Industry Solutions

The manufacturing sector is under the dual pressure of knowledge transfer gaps and digital transformation. LargitData's RAGi enterprise AI knowledge base and QubicX on-premise AI platform help manufacturers consolidate technical documents, SOPs, and expert knowledge scattered across departments into an intelligent knowledge management system, enabling effective knowledge transfer and improved operational efficiency.

Industry Challenges

As manufacturing moves toward Industry 4.0, knowledge management and operational efficiency are the most pressing problems to solve:

• Severe Knowledge Silos：Critical documents — including engineering drawings, process parameters, quality specifications, and equipment maintenance manuals — are scattered across different departments and systems, requiring engineers to search across multiple systems to locate information, a process that is time-consuming and prone to gaps.
• Risk of Expert Knowledge Loss：When senior engineers and technicians retire or resign, the large body of tacit knowledge residing in their personal experience — such as troubleshooting techniques and process tuning know-how — is difficult to transfer effectively.
• Low Efficiency in Equipment Troubleshooting：When production line equipment malfunctions, maintenance personnel must leaf through thick equipment manuals or wait for senior engineers to come on-site, resulting in excessive production line downtime.
• Vast Volume of Quality Management Documents：ISO quality management system documents, customer audit files, and FMEA analysis reports are voluminous, making it difficult for quality personnel to locate what they need when preparing for audits or handling customer complaints.
• Insufficient Supply Chain Information Integration：Supplier specifications, incoming inspection reports, and contract terms are managed in disparate locations, making it difficult for procurement and quality teams to quickly compare and retrieve information.

Industry Solutions

LargitData provides knowledge management-centered AI solutions for the manufacturing industry:

RAGi — Intelligent Knowledge Base for Manufacturing

• Consolidates technical documents, equipment manuals, process SOPs, quality standards, and more into an AI-driven enterprise knowledge base.
• Engineers can quickly retrieve technical information through natural-language queries — for example, "Possible causes and troubleshooting steps for abnormal spindle vibration in a CNC machining center."
• AI 從相關文件中擷取答案並標註出處，讓工程師能回頭核對原始段落；出處標註的完整度取決於文件的結構與數位化品質。
• 支援將資深工程師的故障排除經驗與製程調校知識數位化，逐步累積為企業知識資產。

Learn MoreRAGi Enterprise AI Retrieval-Augmented Generation Engine

QubicX — Factory On-Premise AI Deployment

• AI 運算在工廠內部伺服器執行，製程參數與設計圖面不會傳送至外部雲端；整體的外洩風險仍取決於網路分區、端點管控與人員權限等配套措施。
• 在網路受限或隔離的環境中，系統可在完成部署後獨立運作，不需仰賴外部雲端推論服務。
• 可整合工廠既有的 MES（製造執行系統）、ERP 與 PLM 系統，實際整合範圍需視各系統開放的介面與版本評估。
• 可依工廠規模彈性擴展，從單一產線到多廠區部署皆可規劃。

Learn MoreQubicX On-Premise AI Platform

Diverse application scenarios

Scenario 1: Intelligent Equipment Fault Diagnosis

一家半導體封裝廠商將設備的維護手冊、歷史維修紀錄與故障排除經驗匯入 RAGi 知識庫。當產線設備出現異常時，維修工程師在現場即可透過手機或平板以自然語言描述故障現象（如「打線機第三軌道送線不順，偶爾斷線」），系統即時從知識庫中比對歷史案例並提供排除步驟建議。導入時要注意兩件事：歷史維修紀錄若填寫過於簡略，可比對的案例會很有限；另外建議把系統建議定位為現場人員的參考資訊，關鍵設備的處置仍應依既有的維修作業規範執行。

Scenario 2: Rapid Onboarding of New Engineers

一間精密機械加工廠面臨資深師傅陸續退休的知識斷層危機。工廠將師傅們數十年的加工經驗、刀具選用原則與製程參數調校要訣整理後匯入 RAGi 知識庫。新進工程師在操作過程中遇到問題時，可隨時向 AI 知識庫提問，如同有一位虛擬的資深師傅隨時在旁指導。實務上最大的工作量不在系統，而在把口耳相傳的經驗整理成可被檢索的文字，建議先從最常被問到的十幾個問題開始建立。

Scenario 3: Fast Retrieval of Quality Audit Documents

某汽車零組件供應商將 IATF 16949 品質管理文件、客戶特殊要求（CSR）、PPAP 文件與歷次稽核紀錄匯入 RAGi 知識庫。當客戶進行供應商稽核時，品管人員可即時查詢「客戶評鑑中關於不良品管控的具體要求為何？」，系統快速從相關文件中彙整答案與出處，減少臨場翻找的時間。由於各客戶的稽核與供應商評鑑要求版本經常更新，建議建立文件版本控管機制，並在知識庫中標示生效日期，避免引用到已失效的版本。

Scenario 4: Unified Supplier Information Management

Procurement and quality teams loaded specifications, Material Safety Data Sheets (MSDS), incoming inspection reports, and contract terms for all suppliers into the RAGi knowledge base. When identifying alternative suppliers or comparing material specifications, a simple natural-language query returns cross-supplier comparison information, replacing the inefficient practice of manually reviewing paper or electronic files one by one.

Scenario 5: Process Standardization and Best Practice Sharing

跨國製造集團將各廠區的製程 SOP、改善提案（Kaizen）紀錄與最佳實踐案例匯入統一的 RAGi 知識庫。A 廠區的工程師可以查詢「B 廠在相同產品上如何解決表面粗糙度不良的問題」，促進跨廠區的知識共享與製程標準化。跨廠區共用知識庫時，需先確認哪些製程資料可跨法域傳輸，並依廠區設定存取範圍。

效益怎麼衡量：導入前先建立基準

知識庫類專案最容易在驗收時陷入爭議，因為改善多半發生在難以直接記錄的查找時間上。建議在導入前先取得下列基準值，並在導入後以相同口徑重測：

若供應商提出具體的改善百分比，建議一併索取樣本數、觀察期間、基準線定義與計算方式。不同工廠的設備類型、文件品質與維修流程差異極大，他廠的數字通常無法直接套用到自家產線。

預期效益方向

• 故障判斷階段更快收斂：知識庫讓維修人員即時取得可能原因與過往處置紀錄，減少等待資深人員到場的時間，實際改善幅度需以自家歷史工單回測。
• 新人上手負擔下降：AI 助手提供隨問隨答的技術指引，減少反覆打斷資深同仁的次數。
• 知識資產可持續累積：將資深員工的隱性知識轉為可檢索的文件，降低因人員異動導致的知識中斷風險，前提是持續維護與汰換。
• 稽核準備更有條理：AI 彙整品質文件與歷史紀錄並附出處，讓稽核應對有據可查。
• 製程資料留在廠內：QubicX 地端部署讓製程參數與設計圖面不傳送至外部雲端，可降低外流風險；仍需搭配網路分區、端點管控、權限管理與稽核留痕，並由資安單位驗證。