AI prescriptions sometimes contradict internal standards because past out‑of‑standard operations accidentally hit the quality sweet spot. This article explains why contradictions occur, how to visualize them, and how companies should define priority rules and responsibility.

AI prescriptions often fail not because of AI limitations but due to gaps in operational continuity. This article explains why strict adherence is unrealistic for all batches and how focusing on priority items maximizes defect reduction and ROI in manufacturing.

Effective AI prescriptions in manufacturing require a clear separation between Controllable and Uncontrollable parameters. This article explains why most critical quality parameters are result values that operators cannot directly adjust, and how human judgment, operational discipline, and proper use of AI target ranges are essential for stabilizing quality and achieving real digitalization results.

Many factories find that AI does not reduce defect rates for certain products because AI can only propose conditions within past production history. When improvement stalls, adjusting long‑static parameters within expert‑defined textbook ranges creates new production data, strengthens the AI model, and reveals opportunities beyond the existing history.

A major South African foundry showed that AI success depends less on algorithms and more on operational culture. Instead of feeding thousands of parameters into AI, the company selected fewer than twenty critical factors and set clear internal standards. By building strict shop‑floor discipline first and treating AI as an extension of these standards, they reduced defects to nearly zero. The case highlights that effective AI requires a strong operational foundation.

This article explains why AI struggles with new products in manufacturing. AI relies entirely on past data, yet factories must constantly launch new items with no production history. The only effective approaches are using similar‑product models and expert‑defined process prescriptions. Based on six years of real factory work, the article shows that true AI implementation begins with designing the environment in which AI can learn.

本記事では、砂型鋳造において「理論生産数」と実際の生産個数が一致しない理由と、その誤差が不良率・原価計算・生産管理を歪める問題を解説します。最も再現性の高い方法として、ショット後の総重量をバッチIDと紐づけて実生産数を算出する手法を紹介し、正確なデータに基づく工場運営を可能にします。

This article explains why the theoretical production quantity in sand casting never matches the actual output and how this gap distorts defect rates, cost accounting, and production control. It introduces a practical method to determine true production quantity by measuring casting weight after shot blasting and linking it with batch IDs, enabling accurate, real‑time data for factory operations.

Many factories in Japan have started digitalization, yet progress often stalls. From my on‑site support experience, the main reasons are clear: factories avoid internet connectivity, visualization becomes the final goal instead of the starting point, data is not linked to management decisions, and data accuracy is taken for granted. Without connecting data to real operational and managerial decisions, digitalization remains a tool—not a driver of improvement.