Supply Chain Automation for Construction Materials Using Excel & Python

Automate the Procurement Process with Excel & Python

Managing the procurement process for construction materials can be complex and time-consuming, especially when relying on manual systems. By leveraging Python and Excel, you can automate key tasks such as material ordering, inventory management, and procurement tracking, streamlining the entire process and ensuring that construction projects are supplied with the necessary materials on time.

Automating procurement allows you to focus on other aspects of project management, saving you both time and money.

Code Example:

import pandas as pd
from datetime import datetime

# Sample materials data
materials_data = {
    'Material ID': [1, 2, 3],
    'Material Name': ['Cement', 'Steel', 'Bricks'],
    'Stock Level': [150, 100, 300],
    'Min Stock Level': [100, 80, 200],
    'Reorder Quantity': [50, 40, 100],
    'Last Order Date': ['2023-05-01', '2023-06-15', '2023-07-20']
}

# Create DataFrame
df_materials = pd.DataFrame(materials_data)

# Check if material needs to be reordered
def check_reorder(row):
    if row['Stock Level'] < row['Min Stock Level']:
        return f"Reorder {row['Reorder Quantity']} units of {row['Material Name']}"
    else:
        return "Stock sufficient"

# Apply function to check reorder status
df_materials['Reorder Status'] = df_materials.apply(check_reorder, axis=1)

# Save the material procurement report to Excel
df_materials.to_excel('material_procurement.xlsx', index=False)

print("Procurement process automated and saved to Excel.")

In this Python example, the procurement process is automated by checking the stock levels against the minimum stock levels. When materials need to be reordered, the system automatically suggests the reorder quantity based on predefined parameters, helping you keep your materials in stock without manual checks.

Use Real-Time Data to Manage Material Stock Levels and Orders

With Excel integrated with Python, you can use real-time data to update material stock levels and automatically generate orders. This real-time tracking ensures that your team has the necessary materials for construction without the risk of delays due to stockouts.

By using Python, data can be pulled from external sources, such as supplier databases or inventory management systems, and seamlessly integrated into Excel, allowing for more precise tracking and reporting.

Code Example:

from openpyxl import load_workbook

# Load the existing procurement Excel file
wb = load_workbook('material_procurement.xlsx')
ws = wb.active

# Example of real-time data update for material stock levels
real_time_stock_data = {
    'Cement': 120,  # New stock level for Cement
    'Steel': 50,    # New stock level for Steel
    'Bricks': 250   # New stock level for Bricks
}

# Update the stock levels in the Excel sheet
for row in range(2, len(df_materials)+2):  # Adjust row index for real Excel data
    material_name = ws[f'A{row}'].value
    if material_name in real_time_stock_data:
        ws[f'C{row}'] = real_time_stock_data[material_name]

# Save the updated file
wb.save('updated_material_procurement.xlsx')

print("Real-time material stock levels updated.")

Here, we update the material stock levels directly in Excel using Python, ensuring that procurement decisions are based on the most up-to-date information. This automated process eliminates the risk of manual errors and ensures that construction materials are always properly tracked.

Reduce Delays and Costs with Streamlined Material Management

Efficient material management is key to reducing delays and costs in construction projects. With automated procurement systems, you can minimize the chances of material shortages and overstocking, both of which can lead to project delays and increased expenses.

By using Excel and Python, you can automate critical tasks such as:

• Material order generation based on predefined stock thresholds.
• Supplier tracking and order history for better decision-making.
• Cost analysis and budget optimization for procurement decisions.

Code Example:

# Sample supplier data for material cost analysis
supplier_data = {
    'Material': ['Cement', 'Steel', 'Bricks'],
    'Supplier': ['Supplier A', 'Supplier B', 'Supplier C'],
    'Unit Price': [10, 15, 5],  # Price per unit
}

df_supplier = pd.DataFrame(supplier_data)

# Calculate total cost for ordering the reorder quantity
def calculate_order_cost(row):
    material_name = row['Material']
    reorder_quantity = df_materials.loc[df_materials['Material Name'] == material_name, 'Reorder Quantity'].values[0]
    unit_price = df_supplier.loc[df_supplier['Material'] == material_name, 'Unit Price'].values[0]
    return reorder_quantity * unit_price

df_materials['Order Cost'] = df_materials.apply(calculate_order_cost, axis=1)

# Save the procurement cost data to Excel
df_materials.to_excel('procurement_with_costs.xlsx', index=False)

print("Material procurement costs calculated and saved.")

In this Python example, we calculate the order cost based on the unit price and reorder quantity, allowing you to analyze and optimize procurement costs. By automating this process, you can identify cost-saving opportunities and make more informed decisions for your construction projects.

Why Choose Lillqvist Strat?

At Lillqvist Strat, we specialize in automating procurement and material management systems for construction companies using Excel, Python, and MongoDB. Our tailored solutions:

• Automate procurement to ensure timely material deliveries.
• Streamline inventory management to reduce waste and delays.
• Optimize costs with real-time tracking and automated analysis.

By choosing Lillqvist Strat, you ensure that your construction materials are always well-managed, reducing operational bottlenecks and saving you time and money. Contact us today to learn how we can automate your supply chain management and improve your construction business operations.