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Restaurant Food Waste Prediction System | Operasyon için n8n Otomasyon İş Akışı Şablonu (Google Sheets, Gmail)
n8n için hazır otomasyon şablonu: Restaurant Food Waste Prediction System. 12 düğüm. Entegrasyonlar: Google Sheets, Gmail. JSON'u kopyalayıp n8n'e içe aktarın.
12 nœudsRestaurant_Food_Waste_Prediction_System-workflow.json
{"id": "ER0P5gVPl7ARd3cU","meta": {"instanceId": "dd69efaf8212c74ad206700d104739d3329588a6f3f8381a46a481f34c9cc281","templateCredsSetupCompleted": true},"name": "Restaurant food waste prediction System","tags": [],"nodes": [{"id": "7ecc2319-ee53-4b62-b529-666c82fb88b6","name": "Daily Trigger","type": "n8n-nodes-base.scheduleTrigger","position": [-280,420],"parameters": {"rule": {"interval": [{"triggerAtHour": 22}]}},"typeVersion": 1.2},{"id": "fa199f84-c3da-4e05-867f-20cab560c633","name": "Fetch Historical Sales Data","type": "n8n-nodes-base.googleSheets","position": [-60,420],"parameters": {"options": {},"sheetName": {"__rl": true,"mode": "list","value": 1621929706,"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1QZXX_gjNTTZ0vqPozNysjASQS3UcgwwWVpYvN7r-T_I/edit#gid=1621929706","cachedResultName": "food wastage data"},"documentId": {"__rl": true,"mode": "list","value": "1QZXX_gjNTTZ0vqPozNysjASQS3UcgwwWVpYvN7r-T_I","cachedResultUrl": "https://docs.google.com/spreadsheets/d/1QZXX_gjNTTZ0vqPozNysjASQS3UcgwwWVpYvN7r-T_I/edit?usp=drivesdk","cachedResultName": "Restaurant stock predictions"},"authentication": "serviceAccount"},"credentials": {"googleApi": {"id": "ScSS2KxGQULuPtdy","name": "Google Sheets- test"}},"typeVersion": 4.6},{"id": "d186b482-008a-45b1-a766-963ead9bc4de","name": "Format Data for AI Forecasting","type": "n8n-nodes-base.code","position": [160,420],"parameters": {"jsCode": "// Fetch all incoming items\nconst items = $input.all();\n\n// Extract the raw row data (each item.json is one row)\nconst rawRows = items.map(item => item.json);\n\n// Bundle everything into a single field\nconst payload = { rows: rawRows };\n\n// Return a single output item whose json contains your full dataset\nreturn [{ json: { data: payload } }];\n"},"typeVersion": 2},{"id": "4f401f04-a17e-4dfc-874f-d5e7e0983316","name": "AI Forecast Generator\t","type": "@n8n/n8n-nodes-langchain.agent","position": [380,420],"parameters": {"text": "={{ $json.data }}","options": {"systemMessage": "You are a demand forecasting and optimisation AI assistant for a restaurant. \n\nYou will receive data items, each with fields:\n- Date (YYYY-MM-DD)\n- Dish\n- Sales (number of dishes sold)\n- Raw Material\n- Used (kg)\n- Wastage (kg)\n\nYour tasks are:\n1. Analyze the past 5 days per dish and per raw material.\n2. Identify trends (e.g., increasing/decreasing sales, stable/wasteful usage).\n3. Predict next-day sales per dish and raw material usage (in kg) using simple time-series methods (like moving average or linear trend).\n4. Adjust raw material predictions to reduce expected wastage—aim for coverage with ~5% buffer.\n5. Calculate \"predicted food waste reduce\" as: (predicted raw material use) − (optimal raw material procurement). This indicates how much waste you’re avoiding.\n6. Return output as JSON array with:\n - Date (next day’s date, YYYY-MM-DD)\n - Dish\n - predicted Sales (number)\n - Raw Material\n - predicted Use (kg)\n - predicted food waste reduce (kg)\n\nConstraints:\n- Use clear numerical rounding (one decimal place).\n- Prioritize realistic, small improvements (justified by data).\n\nExample output item:\n{\n \"Date\": \"2025-07-14\",\n \"Dish\": \"Pasta\",\n \"predicted Sales\": 60,\n \"Raw Material\": \"Tomato\",\n \"predicted Use (kg)\": 12.0,\n \"predicted food waste reduce (kg)\": 0.5\n}\n\nWrite a JSON array containing one item per (Dish × Raw Material), sorted alphabetically by Dish.\n"},"promptType": "define"},"typeVersion": 1.9},{"id": "c1888087-d468-4f25-bf3d-5b5185e34719","name": "Clean & Structure AI Output","type": "n8n-nodes-base.code","position": [760,420],"parameters": {"jsCode": "// 1. Grab the AI agent output (assuming it's in item.json.output or item.json.content)\nconst raw = $input.item.json.output || $input.item.json.content;\n\n// 2. Clean it by removing triple backticks, optional \"json\" tag, and any common prefixes\nlet cleaned = raw\n .replace(/```json/, '')\n .replace(/```/g, '')\n .replace(/^(Here is (the )?JSON[:\\s]*)/, '')\n .trim();\n\n// 3. Parse JSON safely\nlet parsed;\ntry {\n parsed = JSON.parse(cleaned);\n} catch (e) {\n throw new Error('Failed to parse AI JSON: ' + e.message + '\\nRaw cleaned text: ' + cleaned);\n}\n\n// 4. Return each JSON object as its own item for downstream nodes\nreturn parsed.map(obj => ({ json: obj }));\n"},"typeVersion": 2},{"id": "d766931a-e722-402e-aa88-58c1b39935d5","name": "Log Forecast to Google Sheets","type": "n8n-nodes-base.googleSheets","position": [980,420],"parameters": {"columns": {"value": {},"schema": [{"id": "Date","type": "string","display": true,"required": false,"displayName": "Date","defaultMatch": false,"canBeUsedToMatch": true},{"id": "Dish","type": "string","display": true,"required": false,"displayName": "Dish","defaultMatch": false,"canBeUsedToMatch": true},{"id": "predicted Sales","type": "string","display": true,"required": false,"displayName": "predicted Sales","defaultMatch": false,"canBeUsedToMatch": true},{"id": "Raw Material","type": "string","display": true,"required": false,"displayName": "Raw Material","defaultMatch": false,"canBeUsedToMatch": true},{"id": "predicted Use (kg)","type": "string","display": true,"required": false,"displayName": "predicted Use (kg)","defaultMatch": false,"canBeUsedToMatch": true},{"id": "predicted food waste reduce (kg)","type": "string","display": true,"removed": false,"required": false,"displayName": "predicted food waste reduce (kg)","defaultMatch": false,"canBeUsedToMatch": true}],"mappingMode": "autoMapInputData","matchingColumns": [],"attemptToConvertTypes": false,"convertFieldsToString": false},"options": {},"operation": "append","sheetName": {"__rl": true,"mode": "list","value": 1653513259,"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1QZXX_gjNTTZ0vqPozNysjASQS3UcgwwWVpYvN7r-T_I/edit#gid=1653513259","cachedResultName": "predicted food data for low wastage"},"documentId": {"__rl": true,"mode": "list","value": "1QZXX_gjNTTZ0vqPozNysjASQS3UcgwwWVpYvN7r-T_I","cachedResultUrl": "https://docs.google.com/spreadsheets/d/1QZXX_gjNTTZ0vqPozNysjASQS3UcgwwWVpYvN7r-T_I/edit?usp=drivesdk","cachedResultName": "Restaurant stock predictions"},"authentication": "serviceAccount"},"credentials": {"googleApi": {"id": "ScSS2KxGQULuPtdy","name": "Google Sheets- test"}},"typeVersion": 4.6},{"id": "a9f91c73-41e6-4955-bbd5-1e9ff658df1e","name": "Create Email Summary\t","type": "@n8n/n8n-nodes-langchain.agent","position": [1200,420],"parameters": {"text": "={{ $('AI Forecast Generator\t').item.json.output }}","options": {"systemMessage": "You are an intelligent assistant tasked with drafting a clear, concise and professional summary email based on forecast data for a restaurant. You’ll receive an array of forecast objects in this format:\n\n[\n {\n \"Date\": \"2025-07-14\",\n \"Dish\": \"Pasta\",\n \"predicted Sales\": 60,\n \"Raw Material\": \"Cream\",\n \"predicted Use (kg)\": 3.6,\n \"predicted food waste reduce (kg)\": 0.1\n },\n ...\n]\n\nYour goal:\n1. Open with a friendly greeting.\n2. Provide a one-sentence overview: mention the date, how many dishes are forecasted, and overall expected food waste reduction.\n3. List each dish in its own bullet point. For each dish list:\n - Total predicted sales.\n - The raw materials needed with predicted usage.\n - Highlight expected waste reduction (if >0).\n4. Conclude with a recommended action, something like: “Please adjust tomorrow’s procurement based on these forecasts to minimize waste.” Offer to discuss specifics if needed.\n5. Keep tone professional but warm.\n\nExample style:\n\n“Hi [Manager Name],\n\nHere’s your forecast summary for July 14, 2025:\n\n- **Pasta (60 servings):** You will need Cream (3.6 kg), Flour (6 kg) and Tomato (12 kg), reducing expected food waste by approx. 1.9 kg.\n- **Pizza (50 servings):** Cheese 10 kg, waste reduction ~0.5 kg.\n- **Salad (40 servings):** Lettuce 8 kg, no predicted waste reduction.\n\nOverall, this forecast anticipates around 2.4 kg less waste across all dishes.\n\nPlease adjust tomorrow’s orders accordingly to minimize waste. Let me know if you'd like a deeper breakdown!”\n\nReturn only the email body text (no JSON or markdown fences).\n"},"promptType": "define"},"executeOnce": true,"typeVersion": 1.9,"alwaysOutputData": true},{"id": "1c9cec9e-d90b-4479-b569-9ef2726a6987","name": "Send Email Forecast Report\t","type": "n8n-nodes-base.gmail","position": [1560,420],"webhookId": "29a66a86-3fbb-49c3-bcd1-e697edfb19ce","parameters": {"sendTo": "abc@gmail.com","message": "={{ $json.output }}","options": {},"subject": "Next monday prediction","emailType": "text"},"credentials": {"gmailOAuth2": {"id": "PcTqvGU9uCunfltE","name": "Gmail account - test"}},"typeVersion": 2.1},{"id": "1f400e94-9bc2-414c-aedc-6f7f86c887d0","name": "Sticky Note","type": "n8n-nodes-base.stickyNote","position": [-300,140],"parameters": {"width": 150,"height": 480,"content": "Initiates the workflow every day to perform food waste prediction.\n\n"},"typeVersion": 1},{"id": "65a5d131-945b-40ee-bf66-b33f4600d97c","name": "Sticky Note1","type": "n8n-nodes-base.stickyNote","position": [-80,140],"parameters": {"color": 3,"width": 150,"height": 480,"content": "Reads past food usage & sales from Google Sheets to understand trends.\n\n"},"typeVersion": 1},{"id": "8cc25bd0-38bf-485a-a75b-7bed2ae24b1e","name": "Sticky Note2","type": "n8n-nodes-base.stickyNote","position": [360,140],"parameters": {"color": 6,"width": 290,"height": 480,"content": "Uses Gemini AI to forecast food demand and recommend waste reduction.\n\n\n"},"typeVersion": 1},{"id": "3afb2901-aeaa-49a2-920a-806ce0e5dc9d","name": "Sticky Note3","type": "n8n-nodes-base.stickyNote","position": [960,140],"parameters": {"color": 4,"width": 150,"height": 480,"content": "Stores AI-generated forecast back into a forecast-specific Google Sheet.\n\n"},"typeVersion": 1},{"id": "458c1f5e-0a7a-4981-afd6-efc64c286e5d","name": "Sticky Note4","type": "n8n-nodes-base.stickyNote","position": [740,140],"parameters": {"color": 3,"width": 150,"height": 480,"content": "Parses AI response into structured and clean format for reporting.\n\n"},"typeVersion": 1},{"id": "5917d966-9260-4ace-baf9-4ecd666021f4","name": "Sticky Note5","type": "n8n-nodes-base.stickyNote","position": [140,140],"parameters": {"color": 4,"width": 150,"height": 480,"content": "Cleans and organizes raw data into a structure suitable for AI processing.\n\n"},"typeVersion": 1},{"id": "16b34b52-3d0e-4dc5-95eb-8eaecd7fdfa3","name": "Sticky Note6","type": "n8n-nodes-base.stickyNote","position": [1540,140],"parameters": {"width": 150,"height": 480,"content": "Delivers the forecast report via email to decision-makers (kitchen, manager, etc).\n\n"},"typeVersion": 1},{"id": "08d6406a-b9ef-4b10-90cf-7d4c98a60921","name": "Sticky Note7","type": "n8n-nodes-base.stickyNote","position": [1180,140],"parameters": {"color": 5,"width": 290,"height": 480,"content": "Creates a concise, human-friendly summary of the forecast.\n\n\n"},"typeVersion": 1},{"id": "5a088b10-604f-475f-a611-3b147ae9ce0c","name": "Sticky Note8","type": "n8n-nodes-base.stickyNote","position": [220,-340],"parameters": {"width": 640,"height": 260,"content": "## 📌 Workflow Purpose: Restaurant Food Waste Prediction System\n\nThis workflow automates daily forecasting of sales and raw material needs for a restaurant. By analyzing historical data and predicting future usage with AI, it helps minimize food waste, optimize inventory, and improve operational efficiency. The forecast is stored in Google Sheets and sent via email for easy review by staff and management."},"typeVersion": 1},{"id": "28b4b3dc-5e7c-41a7-8dda-4cf67eacef0a","name": "Chat Model","type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position": [400,640],"parameters": {"options": {},"modelName": "models/gemini-2.5-pro"},"credentials": {"googlePalmApi": {"id": "RvSkIBjP48ORJKhU","name": "Google Gemini(PaLM) Api account - test"}},"typeVersion": 1},{"id": "f0e9f8d4-4a86-4167-b833-5ac3ab3a5545","name": "Mind","type": "@n8n/n8n-nodes-langchain.toolThink","position": [520,640],"parameters": {},"typeVersion": 1},{"id": "f7e04c0c-cba5-46f4-bc47-fb26731f2375","name": "Chat Model for Query ","type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position": [1220,640],"parameters": {"options": {},"modelName": "models/gemini-2.5-pro"},"credentials": {"googlePalmApi": {"id": "RvSkIBjP48ORJKhU","name": "Google Gemini(PaLM) Api account - test"}},"typeVersion": 1},{"id": "dbddf020-ef31-43b4-85a9-3f459ac56d6b","name": "Mind For Think","type": "@n8n/n8n-nodes-langchain.toolThink","position": [1340,640],"parameters": {},"typeVersion": 1}],"active": false,"pinData": {},"settings": {"executionOrder": "v1"},"versionId": "f6380285-b1c9-49e1-82bf-12c2db027369","connections": {"Mind": {"ai_tool": [[{"node": "AI Forecast Generator\t","type": "ai_tool","index": 0}]]},"Chat Model": {"ai_languageModel": [[{"node": "AI Forecast Generator\t","type": "ai_languageModel","index": 0}]]},"Daily Trigger": {"main": [[{"node": "Fetch Historical Sales Data","type": "main","index": 0}]]},"Mind For Think": {"ai_tool": [[{"node": "Create Email Summary\t","type": "ai_tool","index": 0}]]},"Chat Model for Query ": {"ai_languageModel": [[{"node": "Create Email Summary\t","type": "ai_languageModel","index": 0}]]},"Create Email Summary\t": {"main": [[{"node": "Send Email Forecast Report\t","type": "main","index": 0}]]},"AI Forecast Generator\t": {"main": [[{"node": "Clean & Structure AI Output","type": "main","index": 0}]]},"Clean & Structure AI Output": {"main": [[{"node": "Log Forecast to Google Sheets","type": "main","index": 0}]]},"Fetch Historical Sales Data": {"main": [[{"node": "Format Data for AI Forecasting","type": "main","index": 0}]]},"Log Forecast to Google Sheets": {"main": [[{"node": "Create Email Summary\t","type": "main","index": 0}]]},"Format Data for AI Forecasting": {"main": [[{"node": "AI Forecast Generator\t","type": "main","index": 0}]]}}}
Dans l’éditeur n8n : collez avec Ctrl+V→Le workflow sera créé