Safe guard detection for unexpected operations in a MES system

1. A method for a safe guard detection of unexpected operations launched by an operator for a manufacturing execution system (MES-System), wherein the MES-System includes:

a first database including:

a set of operations;

a set of operators;

calendar information for a shift;

calendar information for equipment of the MES-System;

a second database including:

a login history of carried out logins of the operator;

a set of rules defining allowed combinations of operations being launched by the operator at a specific time according to a content of the first database;

which method comprises the steps of:

checking launched operations as to whether the launched operations comply with the set of rules;

storing a launched operation together with data identifying the operator and operator login data as an entry in an event trace file in case of non-compliance of the launched operation;

analyzing entries in the event trace file by an intrusion detection system;

generating alerts based on an analysis of the entries in the event trace file; and

wherein the step of checking launched operations includes at least one step selected from the group consisting of:

1) crosschecking of logins for detecting, whether the operator logs-in himself at different places at a same time where such simultaneous logins may not be expected,

2) determining whether an operation complies with an order scheduling being defined by the calendar information in the first database, and

3) performing a comparison of a login time of the operator with the calendar information for a shift stored in the first database.

2. The method according to claim 1 , wherein the step of checking launched operations includes the step of crosschecking of logins for detecting, whether the operator logs-in himself at different places at a same time where such simultaneous logins may not be expected.

3. The method according to claim 1 , wherein the step of checking launched operations includes the step of determining whether an operation complies with an order scheduling being defined by the calendar information in the first database.

4. The method according to claim 1 , wherein the step of checking launched operations includes the step of performing a comparison of a login time of the operator with the calendar information for a shift stored in the first database.

5. A method for a safe guard detection of unexpected operations launched by an operator for a manufacturing execution system (MES-System), where the MES-System includes:

a first database including:

a set of operations;

a set of operators;

calendar information for a shift;

calendar information for equipment of the MES-System;

a second database including a login history of carried out logins of the operator;

a machine learning model;

which method comprises the steps of:

cataloging the operator in a given role in the MES-System;

feeding, in a learning phase, a learning module being part of a machine learning model with launched operations for that role;

checking, in a run time phase, the launched operations as to whether the launched operations comply with the operations in the learning module;

storing a launched operation together with data identifying the operator and operator login data as an entry in an event trace file in case of non-compliance of the launched operation;

analyzing entries in the event trace repository by an intrusion detection system and based on an analysis generating alerts; and

wherein the step of checking the launched operations includes at least one step selected from the group consisting of:

1) crosschecking of logins for detecting, whether the operator logs-in himself at different places at a same time where such simultaneous logins may not be expected,

2) determining whether an operation complies with an order scheduling being defined by the calendar information in the first database, and

3) performing a comparison of a login time of the operator with the calendar information for a shift stored in the first database.

6. The method according to claim 5 , which further comprises repeating the learning phase in order to get a refinement of allowed operations.

7. The method according to claim 5 , wherein the machine learning model input comprises:

a user role;

operations to be performed or launched; and

a time frame.

8. The method according to claim 5 , wherein the step of checking launched operations includes the step of crosschecking of logins for detecting, whether the operator logs-in himself at different places at a same time where such simultaneous logins may not be expected.

9. The method according to claim 5 , wherein the step of checking launched operations includes the step of determining whether an operation complies with an order scheduling being defined by the calendar information in the first database.

10. The method according to claim 5 , wherein the step of checking launched operations includes the step of performing a comparison of a login time of the operator with the calendar information for a shift stored in the first database.

11. The method according to claim 10 , wherein the calendar information for the shift indicates whether the operator is meant to be at work and a geographical area at which the operator is expected to be at work.

12. The method according to claim 4 , wherein the calendar information for the shift indicates whether the operator is meant to be at work and a geographical area at which the operator is expected to be at work.