Using historical sales data from ORDERS and ORDER_ITEMS, current stock levels from PRODUCT_INVENTORY, and supplier lead times (assume an average lead time of 14 days if not explicitly available per product), calculate the reorder point (ROP) and optimal stock level for the top 20 products by sales volume. The output should be a CSV file or table listing each product with: 1) Current stock. 2) Calculated ROP. 3) Calculated optimal stock level. 4) Calculated safety stock. 5) Estimated days of stock remaining. Briefly explain the formulas used (e.g., Economic Order Quantity - EOQ, if applicable).
I'll help you calculate reorder points and optimal stock levels for your top-selling products. Let me first check what data sources are available to us.
Now, let's examine the structure of the relevant tables in the nova_glow database to understand what data we have available.
Column | Type | Nullable | Primary |
---|---|---|---|
ORDER_ID | VARCHAR(16777216) | No | No |
CUSTOMER_ID | VARCHAR(16777216) | No | No |
ORDER_DATE | TIMESTAMP_NTZ(9) | No | No |
ORDER_STATUS | VARCHAR(16777216) | No | No |
SHIPPING_STREET_ADDRESS | VARCHAR(16777216) | No | No |
SHIPPING_CITY | VARCHAR(16777216) | No | No |
SHIPPING_STATE | VARCHAR(16777216) | No | No |
SHIPPING_ZIP_CODE | VARCHAR(16777216) | No | No |
SHIPPING_METHOD | VARCHAR(16777216) | No | No |
PAYMENT_METHOD_USED | VARCHAR(16777216) | No | No |
PROMO_CODE_USED | VARCHAR(16777216) | No | No |
SUBTOTAL_AMOUNT | FLOAT | No | No |
DISCOUNT_AMOUNT | FLOAT | No | No |
SHIPPING_COST | FLOAT | No | No |
TAX_AMOUNT | FLOAT | No | No |
TOTAL_ORDER_AMOUNT | FLOAT | No | No |
Column | Type | Nullable | Primary |
---|---|---|---|
ORDER_ITEM_ID | VARCHAR(16777216) | No | No |
ORDER_ID | VARCHAR(16777216) | No | No |
PRODUCT_ID | VARCHAR(16777216) | No | No |
QUANTITY | NUMBER(38,0) | No | No |
PRICE_PER_UNIT_AT_PURCHASE | FLOAT | No | No |
TOTAL_ITEM_PRICE | FLOAT | No | No |
Column | Type | Nullable | Primary |
---|---|---|---|
INVENTORY_ID | VARCHAR(16777216) | No | No |
PRODUCT_ID | VARCHAR(16777216) | No | No |
CURRENT_STOCK_LEVEL | NUMBER(38,0) | No | No |
REORDER_POINT_THRESHOLD | NUMBER(38,0) | No | No |
TARGET_STOCK_LEVEL | NUMBER(38,0) | No | No |
SUPPLIER_LEAD_TIME_DAYS | NUMBER(38,0) | No | No |
LAST_RESTOCK_DATE | DATE | No | No |
LAST_STOCK_CHECK_DATE | TIMESTAMP_NTZ(9) | No | No |
Column | Type | Nullable | Primary |
---|---|---|---|
PRODUCT_ID | VARCHAR(16777216) | No | No |
PRODUCT_NAME | VARCHAR(16777216) | No | No |
CATEGORY | VARCHAR(16777216) | No | No |
SUPPLIER_NAME | VARCHAR(16777216) | No | No |
COST_PRICE | FLOAT | No | No |
SELLING_PRICE | FLOAT | No | No |
DESCRIPTION | VARCHAR(16777216) | No | No |
DATE_ADDED | DATE | No | No |
Great! I have all the necessary tables to complete this task. Let's create a notebook to calculate the reorder points and optimal stock levels for the top 20 products by sales volume.
Now, let's start building our analysis. First, I'll add a markdown cell to explain the purpose of this notebook.
# Inventory Reorder Point and Optimal Stock Level Analysis
This notebook calculates the reorder ...
Now, let's add a Python cell to import the necessary libraries and set up our environment.
# Import necessary libraries
import pandas as pd
import numpy as np
import math
from datetime imp...
Now, let's retrieve the data we need from the database. First, let's get the historical sales data to identify the top 20 products by sales volume.
# Query to get historical sales data for the past year
sql_sales_data = """
SELECT
oi.PRODUC...
It seems we don't have any sales data for the past year. Let's modify our query to get all available sales data without the date restriction.
# Query to get all historical sales data
sql_sales_data = """
SELECT
oi.PRODUCT_ID,
p.PR...
Great! Now we have sales data. Let's also get the current inventory data.
# Query to get current inventory data
sql_inventory_data = """
SELECT
pi.PRODUCT_ID,
p.P...
Now, let's process the sales data to identify the top 20 products by sales volume and calculate the average daily demand and its standard deviation.
# Convert order_date to datetime
sales_data['order_date'] = pd.to_datetime(sales_data['order_date...
Now, let's calculate the daily demand statistics for these top 20 products.
# Calculate daily demand statistics for the top 20 products
daily_demand_stats = []
for _, produ...
I notice that the standard deviation of daily demand is showing as 0.00 for all products. This is likely because the sales data is aggregated at a daily level and doesn't show much variation. Let's try a different approach to calculate the standard deviation by looking at weekly or monthly sales patterns.
# Calculate weekly demand statistics for the top 20 products
weekly_demand_stats = []
for _, pro...
Now, let's merge the demand statistics with the inventory data to calculate the reorder points and optimal stock levels.
# Merge demand statistics with inventory data
inventory_analysis = pd.merge(demand_stats_df, inve...
Let's save this data to the store and create a visualization to better understand the inventory status.
# Save the inventory analysis to the store
store.set("inventory_analysis", inventory_analysis)
#...
Now, let's create another visualization to show the days of stock remaining for each product.
# Create a visualization for days of stock remaining
days_chart = alt.Chart(inventory_analysis).m...
Let's fix the visualization for days of stock remaining:
# Create a visualization for days of stock remaining
# Add a column for color category based on d...
Now, let's create a final table with all the key metrics for the top 20 products:
# Create a final table with all the key metrics
final_table = inventory_analysis[['product_id', '...
Let's export the final table to CSV:
# Export the final table to CSV
csv_data = final_table.to_csv(index=False)
# Display the first f...
Finally, let's add a summary and conclusion to our analysis:
# Summary of the inventory analysis
print("INVENTORY ANALYSIS SUMMARY")
print("=" * 50)
# Count ...
Let's add a final markdown cell to summarize our findings and explain the formulas used:
# Summary and Conclusions
## Key Findings
1. **Immediate Reordering Required**:
- **PulseWa...
I'll now create a final visualization to compare the current reorder point thresholds in the system with our calculated reorder points:
# Compare current reorder point thresholds with calculated reorder points
comparison_df = invento...
Let's add one final markdown cell to explain how to use the results of this analysis:
# How to Use This Analysis
## Implementing the Results
1. **CSV Export**:
- The complete an...
can you make an cool/informational data visualuzation of the products
I'll create a more informative and visually appealing data visualization for the products. Let's add a new visualization that combines multiple metrics in an interesting way.
# Create an enhanced visualization that combines multiple metrics
# First, prepare the data with ...
Let's create one more visualization that shows the inventory status in a more creative way:
# Create a radial visualization of inventory status
# First, prepare the data
radial_data = inven...
Let's add a markdown cell to explain these new visualizations:
# Advanced Product Inventory Visualizations
I've created several advanced visualizations to prov...