├── .github └── workflows │ └── static.yml ├── 1_Proposal.pdf ├── 2_Mid_Term.pdf ├── 3_Final_report.pdf ├── README.md └── index.html /.github/workflows/static.yml: -------------------------------------------------------------------------------- 1 | # Simple workflow for deploying static content to GitHub Pages 2 | name: Deploy static content to Pages 3 | 4 | on: 5 | # Runs on pushes targeting the default branch 6 | push: 7 | branches: ["main"] 8 | 9 | # Allows you to run this workflow manually from the Actions tab 10 | workflow_dispatch: 11 | 12 | # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages 13 | permissions: 14 | contents: read 15 | pages: write 16 | id-token: write 17 | 18 | # Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. 19 | # However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. 20 | concurrency: 21 | group: "pages" 22 | cancel-in-progress: false 23 | 24 | jobs: 25 | # Single deploy job since we're just deploying 26 | deploy: 27 | environment: 28 | name: github-pages 29 | url: ${{ steps.deployment.outputs.page_url }} 30 | runs-on: ubuntu-latest 31 | steps: 32 | - name: Checkout 33 | uses: actions/checkout@v4 34 | - name: Setup Pages 35 | uses: actions/configure-pages@v5 36 | - name: Upload artifact 37 | uses: actions/upload-pages-artifact@v3 38 | with: 39 | # Upload entire repository 40 | path: '.' 41 | - name: Deploy to GitHub Pages 42 | id: deployment 43 | uses: actions/deploy-pages@v4 44 | -------------------------------------------------------------------------------- /1_Proposal.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/repo-sumit/BDM_Project/eca376a0652cdc77f077a41c3238ca17cf063989/1_Proposal.pdf -------------------------------------------------------------------------------- /2_Mid_Term.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/repo-sumit/BDM_Project/eca376a0652cdc77f077a41c3238ca17cf063989/2_Mid_Term.pdf -------------------------------------------------------------------------------- /3_Final_report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/repo-sumit/BDM_Project/eca376a0652cdc77f077a41c3238ca17cf063989/3_Final_report.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 📊 BDM Capstone Dashboard – Jai Maa Jhandewali Store 2 | 3 | > _“Optimizing Inventory and Operations for Sustainable Growth”_ 4 | --- 5 | 6 | 7 | | Project Website : | https://repo-sumit.github.io/BDM_Project/ | 8 | |-------------------|----------------------------------------------| 9 | | Viva PPT Slides : | https://rb.gy/vq3o72 | 10 | 11 | 12 | ## 🏪 Project Overview 13 | 14 | | 🧾 Detail | 📍 Information | 15 | |-------------------|----------------------------------------------| 16 | | **Store Name** | Jai Maa Jhandewali Store | 17 | | **Location** | VP Block, Pitampura, Delhi | 18 | | **Store Type** | 🛒 Family-run B2C Grocery | 19 | | **Owner** | Mr. Satyam Prakash | 20 | | **Time Period** | 📅 Jan 2024 – Jan 2025 | 21 | | **Team Member** | 👤 Sumit Kumar (`22f2000848@ds.study.iitm.ac.in`) | 22 | 23 | --- 24 | 25 | ## ❓ Problem Statement 26 | 27 | ### 🔴 Key Challenges 28 | 29 | - ❌ Overstocking and Stockouts (especially perishables) 30 | - 📦 Severe Space Constraints 31 | - 📉 Manual Operations = Limited Scalability 32 | 33 | --- 34 | 35 | ## 📁 Data Collection 36 | 37 | - 📌 **Source**: Primary – Digitized Records & Interviews 38 | - 📊 **Scope**: Daily SKU Sales (13 items), 398 days 39 | - 🧹 **Format**: Cleaned Excel Data 40 | - 📈 [👉 View Dataset on Google Sheets](https://docs.google.com/spreadsheets/d/1hMAmaAbf4dJ4aiPyztVxLYa6_tDHUOJYUaob6Naxx18/edit?usp=sharing) 41 | 42 | --- 43 | 44 | ## 🔬 Analysis Techniques 45 | 46 | | 🧠 Method | 💡 Use Case | 47 | |------------------------|---------------------------------------------------------------| 48 | | 📉 Descriptive Stats | Understand baseline sales metrics (mean, std, median) | 49 | | 📈 Trend Analysis | Seasonal, weekly patterns using charts | 50 | | ©️ ABC Segmentation | Categorize SKUs by volume share | 51 | | 🔗 Correlation Matrix | Relationship between product categories | 52 | | 🤖 Linear Regression | Basic sales forecasting (e.g., Milk prediction) | 53 | 54 | --- 55 | 56 | ## 📊 Visual Insights 57 | 58 | | 📌 Insight | 🖼️ Chart Example | 59 | |-----------------------------------|-------------------------------------------------------------------| 60 | | Monthly Sales Distribution | ![Sales Bar](https://i.ibb.co/vvgmkBBK/Screenshot-2025-04-22-074654.png)| 61 | | Category Trend Line Chart | ![Trend Line](https://i.ibb.co/4ZFgPwMQ/Screenshot-2025-04-22-074936.png) | 62 | | ABC Volume Segmentation | ![ABC Chart](https://i.ibb.co/vptmtvT/Screenshot-2025-04-22-075705.png) | 63 | | Product Correlations | ![Correlation](https://i.ibb.co/VWT6kBq2/Screenshot-2025-04-22-075258.png)| 64 | | Sales Forecast (Milk) | ![Prediction](https://i.ibb.co/tTp4VPps/Screenshot-2025-04-22-075506.png)| 65 | 66 | --- 67 | 68 | ## 💡 Recommendations Summary 69 | 70 | ### 📦 Inventory Management 71 | - ✅ Use ABC Classification to adjust stocking 72 | - 🧊 Apply JIT for perishable items (Milk, Butter) 73 | - 📈 Begin with simple forecasts (Excel/Linear Models) 74 | 75 | ### 🧱 Space Optimization 76 | - 📐 Invest in vertical shelves 77 | - 🧮 Allocate space to high-selling SKUs (Cat A & B) 78 | - 🔄 Reconfigure layout based on correlations (FMCG + Dairy) 79 | 80 | ### 🚀 Business Growth 81 | - 💻 Adopt POS for sales tracking 82 | - 🛵 Offer home delivery for nearby orders 83 | - 🧠 Use promotions tied to seasonal demand 84 | 85 | --- 86 | 87 | ## 📈 Projected Impact 88 | 89 | | 📊 Financial Gains | ⚙️ Operational Gains | 🧭 Strategic Benefits | 90 | |------------------------|----------------------------|------------------------------| 91 | | 💵 Increased Revenue | 📉 Reduced Stockouts | 🚀 Improved Scalability | 92 | | 🧊 Lower Wastage Cost | 📦 Optimized Space Usage | 🤝 Customer Satisfaction | 93 | | 💰 Better Cash Flow | ⏱️ Reduced Manual Overload | 🏁 Competitive Readiness | 94 | 95 | --- 96 | 97 | ## 🛠️ Tech Stack 98 | 99 | - 💻 **Frontend**: HTML5, Tailwind CSS 100 | - 📊 **Charts**: Chart.js 101 | - 🧠 **Analysis**: Excel, Python 102 | - 📋 **Data Source**: Google Sheets 103 | 104 | --- 105 | 106 | ## 🧠 Author 107 | 108 | **Sumit Kumar** 109 | 📧 `22f2000848@ds.study.iitm.ac.in` 110 | 🎓 BDM Capstone Project – IIT Madras 111 | 112 | --- 113 | 114 | ## 🪪 License 115 | 116 | MIT License – For educational and non-commercial use. Attribution encouraged 🙏 117 | 118 | --- 119 | -------------------------------------------------------------------------------- /index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | Jai Maa Jhandewali Store - BDM Capstone Dashboard (Revised) 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 122 | 123 | 124 | 125 | 126 | 127 |
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Optimizing Inventory and Operations for Sustainable Growth

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Jai Maa Jhandewali Store - BDM Capstone Project

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About Jai Maa Jhandewali Store

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Store Overview:

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  • Type: Family-run B2C grocery store
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  • Location: VP Block, Pitampura, Delhi
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  • Owner: Mr. Satyam Prakash (Student)
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  • Established: ~2 years ago
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  • Products: Daily essentials, dairy, groceries, FMCG
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  • Operations: 9 AM - 9 PM, Family-run
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Problem Statement & Background

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Identified Objectives:

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  • 216 | Inventory Management Issues: Difficulty maintaining optimal stock, managing fluctuations for perishables/seasonal items, avoiding wastage/missed sales. 217 |
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  • 219 | Space Constraints: Limited physical space restricts efficient storage, product range expansion, and managing slow-moving products. 220 |
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  • 222 | Limited Business Growth: Reliance on family-run operations & manual systems hinders scalability and operational efficiency. 223 |
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Problem Background:

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The store handles diverse SKUs with varying shelf lives. The owner's academic commitments limit time for detailed inventory management, leading to overstocking or stockouts, especially for high-demand/seasonal items. Lack of automated systems worsens this.

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Operating from a single room attached to home creates significant space limits. Excess inventory stored in the house is inefficient. This restricts product range expansion and complicates stock handling.

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The family-run model faces scalability challenges due to potential inconsistencies and lack of professional systems (sales/inventory tracking). Competition from quick-commerce platforms necessitates exploring options like home delivery.

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Data Overview & Collection

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Analysis Methods Used

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1. Descriptive Stats

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Calculated stats (Mean, Median, StdDev) to understand SKU sales patterns.

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2. Trend Analysis

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Identified seasonal/weekly patterns using Box Plots & Line Charts.

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3. ABC Segmentation

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Classified products (A, B, C) by sales volume contribution.

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4. Correlation Analysis

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Used Pearson Correlation & Heatmap for category relationships.

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5. Sales Prediction (ML)

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Applied Linear Regression (Milk example) to show forecast feasibility.

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Results & Findings

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Average Monthly Sales Distribution

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(Aggregated Daily Units - Placeholder Data)

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  • ▸ Peak Average Sales: Oct & Dec
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  • ▸ Summer Peak: May-July
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  • ▸ Lowest Average Sales: Jan & Feb
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  • (Note: Bar chart shows averages)
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Monthly Sales Trend by Category

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(Placeholder Data)

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  • FMCG leads sales volume.
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  • Dairy consistently second.
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  • Groceries lowest volume.
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  • ▸ Seasonal peak for FMCG in Summer (May-July).
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ABC Analysis (% Volume Contribution)

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(Placeholder Data)

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  • Category A (~76%): Prioritize (Milk, Bread, Chips...)
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  • Category B (~17%): Monitor (Pulse, Sugar, Butter...)
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  • Category C (~7%): Optimize (Rice, Spices, Salt...)
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Key Category Sales Correlations

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(Placeholder Data - Replaced Heatmap)

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Key Correlations:

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  • FMCG & Groceries: ~0.65
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  • FMCG & Dairy: ~0.61
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  • Dairy & Groceries: ~0.43
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Suggests bundling/placement opportunities.

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Sales Prediction Example: Milk

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(Linear Regression vs. Actual Monthly - Placeholder Data)

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  • ▸ Model captures general trend, useful for baseline planning.
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  • ▸ Misses volatility; advanced models (ARIMA) needed for accuracy.
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  • Validation (RMSE) required before use.
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Recommendations

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Optimizing Inventory:

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RecommendationRationalePriority
JIT/Lean for Cat A perishablesHigh volume, wastage riskHigh
Adjust stock via ABC AnalysisAlign stock with salesHigh
Use Sales Forecasts (start simple)Reduce guessworkMedium
Improve supplier communicationEnsure peak availabilityMedium
Discount near-expiry itemsReduce spoilage lossMedium
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Addressing Space & Layout:

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RecommendationRationalePriority
Prioritize shelf space for Cat A & BMaximize revenue/spaceHigh
Optimize layout: Place correlated items nearbyEncourage cross-sellingMedium
Invest in vertical shelvingMaximize storage capacityMedium
Reduce stock of Cat C itemsFree up valuable spaceMedium
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Enhancing Business Growth:

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RecommendationRationalePriority
Automate Tracking (Excel -> POS)Reduce manual effort, enable analysisHigh
Hire Temporary Support (Peaks/Exams)Manage bottlenecks, owner focusHigh
Introduce Targeted Home DeliveryCompete, add convenienceMedium
Implement Seasonal/Bundled PromotionsBoost sales, use correlationsMedium
Establish Backup Operational PlanEnsure business continuityMedium
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Projected Benefits:

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  • ▸ Profit Margin Increase
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  • ▸ Reduced Wastage Costs
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  • ▸ Improved Cash Flow
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  • ▸ Revenue Growth Potential
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  • ▸ Fewer Stockouts
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  • ▸ Optimized Inventory/Space
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  • ▸ Streamlined Processes
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  • ▸ Better Demand Preparedness
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  • ▸ Enhanced Customer Satisfaction
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  • ▸ Improved Competitiveness
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  • ▸ Sustainable Growth Path
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  • ▸ Reduced Owner Burden
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