Help Me Finish A Table To Compute And Display KOSU Values ✓ Solved

Help me finish a table to compute and display KOSU values pe

Help me finish a table to compute and display KOSU values per row with these requirements: - When a reference is entered the program shows its KOSU obj, coeffic and segment from the lookup table. - Use the same reference count and the same coeffic for all 'reference' cells in a row because they refer to the same reference and segment. - Compute 'KOSU réel équivalent' per row with the formula: nombre des opérateurs temps net de travail / (Bon coeffic). - The fields 'nombre des opérateurs', 'temps net de travail', and 'Bon' are typed per row and vary across rows, so 'KOSU réel équivalent' should vary accordingly. - Place an 'X' in different columns per row depending on comparison between KOSU réel équivalent and KOSU obj (i.e., which KOSU range contains the KOSU réel équivalent). - Highlight cells (coloring) to indicate how far KOSU is from KOSU réel équivalent. Produce guidance, algorithm, UI logic, example calculations, and a C# implementation approach to complete the table as in the provided example images.

Paper For Above Instructions

Overview

This document describes a complete approach to finishing the interactive KOSU table so each row auto-populates reference metadata, computes KOSU réel équivalent dynamically from user input, determines the target column for an 'X', and uses color cues to show deviation from KOSU objective. The guidance covers data design, UI logic, algorithm steps, example calculations, and a C# implementation outline suitable for WinForms or WPF data grids (Microsoft, 2024; Troelsen, 2019).

Data model and lookup

Design a small in-memory lookup table (or database) keyed by reference code. Each record must include: reference id, KOSU_obj (numeric target), coeffic (numeric), and segment (string). When a user types or selects a reference in a row, the app performs a lookup and sets the row's KOSU_obj, coeffic and segment fields to the lookup values (binding or programmatic fill) (Microsoft, 2024a; Microsoft, 2024b).

Column behavior and invariants

  • Reference: user-entered (or selected). Triggers lookup event.
  • KOSU obj: read-only, populated from lookup.
  • Coeffc: read-only, populated from lookup and identical across the row's reference cells.
  • Nombre des opérateurs, Temps net de travail, Bon: user-entered numeric inputs per row.
  • KOSU réel équivalent: computed field equal to (nombre_des_opérateurs temps_net_de_travail) / (Bon coeffic).
  • Range columns: expected KOSU intervals (e.g., buckets). Place 'X' in the bucket whose range contains the computed KOSU réel équivalent.
  • Coloring: apply conditional formatting to show difference between KOSU_obj and KOSU réel équivalent as percent or absolute difference.

Algorithm (step-by-step)

  1. OnReferenceChanged(row): lookup = FindLookup(reference). If found set row.KOSU_obj, row.Coeffic, row.Segment.
  2. OnInputChanged(row) when any of (nombre, temps_net, Bon) changes: parse inputs to numeric; if valid, compute KosuReel = (nombre temps_net) / (Bon coeffic).
  3. Round or format KosuReel to required decimal places.
  4. Determine the bucket: iterate configured KOSU ranges and find the range where KosuReel falls; set 'X' in that bucket column and clear others.
  5. Compute deviation = (KosuReel - KOSU_obj) / KOSU_obj (or absolute difference); map deviation to a color scale (green small difference, yellow moderate, red large difference).
  6. Apply cell background color to the KOSU columns or an indicator column according to deviation thresholds.
  7. Persist/save or export row if needed.

Example calculation

Assume lookup yields coeffic = 1.25 and KOSU_obj = 8.0 for a given reference. For a row with nombre des opérateurs = 4, temps net de travail = 420 (minutes), Bon = 200:

KOSU réel équivalent = (4 420) / (200 1.25) = 1680 / 250 = 6.72

Compare 6.72 to KOSU_obj 8.0: deviation = (6.72 - 8.0) / 8.0 = -0.16 → -16% (under target). Based on bucket definitions (for example buckets: <5, 5–7, 7–9, >9) KosuReel=6.72 falls in the 5–7 bucket so place an 'X' in that column. Color could be amber/yellow for -16% (moderate underperformance) (Few, 2009).

UI and UX design considerations

Use an editable grid control (DataGridView in WinForms or DataGrid in WPF) with proper column types: text for reference, numeric for inputs, read-only numeric for computed columns, and templated columns for 'X' markers and coloring. Provide immediate validation feedback for numeric entries and show tooltips or inline help explaining the formula and meaning of color codes (Preece et al., 2015).

C# implementation approach (outline)

Key classes and responsibilities:

  • ReferenceLookup: provides Find(referenceCode) → returns object {KOSUObj, Coeffic, Segment} (could be a Dictionary or DB query).
  • RowModel: properties for Reference, NombreOperateurs, TempsNet, Bon, KOSUObj, Coeffic, KosuReel, BucketIndex, DeviationPercent.
  • ViewModel / Binding: expose ObservableCollection if using WPF, or use a BindingList<RowModel> for WinForms (Microsoft, 2024).
  • Event handlers: OnReferenceChanged triggers lookup and sets KOSUObj/Coeffic; OnCellValueChanged triggers recompute and UI updates.

Example pseudocode (simplified):

void OnReferenceChanged(RowModel r) {


var meta = lookup.Find(r.Reference);


if (meta != null) {


r.KOSUObj = meta.KOSUObj;


r.Coeffic = meta.Coeffic;


r.Segment = meta.Segment;


Recompute(r);


}


}


void Recompute(RowModel r) {


if (r.Nombre>0 && r.TempsNet>0 && r.Bon>0 && r.Coeffic>0) {


r.KosuReel = (r.Nombre  r.TempsNet) / (r.Bon  r.Coeffic);


r.DeviationPercent = (r.KosuReel - r.KOSUObj) / r.KOSUObj;


r.BucketIndex = FindBucketIndex(r.KosuReel);


ApplyColoring(r);


RefreshRowUI(r);


}


}

Conditional formatting and coloring

Define thresholds for deviation to map to colors. For example:

  • |deviation| <= 5% → green
  • 5% < |deviation| <= 15% → yellow
  • |deviation| > 15% → red

Implement coloring at the cell renderer level in WinForms or via DataTriggers in WPF. Use accessible color choices and optionally icons (arrows up/down) and textual percent values to improve clarity (Few, 2009; Preece et al., 2015).

Edge cases and validation

  • Protect against division by zero (Bon or coeffic must be non-zero).
  • Handle missing lookup entries gracefully (show warning and allow manual override or selection prompt).
  • Normalize units (ensure temps_net and Bon units align; document unit expectations).
  • Allow configurable buckets and thresholds via settings so the UI can match business rules.

Testing and verification

Create unit tests for the Recompute method (various numeric combos, negative values, zeros) and UI integration tests for behavior when reference lookup returns different coeffic values (Sommerville, 2011; Martin, 2008). Validate examples against expected outputs and ensure the 'X' placement and color map correctly reflect computed values.

Summary

This approach ensures the table stays synchronized: a single reference determines KOSU_obj and coeffic, row inputs produce distinct KOSU réel équivalent values, 'X' markers move according to computed value, and color indicates deviation magnitude. The C# binding approach with event-driven recomputation and conditional cell styling offers a robust, maintainable solution (Microsoft, 2024; Troelsen, 2019).

References

  • Microsoft. "Data Binding in Windows Forms." Microsoft Docs. 2024. https://learn.microsoft.com/windows/forms/data-binding (Microsoft, 2024)
  • Microsoft. "WPF Data Binding Overview." Microsoft Docs. 2024. https://learn.microsoft.com/dotnet/desktop/wpf/data/data-binding-overview (Microsoft, 2024)
  • Microsoft. "Conditional formatting in Excel." Microsoft Support. 2023. https://support.microsoft.com/excel/conditional-formatting (Microsoft, 2023)
  • Troelsen, A., & Japikse, P. "Pro C# 8 with .NET Core 3." Apress, 2019. (Troelsen, 2019)
  • Preece, J., Rogers, Y., Sharp, H. "Interaction Design: Beyond Human-Computer Interaction." Wiley, 2015. (Preece et al., 2015)
  • Few, S. "Now You See It: Simple Visualization Techniques for Quantitative Analysis." Analytics Press, 2009. (Few, 2009)
  • Sommerville, I. "Software Engineering." 10th ed., Pearson, 2011. (Sommerville, 2011)
  • Martin, R. C. "Clean Code: A Handbook of Agile Software Craftsmanship." Prentice Hall, 2008. (Martin, 2008)
  • Kleppmann, M. "Designing Data-Intensive Applications." O'Reilly Media, 2017. (Kleppmann, 2017)
  • Harvard Business Review. "A Guide to Setting and Using KPIs." HBR Collection, 2016. (Harvard Business Review, 2016)

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