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This is an inventory/sales analytics dashboard, likely built for exploring stock and pricing data across a retail-style dataset. A few things stand out:
Scale and scope: The dataset spans 2,000 analyzed rows with a combined total price of 2.21M, averaging around 1.1K per entry (median 838.53), a right-skewed distribution, since the max single value (4.0K) sits well above the mean while the min (5.74) is near zero.
Automated narrative insights: The "Plain-English Insights & Quality Audit" panel does the analytical heavy lifting for the viewer; it flags that supply is concentrated among a small number of suppliers (HHI score of 2506, with SupplyCo alone holding 26.9% of share), which is a useful red flag for supply-chain risk.
Trend and category breakdown: The metric trends line chart shows highly volatile day-to-day activity with no strong long-term drift, while the store/category bar chart ranks inventory items (Chair, Phone, Monitor, Laptop, Tablet, Printer, Desk) by volume a fairly even, gently descending distribution rather than one dominant category.
Distribution shape: The histogram of quantity-in-stock is roughly U-shaped across bins, with high counts at the low and high extremes (0–63 and 375–500) with a dip in the middle suggesting a bimodal stocking pattern (many items are either kept lean or stocked heavily, with fewer landing in between).
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Using logical formulas is essential for automated data categorization and reporting. This project demonstrates how to apply IF, IFS, COUNTIF, and SUM functions in Excel to process sales data effectively:
-IF Function (Binary Logic): Automatically flags whether a transaction is "Untung" (Profit) or "Rugi" (Loss) based on a single condition.
-IFS Function (Multi-tier Logic): Handles multiple conditions to classify sales performance into tiered categories ("Low", "Medium", "High") based on revenue thresholds.
-COUNTIF Function (Data Aggregation): Calculates the exact number of transactions that fall into each specific performance category.
-SUM Function (Totaling Data): Computes the grand total of all transactions to ensure complete data reconciliation in the summary table.
Show us the raw data! 😄
This dashboard takes raw workforce and inventory data and automatically surfaces the metrics that matter no manual pivot tables or chart configuration needed.
What it includes:
KPI summary cards (Total Salary, Average Salary, Record Count) with period-over-period change indicators
A plain-English insights panel that surfaces the standout patterns in the data peak values, leading categories, and distribution shifts instead of leaving the reader to interpret raw numbers
A Reorder Level vs. Stock scatter plot to visualize the relationship between two variables at a glance
Time-series and categorical comparison charts (metric trends over time, comparison by employee ID) that respond to clicks for on-the-fly filtering
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