AI Production Planning System
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AI Production Planning System

Next.jsReactTypeScriptExcel Parsing (XLSX)AI Planning CopilotZustandMulti-language UI
projectDetail.role

Full Stack Planning Developer

projectDetail.year

2026

projectDetail.duration

Ongoing

projectDetail.type

projectDetail.industrialAutomation

projectDetail.theChallenge

Before this project, whenever a customer sent a new washing machine planning file, the planning team had to manually copy, paste, and reformat spreadsheet data into internal planning sheets before any real analysis could begin. Column positions were not always consistent, BOM references had to be cross-checked by hand, and matching customer demand with internal process planning consumed several hours per day. This manual workflow slowed decision-making, increased the risk of mapping mistakes, and made it difficult to respond quickly when planning priorities changed.

projectDetail.theSolution

I built an AI-assisted production planning platform that allows planners to upload customer and internal Excel files directly, automatically detect key planning columns, parse daily production schedules, and connect the data with BOM and process master data in one interface. The system combines dashboard analytics, planning tables, master-data management, and AI-generated planning summaries so the team can move from raw demand files to actionable production plans in a few minutes instead of spending hours on manual spreadsheet preparation.

projectDetail.transformationEvidence

After
Before
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Planning Workflow
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projectDetail.systemDemo

AI Production Planning System demo preview

projectDetail.systemArchitecture

projects.systemArchitectureLive

Input

Customer/Internal Excel Upload

Parsing

Smart Header Mapping + XLSX

Planning

Dashboard + AI Planning Copilot

Control

BOM + Capacity + Calendar
projects.latency: 12ms
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projectDetail.lessonsLearned

- Parser Robustness (Data Integration): Customer and internal planning files do not keep perfectly fixed column layouts. I designed the parser to scan for real header labels and column aliases so the system can keep working even when spreadsheet structures shift. - Planning Visibility (UX): A large planning table alone is not enough for fast decisions. I added dashboard summaries, BOM match coverage views, line/tool filters, and exception indicators so planners can detect mismatches and capacity risks much faster. - AI Decision Support (Operations): AI suggestions are only useful when planners can verify the reason behind them. I paired AI summaries with demand, capacity, and BOM evidence so the team can validate each exception before acting on it.

projectDetail.businessImpact

Planning Cycle Reduced from Hours to Minutes | AI Risk Summaries for Load and Material Issues | Unified Demand, Process, and BOM Visibility

projects.detailHeading

AI Production Planning System is a production planning workspace designed to turn incoming customer Excel files into actionable internal production plans without the usual manual spreadsheet preparation. Project Scope: - Built a centralized web application for uploading customer demand files, internal process plans, and BOM reference files. - Automated the parsing of variable Excel structures into typed planning data for lines, models, tools, due dates, and daily quantities. - Combined dashboard analytics, planning review, master-data maintenance, and AI-assisted exception handling into one responsive interface. System Workflow: 1. Upload Layer: Planners upload customer demand files and internal planning files directly through the web interface. 2. Parsing Layer: The application auto-detects key headers, maps daily quantity columns, and normalizes spreadsheet data into structured planning records. 3. Planning Layer: Users filter by line, tool, color, and status to review schedule quantities, demand IDs, and process matching in one place. 4. Master Data Layer: BOM mappings, capacities, inventory data, and working calendars are maintained directly in the application. 5. Decision Layer: The AI planning copilot summarizes overload risks, BOM gaps, unmapped demand, and recommended next actions for faster daily planning decisions. Key Features: - Auto Column Detection: Identifies relevant fields from changing customer Excel layouts without requiring repetitive manual remapping. - Daily Schedule Parsing: Converts date-based spreadsheet columns into structured daily production quantities for analysis and review. - BOM and Process Linking: Connects customer model demand with internal part mapping and process planning logic across Top, Outer, Plan LID, and Vibration stages. - AI Planning Copilot: Generates planning summaries, shortage warnings, capacity imbalance alerts, and recommended next checks for planners. - Master Data Management: Supports maintenance of part mappings, capacities, inventory, and working calendars in one system. - Multi-Language Access: Provides a localized interface for English, Korean, and Thai users. - Persistent Planning Workspace: Keeps uploaded data and planning state available for continued review across sessions. Business Impact: - Reduced the planning team's daily preparation work from several hours to just a few minutes. - Lowered the risk of spreadsheet copy/paste errors during demand-to-plan translation. - Improved visibility between customer demand, internal process planning, and BOM readiness in one operational screen. - Added earlier warning signals for overload, shortage, and mapping exceptions before daily planning meetings.

projects.technologies

  • Next.js
  • React
  • TypeScript
  • Excel Parsing (XLSX)
  • AI Planning Copilot
  • Zustand
  • Multi-language UI

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projects.totalAssets: 4
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