occurring at the manufacturing facility, such as weekly deviations 802, repeat deviations 804 (e.g., multiple instances of the same deviation occurring within a predefined time range), and past due deviations 806 (e.g., deviations awaiting corrective action). The user interface 800 also includes risk analytics associated with tasks performed at the manufacturing facility, such as overdue tasks 808, tasks performed 810, and percent of tasks overdue 812. The user interface 800 also includes risk analytics associated with manufacturing audits, such as customer complaints 814, audits underway 816, and upcoming audits 818.
FIG. 9 is an interface diagram illustrating a user interface 900 for providing an overview of deviations occurring at a manufacturing facility, according to example embodiments. The information presented in the user interface 900 is developed by the data analysis module 210 based on an analysis of deviation report data and operational data associated with the manufacturing facility. As shown, the user interface 900 includes a graph 902 that illustrates deviations that have occurred at the manufacturing facility over a period of time. The user interface 900 also includes a window 904 displaying a total count of current deviations, and a window 906 displaying a year-to-date count of deviations. The user interface 900 also includes a table 908 showing a count of deviations occurring by type and location. The location of a deviation refers to the location within the manufacturing facility where the deviation occurred (e.g., a particular product line or work center).
FIG. 10 is an interface diagram illustrating a user interface 1000 for providing a detailed breakdown of deviations occurring at a manufacturing facility, according to example embodiments. The information presented in the user interface 1000 is developed by the data analysis module 210 based on an analysis of deviation report data and operational data associated with the manufacturing facility. As shown, the user interface 1000 includes multiple charts displaying a number of deviations occurring in the manufacturing facility by subtype 1002, root cause 1004, work center 1006, line-operation 1008, product 1010, and criticality 1020.
FIG. 11 is an interface diagram illustrating a user interface 1100 for presenting information related to chronic deviations in a manufacturing facility, according to example embodiments. The information presented in the user interface 1100 is developed by the data analysis module 210 based on an analysis of deviation report data and operational data associated with the manufacturing facility. As shown, the user interface 1100 includes a graph 1102 illustrating deviations occurring at the manufacturing facility over a period of time, along with repeat deviations occurring at the manufacturing facility over the same period of time. The user interface 1100 also includes a window 1104 displaying the total number of deviations over the period of time, and a window 1106 displaying the percentage of the deviations that are repeat deviations. The user interface 1100 also includes a chart 1108 displaying a breakdown of repeat deviations by type. The chart 1108 includes a display of the number of each type of deviation that are repeat deviations. The chart 1108 also includes indicators (e.g., colors) of criticality (e.g., minor, major, critical) for each repeat deviation.
FIG. 12 is an interface diagram illustrating a user interface 1200 for presenting deviation timelines, according to example embodiments. The information presented in the user interface 1200 is developed by the data analysis module 210 based on an analysis of deviation report data and operational data associated with a manufacturing facility. As shown, the user interface 1200 includes timelines 1201-1208, each of which corresponds to a repeat deviation. The user interface 1200 also indicates a line 1210 (e.g., manufacturing procedure) from which each repeat deviation originated and a root cause 1212 corresponding to each of the timelines 1201-1208. Each of the timelines 1201-1208 further includes an indicator (e.g., a rectangle) of remediation actions taken to remedy the deviations along with an indication of the status (e.g., complete or incomplete) of the remediation action. For example, rectangles are used in the user interface 1200 to denote remediation actions, and the color of each rectangle is used to denote the status of the corresponding remediation action. Further, the duration of each of the remediation actions is denoted by the length of the corresponding rectangle. Each of the timelines 1201-1208 also includes an indicator (e.g., colored circle) of criticality (e.g., minor, major, critical) for the corresponding repeat deviation.
FIG. 13 is an interface diagram illustrating a user interface 1300 for presenting risk analytics related to staffing conditions, according to example embodiments. As shown, the user interface 1300 includes staffing-related risk analytics over a specified time period. A user may specify the time period of analysis using date drop-down menus 1302. As shown, the user interface 1300 includes a window 1304 for displaying a current risk level (e.g., high, medium, or low) and a window 1306 for displaying a projected risk level, which in this example corresponds to the peak risk level for the next three months.
The risk levels displayed in the windows 1304 and 1306 are determined by the risk scoring module 225 based on an analysis of operational data (historical, current, and projected) in light of various risk metrics included in a risk data model corresponding to a manufacturing facility. In this example, the risk metrics relate to staffing conditions of the manufacturing facility. In particular, in this example, the risk metrics include headcount change, productivity, percent overtime, and percent temporary employees. As shown, the user interface 1300 includes charts 1308-1311, each of which corresponds to a risk metric included in the risk data model. In particular, each of the charts 1308-1311 includes historical, current, and projected risk metric values for the corresponding risk metric over the period of time specified by the date drop-down menus 1302. The risk metric values are determined by the risk scoring module 225 based on analysis of the manufacturing facility's operational data. Each of the charts 1308-1311 also includes an indicator of the risk level associated with the corresponding risk metric value, which is denoted by color. The risk level of each risk metric value corresponds to the weighted risk metric value determined during the process of calculating a risk score associated with the manufacturing facility.
As an example, the chart 1308 includes historical, current, and projected values for headcount change, which represents the change to the total number of employees of the manufacturing facility, over the period of time specified by the date drop-down menus 1302. The chart 1309 includes historical, current, and projected values for productivity, which represents an amount of goods created at the manufacturing facility relative to the amount of people creating the goods, over the period of time specified by the date drop-down menus 1302. The chart 1310 includes historical, current, and projected values for percent overtime, which represents an amount of overtime hours worked at the manufacturing facility relative to the total number of hours worked at the manufacturing facility, over the period of time specified by the date drop-down menus 1302. The chart 1311 includes historical, current, and projected values for percent temporary employees, which represents a number of temporary employees of the manufacturing facility relative to the total number of employees in the manufacturing facility workforce, over the period of time specified by the date drop-down menus 1302.
The user interface 1300 also includes a chart 1312 illustrating historical, current, and projected values for recovery of the manufacturing facility over the period of time specified by the date drop-down menus 1302. The recovery of the manufacturing facility represents an amount of goods being created (e.g., amount in dollars of revenue generated) by the manufacturing facility. The chart 1312 also includes an indicator of risk level for each recovery value. The risk scoring module 225 determines the risk level associated with each historical, current, and projected recovery value based on an analysis of the manufacturing facility's operational data in light of the risk data model associated with the manufacturing facility.
FIG. 14 is an interface diagram illustrating a user interface 1400 for presenting risk analytics associated with future projections, according to example embodiments. As shown, the user interface 1400 includes buttons 1402 and 1404 for receiving user input indicative of projected operational data of a manufacturing facility. In particular, the buttons 1402 and 1404 are used to specify projected operational data that may have an effect on one or more risk metrics included in a risk data model corresponding to the manufacturing facility. More specifically, in this example, the button 1402 may be used to input a projected value for a number of temporary employees who will be converted into full-time employees, and the button 1404 may be used to input a projected value for a number of new hires who are temporary employees.
Inputs received at either of the buttons 1402 and 1404 may change one or more risk metric values, and thus, change not only the projected overall risk score, but the individual risk levels associated with each risk metric as well. Accordingly, upon receiving inputs at either one of the buttons 1402 or 1404 indicative of projected operational data, the risk scoring module 225 recalculates risk levels for at least a portion of the risk metrics in the risk data model, and the recalculated risk levels are displayed in the user interface 1400. In particular, the risk scoring module 225 recalculates risk levels for a set of risk metric pairs (e.g., overtime and headcount) in light of the input received via the buttons 1402 and 1404. The risk level for each risk metric pair is displayed in the user interface 1400 in a different block so as to illustrate the relationship of each risk metric pair. For example, a block 1406 represents a +5% change in overtime hours and a −5% change in headcount. Similarly, a block 1408 represents a −7% change in overtime hours and a +5% change in headcount. In the user interface 1400, the risk level of each block is denoted by color. It shall be appreciated that although FIG. 14 illustrates only change in overtime hours and change in headcount, the user interface 1400 is not limited to these risk metrics, and other risk metrics may be used in other embodiments.
FIG. 15 is an interface diagram illustrating a user interface 1500 for presenting an overview of tasks associated with a manufacturing facility, according to example embodiments. The information presented in the user interface 1500 is developed by the data analysis module 210 based on an analysis of task data included as part of operational data associated with the manufacturing facility. As shown, the user interface 1500 includes a drop-down menu 1502 that allows a user to input a date for inspection. The user interface 1500 includes a window 1504 that displays a number of overdue tasks, a window 1506 that displays a number of tasks that are overdue beyond a threshold period of time (e.g., past 30 days), a window 1508 that displays a number of tasks due during the period of time, and a window 1510 that displays a number of tasks due during an upcoming period of time.
The user interface 1500 further includes a graph 1512 that displays overdue tasks over the period of time. The user interface 1500 further includes a graph 1514 that displays planned and upcoming tasks over the period of time.
FIG. 16 is an interface diagram illustrating a user interface 1600 for presenting a detailed breakdown of tasks associated with a manufacturing facility, according to example embodiments. The information presented in the user interface 1600 is developed by the data analysis module 210 based on an analysis of task data included as part of operational data associated with the manufacturing facility. As shown, the user interface 1600 includes charts 1602-1606 for displaying task-related information. In particular, the chart 1602 shows number of tasks by owner (e.g., an employee assigned to complete the task), the chart 1603 shows number of tasks by days overdue, the chart 1604 shows number of tasks by due date, the chart 1605 shows number of tasks by status, and the chart 1606 shows number of tasks by criticality. The charts 1602, 1605, and 1606 further include indicators of task status (e.g., overdue for more than 30 days, overdue, scheduled, and upcoming) as denoted by color.
FIG. 17 is a block diagram illustrating components of a machine 1700, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically, FIG. 17 shows a diagrammatic representation of the machine 1700 in the example form of a computer system, within which instructions 1716 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 1700 to perform any one or more of the methodologies discussed herein may be executed. Additionally, or alternatively, the machine 1700 may correspond to any one of the client device 102, the web server 110, the application server 112, or the third-party computing system 118. The instructions transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine 1700 operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 1700 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 1700 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 1716, sequentially or otherwise, that specify actions to be taken by the machine 1700. Further, while only a single machine 1700 is illustrated, the term “machine” shall also be taken to include a collection of machines 1700 that individually or jointly execute the instructions 1716 to perform any one or more of the methodologies discussed herein.
The machine 1700 may include processors 1710, memory/storage 1730, and L/O components 1750, which may be configured to communicate with each other such as via a bus 1702. In an example embodiment, the processors 1710 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 1712 and a processor 1714 that may execute the instructions 1716. The term “processor” is intended to include multi-core processor that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 17 shows multiple processors, the machine 1700 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.
The memory/storage 1730 may include a memory 1732, such as a main memory, or other memory storage, and a storage unit 1736, both accessible to the processors 1710 such as via the bus 1702. The storage unit 1736 and memory 1732 store the instructions 1716 embodying any one or more of the methodologies or functions described herein. The instructions 1716 may also reside, completely or partially, within the memory 1732, within the storage unit 1736, within at least one of the processors 1710 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 1700. Accordingly, the memory 1732, the storage unit 1736, and the memory of the processors 1710 are examples of machine-readable media.
As used herein, “machine-readable medium” means a device able to store instructions and data temporarily or permanently, and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)), and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store the instructions 1716. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions 1716) for execution by a machine (e.g., machine 1700), such that the instructions, when executed by one or more processors of the machine (e.g., processors 1710), cause the machine to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se.
The I/O components 1750 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1750 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 1750 may include many other components that are not shown in FIG. 17. The I/O components 1750 are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components 1750 may include output components 1752 and input components 1754. The output components 1752 may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components 1754 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.
In further example embodiments, the I/O components 1750 may include biometric components 1756, motion components 1758, environmental components 1760, or position components 1762 among a wide array of other components. For example, the biometric components 1756 may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components 1758 may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components 1760 may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components 1762 may include location sensor components (e.g., a Global Position System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.
Communication may be implemented using a wide variety of technologies. The I/O components 1750 may include communication components 1764 operable to couple the machine 1700 to a network 1780 or devices 1770 via a coupling 1782 and a coupling 1772, respectively. For example, the communication components 1764 may include a network interface component or other suitable device to interface with the network 1780. In further examples, the communication components 1764 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 1770 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB)).
Moreover, the communication components 1764 may detect identifiers or include components operable to detect identifiers. For example, the communication components 1764 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF4117, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 1764. such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.
In various example embodiments, one or more portions of the network 1780 may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network 1780 or a portion of the network 1780 may include a wireless or cellular network and the coupling 1782 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular or wireless coupling. In this example, the coupling 1782 may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long range protocols, or other data transfer technology.
The instructions 1716 may be transmitted or received over the network 1780 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 1764) and using any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 1716 may be transmitted or received using a transmission medium via the coupling 1772 (e.g., a peer-to-peer coupling) to the devices 1770. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 1716 for execution by the machine 1700, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.
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.
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.
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.
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:
15. The system of claim 12, wherein the risk scoring module is further configured to calculate the risk score by performing operations comprising:
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.
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;
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;
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.
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