📦 Warehouse Operations Performance Report

PowerBI · Supply Chain Analytics · Industry 4.0

An interactive multi-page PowerBI report analyzing warehouse operations performance, inventory management, logistics efficiency and KPI tracking.

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📊 Report Overview

The report consists of 7 pages with interactive navigation:

Page	Content
🏠 Index	Interactive navigation hub with page buttons
📊 Executive Overview	KPI cards + Orders distribution by category
📦 Stock & Inventory	Stock levels vs reorder points + Stock by zone
⚡ Picking & Efficiency	Picking time vs handling cost + Layout efficiency
🚚 Shipment & Logistics	Delay analysis table + Delay distribution by cause
📈 Inventory Performance	Rotation index + Unit price by category
🏆 KPI Scorecard	Performance matrix + KPI flag + Evasion rate

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🛠️ Tech Stack

Tool	Purpose
PowerBI Desktop	Dashboard development
Power Query	Data cleaning and transformation
DAX	Custom measures and KPI calculation
Kaggle	Source datasets

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📐 Data Model

Two datasets connected via relationship:

Dataset 1 — Warehouse Operations

• 500 rows · 16 columns
• Source: Kaggle — Logistics Warehouse Dataset
• Key fields: Category, Stock Level, Reorder Point, Picking Time, Order Fulfillment Rate, KPI Score

Dataset 2 — Logistics Performance

• 500 rows · 11 columns
• Source: Kaggle — Smart Logistics Supply Chain Dataset
• Key fields: Asset ID, Shipment Status, Waiting Time, Asset Utilization, Logistics Delay Reason

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🔢 DAX Measures

-- Tasso Evasione %
-- Average order fulfillment rate across all products
Tasso Evasione % =
AVERAGE('Warehouse Operations'[Tasso Evasione Ordini])

-- Performance Flag
-- Classifies each category as Top, Media or Critica based on KPI Score
Performance Flag =
SWITCH(
    TRUE(),
    AVERAGE('Warehouse Operations'[KPI Score]) >= 535, "🟢 Top",
    AVERAGE('Warehouse Operations'[KPI Score]) >= 520, "🟡 Media",
    "🔴 Critica"
)

-- Obiettivo KPI
-- Fixed KPI target value for gauge visualization
Obiettivo KPI = 535

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🎨 Design

• Theme: Custom color palette
• Primary color: #1A2B4A Navy Blue
• Accent color: #E67E22 Orange
• Positive indicator: #2ECC71 Green
• Font: Segoe UI
• Background: #F5F7FA Light Gray

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📋 Key Insights

The report reveals several operational patterns across the warehouse:

Stock Management — All product categories maintain stock levels significantly above their reorder points, suggesting adequate inventory management but potential overstock in some zones.

Picking Efficiency — Apparel shows the highest handling costs despite similar picking times to other categories, indicating potential process optimization opportunities.

Logistics Delays — Weather and Traffic are the primary causes of logistics delays, each accounting for roughly equal share of total disruptions.

KPI Performance — Electronics leads with the highest KPI Score (Top performance), while Automotive and Groceries fall in the Critica range, suggesting areas for operational improvement.

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⚙️ Setup

1. Download the .pbix file
2. Open with PowerBI Desktop (free download at powerbi.microsoft.com)
3. Data is embedded — no external connections needed
4. Navigate using the interactive buttons on the Index page

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🏭 Industrial Context

This report simulates the type of operational reporting used in automated warehouse environments. Key metrics such as order fulfillment rate, stock rotation index and layout efficiency score are directly relevant to companies operating AGV fleets and automated logistics systems.

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📄 License

MIT — feel free to use, modify and share.

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👤 Author

Matteo Daviddi Data Analyst & Process Automation LinkedIn · GitHub