流程製造中的數位化與人工智慧應用：2026 年

流程製造業面臨許多壓力，例如能源價格波動、能源轉型帶來的碳排放成本、熟練勞動力短缺。儘管這些產業在數位轉型方面歷來落後於離散製造業，但數據主導的「工業4.0」時代正在推動其向大規模、靈活和客製化生產模式轉變。

本報告系統分析了流程製造企業如何將數位化工具和人工智慧融入其整個營運流程中。該研究概述了目前的技術重點、實施階段以及未來幾年的投資報酬率預期。

待解答的問題

• 流程製造企業在數位化轉型過程中面臨的首要任務和挑戰是什麼？
• 流程製造企業在實施關鍵技術和應用案例方面處於什麼階段？
• 該軟體主要部署在哪裡（本地部署還是雲端部署），哪些用戶正在遷移到公共雲端？
• 遷移到基於雲端的製造軟體的主要挑戰是什麼？
• 流程製造企業預計人工智慧將在未來 3-5 年內對其核心應用產生多大程度的影響？
• 預計哪種用例的影響最大？預計能節省多少成本？
• 人工智慧在研發領域的應用程度如何？其應用的主要障礙是什麼？
• 人力資源最大的挑戰是什麼？哪些人工智慧工具可望解決這些挑戰？

提及的公司

• Apollo Tyres
• BASF
• Borouge
• Dow Chemicals
• Forza Steel
• Georgia-Pacific
• Honeywell
• Norsk Hydro
• Yokogawa

目錄

第1章執行摘要

第2章：引言

• 章節概要
• 歷史背景：製造業經歷了許多技術創新而不斷發展。
• 人們對智慧製造和工業4.0的興趣日益濃厚。
• 當今技術重點：安全、自動化、軟體、人工智慧
• 未來幾年的願景：擴充性且易於維護的自動化工業設施
• 流程製造與離散製造有著本質上的差異。
• 每個流程製造業都有其獨特的特色。
• 整體挑戰
• 流程工業：通常被認為是製造業中數位化程度較低的領域。
• 目前，流程製造企業的執行長們正在積極推動人工智慧的應用。
• 案例研究：BASF如何應用人工智慧
• 案例研究：博祿如何打造業界首個人工智慧驅動的自主化工廠
• 本報告檢驗了流程製造業如何採用數位技術。
• 參與調查的公司樣本

第3章 流程製造商的優先事項

• 章節概要
• 業務轉型中的優先事項
• 公司優先事項：範例 1 - Norsk Hydro 專注於趨勢 1、2、3 和 4。
• 公司優先事項：範例 2 - 陶氏化學專注於趨勢 1、2、3 和 4。

第4章：流程製造數位化現狀

• 章節定義
• 章節概要
• 技術與應用案例
• 技術介紹
• 用例
• 軟體環境
• 軟體應用
• 雲端遷移面臨的挑戰
• 任務
• 數位科技普及的障礙
• 預期投資報酬率
• 透過數位轉型降低成本
• 科技對OEE的影響
• 透過數位轉型降低成本

第5章：人工智慧在流程製造中的作用

• 章節概要
• 人工智慧實施的起點：流程製造企業主管的 4 個關鍵點
• 例如：一家鋼鐵製造商如何為人工智慧建構資料基礎設施
• 數據和分析在製程製造中的重要性
• 探索人工智慧技術
• 人工智慧對核心應用的影響
• 生成式人工智慧在流程製造中的應用實例

第6章：深度解析：人工智慧在研發上的應用

• 章節概要
• 在研發中利用人工智慧工具
• 人工智慧驅動的研發方法所引入的障礙
• 研究與開發中的關注與挑戰
• 人工智慧對研發的影響
• 一家輪胎製造商如何利用人工智慧驅動的虛擬原型製作加速研發。

第7章：深度解析：利用人工智慧來應對人力資源挑戰

• 章節概要
• 人力資源發展面臨的挑戰
• 人工智慧解決方案應對人力資源挑戰
• 一家紙漿和造紙公司如何為其操作員開發指導聊天機器人的過程。

第8章：調查方法

第9章：關於物聯網分析

A 112-page report on how process manufacturers (chemicals, metals, pulp & paper, …) are adopting digital tools across their operations with a focus on AI adoption.

Questions answered

• What are the top priorities and challenges for process manufacturers in their digital transformation journey?
• At what stage are process manufacturers in adopting key technologies and use cases?
• Where is software predominantly deployed (on-premises vs. cloud), and which applications are migrating to the public cloud?
• What are the main challenges in migrating to cloud-based manufacturing software?
• To what extent do process manufacturers expect AI to impact their core applications over the next 3–5 years?
• Which use cases are expected to have the biggest impact and how large are the expected cost savings?
• How frequently is AI used in R&D, and what are the key barriers to adoption?
• What are the biggest workforce challenges, and which AI tools are expected to address them?

Companies mentioned

• Apollo Tyres
• BASF
• Borouge
• Dow Chemicals
• Forza Steel
• Georgia-Pacific
• Honeywell
• Norsk Hydro
• Yokogawa

About the report

Process manufacturing organizations face increasing pressure from energy price volatility, carbon costs associated with the energy transition, and shrinking skilled labor pools. While these industries have historically trailed discrete manufacturing in digital adoption, the data-driven era of Industry 4.0 is driving a shift toward customized production at scale and flexibility.

The Digital & AI adoption in Process Manufacturing 2026 report provides a structured analysis of how process manufacturers are integrating digital tools and AI across their operations. Based on a survey of a large group of senior stakeholders across several industries and major world regions, the research outlines current technology priorities, deployment stages, and ROI expectations for the coming years.

Report at a glance

• Adoption report: Details the adoption of digital tools across process manufacturing operations with a focus on AI.
• Stakeholder insights: Comprises data from senior decision-makers, including CxOs and directors, at organizations with more than 1,000 employees.
• Industry breadth: Analyzes several process manufacturing sub-sectors, including general chemicals, pulp and paper, petrochemicals, rubber and plastics, basic metals, fertilizers, and non-metallic minerals.
• Technology and use case tracking: Examines the deployment status of various technologies and operational use cases.
• ROI models: Details expected average cost reductions from digital initiatives by 2028.

Key areas of analysis

• Transformation priorities: Identifies revenue growth and operational efficiency as the primary drivers, with a vast majority of surveyed manufacturers rating each as a top or significant priority.
• Technology adoption landscape: Outlines the widespread deployment of smart sensors and process automation, concurrently identifying AI optimization and AI-driven R&D optimization as leading exploration areas.
• Software and cloud migration: Details that foundational applications such as SCM and process control have reached near-universal adoption. Concurrently, it indicates that migration to the public cloud remains slow due to high costs cited by most respondents.
• AI impact expectations: Examines where AI is expected to have the strongest impact, with predictive quality analytics and energy management ranking at the top.
• AI in R&D deep dive: Details how a portion of manufacturers utilize AI tools in research to address manual data processing challenges that affect a majority of organizations.
• Frontline workforce solutions: Analyzes pressing workforce gaps, such as remote support and real-time troubleshooting, and evaluates the role of generative AI in addressing these challenges.
• Case studies in transformation: Features detailed implementations from BASF, Forza Steel, Apollo Tyres, Borouge, and Georgia-Pacific.

Table of Contents

1. Executive summary

• Executive summary (4 parts)
• Summary: Where process manufacturers are in their digital maturity journey
• Key action items from this report

2. Introduction

• Introduction: Chapter overview
• Historical context: Manufacturing has changed in several technology waves
• Interest in smart manufacturing and Industry 4.0 has been growing
• Today’s technology priorities: Security, automation, software and AI
• The vision for the coming years: Automated industrial sites that are scalable and serviceable
• Process manufacturing is uniquely different to discrete manufacturing
• Every process manufacturing industry has unique characteristics
• Overarching Challenge 1, Challenge 2, Challenge 3
• Process industries are typically digital laggards in manufacturing
• Now CEOs of process manufacturers are driving AI adoption
• Case study: How BASF is adopting AI (4 parts)
• Case study: How Borouge is building the industry’s first AI-powered autonomous chemicals facility
• This report examines how process manufacturers adopt digital technologies
• Sample companies that took part in the survey

3. Priorities of process manufacturers

• Priorities of process manufacturers: Chapter overview
• Priorities for transforming operations (3 parts)
• Corporate priorities: Example 1—Norsk Hydro focuses on Trend 1, Trend 2, Trend 3, and Trend 4
• Corporate priorities: Example 2—Dow Chemicals focuses on Trend 1, Trend 2, Trend 3, and Trend 4

4. State of digital in process manufacturing

• Chapter definitions (2 parts)
• State of digital in process manufacturing—Technology and use cases: Chapter overview
• Technology adoption (4 parts)
• Use case adoption (3 parts)
• State of digital in process manufacturing: Software landscape: Chapter overview
• Software applications (4 parts)
• Challenges when migrating to the cloud (2 parts)
• State of digital in process manufacturing: Challenges: Chapter overview
• Roadblocks in adopting digital technologies (3 parts)
• State of digital in process manufacturing: ROI expectations—Chapter overview
• Digital transformation cost savings (3 parts)
• Impact of technologies on OEE (2 parts)
• Cost savings by digital transformation activity (3 parts)

5. The role of AI for process manufacturers

• The role of AI for process manufacturers: Chapter overview
• Starting point for AI: 4 things on the minds of process manufacturing executives
• Example: How a steel manufacturer created a data foundation for AI
• Importance of data & analytics in process manufacturing
• Exploration of AI technologies
• Impact of AI in core applications (2 parts)
• Generative AI use cases in process manufacturing

6. Deep-dive: AI in R&D

• AI in R&D: Chapter overview
• Use of AI tools in R&D (2 parts)
• Barriers in adopting AI-driven R&D methods (3 parts)
• R&D concerns and challenges (3 parts)
• Impact of AI in R&D activities (3 parts)
• How a tire manufacturer accelerated R&D with AI-driven virtual prototyping

7. Deep-dive: AI for workforce challenges

• AI for workforce challenges: Chapter overview
• Workforce Challenge 1
• AI solutions to workforce challenges
• How a pulp and paper company created a chatbot for operator guidance

8. Methodology

9. About IoT Analytics