Automating Supply Chain Management for Grocery Stores with Python

Streamline Supplier Data Management and Automate Ordering Processes

Managing supplier relationships and ordering processes can be time-consuming for grocery stores. By leveraging Python and pandas, you can automate supplier data management and the ordering process, ensuring that the store always has the right products at the right time. Automating these processes reduces manual errors, saves time, and enhances communication with suppliers.

Code Example:

import pandas as pd
import numpy as np

# Load supplier data (supplier_id, product_id, price_per_unit, lead_time, last_order_date)
supplier_data = pd.read_csv('supplier_data.csv')

# Calculate days since last order
supplier_data['days_since_last_order'] = (pd.to_datetime('today') - pd.to_datetime(supplier_data['last_order_date'])).dt.days

# Identify products with low stock that need reordering (example threshold of 10 units)
low_stock_products = supplier_data[supplier_data['product_id'].isin([1, 2, 3])]  # example products needing reordering

# Automate order suggestions based on low stock levels and supplier lead time
low_stock_products['order_quantity'] = np.maximum(10 - low_stock_products['days_since_last_order'], 0)  # Adjust order quantity

# Save the automated reorder list
low_stock_products[['supplier_id', 'product_id', 'order_quantity']].to_csv('automated_reorder_list.csv', index=False)
print("Automated reorder list saved.")

In this Python script, we load supplier data and calculate the days since the last order. For products with low stock, we automatically generate an order list based on the required reorder quantity, ensuring that the store stays stocked up without human intervention.

Integrate Real-Time Tracking and Supply Chain Analytics Using Python

Integrating real-time tracking into your supply chain management system can provide invaluable insights into product movement and supplier performance. With Python, you can integrate live data from suppliers and track products throughout the supply chain. This allows grocery stores to respond proactively to delays, issues, or supply shortages, improving supply chain efficiency.

Code Example:

import requests
import pandas as pd

# Real-time tracking API for supplier deliveries (example endpoint)
api_url = "https://supplier-api.com/tracking"
response = requests.get(api_url)

# Parse the response data (assuming JSON format)
tracking_data = response.json()

# Load previous supplier data for comparison
previous_tracking_data = pd.read_csv('previous_tracking_data.csv')

# Merge real-time tracking data with previous data to analyze delays or discrepancies
merged_data = pd.merge(previous_tracking_data, pd.DataFrame(tracking_data), on='order_id', how='left')

# Identify delayed shipments
merged_data['shipment_delay'] = pd.to_datetime(merged_data['expected_delivery_date']) - pd.to_datetime(merged_data['actual_delivery_date'])
delayed_shipments = merged_data[merged_data['shipment_delay'] > pd.Timedelta(0)]

# Save the report on delayed shipments
delayed_shipments[['order_id', 'supplier_id', 'product_id', 'shipment_delay']].to_csv('delayed_shipments_report.csv', index=False)
print("Delayed shipments report saved.")

This Python script integrates real-time tracking with your supply chain data by pulling live data from a tracking API and merging it with existing records. It identifies delayed shipments and generates a report for the store’s management, enabling timely intervention if necessary.

Improve Operational Efficiency with Automated Supply Chain Solutions

Automating supply chain processes doesn’t just streamline ordering but also enhances overall operational efficiency by reducing delays and minimizing errors. By using Python, pandas, and MongoDB, grocery stores can create a comprehensive solution that automates ordering, tracks shipments, and generates performance analytics, ultimately saving time and improving decision-making.

Code Example:

import pandas as pd
from datetime import datetime, timedelta

# Load supply chain data (supplier_id, product_id, order_date, expected_delivery_date, status)
supply_chain_data = pd.read_csv('supply_chain_data.csv')

# Calculate delivery delays
supply_chain_data['actual_delivery_date'] = pd.to_datetime(supply_chain_data['actual_delivery_date'])
supply_chain_data['expected_delivery_date'] = pd.to_datetime(supply_chain_data['expected_delivery_date'])
supply_chain_data['delivery_delay'] = (supply_chain_data['actual_delivery_date'] - supply_chain_data['expected_delivery_date']).dt.days

# Automatically update status based on delay (example: "Delayed" if delay > 3 days)
supply_chain_data['status'] = supply_chain_data['delivery_delay'].apply(lambda x: 'Delayed' if x > 3 else 'On Time')

# Save the updated supply chain data
supply_chain_data.to_csv('updated_supply_chain_data.csv', index=False)
print("Operational efficiency data updated and saved.")

In this script, we calculate delivery delays and automatically update the status of shipments based on the delay threshold. By automating these processes, grocery stores can better manage their supply chains and respond quickly to potential issues.

Why Choose Lillqvist Strat?

At Lillqvist Strat, we specialize in automating supply chain management for grocery stores. Our Python-based solutions help you:

• Automate supplier data management and ordering processes.
• Track shipments in real-time and generate performance analytics to minimize delays.
• Improve operational efficiency by automating various aspects of supply chain management.

Contact Lillqvist Strat today to implement a custom supply chain solution that will streamline your operations, enhance efficiency, and help you stay ahead of the competition.