Supply Chain Analysis

Analyzing your retail data...

Key Insights

\n \n

Inventory Recommendations

\n \n

Sales Trends

\n \n

Forecasts

\n )}\n

\n );\n}\n\nexport default SupplyChainAnalysis;\n```\n\n### 3. AI Service\n\nNow, let's create a service to interact with the AI:\n\n```javascript:README.md\n// services/aiService.js\n\nexport async function analyzeRetailData(data) {\n try {\n const response = await fetch('/api/analyze-mobile-retail', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({ data }),\n });\n \n if (!response.ok) {\n throw new Error('Failed to analyze data');\n }\n \n return await response.json();\n } catch (error) {\n console.error('Error in AI analysis service:', error);\n throw error;\n }\n}\n```\n\n### 4. Backend API Endpoint\n\nFor the backend, we'll need an endpoint to process the data and interact with the AI:\n\n```javascript\n// api/analyze-mobile-retail.js\nimport { OpenAI } from 'openai';\n\nconst openai = new OpenAI({\n apiKey: process.env.OPENAI_API_KEY,\n});\n\nexport default async function handler(req, res) {\n if (req.method !== 'POST') {\n return res.status(405).json({ error: 'Method not allowed' });\n }\n\n try {\n const { data } = req.body;\n \n // Create a specialized prompt based on data type\n let prompt = '';\n \n if (data.type === 'purchase_orders') {\n prompt = createPurchaseOrderPrompt(data);\n } else if (data.type === 'sales_data') {\n prompt = createSalesDataPrompt(data);\n } else if (data.type === 'inventory') {\n prompt = createInventoryPrompt(data);\n } else {\n prompt = createGeneralPrompt(data);\n }\n \n // Call the AI\n const completion = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n { \n role: \"system\", \n content: \"You are a senior supply chain expert with 20+ years of experience in mobile device retail. You analyze data and provide actionable insights with a focus on inventory optimization, vendor management, and sales forecasting for smartphone and mobile accessory retailers.\" \n },\n { role: \"user\", content: prompt }\n ],\n temperature: 0.2,\n });\n \n // Process the AI response\n const analysisText = completion.choices[0].message.content;\n \n // Parse the response into structured data\n const analysisData = parseAnalysisResponse(analysisText, data.type);\n \n return res.status(200).json(analysisData);\n } catch (error) {\n console.error('Error analyzing mobile retail data:', error);\n return res.status(500).json({ error: 'Failed to analyze data' });\n }\n}\n\n// Create specialized prompts based on data type\nfunction createPurchaseOrderPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following purchase order data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Vendor performance (delivery times, order fulfillment)\n 2. Product category insights (smartphones, accessories, etc.)\n 3. Inventory management recommendations\n 4. Cost optimization opportunities\n 5. Order cycle time improvements\n \n Provide specific actionable recommendations for improving the supply chain.\n `;\n}\n\nfunction createSalesDataPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following sales data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Sales trends by product category and specific models\n 2. Profitability analysis by product type\n 3. Seasonal patterns and forecasting\n 4. Product mix optimization\n 5. Inventory replenishment recommendations based on sales velocity\n \n Provide specific actionable recommendations for improving sales and inventory alignment.\n `;\n}\n\nfunction createInventoryPrompt(data) {\n // Similar structure for inventory data\n}\n\nfunction createGeneralPrompt(data) {\n // Fallback for other data types\n}\n\n// Parse the AI response into structured data\nfunction parseAnalysisResponse(text, dataType) {\n // Basic parsing logic - this could be enhanced with more sophisticated parsing\n const sections = {\n keyInsights: extractSection(text, \"Key Insights\"),\n inventoryAnalysis: extractSection(text, \"Inventory Analysis\"),\n inventoryRecommendations: extractSection(text, \"Inventory Recommendations\"),\n vendorAnalysis: extractSection(text, \"Vendor Analysis\"),\n vendorRecommendations: extractSection(text, \"Vendor Recommendations\"),\n salesTrends: extractSection(text, \"Sales Trends\"),\n salesForecasts: extractSection(text, \"Sales Forecasts\")\n };\n \n // Add placeholder metrics and chart data\n // In a real implementation, you might extract these from the AI response\n // or calculate them directly from the data\n const metrics = {\n inventoryTurnover: \"4.2\",\n fulfillmentRate: \"92.5\",\n avgDaysOnOrder: \"6.3\"\n };\n \n const charts = {\n inventoryTurnover: [],\n categoryPerformance: [],\n vendorPerformance: [],\n salesVsPurchases: []\n };\n \n return {\n ...sections,\n metrics,\n charts\n };\n}\n\nfunction extractSection(text, sectionName) {\n const regex = new RegExp(`${sectionName}[:\\\\s]+(.*?)(?=\\\\d+\\\\.\\\\s+[A-Z]|#|$)`, 's');\n const match = text.match(regex);\n return match ? match[1].trim() : '';\n}\n```\n\n### 5. Main App Component\n\nFinally, let's put it all together:\n\n```javascript\nimport React, { useState } from 'react';\nimport DataUploader from './components/DataUploader';\nimport SupplyChainAnalysis from './components/SupplyChainAnalysis';\n\nfunction RetailAnalysisMCP() {\n const [retailData, setRetailData] = useState(null);\n \n const handleDataProcessed = (data) => {\n setRetailData(data);\n };\n \n return (\n

Retail Supply Chain Analysis

Upload your retail data for expert supply chain analysis

\n \n

\n \n \n {retailData && }\n

\n );\n}\n\nexport default RetailAnalysisMCP;\n```\n\n## Next Steps\n\nTo make this MCP more powerful:\n\n1. **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.\n\n2. **Data Visualization**: Add charts and graphs using libraries like Chart.js or D3.js to visualize trends and forecasts.\n\n3. **Conversational Interface**: Add a chat interface where users can ask follow-up questions about the analysis.\n\n4. **Domain-Specific Prompting**: Refine the AI prompts with specific supply chain terminology and frameworks based on your industry.\n\n5. **Data Preprocessing**: Add more sophisticated data cleaning and normalization before analysis.\n\nWould you like to share examples of your data so I can help tailor this solution more specifically to your needs?\n\n###\n\nYes, I'd love to share examples. Can I upload the files for you to see them?\n\nYes, 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.\nWhen you upload the files, I'll be able to:\nAnalyze the structure and format of your data\nIdentify key fields and relationships\nSuggest specific preprocessing steps needed\nRefine the AI prompts to better interpret your particular retail data\nRecommend appropriate visualizations for the insights\nPlease 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.\n\n###\n\nI'll share 4 screen shots.\n\nFirst - Aberdeen PO's:\nSecond - Huron PO's:\nThird - Luverne PO's:\nFourth - Sales Data from July 2023 through February 2024:\n\n\nThank 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:\n\nData Analysis\n1. Purchase Order Reports\nThe first three screenshots show purchase order details for different locations (Aberdeen, Huron, and Luverne) with similar structures:\nKey fields: PO #, Status, Receive At, Vendor information, Product SKU/ID, Product Name, Category\nQuantity data: # Ordered, # Received, # Outstanding\nFinancial data: Cost Per Item, Cost Received, Cost Outstanding\nTiming information: Committed On, Ordered At, Days on Order, ETA\nAdditional info: Ordering Comments, Receiving Comments, Order Entry #\nThese reports track mobile devices and accessories (smartphones, cables, chargers, etc.) with detailed procurement information.\n2. Sales Data\nThe fourth screenshot shows sales data with:\nTransaction details: Invoice #, Received dates, Sale By information\nProduct information: Various mobile devices and accessories\nPricing data: Multiple price-related columns\nLocation/store information\nMCP Design Based on Your Data\nNow that I understand your data structure, here's a more tailored approach for your retail supply chain MCP:\n\n// components/MobileRetailAnalysis.js\nimport React, { useState, useEffect } from 'react';\nimport { analyzeRetailData } from '../services/aiService';\nimport { \n InventoryTurnoverChart, \n ProductCategoryChart,\n VendorPerformanceChart,\n SalesVsPurchasesChart\n} from './charts';\n\nfunction MobileRetailAnalysis({ data }) {\n const [analysis, setAnalysis] = useState(null);\n const [isAnalyzing, setIsAnalyzing] = useState(false);\n const [selectedView, setSelectedView] = useState('overview');\n \n useEffect(() => {\n if (data) {\n performAnalysis();\n }\n }, [data]);\n \n const performAnalysis = async () => {\n setIsAnalyzing(true);\n try {\n const result = await analyzeRetailData(data);\n setAnalysis(result);\n } catch (error) {\n console.error(\"Error analyzing data:\", error);\n } finally {\n setIsAnalyzing(false);\n }\n };\n \n // Render different views based on selection\n const renderAnalysisView = () => {\n if (!analysis) return null;\n \n switch(selectedView) {\n case 'overview':\n return (\n

Key Insights

\n \n

Inventory Turnover

{analysis.metrics.inventoryTurnover}

Order Fulfillment Rate

{analysis.metrics.fulfillmentRate}%

Average Days on Order

{analysis.metrics.avgDaysOnOrder}

\n );\n \n case 'inventory':\n return (\n

Inventory Analysis

\n \n

Top Products by Turnover

\n \n \n

Category Performance

\n \n \n

Inventory Recommendations

\n );\n \n case 'vendor':\n return (\n

Vendor Performance

\n \n

Vendor Delivery Performance

\n \n \n

Vendor Recommendations

\n );\n \n case 'sales':\n return (\n

Sales Trends

\n \n

Sales vs Purchases

\n \n \n

Sales Forecasts

\n );\n \n default:\n return null;\n }\n };\n \n return (\n

Mobile Retail Supply Chain Analysis

\n \n \n \n \n

\n \n {isAnalyzing ? (\n

Analyzing your mobile retail data...

\n ) : renderAnalysisView()}\n

\n );\n}\n\nexport default MobileRetailAnalysis;\n\n###\n\nNow, let's update the AI service to be more specific to your mobile retail data:\n\n// services/aiService.js\nexport async function analyzeRetailData(data) {\n try {\n const response = await fetch('/api/analyze-mobile-retail', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({ data }),\n });\n \n if (!response.ok) {\n throw new Error('Failed to analyze data');\n }\n \n return await response.json();\n } catch (error) {\n console.error('Error in AI analysis service:', error);\n throw error;\n }\n}","English","tag","en-US",{"_29":41,"_33":45,"_38":46},{"_31":42,"_33":43,"_35":44},"Permite a los usuarios cargar datos minoristas, analizar tendencias, optimizar el inventario y pronosticar ventas utilizando información impulsada por inteligencia artificial, actuando como un experto senior en la cadena de suministro.","Análisis de la cadena de suministro minorista MCP","# Herramienta de análisis de la cadena de suministro minorista\n\nUna herramienta impulsada por IA para analizar datos de la cadena de suministro minorista, proporcionando información útil para la optimización del inventario, la gestión de proveedores y la previsión de ventas.\n\n## Características\n\n* Subir y analizar múltiples archivos Excel/CSV\n* Soporte para órdenes de compra, datos de ventas y archivos de inventario\n* Detección automática del tipo de archivo\n* Análisis cruzado combinado de múltiples fuentes de datos\n* Visualizaciones y métricas detalladas\n* Recomendaciones prácticas para optimizar la cadena de suministro\n\n## Empezando\n\n1. Clonar el repositorio\n2. Instalar dependencias: `npm install`\n3. Inicie el servidor de desarrollo: `npm run dev`\n4. Abra en su navegador\n\n## Uso\n\n1. Cargue uno o más archivos Excel/CSV que contengan datos minoristas\n2. Seleccione el tipo de datos apropiado para cada archivo si no se detecta automáticamente\n3. Haga clic en \"Analizar archivos\" para procesar los datos.\n4. Vea información en diferentes categorías (Descripción general, Inventario, Proveedores, Ventas)\n5. Cambiar entre análisis combinado y análisis de tipo de archivo individual\n\n## Tecnologías\n\n* Next.js para las rutas de API y frontend\n* Claude AI para el análisis inteligente de datos\n* Chart.js para visualización de datos\n* XLSX para el procesamiento de archivos de Excel\n\n## Variables de entorno\n\nCree un archivo `.env.local` con las siguientes variables:\n\n```\nANTHROPIC_API_KEY=your_api_key_here\n```\n\n## Licencia\n\n[Instituto Tecnológico de Massachusetts (MIT)](LICENSE)\n\nMe gustaría crear un MCP donde pueda cargar hojas de cálculo de Excel con datos de la tienda minorista, incluyendo informes de inventario, informes de ventas, etc., y luego permitir que la IA analice los datos para identificar tendencias, pronósticos y otros datos, como si la IA fuera un experto sénior en cadena de suministro del minorista. ¿Podrían ayudarme con eso?\n\nTengo ejemplos de los datos si son útiles.\n\nAnálisis de la cadena de suministro minorista (MCP) Sí, definitivamente puedo ayudarte a crear un Personaje Conversacional Multimodal (MCP) que analiza los datos de las tiendas minoristas como un experto sénior en cadena de suministro. Este es un excelente caso de uso para el análisis de IA. Enfoque de alto nivel Así es como podríamos estructurar este proyecto: Carga y procesamiento de datos: Crear una interfaz para cargar archivos de Excel que contengan inventario, ventas y otros datos minoristas. Análisis de datos: Procesar los datos para extraer información significativa. Personaje experto: Diseñar la IA para comunicar información como un experto sénior en cadena de suministro. Visualización: Proporcionar representaciones visuales de tendencias y pronósticos.\n\nPasos de implementación\n\n1. Componente de carga de datos Primero, necesitaremos una forma de cargar y procesar archivos de Excel:\n\nimportar React, { useState } de 'react'; importar \\* como XLSX de 'xlsx';\n\nfunción RetailDataProcessor({ onDataProcessed }) { const \\[isProcessing, setIsProcessing] = useState(false); const \\[dataType, setDataType] = useState('pedidos\\_de\\_compra'); const \\[location, setLocation] = useState('');\n\nconst handleFileUpload = async (e) => { const archivo = e.target.files\\[0]; setIsProcessing(true);\n\n```\nconst reader = new FileReader();\nreader.onload = (event) => {\n try {\n const data = new Uint8Array(event.target.result);\n const workbook = XLSX.read(data, { type: 'array' });\n \n // Process the first sheet by default\n const worksheet = workbook.Sheets[workbook.SheetNames[0]];\n const jsonData = XLSX.utils.sheet_to_json(worksheet);\n \n // Detect data type based on column headers\n const detectedType = detectDataType(jsonData);\n \n // Process and normalize the data based on type\n const processedData = {\n type: detectedType || dataType,\n location: location,\n data: normalizeData(jsonData, detectedType || dataType)\n };\n \n onDataProcessed(processedData);\n } catch (error) {\n console.error(\"Error processing Excel file:\", error);\n } finally {\n setIsProcessing(false);\n }\n};\n\nreader.readAsArrayBuffer(file);\n```\n\n};\n\n// Detectar el tipo de datos según los encabezados de columna const detectDataType = (data) => { if (!data || data.length === 0) return null;\n\n```\nconst headers = Object.keys(data[0]);\n\nif (headers.includes('PO #') && headers.includes('# Ordered')) {\n return 'purchase_orders';\n} else if (headers.includes('Invoice #') || headers.includes('Sale By')) {\n return 'sales_data';\n} else if (headers.includes('On Hand') || headers.includes('In Stock')) {\n return 'inventory';\n}\n\nreturn null;\n```\n\n};\n\n// Normalizar datos según el tipo 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'], amountOrdered: item\\['# Ordered'], amountReceived: item\\['# Received'], amountOutstanding: item\\['# Outstanding'], costPerItem: item\\['Cost Per Item'], costReceived: item\\['Cost Received'], costOutstanding: item\\['Cost Outstanding'], ordersAt: item\\['Ordered At'], daysOnOrder: item\\['Days on Order'], eta: item\\['ETA'] }));\n\n```\n case 'sales_data':\n // Normalize sales data based on your fourth screenshot\n return data.map(item => ({\n invoiceNumber: item['Invoice #'],\n saleDate: item['Date'],\n saleBy: item['Sale By'],\n product: item['Product Name'],\n category: item['Category'],\n quantity: item['Qty'],\n price: item['Price'],\n cost: item['Cost'],\n profit: item['Profit'],\n location: item['Location'] || location\n }));\n \n default:\n return data;\n}\n```\n\n};\n\ndevolver (Tipo de datos: \\ setLocation(e.target.value)}\n placeholder=\"Aberdeen, Huron, Luverne, etc.\"\n />\n

\n \n

\n \n {isProcessing &&

Processing data...

}\n

\n```\n\n); }\n\nexportar predeterminado RetailDataProcessor;\n\n### 2. Componente de análisis de la cadena de suministro\n\nA continuación, crearemos un componente para manejar el análisis de IA:\n\n```javascript\nimport React, { useState, useEffect } from 'react';\nimport { analyzeRetailData } from '../services/aiService';\n\nfunction SupplyChainAnalysis({ data }) {\n const [analysis, setAnalysis] = useState(null);\n const [isAnalyzing, setIsAnalyzing] = useState(false);\n \n useEffect(() => {\n if (data) {\n performAnalysis();\n }\n }, [data]);\n \n const performAnalysis = async () => {\n setIsAnalyzing(true);\n try {\n // This would call your AI service\n const result = await analyzeRetailData(data);\n setAnalysis(result);\n } catch (error) {\n console.error(\"Error analyzing data:\", error);\n } finally {\n setIsAnalyzing(false);\n }\n };\n \n return (\n

Supply Chain Analysis

\n \n {isAnalyzing &&

Analyzing your retail data...

}\n \n {analysis && (\n

Key Insights

\n \n

Inventory Recommendations

\n \n

Sales Trends

\n \n

Forecasts

\n )}\n

\n );\n}\n\nexport default SupplyChainAnalysis;\n```\n\n### 3. Servicio de IA\n\nAhora, creemos un servicio para interactuar con la IA:\n\n```javascript:README.md\n// services/aiService.js\n\nexport async function analyzeRetailData(data) {\n try {\n const response = await fetch('/api/analyze-mobile-retail', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({ data }),\n });\n \n if (!response.ok) {\n throw new Error('Failed to analyze data');\n }\n \n return await response.json();\n } catch (error) {\n console.error('Error in AI analysis service:', error);\n throw error;\n }\n}\n```\n\n### 4. Punto final de la API de backend\n\nPara el backend, necesitaremos un punto final para procesar los datos e interactuar con la IA:\n\n```javascript\n// api/analyze-mobile-retail.js\nimport { OpenAI } from 'openai';\n\nconst openai = new OpenAI({\n apiKey: process.env.OPENAI_API_KEY,\n});\n\nexport default async function handler(req, res) {\n if (req.method !== 'POST') {\n return res.status(405).json({ error: 'Method not allowed' });\n }\n\n try {\n const { data } = req.body;\n \n // Create a specialized prompt based on data type\n let prompt = '';\n \n if (data.type === 'purchase_orders') {\n prompt = createPurchaseOrderPrompt(data);\n } else if (data.type === 'sales_data') {\n prompt = createSalesDataPrompt(data);\n } else if (data.type === 'inventory') {\n prompt = createInventoryPrompt(data);\n } else {\n prompt = createGeneralPrompt(data);\n }\n \n // Call the AI\n const completion = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n { \n role: \"system\", \n content: \"You are a senior supply chain expert with 20+ years of experience in mobile device retail. You analyze data and provide actionable insights with a focus on inventory optimization, vendor management, and sales forecasting for smartphone and mobile accessory retailers.\" \n },\n { role: \"user\", content: prompt }\n ],\n temperature: 0.2,\n });\n \n // Process the AI response\n const analysisText = completion.choices[0].message.content;\n \n // Parse the response into structured data\n const analysisData = parseAnalysisResponse(analysisText, data.type);\n \n return res.status(200).json(analysisData);\n } catch (error) {\n console.error('Error analyzing mobile retail data:', error);\n return res.status(500).json({ error: 'Failed to analyze data' });\n }\n}\n\n// Create specialized prompts based on data type\nfunction createPurchaseOrderPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following purchase order data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Vendor performance (delivery times, order fulfillment)\n 2. Product category insights (smartphones, accessories, etc.)\n 3. Inventory management recommendations\n 4. Cost optimization opportunities\n 5. Order cycle time improvements\n \n Provide specific actionable recommendations for improving the supply chain.\n `;\n}\n\nfunction createSalesDataPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following sales data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Sales trends by product category and specific models\n 2. Profitability analysis by product type\n 3. Seasonal patterns and forecasting\n 4. Product mix optimization\n 5. Inventory replenishment recommendations based on sales velocity\n \n Provide specific actionable recommendations for improving sales and inventory alignment.\n `;\n}\n\nfunction createInventoryPrompt(data) {\n // Similar structure for inventory data\n}\n\nfunction createGeneralPrompt(data) {\n // Fallback for other data types\n}\n\n// Parse the AI response into structured data\nfunction parseAnalysisResponse(text, dataType) {\n // Basic parsing logic - this could be enhanced with more sophisticated parsing\n const sections = {\n keyInsights: extractSection(text, \"Key Insights\"),\n inventoryAnalysis: extractSection(text, \"Inventory Analysis\"),\n inventoryRecommendations: extractSection(text, \"Inventory Recommendations\"),\n vendorAnalysis: extractSection(text, \"Vendor Analysis\"),\n vendorRecommendations: extractSection(text, \"Vendor Recommendations\"),\n salesTrends: extractSection(text, \"Sales Trends\"),\n salesForecasts: extractSection(text, \"Sales Forecasts\")\n };\n \n // Add placeholder metrics and chart data\n // In a real implementation, you might extract these from the AI response\n // or calculate them directly from the data\n const metrics = {\n inventoryTurnover: \"4.2\",\n fulfillmentRate: \"92.5\",\n avgDaysOnOrder: \"6.3\"\n };\n \n const charts = {\n inventoryTurnover: [],\n categoryPerformance: [],\n vendorPerformance: [],\n salesVsPurchases: []\n };\n \n return {\n ...sections,\n metrics,\n charts\n };\n}\n\nfunction extractSection(text, sectionName) {\n const regex = new RegExp(`${sectionName}[:\\\\s]+(.*?)(?=\\\\d+\\\\.\\\\s+[A-Z]|#|$)`, 's');\n const match = text.match(regex);\n return match ? match[1].trim() : '';\n}\n```\n\n### 5. Componente principal de la aplicación\n\nFinalmente, pongámoslo todo junto:\n\n```javascript\nimport React, { useState } from 'react';\nimport DataUploader from './components/DataUploader';\nimport SupplyChainAnalysis from './components/SupplyChainAnalysis';\n\nfunction RetailAnalysisMCP() {\n const [retailData, setRetailData] = useState(null);\n \n const handleDataProcessed = (data) => {\n setRetailData(data);\n };\n \n return (\n

Retail Supply Chain Analysis

Upload your retail data for expert supply chain analysis

\n \n

\n \n \n {retailData && }\n

\n );\n}\n\nexport default RetailAnalysisMCP;\n```\n\n## Próximos pasos\n\nPara hacer que este MCP sea más potente:\n\n1. **Ejemplos de datos** : Sí, ver ejemplos de sus datos reales sería extremadamente útil para ajustar el análisis y garantizar que la IA pueda interpretar correctamente sus formatos de datos minoristas específicos.\n\n2. **Visualización de datos** : agregue gráficos y tablas utilizando bibliotecas como Chart.js o D3.js para visualizar tendencias y pronósticos.\n\n3. **Interfaz conversacional** : agregue una interfaz de chat donde los usuarios puedan hacer preguntas de seguimiento sobre el análisis.\n\n4. **Indicación específica del dominio** : refine las indicaciones de IA con terminología y marcos de cadena de suministro específicos según su industria.\n\n5. **Preprocesamiento de datos** : agregue una limpieza y normalización de datos más sofisticadas antes del análisis.\n\n¿Le gustaría compartir ejemplos de sus datos para que pueda ayudarle a adaptar esta solución más específicamente a sus necesidades?\n\nSí, me encantaría compartir ejemplos. ¿Puedo subir los archivos para que los veas?\n\nSí, puede subir sus archivos de Excel para que los revise. Esto me ayudará a adaptar la solución específicamente a su estructura de datos y contexto minorista. Cuando suba los archivos, podré: Analizar la estructura y el formato de sus datos Identificar campos y relaciones clave Sugerir pasos específicos de preprocesamiento necesarios Refinar las indicaciones de IA para interpretar mejor sus datos minoristas particulares Recomendar visualizaciones apropiadas para los insights Por favor, continúe y suba sus informes de inventario de ejemplo, datos de ventas o cualquier otra hoja de cálculo minorista que le gustaría que examinara. Una vez que vea los datos reales, puedo brindar una guía mucho más específica sobre cómo estructurar el MCP para una máxima efectividad.\n\nCompartiré 4 capturas de pantalla.\n\nPrimero - Oficinas de correos de Aberdeen: Segundo - Oficinas de correos de Huron: Tercero - Oficinas de correos de Luverne: Cuarto - Datos de ventas de julio de 2023 a febrero de 2024:\n\n¡Gracias por compartir estas capturas de pantalla! Ahora puedo ver la estructura de tus datos de venta minorista, lo que me ayudará a diseñar una solución MCP más personalizada. Analizo lo que veo:\n\nAnálisis de datos\n\n1. Informes de órdenes de compra Las primeras tres capturas de pantalla muestran detalles de órdenes de compra para diferentes ubicaciones (Aberdeen, Huron y Luverne) con estructuras similares: Campos clave: N.° de orden de compra, Estado, Recibir en, Información del proveedor, SKU/ID del producto, Nombre del producto, Categoría Datos de cantidad: N.° de pedidos, N.° de recibidos, N.° de pendientes Datos financieros: Costo por artículo, Costo recibido, Costo pendiente Información de tiempo: Comprometido el, Pedido en, Días en el pedido, ETA Información adicional: Comentarios de pedidos, Comentarios de recepción, N.° de entrada de pedido Estos informes rastrean dispositivos móviles y accesorios (teléfonos inteligentes, cables, cargadores, etc.) con información detallada de adquisiciones.\n2. Datos de ventas La cuarta captura de pantalla muestra datos de ventas con: Detalles de transacción: Número de factura, Fechas de recepción, Información de venta Información del producto: Varios dispositivos móviles y accesorios Datos de precios: Varias columnas relacionadas con los precios Información de ubicación/tienda Diseño de MCP basado en sus datos Ahora que entiendo su estructura de datos, aquí hay un enfoque más personalizado para su MCP de cadena de suministro minorista:\n\n// componentes/MobileRetailAnalysis.js importar React, { useState, useEffect } de 'react'; importar { analyzeRetailData } de '../services/aiService'; importar { InventoryTurnoverChart, ProductCategoryChart, VendorPerformanceChart, SalesVsPurchasesChart } de './charts';\n\nfunción MobileRetailAnalysis({ datos }) { const \\[análisis, establecerAnalisis] = useState(null); const \\[isAnalyzing, establecerIsAnalyzing] = useState(false); const \\[selectedView, establecerSelectedView] = useState('descripción general');\n\nuseEffect(() => { if (datos) { performAnalysis(); } }, \\[datos]);\n\nconst performAnalysis = async () => { setIsAnalyzing(true); try { const result = await analyzeRetailData(data); setAnalysis(result); } catch(error) { console.error(\"Error al analizar los datos:\", error); } finally { setIsAnalyzing(false); } };\n\n// Representar diferentes vistas según la selección const renderAnalysisView = () => { if (!analysis) return null;\n\n```\nswitch(selectedView) {\n case 'overview':\n return (\n

Key Insights

\n \n

Inventory Turnover

{analysis.metrics.inventoryTurnover}

Order Fulfillment Rate

{analysis.metrics.fulfillmentRate}%

Average Days on Order

{analysis.metrics.avgDaysOnOrder}

\n );\n \n case 'inventory':\n return (\n

Inventory Analysis

\n \n

Top Products by Turnover

\n \n \n

Category Performance

\n \n \n

Inventory Recommendations

\n );\n \n case 'vendor':\n return (\n

Vendor Performance

\n \n

Vendor Delivery Performance

\n \n \n

Vendor Recommendations

\n );\n \n case 'sales':\n return (\n

Sales Trends

\n \n

Sales vs Purchases

\n \n \n

Sales Forecasts

\n );\n \n default:\n return null;\n}\n```\n\n};\n\nAnálisis de la cadena de suministro minorista móvil \\

\n```\n\n); }\n\nexportar predeterminado MobileRetailAnalysis;\n\nAhora, actualicemos el servicio de IA para que sea más específico para sus datos minoristas móviles:\n\n// servicios/aiService.js exportar función asíncrona analizarRetailData(datos) { intentar { constante respuesta = esperar fetch('/api/analyze-mobile-retail', { método: 'POST', encabezados: { 'Tipo de contenido': 'application/json', }, cuerpo: JSON.stringify({ datos }), });\n\n```\nif (!response.ok) {\n throw new Error('Failed to analyze data');\n}\n\nreturn await response.json();\n```\n\n} catch (error) { console.error('Error en el servicio de análisis de IA:', error); throw error; } }","Spanish","es-ES",{"_29":48,"_33":52,"_38":53},{"_31":49,"_33":50,"_35":51},"ユーザーが小売データをアップロードし、トレンドを分析し、在庫を最適化し、AI を活用した洞察を使用して売上を予測し、上級サプライチェーンの専門家として機能できるようにします。","小売サプライチェーン分析MCP","# 小売サプライチェーン分析ツール\n\n小売サプライチェーンデータを分析し、在庫の最適化、ベンダー管理、売上予測のための実用的な洞察を提供する AI 搭載ツールです。\n\n## 特徴\n\n* 複数の Excel/CSV ファイルをアップロードして分析する\n* 発注書、販売データ、在庫ファイルのサポート\n* ファイルタイプの自動検出\n* 複数のデータソースを組み合わせたクロス分析\n* 詳細な視覚化と指標\n* サプライチェーンの最適化のための実用的な推奨事項\n\n## はじめる\n\n1. リポジトリをクローンする\n2. 依存関係をインストール: `npm install`\n3. 開発サーバーを起動します: `npm run dev`\n4. ブラウザでを開きます。\n\n## 使用法\n\n1. 小売データを含む1つ以上のExcel/CSVファイルをアップロードします\n2. 自動的に検出されない場合は、各ファイルに適切なデータタイプを選択します。\n3. 「ファイルを分析」をクリックしてデータを処理します\n4. さまざまなカテゴリ（概要、在庫、ベンダー、売上）にわたる分析情報を表示します\n5. 複合分析と個別のファイルタイプ分析を切り替える\n\n## テクノロジー\n\n* フロントエンドとAPIルート用のNext.js\n* インテリジェントなデータ分析のためのClaude AI\n* データ視覚化のためのChart.js\n* Excelファイル処理用のXLSX\n\n## 環境変数\n\n次の変数を含む`.env.local`ファイルを作成します。\n\n```\nANTHROPIC_API_KEY=your_api_key_here\n```\n\n## ライセンス\n\n[マサチューセッツ工科大学](LICENSE)\n\n在庫レポートや売上レポートなどの小売店データをExcelスプレッドシートにアップロードし、AIがまるで小売業者のシニアサプライチェーンエキスパートであるかのように、トレンドや予測、その他のインサイトを分析できるMCPを作成したいと考えています。何かお手伝いできることはありますか？\n\n参考になればとデータの例を挙げておきます。\n\n小売サプライチェーン分析MCP はい、シニアサプライチェーンエキスパートのように小売店のデータを分析するマルチモーダル会話型ペルソナ（MCP）の作成をお手伝いできます。これはAI分析の優れたユースケースです。ハイレベルアプローチこのプロジェクトの構成は以下のとおりです。データのアップロードと処理：在庫、売上、その他の小売データを含むExcelファイルをアップロードするためのインターフェースを作成する。データ分析：データを処理して有益な洞察を抽出する。エキスパートペルソナ：シニアサプライチェーンエキスパートのように洞察を伝えるAIを設計する。可視化：傾向と予測を視覚的に表現する。\n\n実装手順\n\n1. データアップロードコンポーネントまず、Excel ファイルをアップロードして処理する方法が必要です。\n\n'react' から React をインポートし、{ useState } を 'xlsx' から XLSX としてインポートします。\n\nfunction RetailDataProcessor({ onDataProcessed }) { const \\[isProcessing, setIsProcessing] = useState(false); const \\[dataType, setDataType] = useState('purchase\\_orders'); const \\[location, setLocation] = useState('');\n\nconst handleFileUpload = async (e) => { const file = e.target.files\\[0]; setIsProcessing(true);\n\n```\nconst reader = new FileReader();\nreader.onload = (event) => {\n try {\n const data = new Uint8Array(event.target.result);\n const workbook = XLSX.read(data, { type: 'array' });\n \n // Process the first sheet by default\n const worksheet = workbook.Sheets[workbook.SheetNames[0]];\n const jsonData = XLSX.utils.sheet_to_json(worksheet);\n \n // Detect data type based on column headers\n const detectedType = detectDataType(jsonData);\n \n // Process and normalize the data based on type\n const processedData = {\n type: detectedType || dataType,\n location: location,\n data: normalizeData(jsonData, detectedType || dataType)\n };\n \n onDataProcessed(processedData);\n } catch (error) {\n console.error(\"Error processing Excel file:\", error);\n } finally {\n setIsProcessing(false);\n }\n};\n\nreader.readAsArrayBuffer(file);\n```\n\n};\n\n// 列ヘッダーに基づいてデータ型を検出します。constdetectDataType = (data) => { if (!data || data.length === 0) return null;\n\n```\nconst headers = Object.keys(data[0]);\n\nif (headers.includes('PO #') && headers.includes('# Ordered')) {\n return 'purchase_orders';\n} else if (headers.includes('Invoice #') || headers.includes('Sale By')) {\n return 'sales_data';\n} else if (headers.includes('On Hand') || headers.includes('In Stock')) {\n return 'inventory';\n}\n\nreturn null;\n```\n\n};\n\n// タイプに基づいてデータを正規化します。const normalizeData = (data, type) => { switch(type) { case 'purchase\\_orders': return data.map(item => ({ poNumber: item\\['PO #'], status: item\\['ステータス'], vendor: item\\['ベンダー'], productSku: item\\['製品 SKU'], productName: item\\['商品名'], category: item\\['カテゴリ'], quantityOrdered: item\\['# Ordered'], quantityReceived: item\\['# Received'], amountOutstanding: 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'] }));\n\n```\n case 'sales_data':\n // Normalize sales data based on your fourth screenshot\n return data.map(item => ({\n invoiceNumber: item['Invoice #'],\n saleDate: item['Date'],\n saleBy: item['Sale By'],\n product: item['Product Name'],\n category: item['Category'],\n quantity: item['Qty'],\n price: item['Price'],\n cost: item['Cost'],\n profit: item['Profit'],\n location: item['Location'] || location\n }));\n \n default:\n return data;\n}\n```\n\n};\n\nreturn ( データ型: \\ setLocation(e.target.value)}\n placeholder=\"Aberdeen, Huron, Luverne, etc.\"\n />\n

\n \n

\n \n {isProcessing &&

Processing data...

}\n

\n```\n\n); }\n\nデフォルトの RetailDataProcessor をエクスポートします。\n\n### 2. サプライチェーン分析コンポーネント\n\n次に、AI 分析を処理するコンポーネントを作成します。\n\n```javascript\nimport React, { useState, useEffect } from 'react';\nimport { analyzeRetailData } from '../services/aiService';\n\nfunction SupplyChainAnalysis({ data }) {\n const [analysis, setAnalysis] = useState(null);\n const [isAnalyzing, setIsAnalyzing] = useState(false);\n \n useEffect(() => {\n if (data) {\n performAnalysis();\n }\n }, [data]);\n \n const performAnalysis = async () => {\n setIsAnalyzing(true);\n try {\n // This would call your AI service\n const result = await analyzeRetailData(data);\n setAnalysis(result);\n } catch (error) {\n console.error(\"Error analyzing data:\", error);\n } finally {\n setIsAnalyzing(false);\n }\n };\n \n return (\n

Supply Chain Analysis

\n \n {isAnalyzing &&

Analyzing your retail data...

}\n \n {analysis && (\n

Key Insights

\n \n

Inventory Recommendations

\n \n

Sales Trends

\n \n

Forecasts

\n )}\n

\n );\n}\n\nexport default SupplyChainAnalysis;\n```\n\n### 3. AIサービス\n\nそれでは、AI と対話するためのサービスを作成しましょう。\n\n```javascript:README.md\n// services/aiService.js\n\nexport async function analyzeRetailData(data) {\n try {\n const response = await fetch('/api/analyze-mobile-retail', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({ data }),\n });\n \n if (!response.ok) {\n throw new Error('Failed to analyze data');\n }\n \n return await response.json();\n } catch (error) {\n console.error('Error in AI analysis service:', error);\n throw error;\n }\n}\n```\n\n### 4. バックエンドAPIエンドポイント\n\nバックエンドでは、データを処理して AI と対話するためのエンドポイントが必要になります。\n\n```javascript\n// api/analyze-mobile-retail.js\nimport { OpenAI } from 'openai';\n\nconst openai = new OpenAI({\n apiKey: process.env.OPENAI_API_KEY,\n});\n\nexport default async function handler(req, res) {\n if (req.method !== 'POST') {\n return res.status(405).json({ error: 'Method not allowed' });\n }\n\n try {\n const { data } = req.body;\n \n // Create a specialized prompt based on data type\n let prompt = '';\n \n if (data.type === 'purchase_orders') {\n prompt = createPurchaseOrderPrompt(data);\n } else if (data.type === 'sales_data') {\n prompt = createSalesDataPrompt(data);\n } else if (data.type === 'inventory') {\n prompt = createInventoryPrompt(data);\n } else {\n prompt = createGeneralPrompt(data);\n }\n \n // Call the AI\n const completion = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n { \n role: \"system\", \n content: \"You are a senior supply chain expert with 20+ years of experience in mobile device retail. You analyze data and provide actionable insights with a focus on inventory optimization, vendor management, and sales forecasting for smartphone and mobile accessory retailers.\" \n },\n { role: \"user\", content: prompt }\n ],\n temperature: 0.2,\n });\n \n // Process the AI response\n const analysisText = completion.choices[0].message.content;\n \n // Parse the response into structured data\n const analysisData = parseAnalysisResponse(analysisText, data.type);\n \n return res.status(200).json(analysisData);\n } catch (error) {\n console.error('Error analyzing mobile retail data:', error);\n return res.status(500).json({ error: 'Failed to analyze data' });\n }\n}\n\n// Create specialized prompts based on data type\nfunction createPurchaseOrderPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following purchase order data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Vendor performance (delivery times, order fulfillment)\n 2. Product category insights (smartphones, accessories, etc.)\n 3. Inventory management recommendations\n 4. Cost optimization opportunities\n 5. Order cycle time improvements\n \n Provide specific actionable recommendations for improving the supply chain.\n `;\n}\n\nfunction createSalesDataPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following sales data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Sales trends by product category and specific models\n 2. Profitability analysis by product type\n 3. Seasonal patterns and forecasting\n 4. Product mix optimization\n 5. Inventory replenishment recommendations based on sales velocity\n \n Provide specific actionable recommendations for improving sales and inventory alignment.\n `;\n}\n\nfunction createInventoryPrompt(data) {\n // Similar structure for inventory data\n}\n\nfunction createGeneralPrompt(data) {\n // Fallback for other data types\n}\n\n// Parse the AI response into structured data\nfunction parseAnalysisResponse(text, dataType) {\n // Basic parsing logic - this could be enhanced with more sophisticated parsing\n const sections = {\n keyInsights: extractSection(text, \"Key Insights\"),\n inventoryAnalysis: extractSection(text, \"Inventory Analysis\"),\n inventoryRecommendations: extractSection(text, \"Inventory Recommendations\"),\n vendorAnalysis: extractSection(text, \"Vendor Analysis\"),\n vendorRecommendations: extractSection(text, \"Vendor Recommendations\"),\n salesTrends: extractSection(text, \"Sales Trends\"),\n salesForecasts: extractSection(text, \"Sales Forecasts\")\n };\n \n // Add placeholder metrics and chart data\n // In a real implementation, you might extract these from the AI response\n // or calculate them directly from the data\n const metrics = {\n inventoryTurnover: \"4.2\",\n fulfillmentRate: \"92.5\",\n avgDaysOnOrder: \"6.3\"\n };\n \n const charts = {\n inventoryTurnover: [],\n categoryPerformance: [],\n vendorPerformance: [],\n salesVsPurchases: []\n };\n \n return {\n ...sections,\n metrics,\n charts\n };\n}\n\nfunction extractSection(text, sectionName) {\n const regex = new RegExp(`${sectionName}[:\\\\s]+(.*?)(?=\\\\d+\\\\.\\\\s+[A-Z]|#|$)`, 's');\n const match = text.match(regex);\n return match ? match[1].trim() : '';\n}\n```\n\n### 5. メインアプリコンポーネント\n\n最後に、すべてをまとめてみましょう。\n\n```javascript\nimport React, { useState } from 'react';\nimport DataUploader from './components/DataUploader';\nimport SupplyChainAnalysis from './components/SupplyChainAnalysis';\n\nfunction RetailAnalysisMCP() {\n const [retailData, setRetailData] = useState(null);\n \n const handleDataProcessed = (data) => {\n setRetailData(data);\n };\n \n return (\n

Retail Supply Chain Analysis

Upload your retail data for expert supply chain analysis

\n \n

\n \n \n {retailData && }\n

\n );\n}\n\nexport default RetailAnalysisMCP;\n```\n\n## 次のステップ\n\nこの MCP をさらに強力にするには:\n\n1. **データの例**: はい、実際のデータの例を確認することは、分析を微調整し、AI が特定の小売データ形式を適切に解釈できることを確認するのに非常に役立ちます。\n\n2. **データの視覚化**: Chart.js や D3.js などのライブラリを使用してチャートやグラフを追加し、傾向や予測を視覚化します。\n\n3. **会話型インターフェース**: ユーザーが分析に関するフォローアップの質問をすることができるチャットインターフェースを追加します。\n\n4. **ドメイン固有のプロンプト**: 業界に基づいて、特定のサプライチェーン用語とフレームワークを使用して AI プロンプトを調整します。\n\n5. **データ前処理**: 分析前に、より高度なデータクリーニングと正規化を追加します。\n\nこのソリューションをより具体的にお客様のニーズに合わせてカスタマイズできるよう、データの例を共有していただけますか?\n\nはい、ぜひ例を共有したいです。ファイルをアップロードしてご覧いただいてもよろしいでしょうか？\n\nはい、Excelファイルをアップロードしていただければ、レビューさせていただきます。これにより、お客様のデータ構造と小売業の状況に合わせてソリューションをカスタマイズすることができます。ファイルをアップロードしていただければ、以下の作業が可能になります。データの構造と形式を分析する。主要なフィールドと関係性を特定する。必要な前処理手順を提案する。AIプロンプトを調整して、お客様の小売データをより適切に解釈できるようにする。得られたインサイトに適した視覚化を提案する。在庫レポート、売上データ、その他、検証をご希望の小売業向けスプレッドシートなど、サンプルデータをアップロードしてください。実際のデータを拝見し、MCPを最大限効果的に構築するためのより具体的なアドバイスを提供できます。\n\nスクリーンショットを4枚共有します。\n\n1位 - アバディーン郵便局：2位 - ヒューロン郵便局：3位 - ルバーン郵便局：4位 - 2023年7月から2024年2月までの販売データ：\n\nスクリーンショットを共有していただきありがとうございます！これで小売データの構造が分かりました。よりカスタマイズされたMCPソリューションを設計する上で役立ちます。分析させてください。\n\nデータ分析\n\n1. 発注書レポート最初の 3 つのスクリーンショットは、次のような同様の構造を持つ、さまざまな場所 (アバディーン、ヒューロン、ルバーン) の発注書の詳細を示しています。主要フィールド: 発注書番号、ステータス、受領場所、ベンダー情報、製品 SKU/ID、製品名、カテゴリ数量データ: 発注数、受領数、未払い数財務データ: アイテムあたりのコスト、受領コスト、未払いコストタイミング情報: コミット日、発注日、発注日数、ETA 追加情報: 発注コメント、受領コメント、注文入力数これらのレポートは、詳細な調達情報を使用して、モバイルデバイスとアクセサリ (スマートフォン、ケーブル、充電器など) を追跡します。\n2. 販売データ 4 番目のスクリーンショットには、次の販売データが表示されています: 取引の詳細: 請求書番号、受領日、販売元情報製品情報: さまざまなモバイルデバイスとアクセサリ価格データ: 複数の価格関連列場所/店舗情報データに基づく MCP 設計データ構造を理解したので、小売サプライチェーンの MCP に合わせたよりカスタマイズされたアプローチを次に示します:\n\n// components/MobileRetailAnalysis.js import React, { useState, useEffect } from 'react'; import { analyzeRetailData } from '../services/aiService'; import { InventoryTurnoverChart, ProductCategoryChart, VendorPerformanceChart, SalesVsPurchasesChart } from './charts';\n\nfunction MobileRetailAnalysis({ data }) { const \\[analysis, setAnalysis] = useState(null); const \\[isAnalyzing, setIsAnalyzing] = useState(false); const \\[selectedView, setSelectedView] = useState('overview');\n\nuseEffect(() => { if (data) { performAnalysis(); } }, \\[データ]);\n\nconst performAnalysis = async () => { setIsAnalyzing(true); try { const result = await analyzeRetailData(data); setAnalysis(result); } catch (error) { console.error(\"データの分析エラー:\", error); } finally { setIsAnalyzing(false); } };\n\n// 選択に基づいて異なるビューをレンダリングします const renderAnalysisView = () => { if (!analysis) return null;\n\n```\nswitch(selectedView) {\n case 'overview':\n return (\n

Key Insights

\n \n

Inventory Turnover

{analysis.metrics.inventoryTurnover}

Order Fulfillment Rate

{analysis.metrics.fulfillmentRate}%

Average Days on Order

{analysis.metrics.avgDaysOnOrder}

\n );\n \n case 'inventory':\n return (\n

Inventory Analysis

\n \n

Top Products by Turnover

\n \n \n

Category Performance

\n \n \n

Inventory Recommendations

\n );\n \n case 'vendor':\n return (\n

Vendor Performance

\n \n

Vendor Delivery Performance

\n \n \n

Vendor Recommendations

\n );\n \n case 'sales':\n return (\n

Sales Trends

\n \n

Sales vs Purchases

\n \n \n

Sales Forecasts

\n );\n \n default:\n return null;\n}\n```\n\n};\n\nreturn ( モバイル小売サプライチェーン分析 \\

\n```\n\n); }\n\nデフォルトの MobileRetailAnalysis をエクスポートします。\n\n次に、モバイル小売データに特化するように AI サービスを更新しましょう。\n\n// services/aiService.js をエクスポートします。async function analyzeRetailData(data) { try { const response = await fetch('/api/analyze-mobile-retail', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ data }), });\n\n```\nif (!response.ok) {\n throw new Error('Failed to analyze data');\n}\n\nreturn await response.json();\n```\n\n} catch (error) { console.error('AI分析サービスでエラーが発生しました:', error); throw error; } }","Japanese","ja-JP",{"_29":55,"_33":59,"_38":60},{"_31":56,"_33":57,"_35":58},"��용자가 AI 기반 통찰력을 활용하여 소매 데이터를 업로드하고, 추세를 분석하고, 재고를 최적화하고, 판매를 예측할 수 있도록 지원하며, 고위 공급망 전문가 역할을 합니다.","소매 공급망 분석 MCP","# 소매 공급망 분석 도구\n\n소매 공급망 데이터를 분석하고 재고 최적화, 공급업체 관리, 판매 예측을 위한 실행 가능한 통찰력을 제공하는 AI 기반 도구입니다.\n\n## 특징\n\n* 여러 개의 Excel/CSV 파일을 업로드하고 분석합니다.\n* 구매 주문서, 판매 데이터 및 재고 파일 지원\n* 자동 파일 유형 감지\n* 여러 데이터 소스의 결합된 교차 분석\n* 자세한 시각화 및 측정 항목\n* 공급망 최적화를 위한 실행 가능한 권장 사항\n\n## 시작하기\n\n1. 저장소를 복제합니다\n2. 종속성 설치: `npm install`\n3. 개발 서버를 시작합니다: `npm run dev`\n4. 브라우저에서 [http://localhost:3000을](http://localhost:3000) 엽니다.\n\n## 용법\n\n1. 소매 데이터가 포함된 하나 이상의 Excel/CSV 파일을 업로드하세요.\n2. 자동으로 감지되지 않으면 각 파일에 적합한 데이터 유형을 선택하십시오.\n3. 데이터를 처리하려면 \"파일 분석\"을 클릭하세요.\n4. 다양한 카테고리(개요, 재고, 공급업체, 판매)에 대한 통찰력 보기\n5. 결합 분석과 개별 파일 유형 분석 간 전환\n\n## 기술\n\n* 프런트엔드 및 API 경로를 위한 Next.js\n* 지능형 데이터 분석을 위한 Claude AI\n* 데이터 시각화를 위한 Chart.js\n* Excel 파일 처리를 위한 XLSX\n\n## 환경 변수\n\n다음 변수를 사용하여 `.env.local` 파일을 만듭니다.\n\n지엑스피1\n\n## 특허\n\n[MIT](LICENSE)\n\n재고 보고서, 판매 보고서 등 소매점 데이터가 포함된 엑셀 스프레드시트를 업로드하면 AI가 마치 소매업체의 시니어 공급망 전문가처럼 데이터를 분석하여 추세, 예측 및 기타 통찰력을 제공하는 MCP를 만들고 싶습니다. 혹시 도와주실 수 있을까요?\n\n도움이 된다면 데이터 예를 보여드리겠습니다.\n\n소매 공급망 분석 MCP 네, 저는 확실히 여러분이 소매점 데이터를 마치 고위 공급망 전문가처럼 분석하는 다중 모드 대화형 페르소나(MCP)를 만드는 것을 도울 수 있습니다. 이는 AI 분석의 훌륭한 활용 사례입니다. 고차원적 접근 방식 이 프로젝트는 다음과 같이 구성될 수 있습니다. 데이터 업로드 및 처리: 재고, 매출 및 기타 소매 데이터가 포함된 Excel 파일을 업로드하는 인터페이스를 만듭니다. 데이터 분석: 의미 있는 인사이트를 추출하기 위해 데이터를 처리합니다. 전문가 페르소나: 고위 공급망 전문가처럼 인사이트를 전달하도록 AI를 설계합니다. 시각화: 추세 및 예측을 시각적으로 표현합니다.\n\n구현 단계\n\n1. 데이터 업로드 구성 요소 먼저 Excel 파일을 업로드하고 처리할 방법이 필요합니다.\n\n'react'에서 React를 가져오고, { useState }를 가져옵니다. 'xlsx'에서 \\*를 XLSX로 가져옵니다.\n\n함수 RetailDataProcessor({onDataProcessed}) { const \\[처리 중임, 처리 중 설정] = useState(false); const \\[데이터 유형, 데이터 유형 설정] = useState('구매\\_주문'); const \\[위치, 위치 설정] = useState('');\n\nconst handleFileUpload = async (e) => { const 파일 = e.target.files\\[0]; 처리 중 설정(true);\n\n```\nconst reader = new FileReader();\nreader.onload = (event) => {\n try {\n const data = new Uint8Array(event.target.result);\n const workbook = XLSX.read(data, { type: 'array' });\n \n // Process the first sheet by default\n const worksheet = workbook.Sheets[workbook.SheetNames[0]];\n const jsonData = XLSX.utils.sheet_to_json(worksheet);\n \n // Detect data type based on column headers\n const detectedType = detectDataType(jsonData);\n \n // Process and normalize the data based on type\n const processedData = {\n type: detectedType || dataType,\n location: location,\n data: normalizeData(jsonData, detectedType || dataType)\n };\n \n onDataProcessed(processedData);\n } catch (error) {\n console.error(\"Error processing Excel file:\", error);\n } finally {\n setIsProcessing(false);\n }\n};\n\nreader.readAsArrayBuffer(file);\n```\n\n};\n\n// 열 머리글을 기준으로 데이터 유형 감지 const detectDataType = (data) => { if (!data || data.length === 0) return null;\n\n```\nconst headers = Object.keys(data[0]);\n\nif (headers.includes('PO #') && headers.includes('# Ordered')) {\n return 'purchase_orders';\n} else if (headers.includes('Invoice #') || headers.includes('Sale By')) {\n return 'sales_data';\n} else if (headers.includes('On Hand') || headers.includes('In Stock')) {\n return 'inventory';\n}\n\nreturn null;\n```\n\n};\n\n// 유형에 따라 데이터 정규화 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'] }));\n\n```\n case 'sales_data':\n // Normalize sales data based on your fourth screenshot\n return data.map(item => ({\n invoiceNumber: item['Invoice #'],\n saleDate: item['Date'],\n saleBy: item['Sale By'],\n product: item['Product Name'],\n category: item['Category'],\n quantity: item['Qty'],\n price: item['Price'],\n cost: item['Cost'],\n profit: item['Profit'],\n location: item['Location'] || location\n }));\n \n default:\n return data;\n}\n```\n\n};\n\n반환(데이터 유형: \\ setLocation(e.target.value)}\n placeholder=\"Aberdeen, Huron, Luverne, etc.\"\n />\n

\n \n

\n \n {isProcessing &&

Processing data...

}\n

\n```\n\n); }\n\n기본 RetailDataProcessor를 내보냅니다.\n\n### 2. 공급망 분석 구성 요소\n\n다음으로, AI 분석을 처리하는 구성 요소를 생성하겠습니다.\n\n```javascript\nimport React, { useState, useEffect } from 'react';\nimport { analyzeRetailData } from '../services/aiService';\n\nfunction SupplyChainAnalysis({ data }) {\n const [analysis, setAnalysis] = useState(null);\n const [isAnalyzing, setIsAnalyzing] = useState(false);\n \n useEffect(() => {\n if (data) {\n performAnalysis();\n }\n }, [data]);\n \n const performAnalysis = async () => {\n setIsAnalyzing(true);\n try {\n // This would call your AI service\n const result = await analyzeRetailData(data);\n setAnalysis(result);\n } catch (error) {\n console.error(\"Error analyzing data:\", error);\n } finally {\n setIsAnalyzing(false);\n }\n };\n \n return (\n

Supply Chain Analysis

\n \n {isAnalyzing &&

Analyzing your retail data...

}\n \n {analysis && (\n

Key Insights

\n \n

Inventory Recommendations

\n \n

Sales Trends

\n \n

Forecasts

\n )}\n

\n );\n}\n\nexport default SupplyChainAnalysis;\n```\n\n### 3. AI 서비스\n\n이제 AI와 상호작용하는 서비스를 만들어 보겠습니다.\n\n```javascript:README.md\n// services/aiService.js\n\nexport async function analyzeRetailData(data) {\n try {\n const response = await fetch('/api/analyze-mobile-retail', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({ data }),\n });\n \n if (!response.ok) {\n throw new Error('Failed to analyze data');\n }\n \n return await response.json();\n } catch (error) {\n console.error('Error in AI analysis service:', error);\n throw error;\n }\n}\n```\n\n### 4. 백엔드 API 엔드포인트\n\n백엔드의 경우 데이터를 처리하고 AI와 상호 작용할 엔드포인트가 필요합니다.\n\n```javascript\n// api/analyze-mobile-retail.js\nimport { OpenAI } from 'openai';\n\nconst openai = new OpenAI({\n apiKey: process.env.OPENAI_API_KEY,\n});\n\nexport default async function handler(req, res) {\n if (req.method !== 'POST') {\n return res.status(405).json({ error: 'Method not allowed' });\n }\n\n try {\n const { data } = req.body;\n \n // Create a specialized prompt based on data type\n let prompt = '';\n \n if (data.type === 'purchase_orders') {\n prompt = createPurchaseOrderPrompt(data);\n } else if (data.type === 'sales_data') {\n prompt = createSalesDataPrompt(data);\n } else if (data.type === 'inventory') {\n prompt = createInventoryPrompt(data);\n } else {\n prompt = createGeneralPrompt(data);\n }\n \n // Call the AI\n const completion = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n { \n role: \"system\", \n content: \"You are a senior supply chain expert with 20+ years of experience in mobile device retail. You analyze data and provide actionable insights with a focus on inventory optimization, vendor management, and sales forecasting for smartphone and mobile accessory retailers.\" \n },\n { role: \"user\", content: prompt }\n ],\n temperature: 0.2,\n });\n \n // Process the AI response\n const analysisText = completion.choices[0].message.content;\n \n // Parse the response into structured data\n const analysisData = parseAnalysisResponse(analysisText, data.type);\n \n return res.status(200).json(analysisData);\n } catch (error) {\n console.error('Error analyzing mobile retail data:', error);\n return res.status(500).json({ error: 'Failed to analyze data' });\n }\n}\n\n// Create specialized prompts based on data type\nfunction createPurchaseOrderPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following purchase order data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Vendor performance (delivery times, order fulfillment)\n 2. Product category insights (smartphones, accessories, etc.)\n 3. Inventory management recommendations\n 4. Cost optimization opportunities\n 5. Order cycle time improvements\n \n Provide specific actionable recommendations for improving the supply chain.\n `;\n}\n\nfunction createSalesDataPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following sales data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Sales trends by product category and specific models\n 2. Profitability analysis by product type\n 3. Seasonal patterns and forecasting\n 4. Product mix optimization\n 5. Inventory replenishment recommendations based on sales velocity\n \n Provide specific actionable recommendations for improving sales and inventory alignment.\n `;\n}\n\nfunction createInventoryPrompt(data) {\n // Similar structure for inventory data\n}\n\nfunction createGeneralPrompt(data) {\n // Fallback for other data types\n}\n\n// Parse the AI response into structured data\nfunction parseAnalysisResponse(text, dataType) {\n // Basic parsing logic - this could be enhanced with more sophisticated parsing\n const sections = {\n keyInsights: extractSection(text, \"Key Insights\"),\n inventoryAnalysis: extractSection(text, \"Inventory Analysis\"),\n inventoryRecommendations: extractSection(text, \"Inventory Recommendations\"),\n vendorAnalysis: extractSection(text, \"Vendor Analysis\"),\n vendorRecommendations: extractSection(text, \"Vendor Recommendations\"),\n salesTrends: extractSection(text, \"Sales Trends\"),\n salesForecasts: extractSection(text, \"Sales Forecasts\")\n };\n \n // Add placeholder metrics and chart data\n // In a real implementation, you might extract these from the AI response\n // or calculate them directly from the data\n const metrics = {\n inventoryTurnover: \"4.2\",\n fulfillmentRate: \"92.5\",\n avgDaysOnOrder: \"6.3\"\n };\n \n const charts = {\n inventoryTurnover: [],\n categoryPerformance: [],\n vendorPerformance: [],\n salesVsPurchases: []\n };\n \n return {\n ...sections,\n metrics,\n charts\n };\n}\n\nfunction extractSection(text, sectionName) {\n const regex = new RegExp(`${sectionName}[:\\\\s]+(.*?)(?=\\\\d+\\\\.\\\\s+[A-Z]|#|$)`, 's');\n const match = text.match(regex);\n return match ? match[1].trim() : '';\n}\n```\n\n### 5. 메인 앱 구성 요소\n\n마지막으로, 모든 것을 종합해 보겠습니다.\n\n```javascript\nimport React, { useState } from 'react';\nimport DataUploader from './components/DataUploader';\nimport SupplyChainAnalysis from './components/SupplyChainAnalysis';\n\nfunction RetailAnalysisMCP() {\n const [retailData, setRetailData] = useState(null);\n \n const handleDataProcessed = (data) => {\n setRetailData(data);\n };\n \n return (\n

Retail Supply Chain Analysis

Upload your retail data for expert supply chain analysis

\n \n

\n \n \n {retailData && }\n

\n );\n}\n\nexport default RetailAnalysisMCP;\n```\n\n## 다음 단계\n\n이 MCP를 더욱 강력하게 만들려면:\n\n1. **데이터 예시** : 그렇습니다. 실제 데이터 예시를 보면 분석을 세부적으로 조정하고 AI가 특정 소매 데이터 형식을 올바르게 해석할 수 있는지 확인하는 데 매우 도움이 됩니다.\n\n2. **데이터 시각화** : Chart.js나 D3.js와 같은 라이브러리를 사용하여 차트와 그래프를 추가하여 추세와 예측을 시각화합니다.\n\n3. **대화형 인터페이스** : 사용자가 분석에 대한 추가 질문을 할 수 있는 채팅 인터페이스를 추가합니다.\n\n4. **도메인별 프롬프팅** : 귀하의 산업에 기반한 특정 공급망 용어와 프레임워크로 AI 프롬프트를 개선합니다.\n\n5. **데이터 전처리** : 분석 전에 보다 정교한 데이터 정리 및 정규화를 추가합니다.\n\n귀하의 데이터 사례를 공유하여 이 솔루션을 귀하의 요구 사항에 맞게 더욱 구체적으로 맞춤화하는 데 도움을 주시겠습니까?\n\n네, 예시를 공유해 드리고 싶습니다. 파일을 업로드해서 보여드릴 수 있을까요?\n\n네, 검토를 위해 Excel 파일을 업로드해 주시면 됩니다. 이를 통해 데이터 구조와 소매 환경에 맞춰 솔루션을 맞춤 설정하는 데 도움이 됩니다. 파일을 업로드하시면 다음과 같은 작업을 할 수 있습니다. 데이터 구조 및 형식 분석 주요 필드 및 관계 식별 필요한 특정 전처리 단계 제안 특정 소매 데이터를 더 잘 해석할 수 있도록 AI 프롬프트 개선 통찰력에 적합한 시각화 제안 예시 재고 보고서, 판매 데이터 또는 검토를 원하는 다른 소매 스프레드시트를 업로드해 주세요. 실제 데이터를 확인하면 최대 효과를 위해 MCP를 구성하는 방법에 대한 훨씬 더 구체적인 지침을 제공할 수 있습니다.\n\n4개의 스크린샷을 공유해드리겠습니다.\n\n1위 - 애버딘 PO: 2위 - 휴런 PO: 3위 - 루번 PO: 4위 - 2023년 7월부터 2024년 2월까지의 판매 데이터:\n\n스크린샷을 공유해 주셔서 감사합니다! 이제 소매 데이터 구조를 확인할 수 있게 되어 더욱 맞춤화된 MCP 솔루션을 설계하는 데 도움이 될 것입니다. 제가 확인한 내용을 분석해 보겠습니다.\n\n데이터 분석\n\n1. 구매 주문 보고서 처음 세 개의 스크린샷은 유사한 구조를 가진 다양한 위치(애버딘, 휴런, 루번)의 구매 주문 세부 정보를 보여줍니다. 주요 필드: 구매 주문 번호, 상태, 수령 장소, 공급업체 정보, 제품 SKU/ID, 제품 이름, 범주 수량 데이터: 주문 수, 수령 수, 미지급 수 재무 데이터: 품목당 비용, 수령 비용, 미지급 비용 타이밍 정보: 약정 날짜, 주문 장소, 주문 일수, 예상 도착 시간 추가 정보: 주문 주석, 수령 주석, 주문 입력 번호 이 보고서는 자세한 조달 정보와 함께 모바일 장치 및 액세서리(스마트폰, 케이블, 충전기 등)를 추적합니다.\n2. 판매 데이터 네 번째 스크린샷은 다음과 같은 판매 데이터를 보여줍니다. 거래 세부 정보: 송장 번호, 수령 날짜, 판매 정보 제품 정보: 다양한 모바일 장치 및 액세서리 가격 데이터: 여러 가격 관련 열 위치/매장 정보 귀하의 데이터에 따른 MCP 설계 이제 귀하의 데이터 구조를 이해했으므로 소매 공급망 MCP에 맞게 더욱 맞춤화된 접근 방식을 알려드리겠습니다.\n\n// 구성요소/MobileRetailAnalysis.js import React, { useState, useEffect } from 'react'; import { analyzeRetailData } from '../services/aiService'; import { InventoryTurnoverChart, ProductCategoryChart, VendorPerformanceChart, SalesVsPurchasesChart } from './charts';\n\n함수 MobileRetailAnalysis({데이터}) { const \\[분석, 분석 설정] = useState(null); const \\[분석 중임, 분석 중임 설정] = useState(false); const \\[선택된 뷰, 선택된 뷰 설정] = useState('개요');\n\nuseEffect(() => { if (데이터) { 수행분석(); } }, \\[데이터]);\n\nconst performAnalysis = async () => { setIsAnalyzing(true); try { const result = await analyzeRetailData(data); setAnalysis(result); } catch (error) { console.error(\"데이터 분석 오류:\", error); } finally { setIsAnalyzing(false); } };\n\n// 선택에 따라 다른 뷰를 렌더링합니다 const renderAnalysisView = () => { if (!analysis) return null;\n\n```\nswitch(selectedView) {\n case 'overview':\n return (\n

Key Insights

\n \n

Inventory Turnover

{analysis.metrics.inventoryTurnover}

Order Fulfillment Rate

{analysis.metrics.fulfillmentRate}%

Average Days on Order

{analysis.metrics.avgDaysOnOrder}

\n );\n \n case 'inventory':\n return (\n

Inventory Analysis

\n \n

Top Products by Turnover

\n \n \n

Category Performance

\n \n \n

Inventory Recommendations

\n );\n \n case 'vendor':\n return (\n

Vendor Performance

\n \n

Vendor Delivery Performance

\n \n \n

Vendor Recommendations

\n );\n \n case 'sales':\n return (\n

Sales Trends

\n \n

Sales vs Purchases

\n \n \n

Sales Forecasts

\n );\n \n default:\n return null;\n}\n```\n\n};\n\n반환 ( 모바일 소매 공급망 분석 \\

\n```\n\n); }\n\n기본 MobileRetailAnalysis를 내보냅니다.\n\n이제 모바일 소매 데이터에 맞게 AI 서비스를 더욱 구체적으로 업데이트해 보겠습니다.\n\n// 서비스/aiService.js 비동기 함수 내보내기 analyzeRetailData(데이터) { try { const response = await fetch('/api/analyze-mobile-retail', { 메서드: 'POST', 헤더: { 'Content-Type': 'application/json', }, 본문: JSON.stringify({ 데이터 }), });\n\n```\nif (!response.ok) {\n throw new Error('Failed to analyze data');\n}\n\nreturn await response.json();\n```\n\n} catch (error) { console.error('AI 분석 서비스에서 오류 발생:', error); throw error; } }","Korean","ko-KR",{"_29":62,"_33":66,"_38":67},{"_31":63,"_33":64,"_35":65},"使用户能够上传零售数据、分析趋势、优化库存并使用人工智能洞察预测销售，充当高级供应链专家。","零售供应链分析 MCP","# 零售供应链分析工具\n\n一种用于分析零售供应链数据的人工智能工具，为库存优化、供应商管理和销售预测提供可行的见解。\n\n## 特征\n\n* 上传并分析多个 Excel/CSV 文件\n* 支持采购订单、销售数据和库存文件\n* 自动文件类型检测\n* 多数据源联合交叉分析\n* 详细的可视化和指标\n* 供应链优化的可行建议\n\n## 入门\n\n1. 克隆存储库\n2. 安装依赖项： `npm install`\n3. 启动开发服务器： `npm run dev`\n4. 在浏览器中打开\n\n## 用法\n\n1. 上传一个或多个包含零售数据的 Excel/CSV 文件\n2. 如果未自动检测到，请为每个文件选择适当的数据类型\n3. 点击“分析文件”来处理数据\n4. 查看不同类别的见解（概览、库存、供应商、销售）\n5. 在组合分析和单个文件类型分析之间切换\n\n## 技术\n\n* 用于前端和 API 路由的 Next.js\n* Claude AI 用于智能数据分析\n* Chart.js 用于数据可视化\n* XLSX 用于 Excel 文件处理\n\n## 环境变量\n\n使用以下变量创建`.env.local`文件：\n\n```\nANTHROPIC_API_KEY=your_api_key_here\n```\n\n## 执照\n\n[麻省理工学院](LICENSE)\n\n我想创建一个 MCP，可以上传包含零售店数据（包括库存报告、销售报告等）的 Excel 电子表格，然后让人工智能分析这些数据，从而得出趋势、预测和其他洞察，就像这个人工智能是零售商的高级供应链专家一样。您能帮我吗？\n\n如果有帮助的话，我有数据示例。\n\n零售供应链分析 MCP 是的，我绝对可以帮助您创建一个多模态对话角色 (MCP)，像高级供应链专家一样分析零售店数据。这是 AI 分析的绝佳用例。高级方法我们可以这样构建这个项目：数据上传和处理：创建一个界面来上传包含库存、销售和其他零售数据的 Excel 文件数据分析：处理数据以提取有意义的见解专家角色：设计 AI，使其像高级供应链专家一样传达见解可视化：提供趋势和预测的可视化表示\n\n实施步骤\n\n1. 数据上传组件首先，我们需要一种上传和处理 Excel 文件的方法：\n\n从 'react' 导入 React，{ useState }；从 'xlsx' 导入 \\* 作为 XLSX；\n\n函数 RetailDataProcessor（{ onDataProcessed }）{ const \\[isProcessing，setIsProcessing] = useState（false）; const \\[dataType，setDataType] = useState（'purchase\\_orders'）; const \\[location，setLocation] = useState（''）;\n\nconst handleFileUpload = async (e) => { const file = e.target.files\\[0]; setIsProcessing(true);\n\n```\nconst reader = new FileReader();\nreader.onload = (event) => {\n try {\n const data = new Uint8Array(event.target.result);\n const workbook = XLSX.read(data, { type: 'array' });\n \n // Process the first sheet by default\n const worksheet = workbook.Sheets[workbook.SheetNames[0]];\n const jsonData = XLSX.utils.sheet_to_json(worksheet);\n \n // Detect data type based on column headers\n const detectedType = detectDataType(jsonData);\n \n // Process and normalize the data based on type\n const processedData = {\n type: detectedType || dataType,\n location: location,\n data: normalizeData(jsonData, detectedType || dataType)\n };\n \n onDataProcessed(processedData);\n } catch (error) {\n console.error(\"Error processing Excel file:\", error);\n } finally {\n setIsProcessing(false);\n }\n};\n\nreader.readAsArrayBuffer(file);\n```\n\n};\n\n// 根据列标题检测数据类型 const detectDataType = (data) => { if (!data || data.length === 0) return null;\n\n```\nconst headers = Object.keys(data[0]);\n\nif (headers.includes('PO #') && headers.includes('# Ordered')) {\n return 'purchase_orders';\n} else if (headers.includes('Invoice #') || headers.includes('Sale By')) {\n return 'sales_data';\n} else if (headers.includes('On Hand') || headers.includes('In Stock')) {\n return 'inventory';\n}\n\nreturn null;\n```\n\n};\n\n// 根据类型规范化数据 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'], amountOrdered: item\\['# Ordered'], amountReceived: item\\['# Received'], amountOutstanding: 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'] }));\n\n```\n case 'sales_data':\n // Normalize sales data based on your fourth screenshot\n return data.map(item => ({\n invoiceNumber: item['Invoice #'],\n saleDate: item['Date'],\n saleBy: item['Sale By'],\n product: item['Product Name'],\n category: item['Category'],\n quantity: item['Qty'],\n price: item['Price'],\n cost: item['Cost'],\n profit: item['Profit'],\n location: item['Location'] || location\n }));\n \n default:\n return data;\n}\n```\n\n};\n\n返回（数据类型：\\ setLocation(e.target.value)}\n placeholder=\"Aberdeen, Huron, Luverne, etc.\"\n />\n

\n \n

\n \n {isProcessing &&

Processing data...

}\n

\n```\n\n); }\n\n导出默认的 RetailDataProcessor；\n\n### 2.供应链分析组件\n\n接下来，我们将创建一个组件来处理 AI 分析：\n\n```javascript\nimport React, { useState, useEffect } from 'react';\nimport { analyzeRetailData } from '../services/aiService';\n\nfunction SupplyChainAnalysis({ data }) {\n const [analysis, setAnalysis] = useState(null);\n const [isAnalyzing, setIsAnalyzing] = useState(false);\n \n useEffect(() => {\n if (data) {\n performAnalysis();\n }\n }, [data]);\n \n const performAnalysis = async () => {\n setIsAnalyzing(true);\n try {\n // This would call your AI service\n const result = await analyzeRetailData(data);\n setAnalysis(result);\n } catch (error) {\n console.error(\"Error analyzing data:\", error);\n } finally {\n setIsAnalyzing(false);\n }\n };\n \n return (\n

Supply Chain Analysis

\n \n {isAnalyzing &&

Analyzing your retail data...

}\n \n {analysis && (\n

Key Insights

\n \n

Inventory Recommendations

\n \n

Sales Trends

\n \n

Forecasts

\n )}\n

\n );\n}\n\nexport default SupplyChainAnalysis;\n```\n\n### 3.人工智能服务\n\n现在，让我们创建一个与AI交互的服务：\n\n```javascript:README.md\n// services/aiService.js\n\nexport async function analyzeRetailData(data) {\n try {\n const response = await fetch('/api/analyze-mobile-retail', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({ data }),\n });\n \n if (!response.ok) {\n throw new Error('Failed to analyze data');\n }\n \n return await response.json();\n } catch (error) {\n console.error('Error in AI analysis service:', error);\n throw error;\n }\n}\n```\n\n### 4. 后端 API 端点\n\n对于后端，我们需要一个端点来处理数据并与 AI 交互：\n\n```javascript\n// api/analyze-mobile-retail.js\nimport { OpenAI } from 'openai';\n\nconst openai = new OpenAI({\n apiKey: process.env.OPENAI_API_KEY,\n});\n\nexport default async function handler(req, res) {\n if (req.method !== 'POST') {\n return res.status(405).json({ error: 'Method not allowed' });\n }\n\n try {\n const { data } = req.body;\n \n // Create a specialized prompt based on data type\n let prompt = '';\n \n if (data.type === 'purchase_orders') {\n prompt = createPurchaseOrderPrompt(data);\n } else if (data.type === 'sales_data') {\n prompt = createSalesDataPrompt(data);\n } else if (data.type === 'inventory') {\n prompt = createInventoryPrompt(data);\n } else {\n prompt = createGeneralPrompt(data);\n }\n \n // Call the AI\n const completion = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n { \n role: \"system\", \n content: \"You are a senior supply chain expert with 20+ years of experience in mobile device retail. You analyze data and provide actionable insights with a focus on inventory optimization, vendor management, and sales forecasting for smartphone and mobile accessory retailers.\" \n },\n { role: \"user\", content: prompt }\n ],\n temperature: 0.2,\n });\n \n // Process the AI response\n const analysisText = completion.choices[0].message.content;\n \n // Parse the response into structured data\n const analysisData = parseAnalysisResponse(analysisText, data.type);\n \n return res.status(200).json(analysisData);\n } catch (error) {\n console.error('Error analyzing mobile retail data:', error);\n return res.status(500).json({ error: 'Failed to analyze data' });\n }\n}\n\n// Create specialized prompts based on data type\nfunction createPurchaseOrderPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following purchase order data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Vendor performance (delivery times, order fulfillment)\n 2. Product category insights (smartphones, accessories, etc.)\n 3. Inventory management recommendations\n 4. Cost optimization opportunities\n 5. Order cycle time improvements\n \n Provide specific actionable recommendations for improving the supply chain.\n `;\n}\n\nfunction createSalesDataPrompt(data) {\n return `\n As a senior supply chain expert for a mobile device retailer, analyze the following sales data for ${data.location || 'our stores'}:\n \n ${JSON.stringify(data.data, null, 2)}\n \n Focus your analysis on:\n 1. Sales trends by product category and specific models\n 2. Profitability analysis by product type\n 3. Seasonal patterns and forecasting\n 4. Product mix optimization\n 5. Inventory replenishment recommendations based on sales velocity\n \n Provide specific actionable recommendations for improving sales and inventory alignment.\n `;\n}\n\nfunction createInventoryPrompt(data) {\n // Similar structure for inventory data\n}\n\nfunction createGeneralPrompt(data) {\n // Fallback for other data types\n}\n\n// Parse the AI response into structured data\nfunction parseAnalysisResponse(text, dataType) {\n // Basic parsing logic - this could be enhanced with more sophisticated parsing\n const sections = {\n keyInsights: extractSection(text, \"Key Insights\"),\n inventoryAnalysis: extractSection(text, \"Inventory Analysis\"),\n inventoryRecommendations: extractSection(text, \"Inventory Recommendations\"),\n vendorAnalysis: extractSection(text, \"Vendor Analysis\"),\n vendorRecommendations: extractSection(text, \"Vendor Recommendations\"),\n salesTrends: extractSection(text, \"Sales Trends\"),\n salesForecasts: extractSection(text, \"Sales Forecasts\")\n };\n \n // Add placeholder metrics and chart data\n // In a real implementation, you might extract these from the AI response\n // or calculate them directly from the data\n const metrics = {\n inventoryTurnover: \"4.2\",\n fulfillmentRate: \"92.5\",\n avgDaysOnOrder: \"6.3\"\n };\n \n const charts = {\n inventoryTurnover: [],\n categoryPerformance: [],\n vendorPerformance: [],\n salesVsPurchases: []\n };\n \n return {\n ...sections,\n metrics,\n charts\n };\n}\n\nfunction extractSection(text, sectionName) {\n const regex = new RegExp(`${sectionName}[:\\\\s]+(.*?)(?=\\\\d+\\\\.\\\\s+[A-Z]|#|$)`, 's');\n const match = text.match(regex);\n return match ? match[1].trim() : '';\n}\n```\n\n### 5. 主应用程序组件\n\n最后，让我们把所有内容放在一起：\n\n```javascript\nimport React, { useState } from 'react';\nimport DataUploader from './components/DataUploader';\nimport SupplyChainAnalysis from './components/SupplyChainAnalysis';\n\nfunction RetailAnalysisMCP() {\n const [retailData, setRetailData] = useState(null);\n \n const handleDataProcessed = (data) => {\n setRetailData(data);\n };\n \n return (\n

Retail Supply Chain Analysis

Upload your retail data for expert supply chain analysis

\n \n

\n \n \n {retailData && }\n

\n );\n}\n\nexport default RetailAnalysisMCP;\n```\n\n## 后续步骤\n\n为了使这个 MCP 更加强大：\n\n1. **数据示例**：是的，查看实际数据示例对于微调分析和确保 AI 能够正确解释您的特定零售数据格式非常有帮助。\n\n2. **数据可视化**：使用 Chart.js 或 D3.js 等库添加图表和图形，以可视化趋势和预测。\n\n3. **对话界面**：添加聊天界面，用户可以在其中询问有关分析的后续问题。\n\n4. **特定领域提示**：根据您的行业，使用特定的供应链术语和框架来细化 AI 提示。\n\n5. **数据预处理**：在分析之前添加更复杂的数据清理和规范化。\n\n您是否愿意分享您的数据示例，以便我能够更准确地根据您的需求定制该解决方案？\n\n是的，我很乐意分享示例。我可以上传文件给你看吗？\n\n是的，您可以上传您的 Excel 文件供我审核。这将有助于我根据您的数据结构和零售环境定制解决方案。上传文件后，我将能够：分析数据的结构和格式；识别关键字段和关系；建议所需的具体预处理步骤；优化 AI 提示，以更好地解读您的特定零售数据；为洞察推荐合适的可视化效果。请继续上传您的示例库存报告、销售数据或任何其他您希望我查看的零售电子表格。一旦我看到实际数据，我就可以提供更具体的指导，指导如何构建 MCP 以实现最大效益。\n\n我将分享 4 张屏幕截图。\n\n第一名 - 阿伯丁邮局：第二名 - 休伦邮局：第三名 - 卢弗恩邮局：第四名 - 2023 年 7 月至 2024 年 2 月的销售数据：\n\n感谢您分享这些截图！现在我可以看到您的零售数据结构，这将有助于我设计更个性化的 MCP 解决方案。让我分析一下我看到的内容：\n\n数据分析\n\n1. 采购订单报告前三张屏幕截图显示了具有类似结构的不同地点（阿伯丁、休伦和卢韦恩）的采购订单详细信息：关键字段：采购订单号、状态、接收地点、供应商信息、产品 SKU/ID、产品名称、类别数量数据：订购数量、已接收数量、未付款数量财务数据：每件成本、已接收成本、未付款成本时间信息：承诺日期、订购时间、订购天数、预计到达时间附加信息：订购备注、接收备注、订单输入号这些报告跟踪移动设备和配件（智能手机、电缆、充电器等），并提供详细的采购信息。\n2. 销售数据第四张屏幕截图显示了销售数据，包括：交易详情：发票号、收货日期、销售依据信息产品信息：各种移动设备和配件定价数据：多个与价格相关的列位置/商店信息基于您的数据的 MCP 设计现在我了解了您的数据结构，下面是更适合您的零售供应链 MCP 的方法：\n\n// components/MobileRetailAnalysis.js 从 'react' 导入 React、{ useState、useEffect }；从 '../services/aiService' 导入 { analyzeRetailData }；从 './charts' 导入 { InventoryTurnoverChart、ProductCategoryChart、VendorPerformanceChart、SalesVsPurchasesChart }；\n\n函数 MobileRetailAnalysis({ data }) { const \\[analysis, setAnalysis] = useState(null); const \\[isAnalyzing, setIsAnalyzing] = useState(false); const \\[selectedView, setSelectedView] = useState('overview');\n\nuseEffect(() => { if (data) { performAnalysis(); } }, \\[data]);\n\nconst performAnalysis = async () => { setIsAnalyzing(true); try { const result = await analyzeRetailData(data); setAnalysis(result); } catch (error) { console.error(\"分析数据时出错：\", error); } finally { setIsAnalyzing(false); } };\n\n// 根据选择渲染不同的视图 const renderAnalysisView = () => { if (!analysis) return null;\n\n```\nswitch(selectedView) {\n case 'overview':\n return (\n

Key Insights

\n \n

Inventory Turnover

{analysis.metrics.inventoryTurnover}

Order Fulfillment Rate

{analysis.metrics.fulfillmentRate}%

Average Days on Order

{analysis.metrics.avgDaysOnOrder}

\n );\n \n case 'inventory':\n return (\n

Inventory Analysis

\n \n

Top Products by Turnover

\n \n \n

Category Performance

\n \n \n

Inventory Recommendations

\n );\n \n case 'vendor':\n return (\n

Vendor Performance

\n \n

Vendor Delivery Performance

\n \n \n

Vendor Recommendations

\n );\n \n case 'sales':\n return (\n

Sales Trends

\n \n

Sales vs Purchases

\n \n \n

Sales Forecasts

\n );\n \n default:\n return null;\n}\n```\n\n};\n\n返回（移动零售供应链分析 \\

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