'We Don't Need AI That Can't Be Used in Factories.' Introducing AI Business Process Reengineering Consulting That 'Follows Through' to Generate Results on the Manufacturing Floor at <NexTech Week 2026 Spring> April 15-17
I.Y.P Consulting will exhibit its 'AI Business Process Reengineering Consulting' for the manufacturing industry at NexTech Week 2026. They solve the problem of generative AI stopping at the PoC stage by linking implementation directly to factory KPIs and on-site adoption.
📋 Article Processing Timeline
- 📰 Published: April 8, 2026 at 18:00
- 🔍 Collected: April 8, 2026 at 09:30
- 🤖 AI Analyzed: April 20, 2026 at 17:33 (296h 2m after Collected)
I.Y.P Consulting Inc. (Headquarters: Chuo-ku, Tokyo; Representative: Jinho Choi; hereinafter 'IYP') will exhibit at 'NexTech Week 2026,' held at Tokyo Big Sight (West Exhibition Halls) from Wednesday, April 15 to Friday, April 17, 2026. In this exhibition, for companies that have introduced generative AI but are not seeing results on the factory or manufacturing floors, we will introduce our 'AI Business Process Reengineering Consulting,' which provides end-to-end support covering literacy development, To-Be design, business process organization, introduction assessment, and on-site implementation and fixation (*What we guide you through in this exhibition is not the sale of specific products, but problem-driven consulting services).
Background of the Exhibition: Introductions are Progressing, but 'Ineffective' on Site
While the verification of generative AI (GenAI) is spreading in the manufacturing industry, scaling across bases and establishing it on-site remain difficult, bringing to light the issue where 'Pilots (PoC) work, but factory KPIs do not improve.' According to a survey summary by Deloitte, while the rate of initiating generative AI pilots is at a high level, the proportion successfully deployed across entire factories and networks is limited.
Furthermore, to scale generative AI, a data-centric roadmap and a 'deployment mechanism' including governance, KPIs, and organizational structure are considered indispensable.
IYP's Proposal: 'AI Business Process Reengineering' Working Backward from Factory Impact
Instead of starting with tool selection, IYP breaks down the decision-making processes on the factory floor (quality judgment, anomaly detection, condition optimization, etc.) down to 'where, who, and on what basis decisions are made.' We design and implement AI utilization in a way that directly links to the improvement of performance indicators (e.g., defect rate, yield rate, downtime, setup time). Ultimately, we work alongside you to connect the site and management until a state where the AI 'continues to be used' is achieved.
## ■ Examples of Themes You Can Consult at Our Booth on the Day
- We implemented generative AI, but factory productivity and quality are not improving (How to select use cases, To-Be design, KPI design)
- While PoCs are increasing, we cannot make investment decisions or prioritize (Evaluation criteria, ROI estimation, roadmaps)
- On-site deployment is halted due to concerns over data, authority, and security (Governance, operational rules, auditability)
- Design of business-specific AI anticipating accuracy, reproducibility, and operational costs 'usable in factories' (Business requirements, operational design)
Background of the Exhibition: Introductions are Progressing, but 'Ineffective' on Site
While the verification of generative AI (GenAI) is spreading in the manufacturing industry, scaling across bases and establishing it on-site remain difficult, bringing to light the issue where 'Pilots (PoC) work, but factory KPIs do not improve.' According to a survey summary by Deloitte, while the rate of initiating generative AI pilots is at a high level, the proportion successfully deployed across entire factories and networks is limited.
Furthermore, to scale generative AI, a data-centric roadmap and a 'deployment mechanism' including governance, KPIs, and organizational structure are considered indispensable.
IYP's Proposal: 'AI Business Process Reengineering' Working Backward from Factory Impact
Instead of starting with tool selection, IYP breaks down the decision-making processes on the factory floor (quality judgment, anomaly detection, condition optimization, etc.) down to 'where, who, and on what basis decisions are made.' We design and implement AI utilization in a way that directly links to the improvement of performance indicators (e.g., defect rate, yield rate, downtime, setup time). Ultimately, we work alongside you to connect the site and management until a state where the AI 'continues to be used' is achieved.
## ■ Examples of Themes You Can Consult at Our Booth on the Day
- We implemented generative AI, but factory productivity and quality are not improving (How to select use cases, To-Be design, KPI design)
- While PoCs are increasing, we cannot make investment decisions or prioritize (Evaluation criteria, ROI estimation, roadmaps)
- On-site deployment is halted due to concerns over data, authority, and security (Governance, operational rules, auditability)
- Design of business-specific AI anticipating accuracy, reproducibility, and operational costs 'usable in factories' (Business requirements, operational design)