Automate NWA Reporting with Claude: Faster Insights, Less Grind

Identify Core NWA Reports and Data Sources

First, pinpoint the specific weekly and monthly reports causing the most headaches. Think about your critical Walmart/Sam's Club metrics: On-Time, In-Full (OTIF), POS sales, inventory levels, forecast accuracy, or promotion performance. For each report, identify the exact systems where the raw data resides. This might be SAP S/4HANA for inventory and order data, JDA for demand forecasts, the WMI portal for OTIF scores, or even internal SQL databases. Clearly define the data points needed for each report and the desired output format, whether it's a summary table, trend analysis, or specific call-outs.

Automate Data Extraction from Key Systems

This is where you stop the manual copy-pasting. Implement scripts or API calls to pull data directly from your identified sources. For SAP S/4HANA, use standard connectors or custom ABAP reports to extract relevant tables. For WMI, explore Robotic Process Automation (RPA) tools if direct APIs aren't available, or utilize existing data warehouse exports. For JDA, leverage its integration capabilities. The goal is to get clean, structured data into a format Claude can easily process, typically CSV or JSON. Ensure these extracts are scheduled to run automatically before your reporting cycle begins.

import pandas as pd
import requests

# Example: Pulling data from a hypothetical internal API
def get_sales_data(start_date, end_date):
    api_url = "https://yourcompany.com/api/sales"
    params = {'start': start_date, 'end': end_date}
    response = requests.get(api_url, params=params)
    response.raise_for_status() # Raise an exception for HTTP errors
    return pd.DataFrame(response.json())

# Example: Loading a weekly WMI export
# wmi_data = pd.read_csv('weekly_wmi_otif_report.csv')

Pre-process and Clean Data for Claude

Raw data often needs cleaning and transformation before it's ready for AI analysis. Use Python with libraries like Pandas to handle missing values, correct data types, merge datasets from different sources (e.g., SAP inventory with WMI sales), and calculate derived metrics like 'weeks of supply.' This step ensures Claude receives accurate, consistent, and relevant information. The cleaner the input, the more reliable Claude's analysis and reports will be. Think about standardizing product IDs, date formats, and unit measures across all your data sources.

import pandas as pd

def clean_and_merge_data(sap_df, wmi_df):
    # Example: Merge SAP inventory with WMI sales on 'Product_ID' and 'Week'
    merged_df = pd.merge(sap_df, wmi_df, on=['Product_ID', 'Week'], how='inner')
    merged_df['Inventory_Weeks_Supply'] = merged_df['Current_Inventory'] / merged_df['Weekly_Sales']
    merged_df = merged_df.fillna(0) # Handle any remaining NaNs
    return merged_df

# merged_report_data = clean_and_merge_data(sap_inventory_df, wmi_sales_df)

Craft Effective Prompts for Claude's Analysis

The quality of your output depends heavily on the prompts you give Claude. Be specific. Instead of 'Summarize this data,' try: 'Analyze the attached weekly Walmart POS sales data for Product Group A. Identify the top 3 and bottom 3 performing SKUs by sales growth percentage compared to the prior week. Highlight any SKUs with inventory below 2 weeks of supply. Provide a concise executive summary of key trends and actionable insights for the operations team.' Include context, desired output format (bullet points, table, narrative), and any specific metrics to focus on. Iterate on your prompts to refine Claude's understanding.

prompt = """
Analyze the attached CSV data containing weekly Walmart POS sales, inventory, and OTIF scores for 'Product Group B' from the last 8 weeks.

Key Analysis Points:
1. Identify current week's top 5 SKUs by POS sales value and their week-over-week growth (%).
2. Flag any SKUs with an OTIF score below 95% for the last 4 weeks.
3. Calculate the average inventory weeks of supply for 'Product Group B'.
4. Provide a concise executive summary (max 150 words) highlighting critical performance issues and potential risks.

Format the output with a clear executive summary first, followed by bullet points for each analysis point.
"""

Integrate Claude API for Report Generation

With your data pre-processed and prompts ready, integrate with the Claude API. Send your cleaned data (e.g., as a CSV string or JSON) along with your crafted prompt. Claude will process this information and return the analyzed report. This step automates the analytical heavy lifting. You'll need to handle the API authentication, error checking, and response parsing. The goal is to get Claude's output in a structured format that can then be easily incorporated into your final report, whether it's an email, a SharePoint document, or a Power BI dashboard.

import anthropic

client = anthropic.Anthropic(api_key="YOUR_CLAUDE_API_KEY")

def generate_claude_report(prompt_text, data_csv_string):
    response = client.messages.create(
        model="claude-3-opus-20240229", # Or your preferred Claude model
        max_tokens=1500,
        messages=[
            {"role": "user", "content": f"{prompt_text}\n\nData:\n```csv\n{data_csv_string}\n```"}
        ]
    )
    return response.content[0].text

# final_report_content = generate_claude_report(prompt, preprocessed_data.to_csv(index=False))

Automate Report Distribution and Monitoring

Once Claude generates the report, automate its distribution. This could mean emailing the summary to your operations team, posting it to a Microsoft Teams channel, or updating a dashboard in Microsoft Power BI. Tools like Microsoft Power Automate or custom Python scripts can handle these tasks. Set up monitoring to ensure the data extraction, processing, and Claude API calls are running successfully. Implement alerts for any failures. Regularly review Claude's output for accuracy and refine your prompts or pre-processing steps as needed to continuously improve the quality and relevance of the reports.