全年生产数据与图表看板

Both reproduce the Week 1 data and correct KPI totals, including 38,880 units, Operator 1 at 4,720, and Machine 3 at 13,300. A leaves weeks 2–48 ready for actual entry, provides automatic averages/totals, and connects its week-range selectors to an Active/Inactive filter field. B fills the unprovided weeks with unsupported production figures and reports 1,898,790 units YTD, undermining its leaderboard. B’s Start/End selectors also drive a separate snapshot rather than the displayed PivotTable filters. A is therefore the more reliable production tracker.

B is the more faithful and usable tracker. Both reproduce the nine Week 1 records, but B leaves Weeks 2–48 available for real entries and uses blank-safe average/total formulas. Its KPI formulas produce 38,880 units, Operator 1 at 4,720, Machine 3 at 13,300, and a weekly operator average of 4,320; its YTD leaderboard correctly totals 38,880. A fills later weeks with unsupported production figures and reports 1,898,790 units YTD, making its YTD analysis misleading. B’s validated week selectors feed selected-range aggregation formulas and an Active/Inactive filter helper, whereas A’s selectors feed a separate snapshot rather than its displayed pivot reports. Both contain four chart objects. B’s full completion remains unconfirmed because Dashboard charts are not rendered here and its Week 1 data render shows no visible top/bottom highlighting.

A is the more faithful, usable tracker, mainly because B invents 47 weeks of production data and A does not. Data sheet: Both have 432 rows (48 weeks × 9 operators), fixed machine and shift assignments, correct Week 1 values, and Average and Total formulas. The task only supplies and asks for Week 1 entries. A loads Week 1 and leaves Weeks 2-48 ready for entry, with formulas that stay blank and numeric validation on the inputs. B fills Weeks 2-48 with invented outputs, and no visible sheet text labels them as illustrative; its footer says only that Week 1 came from the input file. Effect of B's invented data: B's YTD pivot, formula leaderboard and YTD chart show fictional totals, such as Operator 1 at 229,065 and a grand total of 1,898,790. That conflicts with the instruction that the graphs be based on Week 1 data, and it would misstate individual operators' performance at production meetings. The real data would also have to overwrite these rows, and B's leaderboard total in J35 is stored as a fixed value. A's YTD figures truthfully equal the only recorded data (38,880 total, Operator 1 at 4,720). Week selection: A's B5 and D5 dropdowns drive a helper Week Filter column (Active/Inactive). Its operator, machine and shift pivots filter on that column, so after Refresh the validation lists control them as requested, and its formula-driven chart tables respond to the selection. B's B5 and D5 dropdowns feed only a separate formula 'Week Range Snapshot'. Its pivots rely on their own Week # filters, so the validation lists do not control the PivotTables. KPIs: Both are correct: 38,880 units; Operator 1 at 4,720; Machine 3 at 13,300; 4,320 per operator; Day 58.4%; Night 41.6%. Layout: A places the selector, KPIs and charts before the pivots. B places its four charts at row 58 and below, after pivots, snapshot and source tables. B's advantage: Its conditional formatting visibly works, with a colour scale and red bottom performers in the render. A claims green/red highlighting, but none appears on its rendered Week 1 rows, which is a real possible miss on an explicit requirement. This is narrower than B's fabricated YTD results and its disconnected selectors, so A remains better overall.

Both workbooks share the same core. Each has correctly named sheets and 432 rows (48 weeks × 9 operators) with fixed machine and shift assignments. Week 1 values match the input exactly, and Average and Total are calculated by formula. Each has data-validation lists for week selection on B5/D5, four charts on the Dashboard, and four pivot-style tables covering operator, machine, shift average and YTD. The Week 1 KPIs are correct in both: 38,880 units; Operator 1 at 4,720; Machine 3 at 13,300; 4,320 per operator; Day 58.4% and Night 41.6%. The decisive difference is that Submission A invented daily outputs for weeks 2–48 (423 rows). The task only supplied, and only asked to populate, Week 1, and A does not label these rows as sample data. As a result, A's YTD leaderboard and YTD pivot are fictitious (for example, Operator 1 at 229,065 and a 1,898,790 grand total). Its YTD bar-chart source (K48:K56) and the week-range snapshot are also fictitious. This contradicts "graphs based on the Week 1 data" and would be misleading at production meetings. A is also fragile as a real tracker: =ROUND(AVERAGE(E:I),1) returns #DIV/0! once the fake rows are cleared. In addition, its validation selectors drive only a formula snapshot, not the PivotTables. A's clear strength is conditional formatting that visibly works: top/bottom 5 highlighting plus a colour scale, shown in the rendered pages. Submission B stays faithful to the input. Weeks 2–48 are blank and ready for entry, with blank-safe formulas (IF(COUNT=0,"",…)) and whole-number validation on the daily output cells. Its From/To selectors feed a helper 'Week Filter' column, which is the page filter for the operator, machine and shift pivots. This directly links the validation lists to the PivotTables, as the task describes. The YTD leaderboard pivot (All) correctly equals actual output to date, and the charts default to Week 1. B's main weakness is that its claimed green/red top/bottom highlighting does not appear on the Week 1 rows in the render, so its conditional formatting is unverified. On balance, A's undisclosed fabricated data, which corrupts the YTD deliverables, is a more serious correctness and usability flaw than B's unverified formatting.