全年生产数据与图表看板

A is the more reliable tracker. It correctly reproduces Week 1, leaves later outputs blank for actual entry, calculates weekly averages/totals, and links selected-period summaries to validated Start/End week controls. Its KPIs reconcile to 38,880 units, Operator 1 at 4,720 and Machine 3 at 13,300, with visible top/bottom highlighting. B invents daily production figures for Weeks 2–48, inflating its YTD leaderboard to 1,898,790 units when only 38,880 units are supported by the reference. This undermines the dashboard’s monitoring purpose.

B accurately loads Week 1, preserves operator machine/shift assignments across 48 weeks, and leaves future outputs ready for entry. Its formulas produce the correct KPIs: 38,880 units, Operator 1 with 4,720, Machine 3 with 13,300, average output of 4,320 per operator, and day/night contributions of 58.42%/41.58%. Its dropdowns feed live summaries, and the rendered data page visibly highlights the weekly top and bottom performers. A instead fills Weeks 2–48 with unsupported production figures and reports 1,898,790 YTD units, making its leaderboard unreliable. Both inventories report four charts, but the available evidence does not establish native PivotTable functionality or the required chart types and quadrant layout, so B’s full completion remains unconfirmed.

Both workbooks share the core structure. Each has 432 rows covering Week 1–48 for 9 operators, with fixed machine and shift assignments. Each loads Week 1 exactly from the input (e.g., Operator 1: 950/930/940/945/955). Both calculate Average and Total Output with formulas, add Start/End week drop-downs, show four pivot-style tables for (a)–(d) and include four charts. Both Week 1 KPI tables are correct: 38,880 total; Operator 1 at 4,720; Machine 3 at 13,300; 4,320 per operator; 58.4% day and 41.6% night. A follows the input faithfully. Weeks 2–48 are left blank with headings ready for weekly entry. Its conditional formatting marks the top and bottom performer within each week, and the render shows Operator 1 in green and Operator 4 in red for Week 1. A's drop-downs (C6/C7, list Z5:Z52) drive the KPIs, live tables and charts directly. They also drive a 'Week Filter' helper column that the pivot tables filter on after refresh, so validation-list selection is tied to the PivotTables. Its leaderboard and YTD chart reflect actual data to date, which is Week 1, matching 'graphs based on the Week 1 data'. B fills weeks 2–48 with invented output numbers that are not labelled as sample data. As a result, its YTD leaderboard and YTD chart report about 1.9M units (Operator 1 at 229,065) as if real. That contradicts the Week 1 basis for the graphs and would mislead a production meeting; the data would have to be wiped before use. B's drop-downs (B5/D5) drive only a separate 'Week Range Snapshot' block, while its pivots rely on their own Week # page filter. Its top/bottom-5 rule spans all 432 rows, so Week 1's best and worst operators are not flagged. There are also minor flaws: a hardcoded leaderboard total (J35 = 1898790) and the text '5 days x 5 days/week'. Overall, A is the more accurate, usable tracker.

Both workbooks have the main parts. Each has 432 rows covering Weeks 1–48. Week 1 matches the input exactly in both. Machine and shift assignments are fixed for every week. Average and Total are formulas in both. Both have four PivotTable-style blocks (operator, machine, shift average, YTD leaderboard), week drop-downs and four charts. Both KPI tables give correct Week 1 values: total 38,880; Operator 1 at 4,720; Machine 3 at 13,300; 4,320 per operator; Day 58.4% and Night 41.6%. The deciding difference is the data for Weeks 2–48. Submission A fills these weeks with invented daily outputs and does not say they are simulated. Its notes only say Week 1 came from the input file. As a result, A's YTD leaderboard pivot (grand total 1,898,790) and YTD chart source (K48:K56 summing all 48 weeks) show fictitious production. That contradicts the instruction that the graphs be based on the Week 1 data, and a manager would have to delete 47 weeks of fake figures before using the tracker. A's conditional formatting is a global top-5/bottom-5 plus a colour scale across all 432 rows, so no Week 1 operator is flagged as top or bottom. A's drop-downs (B5/D5) drive only a separate formula snapshot, not the PivotTables, which rely on their own Week # filter. Submission B leaves Weeks 2–48 blank for weekly entry, with blank-safe Average and Total formulas. So its YTD leaderboard (Operator 1 at 4,720 down to Operator 4 at 3,800; total 38,880) and YTD chart correctly reflect the Week 1 data. B's conditional formatting highlights the top and bottom performer within each week; the render shows Operator 1 green and Operator 4 red. B's Start/End week drop-downs (C6/C7) feed a 'Week Filter' helper column used by the PivotTables (after refresh), the KPI table and the charts. B also adds input validation and consistency checks. B is therefore more faithful to the source data and more usable for real weekly meetings.