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1 Functions
2 Reference Architecture
Technical Specification: Compression and Understanding of Industrial Data (MPAI-CUI) – Company Performance Prediction (CUI-CPP) V2.0 assumes that workflows are based on Technical Specification: AI Framework (MPAI-AIF) V2.1 specifying the standard AI Framework (AIF) that enables initialisation, dynamic configuration, execution, and control of AI Workflows (AIW) composed of interconnected AI Modules (AIM).
The CUI-CPP V2.0 AI-Workflow supersedes those specified by previous MPAI-CUI specifications. These can still be used if their version is explicitly indicated.
Figure 1 – Company Performance Prediction V2.0 Reference Model
3 I/O Data
Table 2 gives the input/output data of the CUI-CPP V2.0 AIW.
Table 1 – I/O data of CUI-CPP V2.0 AIW
| Input data | From | Description |
| Prediction Horizon | CUI-CPP user | Prediction time in months. |
| Primary Risk Statements | External source | Statements by the Company being assessed about the Risks for which compliant Machine Learning Modules are available in the CUI-CAP. |
| Governance Statements | Company | Statements by the Company being assessed about Company governance. |
| Financial Statements | Company | Statements by the Company being assessed about Company financial data. |
| Secondary Risk Statements | Company | Statements by the Company being assessed about the Company-perceived first level Risks of the Risk Taxonomy. |
| Output data | To | Description |
| Organisation Descriptors | User | |
| Primary Default Descriptors | User | |
| Primary Discontinuity Descriptors | User | |
| Secondary Risk Probability | User |
4 Functions of AI Modules
Table 2 gives the functions of all CUI-CPP V2.0 AIMs. Each link provides the AIM function, reference model, Input/Output Data, and JSON metadata.
Table 2 – Functions of CUI-CPP V2.0 AIMs
| AIMs | Function |
| Governance Assessment | Processes Governance Statements and Financial Statements to provide Governance Descriptors to Company Assessment and Prediction AIM. |
| Financial Assessment | Processes Financial Statements to provide Financial Descriptors to Company Assessment and Prediction AIM. |
| Risk Matrix Generation | Process Secondary Risk Statements to provide the Secondary Risk Matrix. |
| Company Assessment and Prediction | Processes Prediction Horizon, Primary Risk Statements, Governance Descriptors, and Financial Descriptors to provide Organisation Descriptors, Primary Default Descriptors, and Primary Discontinuity Descriptors. |
| Prediction Result Perturbation | Processes Governance Descriptors, Financial Descriptors, Primary Default Descriptors, Secondary Risk Matrix, and Prediction Horizon to produce Secondary Business Discontinuity Probability by perturbing the Governance Descriptors and Financial Descriptors. |
5 I/O Data of AI Modules
Table 3 provides the link to the specified AIMs.
Table 3 – I/O Data of CUI-CPP V2.0 AIMs
| Acronym | Input | Output |
| CUI-CAP | Prediction Horizon Primary Risk Statement Governance Descriptors Financial Descriptors |
Organisation Descriptors Primary Default Descriptors Primary Discontinuity Descriptors |
| CUI-FNA | Financial Statements | Financial Descriptors |
| CUI-GVA | Governance Statements Financial Statements |
Governance Descriptors |
| CUI-PRP | Primary Default Descriptors Secondary Risk Matrix |
Primary Discontinuity Probability |
| CUI-RMG | Secondary Risk Statements | Secondary Risk Matrix |
6 AIWs and JSON Metadata
Table 4 provides the links to the AIW and AIW specifications and to the JSON Metadata.
Table 4 – AIWs and JSON Metadata
| AIW | AIMs | Name | JSON |
| CUI-CPP | Company Performance Prediction | X | |
| CUI-CAP | Company Assessment and Prediction | X | |
| CUI-FNA | Financial Assessment | X | |
| CUI-GVA | Governance Assessment | X | |
| CUI-PRP | Prediction Result Perturbation | X | |
| CUI-RMG | Risk Matric Generation | X |
7 Reference Software
As a rule, MPAI provides Reference Software implementing the Technical Specification released with the BSD-3-Clause licence and the following disclaimers:
- The purpose of the Reference Software is to demonstrate a working Implementation of an AIW, not to provide a ready-to-use product.
- MPAI disclaims the suitability of the Software for any other purposes that those of the MPAI-HMC Standard and does not guarantee that it is secure.
- Users shall verify that they have the right to use any third-party software required by the Reference Software Implementation.
- Users should note that the Reference Software Implementation may require the acceptance of licences from third-party repositories.
8 Conformance Testing
An implementation of an AI Workflow conforms with MPAI-HMC if it accepts as input and produces as output Data and/or Data Objects (Data and its Qualifier) conforming with those specified or referenced by MPAI-HMC.
The Conformance of an instance of a Data is to be expressed by a sentence like “Data validates against the Data Type Schema”. This means that:
- Any Data Sub-Type is as indicated in the Qualifier.
- The Data Format is indicated by the Qualifier.
- Any File and/or Stream have the Formats indicated by the Qualifier.
- Any Attribute of the Data is of the type or validates against the Schema specified in the Qualifier.
9 Performance Assessment
Performance is a multidimensional entity because it can have various connotations. Therefore, the Performance Assessment Specification provides methods to measure how well an AIW performs its function, using a metric that depends on the nature of the function, such as:
- Quality: the Performance of a Communicating Entities in Context AIW can measure how well the AIW responds to a question.
- Bias: Performance of a Communicating Entities in Context AIW can measure the quality of responses in dependence of the type of message received.
- Legal compliance: the Performance of an AIW can measure the compliance of the AIW to a regulation, e.g., the European AI Act.
- Ethical compliance: the Performance Assessment of an AIW can measure the compliance of an AIW to a target ethical standard.
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