Adoption View
Quality KIT
Vision & Mission
Vision
Our vision is to move from parts-based quality management on a bilateral level between supplier and customer to a data-based approach across the OEM-n-Tier chains of value creation to enable a network approach for producing and ensuring product quality.
The Quality KIT enables data provider and consumer to exchange and analyse existing data across company boundaries on a daily basis, securely and easily. By merging the OEM's field quality data and the supplier's product information, a new level of transparency is achieved in a joint analysis (single point of information). This leads to earlier failure detection, faster cooperation between partners and acceleration of root cause analysis. Once the root cause of the problem is known, corrective actions can be targeted to the products and vehicles that have the quality problem using Catena-X traceability capabilities. The containment minimises the number of parts & vehicles for which corrective actions need to be implemented.
This KIT enables quality app provider to deliver solutions for quality data analysis including tracebility and notification functionalities.
Mission
The Quality KIT provides the necessary standards, aspect models, technical data pipeline specifications and business logics on how to estabilish a soverein exchange of quality related data along the OEM-Tier n chain. All the components mentioned are based on the following principles:
- We bring together data from suppliers and manufacturers (OEM).
- Data exchange between data provider and consumer complies with the Catena-X network's data sovereignty principles.
- Data exchange enables each partner to use the applications of their choice for data analysis features like Early Warning and Root Cause Analysis.
- We standardize data models incl. their assets
- Data exchange in the current Quality KIT version is described as a common requirement.
- Analysis methods and algorithms that are realized in the quaity applications are not part of any standardization. It is desirable that different tools deliver different results Specialisation of tools is valuable.
In sum this KIT enables quality management to substantially increase speed in resolving quality problems and reach a new level on transparency and traceability.
Customer Journey
With the Quality KIT, we support the Catena-X customer journey for our adopters and solutions providers.
Business Value
Through the standardized specifications described in the “Quality-KIT” – mainly the semantic models and data exchange process – data providers & consumers can build up a soverreign and trusty data exchange pipeline with their partner companies and reduce investment and implementation costs to integrate data based quality processes in their company inhouse process and IT landscape.
Furthermore, quality application providers can also reduce the implementation effort and enter potential new markets providing specific analytic capabilities.
Use Case
Status Quo / Today's challenge
In today’s global and complex collaboration models quality does not emerge as the sum of the quality contributions of the individual partners in the value chain of OEM and suppliers, but rather because of the networking of the partners involved.
The existing conventional bilateral working models do not account for this. There is no operative network in the industry with a substantial coverage of elements of the value chains that provides the necessary means for collaborative quality management with all involved partners.
From Quality Management perspective, the main challenge within the automotive industry is to define and implement inter-organizational end-to-end data chains across the whole automotive partner chain to empower data driven quality use cases.
Main challenges to ensure a trustful and scalable cooperation are:
- Trustful and sovereign data exchange mechanism including ...
- legal contracts and access/usage policy framework along the complete data chain
- Standardized data pipeline
- Aligned standard data exchange, e.g. file format and transfer
- Standardized data models
Benefits
OEM and large automotive suppliers
The Quality KIT from Catena-X enables companies to realize trustful and sovereign data exchange with their partners and utilize the data in a cooperative way for an Early Warning of known and unknown quality issues. Root causes can be analysed und understood much faster what leads to an earlier and focussed counter measure. In sum companies can realize economic benefit by reduction of warranty costs while at the same time increasing customer satisfaction due to a maximum availability of vehicles, products and services.
SME
The defined standards like data models and data exchange pipelines enforce a flexible and low-barries approach to integrate quality use cases and features according to SME need. An easy access to analytic capabilities or transparent analytic results from partner companies leads to an economic benefit from warranty costs reduction via faster on more focused activities related to quality issues.
Solution Provider
Solution providers have the potential to scale customer groups via platform effects and standardization of data models and their exchange. Additional new market potentials can be accessed via marketplace and shared service network.
Example: Benefits of using early warning and root cause analyses in active field monitoring of a vehicle component
OEM A and supplier B agree to carry out quality analyses with field data from the OEM and production data from the supplier based on Catena-X Use Case Quality Methodology (live control loop see above) and with Catena-X-certified tools. For this purpose, a quality case with framework conditions is agreed to in the use case. A component and the associated data are selected. After technical and organizational onboarding and the agreed data exchange, the joint analysis room is available and collaborative quality work can be started.
In general, one of the partners carries out continuous monitoring of the components using the common database. This allows, for example, error messages in the vehicle, repairs and claims to be monitored and anomalies are immediately visible.
In our example, an engine component passes on various error messages (DTCs = Diagnosis Trouble Codes) to the vehicle via the central engine control unit. After 4 weeks, it is visible in the Catena-X certified tool that a DTC in the field is slowly but steadily increasing. With Catena-X Tooling, this is immediately recognized, although no increasing workshop visits and repairs are yet visible in the database. An employee of a partner immediately notices this and shares this observation with the joint team. At the same time, the employee begins to clarify through initial analyses whether the anomaly is actually a problem. Since it quickly becomes clear from the data that this is a potentially critical fault pattern with the result of increasing repair cases and that a replacement of parts may be necessary, the employee reports this to the joint team (early warning).
The team decides to carry out a root cause analysis together. Various hypotheses about the cause of the fault are examined: running times are compared, software levels, environmental conditions at the time the fault occurred, etc. The cause of the fault is a diagnostic algorithm modified in a software update, which results in the abnormal DTC appearing more often in the field at hot temperatures. This is caused by the production of vehicles from a certain point in time with the new software version or the installation of a new software version for vehicles in the field, e.g. during a service visit to a workshop.
As a jointly defined corrective measure between OEM and supplier, a modified algorithm will be integrated into the next regular software update. This starts as soon as possible in vehicle production and vehicles with the faulty software version receive a software update the next time they visit the workshop. For this purpose, repair shops are informed that the displayed error (DTC) for a particular software version is a software problem and does not require any repair. This minimizes costs due to unnecessary repairs.
The affected component continues to be monitored regularly. After a few months, there is a decrease in the conspicuous DTC corresponding to the reduction in the number of vehicles in the field with the faulty software version (proof of effectiveness of the corrective measure adopted).
The image below shows user feedback, challenges, results and benefits of the new data-based way of working using the example of the Early Warning & Root Cause Analysis process steps.
Conclusion:
The example impressively shows that with the Catena-X methodology (live control loop), quality problems can be identified earlier, the causes of faults can be found more quickly, corrective measures can be carried out in a more targeted manner and the affected vehicles can be narrowed down more precisely. There are similar examples of the conversion of production parameters at the supplier or design errors in the design of vehicle components.
(Source: The example is based on real project results from piloting the Catena-X methodology at an OEM with 5 selected suppliers)
Tutorials
The following videos gives an overview of the presented Quality Improvement Use Case.
Overview about how Quality Management is improved by Catena-X
For more technical details take a look at the video in the Operation View
Data driven Quality Management with Catena-X - Statements from the consortial partners
Semantic Models
Semantic Structure
Overview Data Model Entities
Download for MS Excel: Quality_KIT_DataModelOverview_v1.0.xlsx
Quality Task
Quality Task is the root element and describes why companies are working together on a quality topic and what they want to do. All involved companies and their contact people are named. In addition, a flag tells what should be done with exchanged data after a Quality Task is closed. A Quality Task (qTask) can be created by both OEM or Supplier and defines why data is exchanged between two or more companies and what insights should be generated out of the transferred data. In addition, there is a flag what happens with the transferred data when this qTask is closed.
Remark: The table contains an overview about the data content as explanation. For the implementation of the valid entity naming and semantic structure please reference to the model definition in Github.
| Entity name | Entity description | Example |
|---|---|---|
| qualityTaskId | An unique quality task identifier for this quality task. Each company generates their own quality task ids using the Catena-X business partner number. | BPN-811_2022_000001 |
| status | Status of this quality task | new |
| creationDate | Timestamp when this quality task was created | 2019-04-01T14:00:00 |
| title | Working title for this quality task | Early Warning A |
| description | Description what should be done in this quality task | Early Warning of vehicle model A |
| component | The component that should be monitored or investigated in this quality task | ComponentA |
| dataDeletion | What should be done with the data after this quality task is closed | delete-data-after-closing |
| cxBPN | Catena-X Business Partner Number (BPN) of the involved company | BPN-8110 |
| name | Name of the involved company | testCompanyA |
| E-Mail of the key contact at involved company | Horst.Schlemmer@testCompanyA.de |
Github Link to semantic data model: CX-00036 Quality Task
Quality Task Attachment
Quality Task Attachment gives the ability to share data that is not standardized in an existing semantic model yet. Non standardized data provisioning is realized as a file transfer. The model contains file parameters and the schema of structured data in the provided file. A Quality Task Attachment can be provided by both OEM or Supplier.
Remark: The table contains an overview about the data content as explanation. For the implementation of the valid entity naming and semantic structure please reference to the model definition in Github.
| Entity name | Entity description | Example |
|---|---|---|
| qualityTaskId | Reference to a Quality Task: A unique identifier. The company creating this quality task sets this identifier. The identifier should contain the BPN to make it unique insidethe CX network. | BPN-811_2022_000001 |
| relatedModelType | Name of the semantic data model, that the attachment belongs to. | fleet diagnotic data |
| fileDescription | Description of the file content | Fleet environmental conditions |
| fileName | Name of the provided file | Histogramm_data.csv |
| sizeInKb | Size of the provided file in KiloByte | 615 |
| fileExtension | Extension of the provided file | csv |
| filePath | Path of the provided file - If file is provided in a folder structure | /subfolder/Histogramm_data.csv |
| delimiter | Delimiter that separates column values in a tabular form like e.g. a "csv" file | semicolon |
| unit | Physical unit of each variable in a tabular schema | degreeCelsius |
| variableName | Name of each variable in a tabular schema | Ambient temperature |
| dataType | Data type of each variable in a tabular schema | double |
| variableDescription | Description of each variable in a tabular schema | This column contains the hourly ambient temperature |
| decimalSeperator | Seperator in a decimal number | comma |
Github Link to semantic data model: CX-00092 Quality Task Attachment
OEM Data: Data structured in the following semantic models are to be delivered by OEM.
Fleet Vehicles Product Description
Master data for each vehicle of a specific population - from an end customer view. This model represents the vehicle as it was sold to the customer. All entities and properties are constant over the lifetime of the vehicle.
Remark: This semantic model contains of two models that are standardized in CX-00091, containing the vehicle population (listOfVehicles) and CX-00037 containing the data entities. The table contains an overview about the data content as explanation. For the implementation of the valid entity naming and semantic structure please reference to the model definition in Github.
| Entity name | Entity description | Example |
|---|---|---|
| anonymizedVin | OEM-specific hashed VIN; link to car data over pseudonymized/hashed VIN or Catena-X unique digital twin identifier | 3747429FGH382923974682 |
| class | Class of the vehicle | A |
| driveType | Drive type of a vehicle according to enumeration | All-Wheel Drive (AWD) |
| emptyWeight | The empty weight of the vehicle in kg as specified | 2000 |
| modelDescription | Detail vehicle model, e.g. "Golf VIII" | CX test model 2 |
| modelIdentifier | OEM-specific model identifier or OEM-specific project name | 689-G8 |
| steeringPos | Position of vehicle steering wheel (e.g. left or right) | Left-Hand Drive (LHD) |
| catenaXId | A fully anonymous Catena-X identifier that is registered in CX Digital twin registry. Can be used for vehicles, parts, workshops, etc. | 580d3adf-1981-44a0-a214-13d6ceed9379 |
| vehicleSeries | Vehicle series, normally one level above model. E.g. vehicle series ="Golf", vehicle model="Golf VIII" | Series1 |
| systemPower | Complete power of this vehicle in KW | 110 |
| hybridizationType | Kind of the hybridization in this vehicle | battery electric vehicle |
| softwareCategory | Some OEMs bring in the software as a complete package for all systems. To identify this software, software category and software version is needed. Software category when his car was built | TZGH64738 |
| softwareVersion | Some OEMs bring in the software as complete package for all systems. To identify this software, software category and software version is needed. Software version when his car was built | 3.4.9837.567 |
| cxBPN | Catena-X business partner number of the OEM company | urn:uuid:4789d3adf-cax_qax1-_oem-13d6ceed9379 |
| wmiCode | Short name/code of the vehicle manufacturer according to world manufacturer information(wmi). The wmiCode is the first 3 chars of the vehicle identification number. | CAX |
| wmiDescription | Name of OEM according to NHTSA or other authorities. Has to be compliant with linked wmiCode attribute. | CatenaX Test OEM |
| colorId | Color code describes the code of a specific color of one vehicle | LY7W |
| colorDescription | Color name describes the color of the color code as a written word | Light grey |
| numberOfDoors | Describes the number of doors of a vehicle | 5 |
| kbaBody | Vehicle variant - Body shapes according to German KBA | Limousine |
| nhtsaBody | Vehicle variant - Body shapes according to US NHTSA | Cargo Van |
| equipmentDescription | The equipment variants description | Sport seats |
| equipmentIdentifier | The identifier of a specific equipment | SDCF34 |
| group | Grouping the special equipment into categories like (e.g. interior) | Interior |
| plantDescription | Long name of the production plant of the vehicle | Wolfsburg |
| plantIdentifier | Plant id of the final assembly of the vehicle | 4711 |
| productionDate | Production date of the vehicle | 2018-01-15T00:00:00 |
| countryCode | Vehicle sold country in ISO 3166 alpha 3 | DEU |
| countryGroup | Region where this car was sold | Europe |
| soldDate | Sold date of the vehicle = warranty start date for this vehicle | 03.02.2018 |
| engineDescription | Description of the engine | 2.0 Diesel |
| engines.engineId | OEM-specific identifier/type of the installed engine | Type100 |
| engineProductionDate | Date when the engine was produced | 2017-10-20T00:00:00 |
| engineSeries | Engine series | Series10 |
| installDate | Date when the engine was installed | 2018-01-10T00:00:00 |
| power | Engine power is the power that an engine can put out | 110 |
| serialNumber | serial number of the installed engine | b11c7587a |
| size | Cubic capacity in a combustion engine - not available in battery-electric vehicles | 1998 |
| kbaFuelType | Description of the fuel according German KBA | Diesel |
| nhtsaFuelType | Description of the fuel according US NHTSA | Diesel |
Github Links to semantic data models:
CX-00037 Vehicle Product Description
Fleet Diagnostic Data
Data model for vehicle diagnostic data suitable for mass data transfer. Diagnostic data coming from multiple vehicles that are affected by an quality issue + Diagnostic data from similar vehicles that are not affected by an quality issue.
Remark: The table contains an overview about the data content as explanation. For the implementation of the valid entity naming and semantic structure please reference to the model definition in Github.
| Entity name | Entity description | Example |
|---|---|---|
| workShopId | OEM internal workshop ID | 8632208 |
| type | Indicator whether this DTC was stored as error or Info | Error |
| swVersion | Current version of the software on this ECU | AA |
| swPartNumber | SW part number of this ecu | SW_A |
| softwareVersion | Software version of this car during the session - only available for OEMs that have a software category on vehicle level | 3.5.0001.001 |
| softwareCategory | Software category of this car during the session - only available for OEMs that have a software category on vehicle level | TZGH64738 |
| sessionId | Format is OEM-specific: A unique session identifier within one OEM. | APD5889H7J6OZV5KR80D0D470833L0A_20190407 |
| readOutDate | Date when this ECU information was read out from the diagnostic session | 2022-10-12T03:59:00 |
| qualityTaskId | A unique quality task identifier, where these lists of session data belong to. Optional to ensure that also diagnostic data without quality task can be exchanged. | BPN-811_2022_000001 |
| occurenceMileage | Mileage in km when the DTC occurred the first time | 30 |
| occurenceDateTime | Date and time when the DTC occurred the first time/was recorded the first time in the ECU | 2022-01-30T14:48:54 |
| occurenceCounterTotal | Counter how often this DTC was set in total | 22 |
| name | Name of ECU | ABS |
| mileage | Current mileage counter of the car during the diagnostic session | 120 |
| measurementUnit | The unit of measurement for the environment condition value. | rpm |
| longitude | Longitude of this workshop | 53,14968808 |
| latitude | Latitude of this workshop | 17,23471843 |
| isMilOn | Describes whether this DTC set the MIL (malfunction indicator light) in the dashboard | true |
| hwVersion | Hardware version of ECU | V1 |
| hwPartNumber | Hardware part number of ECU | HW_A1 |
| fullName | Combined string of DTC name plus the so called DTC sub type or DTC failure byte. Both string values are concatenated using a "-" as eparator. DTC name is: B|C|P|U + 4 hex chars DTC failure byte: 2 hex chars In some rare cases this could be just a hex string | P0001-00 |
| fullDescription | Description of DTC and failure byte. Both description strings are concatenated using a "-" as separator | Catena-X test dtc 1 |
| freezeFrame | Undecoded freeze frame from ECU. The freeze frame records many parameters of the DTC and surrounding parameters like outside temperature when the DTC was set. It is a very long HEX string with many OEM-specific and ECU-specific content in | Example_freeze_frame |
| faultPathDescription | OEM-specific description of DTC fault path | Shortage to plus |
| faultPath | OEM-specific: Fault path for this DTC. Allows further analysis | 1000761 |
| eventValue | The value of this event. For example, the calibration file used. | CAL366474-4848 |
| eventId | OEM-specific: Primary key for this event | ABS_CAL1234 |
| eventDescription | The description of the event | Calibration of ABS ecu with calib file - see value |
| eventCreationDate | Date and time when this event occured | 2022-10-12T03:59:00 |
| ecuSerialPartNumber | Unique serial number of ECU | 60284BD6790 |
| ecuSerialPartNumber | Serial number of ECU | 60284BD6790 |
| ecuSerialPartNumber | Serial number of ECU | 60284BD6790 |
| ecuSerialPartNumber | Serial number of ECU | 60284BD6790 |
| dtcList.state | OEM-specific state of DTC: 0;1 (permanent/temporary/intermediate), could also be a string with permanent, temporary, intermediate, etc. | permanent |
| dtcHexValue | Hex value of this DTC | 4337499FF |
| dtcHexValue | Hex value of this DTC | 4337499FF |
| dtcHexValue | Hex value of this DTC | 4337499FF |
| description | Long name of ECU | Anti-blocking control unit |
| creationDate | Date-timestamp for this session according to ISO 8601 when this session was created. Depending on OEM this attribute reflects the start or end date of one diagnostic session. | 2022-02-04T14:48:54 |
| countryCode | Country code in ISO 3166-1 alpha-3 codes, where this session took place | DEU |
| conditionValue | The numeric value (if applicable) of the stored environment condition at the time of the DTC. | 4000 |
| conditionId | OEM-specific: Primary key for this condition consists of unique identifier of env. condition and DTC | DTC1_EnvCond1 |
| conditionDescription | The description of the environment condition/information | RPM |
| conditionCreationDate | Date and time when this condition/information was created. | 2022-10-12T03:59:00 |
| catenaXId | A fully anonymous Catena-X identifier that is registered in the C-X Digital twin registry. This property can be used for vehicles, parts, workshops, etc. Optional: Not always available | urn:uuid:f5a1a3e716-cax_-qax1-test-1a8c38ea27 |
| catenaXId | A fully anonymous Catena-X identifier that is registered in the C-X Digital twin registry. This property can be used for vehicles, parts, workshops, etc. Optional: Not always available | urn:uuid:b11c7587af-cax_-qax1-car-f810a2cadc |
| assemblyPartNumberVersion | OEM-specific ECU assembly version | 1 |
| assemblyPartNumber | OEM-specific ECU assembly from hardware and software | V039352784 |
| anonymizedVIN | OEM-specific hashed VIN; link to car data over pseudonymized/hashed VIN or Catena-X unique digital twin identifier | APD5889H7J6OZV5KR80D0D470833L0A |
Github Link to semantic data model: CX-00038 Fleet Diagnostic Data
Fleet Claim Data
Customer complaints that are linked to this QualityTask +Data about the exchange of potentially faulty parts.
Remark: The table contains an overview about the data content as explanation. For the implementation of the valid entity naming and semantic structure please reference to the model definition in Github.
| Entity name | Entity description | Example |
|---|---|---|
| claimId | Claim ID is unique for each OEM | B798JI26D |
| qualityTaskId | Reference to a Quality Task: A unique identifier. The company creating this quality task sets this identifier. The identifier should contain the BPN to make it unique inside the CX network. | BPN-811_2022_000001 |
| listOfDiagnosticSessionId | References to a list of diagnostic session IDs | APD5889H7J6OZV5KR80D0D470833L0A_20190407 |
| repairMileage | Mileage of the car when the claim was reported | 120 |
| repairDate | References the date when the claim was initially reported | 43562 |
| technicianComment | Short description of the claim from the technician | Technician comment |
| customerComment | Short description of the claim from customer view (vehicle owner) | Customer comment |
| damageCode | OEM-specific damage code | G300 |
| vehicleCatenaXId | Catena-X car ID /digital twin of car | urn:uuid:b11c7587af-cax_-qax1-car-f810a2cadc |
| anonymizedVIN | OEM-specific hashed VIN; link to car data over pseudonymized/hashed VIN or Catena-X unique digital twin identifier | APD5889H7J6OZV5KR80D0D470833L0A |
| isPartReplaced | Flag is set if part was replaced. true: replaced false: not replaced | true |
| isPartCausal | Flag set to true if part was causing the problem. true: part caused the problem. false: part did not cause the problem. | true |
| amountOfReplacedParts | Counter for non-serial parts which have been replaced | 1 |
| replacedPart.name | OEM specific name of the part | zehn |
| replacedPart.number | OEM specific part number | 8D34393E7FFE |
| replacedPart.catenaXId | A fully anonymous Catena-X identifier that is registered in C-X Digital twin registry. This property is being used for vehicles, parts, workshops, etc. Optional, not always available. | urn:uuid:b11c7587af-cax_-qax1-part-f810a2cadc |
| replacedPart.serialNumber | OEM serial part number of the part - only available for serial parts | 1 |
| replacedPart.supplierId | OEM-specific ID of the supplier that manufactured the part that was put out - available if known | ZF2064600502 |
| sparePart.name | OEM specific name of the part | zehn |
| sparePart.number | OEM specific part number | 8D34393E7FFE |
| sparePart.catenaXId | A fully anonymous Catena-X identifier that is registered in C-X Digital twin registry. This property is being used for vehicles, parts, workshops, etc. Optional, not always available. | urn:uuid:b11c7587af-cax_-qax1-part-f810a2cad6 |
| sparePart.serialNumber | OEM serial part number of the part - only available for serial parts | 1000 |
| sparePart.supplierId | OEM-specific ID of the supplier that manufactured the part that was put in - available if known | ZF2064600502 |
Github Link to semantic data model: CX-00039 Fleet Claim Data
Supplier Data: Data structured in the following semantic models are to be delivered by Supplier (Tier n).
Manufactured Parts Quality Information
A selection of manufacturing-related parameters that help to solve a quality issue.
Remark: The table contains an overview about the data content as explanation. For the implementation of the valid entity naming and semantic structure please reference to the model definition in Github.
| Entity name | Entity description | Example |
|---|---|---|
| catenaXId | The fully anonymous Catena-X ID of the manufactured part only available after digital twin registry is fully operational | urn:uuid:b11c7587af-cax_-qax1-part-f810a2cadc |
| qualityTaskId | A unique quality task identifier where this manufacturing information belongs to. Optional to ensure that there is also data exchange without having a quality task. | BPN-811_2022_000001 |
| manufacturerId | Identifier assigned by the manufacturer for this specific part. In case of common parts: This identifier is not unique. | 123-0.740-3434-A |
| manufacturerSerialPartNumber | Serial part number given by the manufacturer. Not available for common parts. | 436347347.4343884384.FTG.000001 |
| nameAtManufacturer | Name of the manufactured part as given by the manufacturer | zehn_Supplier |
| date | Production date of the component | 2018-10-01T14:24:00 |
| country | Country code where the part was manufactured | DEU |
| plantId | Manufacturer-specific identifier of theproduction plant of this part | 00001 |
| plantDescription | Manufacturer-specific description of the production plant of this part | Supplier_Plant_1 |
| batchId | Manufacturer-specific batch identifier: In which batch was this part manufactured | 20181001_14 |
| productionLine | On which production line was this part produced | Line_1 |
| hasBeenReworked | Indicator whether this part was reworkedduring manufacturing and before delivery | FALSE |
| numberOfConductedEOLTests | Number how often this part went through the EOL test | 1 |
| addtionalInformation.key | Key identifier for this additional information | SteelQuality |
| addtionalInformation.value | Value for this additional information | StainlessSteel |
Github Link to semantic data model: CX-00041 Manufactured Parts Quality Information
Parts Analyses
Analyses results of replaced and potentially faulty parts, that are linked to this Quality Task.
Remark: The table contains an overview about the data content as explanation. For the implementation of the valid entity naming and semantic structure please reference to the model definition in Github.
| Entity name | Entity description | Example |
|---|---|---|
| anonymizedVin | OEM-specific hashed VIN; link to car data over pseudonymized/hashed VIN or Catena-X unique digital twin identifier | 3747429FGH382923974682 |
| manufacturerAnalysisID | Component manufacturer specific identifier of the analysis process | TIER-647439403403 |
| customerAnalysisID | Customer specific identifier of the analysis process | OE-43673473438 |
| catenaXIdentifier | The fully anonymous Catena-X ID of the analyzed part - only available after digital twin registry is fully operational | urn:uuid:580d3adf-1981-44a0-a214-13d6ceed9000 |
| qualityTaskId | A unique quality task identifier to which this list of parts analysis belongs to | BPN-811_2022_000001 |
| manufacturerPartIdentifier | Part Id of the analyzed part as assigned by the manufacturer of the part. The Part Id identifies the part type and is not unique for each serial part. | 123-0.740-3434-A |
| manufacturerSerialPartNumber | Serial Part Number of the analyzed part as assigned by the manufacturer of the part. The serial part number is unique for each serial part. Not available for all kinds of parts | 436347347.4343884384.FTG.000001 |
| customerPartIdentifier | Part ID as assigned by Original Equipment Manufacturer (OEM) | 8D34393E7FFE |
| nameAtManufacturer | Name of the analyzed part as assigned by the manufacturer of the part | zehn_Supplier |
| status | Status of this part analysis | new |
| isDefect | True: Analysis turned out that analyzed part is defect according to part's specification. | false |
| resultsDescription | Detailed description of part analysis results | Corrosion on component part_A |
Github Link to semantic data model: CX-00040 Parts Analyses
Logic & Schema
Business Logic
The prerequisite for faster faster problem solving is the earliest possible detection of a problem (early warning) and the fastest possible understanding of the error chain and cause (root cause analysis). Early Warning in general has to be realized at all relevant points along the value chain.
Early Warning in the Field, an early warning system for issues in a vehcile fleet, enables the earliest possible detection of quality problems in products in vehicles after delivery. Vehicle data from the OEM is used for the analysis, in particular fault codes that are stored in ECUs and read out during a workshop visit or frequently "over the air". Increases in product-specific fault codes across the vehicle population provide a reliable indicator of quality problems much earlier than through parts replacement and analysis.
Early Warning in the Production focuses on early detection in the production of products. Various practical scenarios have been developed and the corresponding technical requirements specified. If, for example, a supplier discovers that a delivered product has a quality defect, the customer can be informed by means of notification. The functionality of traceability (Catena-X Use Case Traceability) in the supply chain makes it possible to trace in which vehicle or follow-up product the affected components are installed. Remedial measures can thus be applied specifically to a limited quantity.
If a problem is detected by early warning in the field or in production, a data-based Root Cause Analysis is started. The aim is to derive hypotheses regarding the cause and effect relationship from the shared database of the customer and supplier and to verify them together. With the Catena-X network functions, this transparency can be achieved much faster. If the root cause is known more quickly, effective counter measures can be defined and implemented much faster.
Architecture Overview
The tier-1 receives data on vehicle master data, existing claims and DTCs. Once the data is received, the Tier-1 supplier is analyzing the data in order to detect patterns based on which DTCs and claims can be explained. The data is shared and consumed as assets via the companies' EDC while the authorization is managed via the the shared services of the consortia.
Quality Components
| Subsystem | Description |
|---|---|
| Data Provisioning | This component provides a company's data to the Catena-X network by transforming it into the Catena-X format and publishing it. In Catena-X, data must be provided to the network based on existing standards from the other Kits. One example that can be used is the Connector Kit that builds a component based on the IDS protocol, e.g. the Connector of the Eclipse Dataspace Components (EDC). The data format used for Quality data is based on the aspects (Sub-)models published in the Semantic Hub. |
| Internal Systems | Existing internal systems of a Catena-X partner which provide data to Quality components. - For Data Provisioning: The data provided to Catena-X via the EDC should be fetched from a partner's internal system. e. g. quality claims, defect code collection system |
| Quality App | Enables traceability functionalities like quality alerts or notifications. When a Traceability App fetches data for digital twins (submodels), there are two options: - Directly access the partner's EDC (and the Digital Twin Registry) to connect to other partner's EDC and retrieve the data from ther - Use a local IRS service to get the data and let the IRS handle the EDC and Digital Twin Registry communication. |
Catena-X Core Services
| Subsystem | Description |
|---|---|
| Eclipse Dataspace Components (EDC) | The Connector of the Eclipse Dataspace Components provides a framework for sovereign, inter-organizational data exchange. It will implement the International Data Spaces standard (IDS) as well as relevant protocols associated with GAIA-X. The connector is designed in an extensible way in order to support alternative protocols and integrate in various ecosystems. Repository of the Catena-X specific EDC. |
| SSI → MIW | The Self-Sovereign Identity is also a life long identity,( when credentials are created and the MIW is not reachable) , the other verifiers should be able to check and validate exisiting valid credentials from distributed databases, directory or DLT. The MIW (also called "Custodian") provides a private/public key pair and related DID for a legal entity along with the onboarding. |
| Discovery Service | The EDC / dataspace discovery interface is a CX network public available endpoint which can get used to retrieve EDC endpoints and the related BPNs, as well as search for endpoints via the BPN. |
Business Process
To realize the Business Logic described in the Quality Kit
all steps of the Business Process (described in the Development View), like data provisioning and consuming by the involved partner companies, are realized in compliance with the Catena-X Data Governance Framework. Under this link you can find the latest version of the framework regulations as download. The documents are seperated in the following levels:
Data Space Level: 10 Golden Rules of Catena-X
Use Case Level: Quality Management specific policy (not released yet)
Data Offering and Usage Level are defined by bi-lateral aligned policies and contracts between the cooperating partner companies. Content is currently in definition.
Standards
Our relevant standards can be downloaded from the official Catena-X Standard Library: