Summary

長い間、人工知能（AI）は製造業分野に革命を起こすパワフルな技術と思われてきた。それには一理あるが、人工知能に近道はない。まず、製造業用途の人工知能は、製品開発におけるジェネレーティブデザイン、在庫管理による製造予測、マシンビジョン、欠陥検査、製造の最適化、製造における予知保全などの、製造プロセスの様々な場面における様々な利用ケースの一環である。次に、人工知能の導入には正確なデータと人員が求められる。工場の既存の多くの機器やツールは、ネットワーク接続されていない。さらに、メーカーは人工知能導入の内製データサイエンスの構築とトレーニングに関する膨大な競争にさらされている。

For the longest time, Artificial Intelligence (AI) has been touted as a powerful technology that will revolutionize the industrial manufacturing space. The sentiment has its validity, but there is no shortcut to AI. Firstly, AI in industrial manufacturing is an ensemble of various use cases at various phases of manufacturing, such as generative design in product development, production forecasting in inventory management, machine vision, defect inspection, production optimization and predictive maintenance on production phase. Secondly, the right data and personnel are needed for AI implementation. Many existing equipment and tools on the factory floor remains unconnected. In addition,  manufacturers are facing enormous competition in building and training in-house data science team for AI implementation.

Once the right foundations are in place, including data architecture, AI frameworks, AI engines and on-premise hardware, manufacturers can start to leverage the capabilities that AI can offer. At the moment, most AI solutions are able to collect data and perform unsupervised machine learning to generate insights and recommendations, with supervision from AI experts. The rise of automated machine learning will free AI experts from the more mundane AI optimization tasks and allow them to explore new use cases for AI.  However, not all AI models need to be complex. There are many low-hanging fruits that simple AI models are more than capable to address in today’s factory.

To provide a clear picture on commercial AI applications, this report explores the roles and offering from different implementors of AI in industrial manufacturing, including cloud service providers, industrial cloud platform vendors, pure-play AI startups, system integrators, chipset and industrial server manufacturers and connectivity service providers. Manufacturers who want to implement AI will definitely need to engage with these companies and partner with them in their AI journey.

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Table of Contents

• 1. EXECUTIVE SUMMARY
• 2. DEFINITION OF ARTIFICIAL INTELLIGENCE
  • 2.1. Classes of Machine Learning
• 3. OVERVIEW OF AI IN INDUSTRIAL MANUFACTURING
  • 3.1. Use Case-Centric
  • 3.2. Location of AI
• 4. KEY TRENDS
  • 4.1. Having the Right Building Blocks Matters
  • 4.2. Unsupervised Learning Is the De-Facto ML Technique in Industrial Manufacturing
  • 4.3. New AI Techniques Are on the Horizon
  • 4.4. Variation in Market Adoption Rate
• 5. BEST PRACTICES OF AI DEPLOYMENT IN INDUSTRIAL MANUFACTURING
• 6. MARKET FORECASTS
• 7. PROFILE OF KEY VENDORS
  • 7.1. Cloud Service Providers
  • 7.2. Smart Manufacturing Platform Vendors
  • 7.3. Pure-Play Industrial AI Platform and Service Providers
  • 7.4. Industrial Edge AI Gateway and Server Vendors
  • 7.5. Chipset Vendors
  • 7.6. System Integrators
  • 7.7. Connectivity Vendor
• 8. KEY RECOMMENDATIONS AND CONCLUSIONS
  • 8.1. Holistic View of AI
  • 8.2. AI Deployment Requires Company-Wide Buy-Ins
  • 8.3. In-House Expertise or Third-Party System Integrators