Things Inc. CEO Atsuya Suzuki Contributes to Nikkan Kogyo Shimbun's "Machine Design" Magazine, Explaining the Transformation of DRBFM/FMEA with Generative AI
Atsuya Suzuki, CEO of Things Inc., a company supporting manufacturing DX, has contributed an article to the June 2026 issue of "Machine Design" monthly magazine. The article explains how generative AI can transform preventative activities in the DRBFM/FMEA domain of manufacturing quality assurance, using practical examples from their product "PRISM." It addresses challenges like knowledge personalization, loss of tacit knowledge, and work inefficiency, demonstrating AI's potential to support designers' thought processes and reduce task times from weeks to minutes.
📋 Article Processing Timeline
- 📰 Published: May 18, 2026 at 20:00
- 🔍 Collected: May 18, 2026 at 11:31
- 🤖 AI Analyzed: May 18, 2026 at 12:05 (34 min after Collected)
An article written by Atsuya Suzuki, CEO of Things, Inc. (HQ: Minato-ku, Tokyo), a company that supports manufacturing DX through the development and provision of the product knowledge utilization platform "PRISM," has been published in the June 2026 special issue (Vol. 70, No. 6) of "Machine Design" magazine, published by Nikkan Kogyo Shimbun.
The article provides a detailed explanation of how generative AI can transform preventative activities in the realms of DRBFM and FMEA, which are crucial for quality assurance in manufacturing, with practical examples from the company's engineering chain platform, "PRISM."
■ Publication Overview
Publication: Nikkan Kogyo Shimbun's "Machine Design," Vol. 70, No. 6 (June 2026 issue)
Special Feature: Basic Understanding and Utilization of FMEA/DRBFM
Article Title: Chapter 5 "Preventative Activities Transformed by Generative AI - The Forefront of AI Utilization in DRBFM/FMEA-"
Author: Atsuya Suzuki, CEO, Things, Inc.
■ Article Highlights
1. Three Structural Issues in a Preventative Context: The fields of DRBFM/FMEA face challenges of "personalization and experience gaps," "loss of tacit knowledge," and "work efficiency barriers."
2. The Decisive Difference and Affinity between Conventional and Generative AI: Generative AI excels at natural language understanding and cross-domain inference, making it highly compatible with the DRBFM process of verbalizing what might happen due to a design change.
3. Practical Creation of DRBFM using PRISM and its Effects: "PRISM," which uses internal technical documents as a knowledge base, incorporates the thought processes of veteran designers to present multifaceted points of concern, compensating for human cognitive biases and achieving high comprehensiveness.
4. How Engineers' Work Styles Will Change: The DRBFM creation process, which traditionally took weeks, will drastically change to a workflow where AI generates a draft in "minutes." This allows designers to focus on essential thinking and accelerates the training of young engineers.
■ About the DRBFM/FMEA Support Service 'PRISM Preventative':
"PRISM Preventative" is a specialized service that enables companies to implement the transformations described in this article. AI analyzes past trouble cases and design knowledge to automatically generate drafts for DRBFM and Process FMEA.
■ About Things, Inc.:
Things, Inc. is tackling DX in the engineering chain under the theme of "unearthing knowledge in manufacturing." By using "PRISM" to extract and share buried technical information, the company aims to create a world where everyone involved in manufacturing shares the same vision.
The article provides a detailed explanation of how generative AI can transform preventative activities in the realms of DRBFM and FMEA, which are crucial for quality assurance in manufacturing, with practical examples from the company's engineering chain platform, "PRISM."
■ Publication Overview
Publication: Nikkan Kogyo Shimbun's "Machine Design," Vol. 70, No. 6 (June 2026 issue)
Special Feature: Basic Understanding and Utilization of FMEA/DRBFM
Article Title: Chapter 5 "Preventative Activities Transformed by Generative AI - The Forefront of AI Utilization in DRBFM/FMEA-"
Author: Atsuya Suzuki, CEO, Things, Inc.
■ Article Highlights
1. Three Structural Issues in a Preventative Context: The fields of DRBFM/FMEA face challenges of "personalization and experience gaps," "loss of tacit knowledge," and "work efficiency barriers."
2. The Decisive Difference and Affinity between Conventional and Generative AI: Generative AI excels at natural language understanding and cross-domain inference, making it highly compatible with the DRBFM process of verbalizing what might happen due to a design change.
3. Practical Creation of DRBFM using PRISM and its Effects: "PRISM," which uses internal technical documents as a knowledge base, incorporates the thought processes of veteran designers to present multifaceted points of concern, compensating for human cognitive biases and achieving high comprehensiveness.
4. How Engineers' Work Styles Will Change: The DRBFM creation process, which traditionally took weeks, will drastically change to a workflow where AI generates a draft in "minutes." This allows designers to focus on essential thinking and accelerates the training of young engineers.
■ About the DRBFM/FMEA Support Service 'PRISM Preventative':
"PRISM Preventative" is a specialized service that enables companies to implement the transformations described in this article. AI analyzes past trouble cases and design knowledge to automatically generate drafts for DRBFM and Process FMEA.
■ About Things, Inc.:
Things, Inc. is tackling DX in the engineering chain under the theme of "unearthing knowledge in manufacturing." By using "PRISM" to extract and share buried technical information, the company aims to create a world where everyone involved in manufacturing shares the same vision.