全年生产数据与图表看板

B better addresses the requested analysis workflow: it provides populated week-filtered operator, machine and shift analysis, a descending YTD leaderboard, and validation selectors linked to an Active/Inactive source field. A’s sections labelled “PivotTable” are ordinary SUMIFS/RANK/INDEX formula summaries rather than the requested PivotTables. Both correctly reproduce Week 1, maintain assignments across 48 weeks, and calculate the required averages and totals. B’s KPIs reconcile to 38,880 units, Operator 1 at 4,720, Machine 3 at 13,300, and 4,320 units per operator. A visibly supplies top/bottom conditional highlighting; B’s supplied renders do not demonstrate it. Four charts are counted in each workbook, but their required types and quadrant arrangement cannot be verified from the supplied pages.

Both correctly load Week 1, preserve machine/shift assignments across 48 weeks, and include average/total formulas and four chart objects. A has populated pivot-style analyses with an Active week filter and a complete, sorted YTD leaderboard. Its Week 1 KPIs correctly show 38,880 units, Operator 1 with 4,720, Machine 3 with 13,300, and 4,320 units per operator. B’s sections labeled “PivotTable” are instead SUMIFS/RANK/LARGE/INDEX formula summaries, so they do not fulfill the explicit PivotTable requirement. B visibly provides top/bottom conditional highlighting; A’s highlighting is not visible in its rendered data sheet. A is closer overall, but full completion cannot be verified from the supplied evidence.

Both workbooks have the two correctly named sheets. Both data sheets have 432 rows (Weeks 1–48 × 9 operators), the Week 1 values entered correctly, fixed machine and shift assignments for all weeks, and auto-calculated Average/Total formulas. Both dashboards have Start/End week dropdowns, 4 charts and a complete Week 1 KPI block (total, top operator and machine with totals, average per operator, day and night %). The main difference is the required PivotTables. Submission A's tables labelled "PivotTable 1–4" are ordinary SUMIFS/INDEX/LARGE formula ranges (C55:C63, I55:I57, C69:C70, I69:J77), not PivotTables. That fails an explicit, central dashboard requirement covering all four sub-items. A also stores no cached values, so its KPI and summary cells appear blank in the text extract; Excel would likely recalculate them on open. Submission B's dashboard shows genuine PivotTable output: - a "Week Filter" page field set to Active, or (All) for YTD; - "Sum of Total Output" and "Average Weekly Output" captions with Grand Total rows; - a descending-sorted YTD leaderboard. These pivots are driven by a helper column L (Active/Inactive) tied to the B5/D5 week dropdowns, plus a refresh instruction. This is a valid way to combine real PivotTables with data-validation selection. B's cached values are correct: 38,880 total; Operator 1 at 4,720; Machine 3 at 13,300; Day/Night averages 4,543 and 4,041.25; Day 58.4% and Night 41.6%. B also adds input validation on daily outputs and usage notes. A's one clear advantage is conditional formatting. Its render shows green top-3 and red bottom-3 highlighting on both the Average and Total columns. B's render shows no highlighting despite its note claiming green/red top/bottom formatting, so B may miss or have a broken conditional-formatting rule. Weighing A's missing PivotTables (four required components) against B's possibly missing conditional formatting (one requirement), B better satisfies the task.

Both workbooks have the correct base structure. Each data sheet has 432 rows (48 weeks × 9 operators) with fixed machine and shift assignments. Each correctly loads the Week 1 Monday–Friday values and uses formula-based Average and Total columns. Each Dashboard has list data validation for start and end week, a Week 1 KPI table covering every required item, and 4 charts. The decisive difference is the explicitly required PivotTables. A has real PivotTable structures in rows 58–70. They show a 'Week Filter' page field set to 'Active' or '(All)', 'Sum of Total Output' and 'Grand Total' rows, and a descending-sorted YTD leaderboard. A ties them to the dropdowns through a helper column (=IF(AND($A2>=Dashboard!$B$5,$A2<=Dashboard!$D$5),"Active","Inactive")). Their values check out: operators 4720…3800 totalling 38880; machines 12935, 12645 and 13300; average shift output 4543 Day and 4041.25 Night. B's four 'PivotTable' sections are SUMIFS/LARGE/INDEX formula tables labelled as PivotTables, not actual PivotTables, so B misses an explicit core requirement. A's KPIs carry cached, verified values: 38880 total; Operator 1 at 4720; Machine 3 at 13300; 4320 average per operator; 58.4% day and 41.6% night share. B's text extraction shows blank cached values for every formula, including the KPIs, tables and data totals, so B depends on recalculation to display anything. B's main advantage is conditional formatting: green top-3 and red bottom-3 highlighting is visible on the Average and Total columns in its render. A's render shows no highlighting even though A claims it exists, so A's conditional formatting is unconfirmed. On balance, A satisfies more of the stated requirements, especially the PivotTable section.