202213957 1 Specification Monitoring an operability of a production system The present invention relates to an apparatus and a computer- implemented method for monitoring an operability of a produc- tion system, as well as a computer program product. Material flow simulations provide great benefit as for examp- le decision support systems and/or operability monitoring systems when being used during the operation phase of produc- tion systems. Ideally, computer simulation models can be ge- nerated from various data sources in an enterprise by means of some automated processing pipeline. However, often the ge- neration and synchronization of the simulation model is quite cumbersome since many different and heterogeneous data sources from the production system need to be considered. Of- ten these raw data sources contain information and data ele- ments that are not required, not updated, inconsistent with each other and thus need to be integrated, transformed, and/or validated before being used for the simulation model generation. Especially the validation and/or consistency che- cking often takes time. Often inconsistencies are only detec- ted piece by piece during the modeling process which requires frequent exchange meetings between simulation experts and factory experts and development iterations of the simulation model. In the worst case, inconsistencies are detected during the productive use of simulation or not even at all, resul- ting in erroneous simulation results. It is therefore an objective of the present invention to im- prove the data validation of a production system for monito- ring an operability of the production system. The objective is solved by the features of the independent claims. The dependent claims contain further developments of the invention. 202213957 2 The invention provides to the first aspect an appa- ratus for monitoring an operability of a production system, the apparatus comprising: - an input unit configured to input production-related data of the production system, - a mapping engine configured to map the production-related data to instance data of a first knowledge graph according to a given mapping definition, - a first validation unit configured to validate a consisten- cy and/or an integrity of the instance data by means of de- clarative constraints and to output a first validation re- sult, - a simulator configured to generate a computer-implemented material flow simulation model of the production system based on the instance data and depending on the first validation result, - a generator configured to generate simulated production logs using the material flow simulation model, - a second validation unit configured to validate the simu- lated production logs against measured production logs of the production system and to output a second validation result, and - an output unit configured to output the second validation result for monitoring the operability of the production sys- tem. The invention provides according to a second aspect a comput- er-implemented method for monitoring an operability of a pro- duction system, the method comprising the steps: - inputting production-related data of the production system, - mapping the production-related data to instance data of a first knowledge graph according to a given mapping defini- tion, - validating a consistency and/or an integrity of the in- stance data by means of declarative constraints and to output a first validation result, - generating a computer-implemented material flow simulation model of the production system based on the instance data and depending on the first validation result, 202213957 3 - generating simulated logs using the material flow simulation model, - validating the simulated production logs against measured production logs of the production system and to output a sec- ond validation result, and - outputting the second validation result for monitoring the operability of the production system. Therefore, the invention provides an integrated use of an ex- plicit representation of simulation relevant data in a uni- form and reusable knowledge graph as a basis for a constraint catalogue that allows the automatic use of for example prede- fined SHACL validation constraints to factory instance data of this knowledge graph. Furthermore, after validation of the production-related data, simulation models can be automati- cally generated. Then, scenarios based on factory data in- stances and the further application of predefined SHACL vali- dation constraints provide a comparison of the real produc- tion data with the simulated data for checking the operabil- ity of the production system. By validating the input data and generating the simulation model depending on the valida- tion result, the simulation can be used to generate simulated production logs that can then be compared to measured produc- tion logs, therefore, allowing to monitor the operability of the production system. It is therefore an advantage of the proposed invention that it reduces the effort for the validation of raw production- related data required for plant simulation models during op- eration and therefore also their generation and application by using declarative constraints, like e.g., SHACL con- straints, for checking the data consistency of raw data re- quired for material flow models. Data inconsistencies can be easily detected. Therefore, material flow simulations can be more easily generated to compare simulated data with measured data from the production. This allows efficient monitoring of the production system during the operation leading to more 202213957 4 benefits such as of production KPIs like throughput, utilization rate, efficiency etc. Additionally, by using a knowledge graph or graph data model as a common representation, the effort for onboarding new da- ta sources or extending the data model by additional concepts is minimized, i.e., it is less time-consuming, less error- prone, more maintainable, and therefore more cost-effective. According to an embodiment the apparatus can further comprise a storage unit configured to store the simulated logs and/or the measured logs in a second knowledge graph. This allows straightforward comparison of the simulated with the measured data logs. The storage unit can be further con- figured to map or transform the simulated logs/log-files and/or the measured logs/log-files to instance log-data of the second knowledge graph. According to further embodiment of the apparatus the second validation unit can be configured to validate the simulated production logs against the measured production logs by means of declarative constraints. Declarative constraints can be understood to provide prede- fined rules for checking respective data. Therefore, data checks and/or comparisons can be automated. According to a further embodiment of the invention the de- clarative constraints can be based on the Shapes Constraint Language (SHACL). According to a further embodiment of the invention the de- clarative constraints can be based on the SPARQL Query Lan- guage. According to a further embodiment of the invention the pro- duction-related data can comprise production orders, a bill of processes, a bill of resources, and/or production events. 202213957 5 According to a further embodiment of the invention the pro- duction-related data can be checked depending on the first validation result. The first validation result provides information about the consistency and/or integrity of the production-related data. Therefore, the first validation result can for example summa- rize violations within the instance data. In case of a viola- tion, the original production-related data can be checked and for example reloaded or requested again. The validation re- sult can for example be exported as a report for an expert to notify about issues in the source data. In addition, a computer program product (non-transitory com- puter readable storage medium having instructions, which when executed by a processor, perform actions) having program in- structions for performing the aforementioned method according to embodiments of the invention is claimed, wherein the me- thods according to embodiments of the invention is performab- le by means of the computer program product each time. The invention will be explained in more detail by reference to the accompanying figures. Fig. 1: shows an embodiment of an apparatus for moni- toring an operability of a production system; and Fig. 2: shows an embodiment of a computer-implemented meth- od for monitoring an operability of a production system. Equivalent parts in the different figures are labeled with the same reference signs. Figure 1 shows an exemplary embodiment of an apparatus 100 for monitoring an operability of a production system SYS. A production system SYS can be for example an automated factory for producing or manufacturing a product. The apparatus 100 can comprise software and/or hardware components. In particu- 202213957 6 lar, the apparatus 100 can at least one processor. The apparatus 100 is preferably coupled with the production system SYS, e.g., to exchange data for monitoring the produc- tion system SYS. In particular, the apparatus 100 is config- ured to generate and run a computer-aided simulation of the production system, e.g., in parallel to the operation of the production system, to monitor the operability, e.g., produc- tivity, efficiency, performance, and/or functionality, of the production system SYS by comparing simulated with measured production logs. The apparatus 100 comprises components to monitor production- related data of the production system to allow conclusions about the operability of the production system SYS. For exam- ple, the apparatus 100 can provide information about perfor- mance of production lines, production progress, status of production etc. based on production logs of the production system. Such information gives insight into the operability of the production system, i.e., whether the production system works as specified. The apparatus 100 comprises an input unit 101, a mapping en- gine 102, a first validation unit 103, a simulator 104, a generator 105, a second validation unit 106, and an output unit 107. Furthermore, the apparatus 100 can comprise a stor- age unit 108. All these units/components are preferably con- nected with each other to exchange data. The input unit 101 is configured to input production-related data PD of the production system SYS. The production-related data can be raw data from different and/or heterogenous data sources related to the production system SYS. Data sources can be for example Enterprise Resource Planning (ERP) Sys- tems, Manufacturing Execution Systems (MES), file-based data (Excel, CSV, …) or other Engineering Tools (CAD, Layout De- signer, …). The production-related data PD can comprise pro- duction orders, a bill of processes, a bill of resources, and/or production events. The production-related data PD are preferably provided in machine-readable formats, e.g., Excel, 202213957 7 CSV files, etc. The related data PD are provided to the mapping engine 102. The mapping engine 102 is configured to map the production- related data PD to instance data KGD of a first knowledge graph model according to a given mapping definition. Mapping the production-related data PD to instance data KGD can in particular involve selecting required parts of the produc- tion-related data PD and/or transforming the production- related data PD to a data format that is required by knowledge graph. The mapping definition comprises rules for mapping the data to the knowledge graph according to a prede- fined schema. In other words, the mapping engine maps the raw data to graph instance data (e.g., RDF) that is aligned with the reusable schema of a knowledge graph model. For the mapping engine 102 existing technologies can be used, e.g., OpenRefine, OntoRe- fine, RMLMapper. A mapping definition, that defines how the raw data is mapped to graph instance data can be provided per data source in a descriptive manner depending on the chosen technology for the mapping engine 102, e.g., General Refine Expression Language (GREL) or RDF Mapping Language (RML). The instance data KGD can be sent to and stored in the graph database DB. The instance data KGD can then be retrieved from the graph database DB by other units. Alternatively, the in- stance data KGD can be provided to the respective other units. The first validation unit 103 is configured to validate a consistency and/or an integrity of the instance data KGD by means of declarative constraints DC and to output a first validation result VAL1. The declarative constraints DC can be for example based on the Shapes Constraint Language (SHACL) or on the SPARQL Query Language. The first validation unit 103 validates the instance data KGD in the graph database by performing consistency and integrity 202213957 8 checks. The first validation 103 is preferably backed by a catalog of validation rules, constraints, and/or conditi- ons. Hence, by applying for example such validation rules, constraints and/or conditions, the consistency and integrity of the instance data KGD can be checked. These validation constraints can be provided by means of e.g., SPARQL queries or SHACL shapes. The catalog can contain a default set of va- lidation rules that is applicable generically to every use case as well as a set of user-provided rules and constraints that can be use-case/customer specific. The first validation result VAL1 can be output, e.g., for checking the production-related data PD. The first validation result VAL1 can be for example exported as part of a report comprising information about the consistency and/or integrity of the input data PD. Depending on the validation result VAL1, the instance data KGD can be provided to the simulator 104. For example, if the validation result VAL1 provides no violation of the con- sistency and/or integrity of the instance data KGD within a given uncertainty range, the instance data KGD can be re- trieved by the simulator 104. The simulator 104 is configured to generate a computer- implemented material flow simulation model SM of the produc- tion system SYS based on the instance data KGD and depending on the first validation result VAL1. Therefore, if the pro- duction data are correct and consistent within the given un- certainty range, the simulator 104 automatically generates the simulation model SM using the master data (e.g., Ma- chines, Orders, Products) from graph database DB. Based on the generated material flow simulation model SM, the generator 105 is configured to generate simulated production logs/log-files SLOG. Preferably, the generator creates simu- lated production logs SLOG by using the generated material flow simulation model SM and a subset of given production or- ders and/or other dynamically changing data elements (e.g., 202213957 9 the availability of the and machines as well as the current status of the production). Simulated logs SLOG typi- cally comprise the start and end time of production processes (process steps), the order and product they belong to and the resource where the process was executed on. Sometimes also more information like the personnel involved or additional tools and equipment is added. The simulated production logs/log-files SLOG as well as respective measured logs/log-files MLOG can then be uploaded to the graph database DB. The measured logs MLOG can be pro- vided for example by sensors of the production system SYS. Preferably, the measured production logs MLOG correspond to the simulated production logs SLOG in e.g., time range of production process etc. In particular, the storage unit 108 is configured to store the simulated logs SLOG and/or the measured logs MLG in a second knowledge graph. Therefore, the storage unit 108 can be configured as a mapping engine for mapping the simulated logs SLOG and/or the measured logs MLOG to graph data of a second knowledge graph. Then, the second validation unit 106 validates the simulated production logs SLOG against the measured production logs MLOG by means of declarative constraints. Preferably, the simulated production logs SLOG are validated against the measured production logs MLOG by means of SHACL or SPARQL constraints applied to the second knowledge graph. The second validation unit 106 provides a second validation result VAL2 comprising information about deviations of the measured production logs MLOG from the simulated production logs SLOG. For example, the second validation result VAL2 can comprise information about no deviation between simulated and measured production logs, pointing out full operability of the production system SYS. Alternatively, the second valida- tion result can comprise information about specific devia- tions of the measured production logs MLOG from the simulated production logs, pointing out possible production failures or problems. 202213957 10 The second validation result VAL2 is provided by the output unit 107 to a user and/or to the production system SYS and can be used for monitoring the operability of the production system SYS. For example, based on the second validation re- sult VAL2, the production can be continued or at least par- tially stopped or interrupted. Therefore, it is possible to provide the second validation result VAL2 to a control unit of the production system SYS for controlling the production system SYS depending on the second validation result VAL2. For example, in case of an inconsistency between the measured production logs MLOG and the simulated production logs SLOG, the production system SYS or the affected part of the produc- tion system SYS can be stopped or decelerated. Preferably, the apparatus 100 can process production-related data as described above in iterative steps for predefined time spans. This allows continuous monitoring of the produc- tion system SYS. Figure 2 shows an exemplary example of a computer-implemented method for monitoring the operability of a production system. The method can be for example performed by an apparatus as described by means of Figure 1. The method comprises the following method steps: In a first step S1 production-related data of the production system can be input, e.g., read in from data sources connect- ed with the production system. In the next step S2 the production-related data are mapped to instance data of a first knowledge graph according to a given mapping definition. Therefore, the production-related data are assigned to the knowledge graph according to a predefined mapping definition. In the next step S3, a consistency and/or an integrity of the instance data is validated by means of declarative con- 202213957 11 straints, e.g., SHACL or constraints, and a first val- idation result is provided. In case of a positive first validation result, i.e., con- sistent and valid instance data according to the first vali- dation, in the next step S4 a computer-implemented material flow simulation model of the production system can be gener- ated based on the instance data. In case of a negative first validation result, i.e., for ex- ample inconsistent instance data, the first validation result is provided, step 13. It is then possible to further check the instance data/the production-related data and for example request new/updated production-related data and repeat steps S1 to S3. In the next step S5, in case of a positive first validation result, simulated production logs are generated using the ma- terial flow simulation model. The simulated production logs can then be stored in a second knowledge graph. In addition, measured production logs from the production systems can be retrieved and also stored in the second knowledge graph. In the next step S6 the simulated production logs are vali- dated against measured production logs using declarative con- straints, e.g., SHACL or SPARQL constraints, and a second validation result is provided. In the next step S7 the second validation result is provided for monitoring the operability of the production system. All of the described and/or drawn features as shown by the embodiments can be advantageously combined within the scope of the invention. Although the present invention has been described in detail with reference to the preferred embodiment, it is to be un- derstood that the present invention is not limited by the disclosed examples, and that numerous additional modifica- 202213957 12 tions and variations could made thereto by a person skil- led in the art without departing from the scope of the inven- tion.