The module opens through the Work group estimator button in the Work group modelling section. This step is used only if the client does not have its own grading. If grading already exists, it can be entered directly into Level 1 / 2 / 3 in the employee data and the estimator can be skipped.
Five-step structure
Step | Name | Description |
1 | Upload existing evaluation | Only if available. |
2 | Set work criteria for your company | Definition of maximum points or weights. |
3 | Run work group modelling | Selection of AI or manual mode. |
4 | Estimate grades for your positions | Scoring of specific positions. |
5 | Update dataset with new grades | Writes the result back into employee data. |
Below the guide are four expandable sections: Characteristics table, Position list, Work group modelling and Position name / seniority.
Steps 1–2: Characteristics table
The characteristics table defines the maximum number of points, or weights, for each evaluation criterion. The criteria measure work complexity, effort, responsibility and working conditions. The structure follows Section 110 of the Labour Code.
The table has three columns: Criterion / Subcriterion, Short description and Limit.
Weighting principle
The main limit for each criterion represents the weight assigned to that criterion by the client in relation to the nature of its business. The example totals 100 points, but this is not a mandatory rule. The client chooses weights that reflect the actual demands of work in the company.
For an office-based company, working conditions usually have a low weight. For mining, manufacturing or other high-risk activities, this weight may be much higher. The settings must reflect the company’s activity and applicable local legislation.
Criterion 1: Complexity / skills — Section 110(3)
Default main limit: 40 points. Typical subcriteria are:
Subcriterion | Short description | Default limit |
1.1 Formal education | Minimum education required for the position. | 8 |
1.2 Practical knowledge | Scope of expert knowledge and length of experience. | 14 |
1.3 Activity complexity | Variability of work and problem solving. | 8 |
1.4 Communication competence | Communication difficulty, empathy and care. | 5 |
1.5 Organisational complexity | Autonomy, initiative and management demands. | 5 |
Criterion 2: Effort — Section 110(3)
This criterion measures physical, mental and sensory effort related to the position. The client defines the main limit and subcriteria according to its own methodology.
Criterion 3: Responsibility — Section 110(3)
Subcriterion | Short description | Default limit |
3.1 Decision-making and impact | Impact of decisions on the organisation. | 8 |
3.2 Financial responsibility | Responsibility for assets and finances. | 8 |
3.3 People management and OHS | Managing people and responsibility for occupational health and safety. | 9 |
Criterion 4: Working conditions — Section 110(4)
Default main limit: 10 points. This criterion refers to Section 110(4), unlike the previous criteria, which refer to Section 110(3).
Subcriterion | Short description | Default limit |
4.1 Environment and risk | Harmful environment and safety risks. | 5 |
4.2 Work regimes | Shift work, night work and flexibility. | 5 |
Position list — mapping catalogue
This section is used to import or define a catalogue of job positions. Each position can have similar names and an assigned grade.
The main use is mapping employees of a subsidiary to an existing group grading system. If the parent group already has a position evaluation system, the group catalogue can be uploaded and positions can be assigned to the correct grade through equivalent-position search.
Action button | Function |
Upload position list | Imports a completed template. |
CSV | Downloads an empty CSV template. |
Excel | Downloads an empty Excel template. |
Catalogue column | Description |
ID | Position sequence number. |
Position name | Main canonical name. |
Similar position names | Alternative labels mapped to the main name. |
Grade | Assigned pay grade. |
Step 3: Work group modelling — four modes
After defining weights and optionally uploading a catalogue, the third step determines how grade boundaries are created.
Mode | Principle | When to use |
Simple grade distribution | Mechanical distribution of the point range into predefined intervals. | Quick start or simple distribution. |
Grade Discovery (AI) | AI determines the optimal number of grades and point ranges from company data. | Organisation without own grading looking for structure. |
Grade Reshuffling (AI) | AI redistributes existing positions to minimise within-grade variance. | Organisation has grading but wants to refine it. |
Manual entry | Fully manual grade boundaries. | Client has a clear internal methodology. |
Example grade ranges visible in the application include grade 3 for 40–59 points, grade 4 for 60–79 points and grade 5 for 80–100 points. Ranges can be changed and additional grades can be added.
Step 4: Position scoring — two variants per position
The Position name / seniority section is the main working area. The table contains one row for every combination of position and seniority from the employee data. The client can evaluate each position in two ways.
Column | Type | Description |
Estimate | Icon | Estimate indicator and contextual help. |
Position name | Text | Position from employee data. |
Seniority | Integer | Seniority level for the position. |
Position | Drop-down | Mapping to the position catalogue. |
Complexity | Integer | Points for criterion 1. |
Effort | Integer | Points for criterion 2. |
Responsibility | Integer | Points for criterion 3. |
Working conditions | Integer | Points for criterion 4. |
Grade source | Switch | POSITION from catalogue or POINTS from scoring. |
Grade | Integer | Resulting grade. |
Pay range min | Decimal | Lower pay-band limit for the grade. |
Pay range max | Decimal | Upper pay-band limit for the grade. |
Variant A: Mapping to an equivalent catalogue position
If a group catalogue has been uploaded, the client can map its position to an equivalent catalogue position. For example, a local Engineer position can be mapped to Software Engineer in the group catalogue, and the grade is transferred automatically.
This workflow is fast and maintains consistency with the parent group.
Variant B: Individual scoring through questionnaire
If no equivalent position exists, the client scores the position individually. A questionnaire opens and the client fills in points for the subcriteria under Complexity, Effort, Responsibility and Working conditions. The position is then classified into the corresponding grade based on the point range.
Note for each position — critical for audit
Each position can include a note. This note is important for audits because it explains why specific values were assigned. If the labour inspectorate or parent group asks why a position received a particular responsibility score, the answer should be documented in the note.
Grade source switch
Each row can use POSITION or POINTS independently. This allows mixed methodology: known positions can use catalogue mapping, while new or disputed positions can be scored manually.
Export and import of questionnaires
The panel supports exporting the full scoring table to Excel, exporting questionnaires for managers and importing completed Excel files or questionnaires back into the system. This enables delegation of scoring to department heads.
Step 5: Writing grades into the dataset
The last step writes the resulting grades back into employee data. The client selects which of the three Level 1 / Level 2 / Level 3 columns should be updated and confirms the overwrite.
Three parallel grading systems
The mechanism can be repeated. The client can create one variant and write it into Level 1, adjust weights or methods and write the second into Level 2, then create a third variant for Level 3. The Simple overview then allows the user to compare results across all three grading systems.
Saving and closing
Save stores the estimator state in the estimator database. Close exits the panel without saving. After saving the scoring, the user must still perform step 5 to update the employee dataset used by the regression.
