Integrations
Used for data visualization of retail supply chain analytics
Used for the frontend and API routes of the retail supply chain analysis tool
Retail Supply Chain Analysis Tool
An AI-powered tool for analyzing retail supply chain data, providing actionable insights for inventory optimization, vendor management, and sales forecasting.
Features
- Upload and analyze multiple Excel/CSV files
- Support for Purchase Orders, Sales Data, and Inventory files
- Automatic file type detection
- Combined cross-analysis of multiple data sources
- Detailed visualizations and metrics
- Actionable recommendations for supply chain optimization
Getting Started
- Clone the repository
- Install dependencies:
npm install
- Start the development server:
npm run dev
- Open http://localhost:3000 in your browser
Usage
- Upload one or more Excel/CSV files containing retail data
- Select the appropriate data type for each file if not automatically detected
- Click "Analyze Files" to process the data
- View insights across different categories (Overview, Inventory, Vendors, Sales)
- Switch between combined analysis and individual file type analysis
Technologies
- Next.js for the frontend and API routes
- Claude AI for intelligent data analysis
- Chart.js for data visualization
- XLSX for Excel file processing
Environment Variables
Create a .env.local
file with the following variables:
License
I'd like to create an MCP where I can upload excel spreadsheets with retail store data including inventory reports, sales reports, etc and then have ai analyze the data for trends, forecasts, and other insights as if the AI is a Sr. Supply Chain expert for the retailer. Is that something you can help me with?
I have examples of the data if it is helpful.
Retail Supply Chain Analysis MCP Yes, I can definitely help you create a Multi-modal Conversational Persona (MCP) that analyzes retail store data like a senior Supply Chain expert. This is an excellent use case for AI analysis. High-Level Approach Here's how we could structure this project: Data Upload & Processing: Create an interface to upload Excel files containing inventory, sales, and other retail data Data Analysis: Process the data to extract meaningful insights Expert Persona: Design the AI to communicate insights like a senior Supply Chain expert Visualization: Provide visual representations of trends and forecasts
Implementation Steps
- Data Upload Component First, we'll need a way to upload and process Excel files:
import React, { useState } from 'react'; import * as XLSX from 'xlsx';
function RetailDataProcessor({ onDataProcessed }) { const [isProcessing, setIsProcessing] = useState(false); const [dataType, setDataType] = useState('purchase_orders'); const [location, setLocation] = useState('');
const handleFileUpload = async (e) => { const file = e.target.files[0]; setIsProcessing(true);
};
// Detect data type based on column headers const detectDataType = (data) => { if (!data || data.length === 0) return null;
};
// Normalize data based on type const normalizeData = (data, type) => { switch(type) { case 'purchase_orders': return data.map(item => ({ poNumber: item['PO #'], status: item['Status'], vendor: item['Vendor'], productSku: item['Product SKU'], productName: item['Product Name'], category: item['Category'], quantityOrdered: item['# Ordered'], quantityReceived: item['# Received'], quantityOutstanding: item['# Outstanding'], costPerItem: item['Cost Per Item'], costReceived: item['Cost Received'], costOutstanding: item['Cost Outstanding'], orderedAt: item['Ordered At'], daysOnOrder: item['Days on Order'], eta: item['ETA'] }));
};
return ( Data Type: <select value={dataType} onChange={(e) => setDataType(e.target.value)} > Purchase Orders Sales Data Inventory
); }
export default RetailDataProcessor;
2. Supply Chain Analysis Component
Next, we'll create a component to handle the AI analysis:
3. AI Service
Now, let's create a service to interact with the AI:
4. Backend API Endpoint
For the backend, we'll need an endpoint to process the data and interact with the AI:
5. Main App Component
Finally, let's put it all together:
Next Steps
To make this MCP more powerful:
- Examples of Data: Yes, seeing examples of your actual data would be extremely helpful to fine-tune the analysis and ensure the AI can properly interpret your specific retail data formats.
- Data Visualization: Add charts and graphs using libraries like Chart.js or D3.js to visualize trends and forecasts.
- Conversational Interface: Add a chat interface where users can ask follow-up questions about the analysis.
- Domain-Specific Prompting: Refine the AI prompts with specific supply chain terminology and frameworks based on your industry.
- Data Preprocessing: Add more sophisticated data cleaning and normalization before analysis.
Would you like to share examples of your data so I can help tailor this solution more specifically to your needs?
Yes, I'd love to share examples. Can I upload the files for you to see them?
Yes, you can upload your Excel files for me to review. This will help me tailor the solution specifically to your data structure and retail context. When you upload the files, I'll be able to: Analyze the structure and format of your data Identify key fields and relationships Suggest specific preprocessing steps needed Refine the AI prompts to better interpret your particular retail data Recommend appropriate visualizations for the insights Please go ahead and upload your example inventory reports, sales data, or any other retail spreadsheets you'd like me to examine. Once I see the actual data, I can provide much more specific guidance on how to structure the MCP for maximum effectiveness.
I'll share 4 screen shots.
First - Aberdeen PO's: Second - Huron PO's: Third - Luverne PO's: Fourth - Sales Data from July 2023 through February 2024:
Thank you for sharing these screenshots! I can now see the structure of your retail data, which will help me design a more tailored MCP solution. Let me analyze what I'm seeing:
Data Analysis
- Purchase Order Reports The first three screenshots show purchase order details for different locations (Aberdeen, Huron, and Luverne) with similar structures: Key fields: PO #, Status, Receive At, Vendor information, Product SKU/ID, Product Name, Category Quantity data: # Ordered, # Received, # Outstanding Financial data: Cost Per Item, Cost Received, Cost Outstanding Timing information: Committed On, Ordered At, Days on Order, ETA Additional info: Ordering Comments, Receiving Comments, Order Entry # These reports track mobile devices and accessories (smartphones, cables, chargers, etc.) with detailed procurement information.
- Sales Data The fourth screenshot shows sales data with: Transaction details: Invoice #, Received dates, Sale By information Product information: Various mobile devices and accessories Pricing data: Multiple price-related columns Location/store information MCP Design Based on Your Data Now that I understand your data structure, here's a more tailored approach for your retail supply chain MCP:
// components/MobileRetailAnalysis.js import React, { useState, useEffect } from 'react'; import { analyzeRetailData } from '../services/aiService'; import { InventoryTurnoverChart, ProductCategoryChart, VendorPerformanceChart, SalesVsPurchasesChart } from './charts';
function MobileRetailAnalysis({ data }) { const [analysis, setAnalysis] = useState(null); const [isAnalyzing, setIsAnalyzing] = useState(false); const [selectedView, setSelectedView] = useState('overview');
useEffect(() => { if (data) { performAnalysis(); } }, [data]);
const performAnalysis = async () => { setIsAnalyzing(true); try { const result = await analyzeRetailData(data); setAnalysis(result); } catch (error) { console.error("Error analyzing data:", error); } finally { setIsAnalyzing(false); } };
// Render different views based on selection const renderAnalysisView = () => { if (!analysis) return null;
};
return ( Mobile Retail Supply Chain Analysis <button className={selectedView === 'overview' ? 'active' : ''} onClick={() => setSelectedView('overview')} > Overview <button className={selectedView === 'inventory' ? 'active' : ''} onClick={() => setSelectedView('inventory')} > Inventory <button className={selectedView === 'vendor' ? 'active' : ''} onClick={() => setSelectedView('vendor')} > Vendor <button className={selectedView === 'sales' ? 'active' : ''} onClick={() => setSelectedView('sales')} > Sales
); }
export default MobileRetailAnalysis;
Now, let's update the AI service to be more specific to your mobile retail data:
// services/aiService.js export async function analyzeRetailData(data) { try { const response = await fetch('/api/analyze-mobile-retail', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ data }), });
} catch (error) { console.error('Error in AI analysis service:', error); throw error; } }
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Enables users to upload retail data, analyze trends, optimize inventory, and forecast sales using AI-powered insights, acting as a senior supply chain expert.
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