📦 Inventory Simulator — EOQ / ROP

Supply Chain Analytics Project | Python · pandas · matplotlib
Part of a logistics optimization portfolio by Emmanuel Beristain Guzmán

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Overview

This tool models optimal inventory replenishment using two foundational supply chain formulas:

• EOQ (Economic Order Quantity) — finds the order size that minimizes total inventory costs
• ROP (Reorder Point) — determines when to trigger a new purchase order

Built to demonstrate applied knowledge of inventory management principles relevant to SAP MM, supply chain consulting, and logistics analytics roles.

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Key Concepts

Concept	Formula	Description
EOQ	√(2DS / H)	Optimal units per order
ROP	d × L + SS	Inventory level that triggers a new order
Safety Stock	Z × σ × √L	Buffer against demand variability
Total Cost	(D/Q)S + (Q/2)H	Annual ordering + holding cost

Variables:
D = Annual demand · S = Ordering cost · H = Holding cost/unit/year
d = Daily demand · L = Lead time (days) · Z = Service level z-score · σ = Demand std dev

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Features

• ✅ EOQ calculation with cost optimization
• ✅ ROP with configurable safety stock and service level
• ✅ Day-by-day inventory simulation (365 days)
• ✅ Visual charts: inventory level + cost curves
• ✅ CSV exports for further analysis (Excel / SAP-ready format)
• ✅ Console summary report with all KPIs

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Output Example

════════════════════════════════════════════════════
  INVENTORY SIMULATOR — EOQ / ROP REPORT
════════════════════════════════════════════════════
  Product          : Industrial Filter A-200
────────────────────────────────────────────────────
  INPUT PARAMETERS
────────────────────────────────────────────────────
  Annual demand    :      4,800  units/year
  Daily demand     :       13.2  units/day
  Ordering cost    :    $150.00  per order
  Holding cost     :     $12.00  per unit/year
  Lead time        :          7  days
  Service level    :        95%
────────────────────────────────────────────────────
  RESULTS
────────────────────────────────────────────────────
  EOQ              :        346  units per order
  Safety stock     :         19  units
  ROP              :        111  units (reorder point)
  Orders/year      :       13.9  times
  Cycle time       :       26.3  days between orders
  Annual cost      :  $4,156.92
════════════════════════════════════════════════════

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Project Structure

inventory-simulator/
│
├── inventory_simulator.py   # Main script — all logic here
├── requirements.txt         # Python dependencies
├── README.md
│
└── data/                    # Auto-generated on first run
    ├── inventory_level.png  # Chart: stock over 365 days
    ├── cost_curves.png      # Chart: EOQ cost optimization
    ├── simulation_results.csv
    └── kpi_summary.csv

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Getting Started

1. Clone the repo

git clone https://github.com/net421/inventory-simulator.git
cd inventory-simulator

2. Install dependencies

pip install -r requirements.txt

3. Run the simulator

python inventory_simulator.py

4. Customize your product

Open inventory_simulator.py and edit the params block at the bottom:

params = {
    "product_name":     "Your Product Name",
    "annual_demand":    4800,    # units per year
    "ordering_cost":    150.0,   # $ per order placed
    "holding_cost":     12.0,    # $ per unit per year
    "lead_time_days":   7,       # supplier lead time
    "service_level":    0.95,    # 95% → z-score 1.65
    "std_daily_demand": 3.5,     # demand variability (std dev)
}

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Business Application

This model directly maps to SAP MM functionality:

This script	SAP MM equivalent
EOQ calculation	MRP lot size optimization (EOQM)
ROP trigger	Reorder point planning (MRP Type = VB)
Safety stock	Safety stock field in material master (MR21)
Lead time	Planned delivery time in purchasing info record
Annual cost	Inventory valuation & cost center reporting

Understanding this logic is foundational for configuring MRP parameters in SAP and for supply chain consulting engagements.

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Charts Generated

Inventory Level (365 days)
Shows stock depletion, ROP trigger points, and order replenishment cycles.

EOQ Cost Curves
Visualizes the trade-off between ordering frequency and holding cost — the intersection is EOQ.

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Tech Stack

Tool	Use
Python 3.10+	Core logic
pandas	Simulation data + CSV export
matplotlib	Charts
numpy	Cost curve calculations

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Skills Demonstrated

• Inventory management modeling (EOQ, ROP, Safety Stock)
• Data simulation and time-series generation
• Data visualization for operational KPIs
• Python scripting for logistics automation
• Analytical thinking applied to supply chain optimization

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About

Emmanuel Beristain Guzmán
Logistics Engineer | Supply Chain Analytics | SAP Functional Trainee
📍 México (Remote) · 📧 emmanuel.beristain.guzman@gmail.com
🔗 github.com/net421

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Part of a series of supply chain analytics projects. See also: demand forecasting, VRP route optimization, and logistics dashboard.