TIDE | Clothing Repair Service Capacity Plan

Prompt

Use the current VIFE Lite to generate a real, editable XLSX, "TIDE | Clothing Repair Service Capacity Plan", for a public Example; label all data "Simulated data · Not real operations". Build 8 weeks × 4 service types (mending, hemming, zippers, refurbishing), 32 rows in total, with the fields Week, Service, Opening backlog, New bookings, Completed, Labor hours per unit, Available labor hours, Closing backlog and Labor utilization; the last two use formulas. At least four sheets: (1) Weekly service detail, with frozen panes, filters, and distinct colors for inputs and formulas; (2) Service summary, using SUMIFS to total bookings, completions and backlog and showing utilization by service; (3) Scenario parameters and comparison, with baseline, overtime and added-volunteers scenarios and clearly listed assumptions; (4) Operations dashboard, with closing backlog, capacity gap, a weekly trend chart and 3 observations about the simulated data. Make units, percentages and weeks clear, and use denim blue, copper orange and warm gray. Generate the final XLSX with the existing openpyxl; check the 32 rows, formulas, charts, that no key values are blank and that the file opens, then deliver it. Do not install software or renderers; finish after checking. Additional acceptance criteria: the public Example's XLSX preview needs cached formula values. xlsxwriter is available, so you can use write_formula(..., value=precomputed result) to write both the formula and its cache, or validate the OOXML <f>/<v> after generation. Make sure the dashboard and summary KPIs show values even in a preview that does not recalculate formulas, then deliver the final file. Also check the units: bookings, completed pieces and backlog pieces must be whole numbers. If scenario output is counted in pieces, round it down and keep the formula caches, summary and QA in sync; if it is in labor hours, label that clearly in the table. Write all content, headings, labels, sheet names and file names in English.

Result

A1fxTIDE | Clothing Repair Service Capacity…
ABCDE1TIDE | Clothing Repair Service Capacity…2Simulated data · Not real operations3Prepared for: public Example | Horizon:…45What this workbook is6A capacity plan for a simulated four-se…78910

TIDE | Clothing Repair Service Capacity Plan — editable XLSX, every sheet labelled Simulated data · Not real operations.

Structure (5 sheets)

  1. Read Me — purpose, sheet map, units and conventions, colour key, week calendar (W1 = w/c Mon 2 Nov 2026 → W8 = w/c 21 Dec 2026).
  2. Weekly Service Detail — 32 records (8 weeks × mending, hemming, zippers, refurbishing), 9 columns. Copper = inputs, warm grey = formulas. Frozen panes at B5, autofilter A4:I36. Closing backlog = opening + bookings − completed and Utilization = completed × h/unit ÷ available hours are live formulas in all 32 rows.
  3. Service Summary — SUMIFS totals of bookings, completions and backlog (plus W1 opening, W8 closing, hours used/available, weighted utilization, backlog share), a bar chart of W8 backlog by service, and five tie-out checks that all read OK.
  4. Scenario Comparison — 10 listed assumptions, editable parameters, and a comparison of baseline / overtime (+12% hours) / added volunteers (24 h/week, 6 h per service), backed by a per-service calculation block using ROUNDDOWN so every piece count is whole.
  5. Operations Dashboard — six KPI cards, weekly trend table, capacity table, a line chart of bookings / completions / closing backlog, and three observations.

Key simulated results

  • 4,838 bookings, 4,355 completed, closing backlog 120 → 596 pieces at W8 (+476); weekly capacity 566 pieces vs ~605 bookings/week.
  • Average labor utilization 95.0%; W8 capacity gap 40 pieces/week; peak backlog week W8.
  • Scenarios leave 596 / 210 / 31 pieces at W8 (baseline / overtime / volunteers); both extra-capacity options clear mending and hemming, leaving the gap in zippers and refurbishing.

Checks run on the saved file (0 problems) Package opens as valid OOXML; 5 sheets in order; exactly 32 unique week × service rows with no blank cell in any of them; formulas present and each carrying a cached value; all 32 caches equal an independent Python recomputation of closing backlog and utilization; bookings/completions/backlog are whole numbers; summary and scenario totals tie to the detail and to the per-service block; backlog and completion orderings hold across scenarios; all KPIs, the trend block and the peak-week lookup show cached values (so previews display numbers without recalculating); no #REF!/#DIV/0! tokens; explicit column widths on every used column.

Limit: per your instruction I installed nothing and did not render the sheets to images, so I verified layout deterministically (widths, merged ranges, wrap, row heights) rather than visually. Opening it once in Excel/LibreOffice is the remaining visual confirmation.

How it was made

Mode
Spreadsheet
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