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1609880

2025-2033 年日本预测维修市场报告（按组件（解决方案、服务）、部署（本地、云端）、最终用户（能源和公用事业、运输、製造、医疗保健等）和区域）

Japan Predictive Maintenance Market Report by Component (Solutions, Services), Deployment (On-premise, Cloud), End User (Energy and Utilities, Transportation, Manufacturing, Healthcare, and Others), and Region 2025-2033

出版日期: 2024年12月05日 | 出版商:

IMARC | 英文 121 Pages | 商品交期: 5-7个工作天内

价格

简介目录

2024 年，日本预测性维护市场规模达到 7.747IMARC Group美元。该市场受到几个重要因素的推动，包括机器对机器（M2M）通讯的广泛使用、为延长老化工业设备的使用寿命而加大投资，以及更多地采用远端监控来进行高级检查。

预测性维护是一种依赖使用状态监测工具和系统进行即时设备效能监控的方法。这种方法结合了物联网 (IoT)、人工智慧 (AI) 和整合系统等技术来连接各种资产并共享和分析关键资料。它包含预测性维护感测器、工业控制以及企业资产管理 (EAM) 和企业资源规划 (ERP) 系统等业务软体等元件。预测性维护的核心功能是利用状态监控设备来评估和分析资产性能。它收集不同的资料点，包括温度、振动和电导率，使工程师能够预测设备或资产故障并规划主动维修或更换。预测性维护具有降低成本、延长设备使用寿命和提高生产力等优势。此外，由于其对安全合规性的贡献以及采取先发制人的纠正措施的能力，其需求正在上升。

日本预测性维护市场趋势：

在日本的技术实力和对优化工业运营的承诺的推动下，日本的预测维护市场正在经历大幅增长。日本工业界迅速采用利用物联网 (IoT)、人工智慧 (AI) 和整合系统等先进技术的预测性维护策略。这些技术用于即时监控和分析关键设备资料，以便及早发现潜在故障或维护需求。日本广泛的製造业，包括汽车和电子产业，已经认识到预测性维护在减少停机时间、降低维护成本以及确保机械和生产线高效运作方面的价值。此外，预测性维护与安全合规措施的整合使其成为工业流程的重要组成部分。随着日本继续在其工业领域优先考虑创新和效率，预测性维护市场预计将在各个领域进一步扩展和采用。

日本预测维护市场区隔：

组件见解：

解决方案
服务

部署见解：

本地部署
云

最终使用者见解：

能源和公用事业
运输
製造业
卫生保健
其他的

竞争格局：

市场研究报告也对竞争格局进行了全面分析。报告涵盖了市场结构、关键参与者定位、最佳制胜策略、竞争仪表板和公司评估象限等竞争分析。此外，也提供了所有主要公司的详细资料。

本报告回答的关键问题：

到目前为止，日本预测性维护市场的表现如何，未来几年将如何表现？
COVID-19 对日本预测性维护市场有何影响？
日本预测性维护市场以组件划分是怎样的？
日本预测性维护市场在部署上的细分如何？
日本预测性维护市场以最终用户划分是怎样的？
日本预测性维护市场价值链的各个阶段是什么？
日本预测性维护的关键驱动因素和挑战是什么？
日本预测性维护市场的结构如何？
日本预测性维护市场的竞争程度如何？

本报告回答的关键问题：

到目前为止，日本预测性维护市场的表现如何，未来几年将如何表现？
COVID-19 对日本预测性维护市场有何影响？
日本预测性维护市场以组件划分是怎样的？
日本预测性维护市场在部署上的细分如何？
日本预测性维护市场以最终用户划分是怎样的？
日本预测性维护市场价值链的各个阶段是什么？
日本预测性维护的关键驱动因素和挑战是什么？
日本预测性维护市场的结构如何？
日本预测性维护市场的竞争程度如何？

能源和公用事业
- 概述
- 历史与当前市场趋势（2019-2024）
- 市场预测（2025-2033）
运输
- 概述
- 历史与当前市场趋势（2019-2024）
- 市场预测（2025-2033）
製造业
- 概述
- 历史与当前市场趋势（2019-2024）
- 市场预测（2025-2033）
卫生保健
- 概述
- 历史与当前市场趋势（2019-2024）
- 市场预测（2025-2033）
其他的
- 历史与当前市场趋势（2019-2024）
- 市场预测（2025-2033）

第 9 章：日本预测维修市场 - 竞争格局

概述
市场结构
市场参与者定位
最佳制胜策略
竞争仪表板
公司评估象限

第 10 章：关键参与者简介

Company A

Business Overview
Product Portfolio
Business Strategies
SWOT Analysis
Major News and Events

Company B

Business Overview
Product Portfolio
Business Strategies
SWOT Analysis
Major News and Events

Company C

Business Overview
Product Portfolio
Business Strategies
SWOT Analysis
Major News and Events

Company D

Business Overview
Product Portfolio
Business Strategies
SWOT Analysis
Major News and Events

Company E

Business Overview
Product Portfolio
Business Strategies
SWOT Analysis
Major News and Events

第 11 章：日本预测维护市场 - 产业分析

驱动因素、限制因素和机会
- 概述
- 司机
- 限制
- 机会
波特五力分析
- 概述
- 买家的议价能力
- 供应商的议价能力
- 竞争程度
- 新进入者的威胁
- 替代品的威胁
价值链分析

第 12 章：附录

简介目录

Product Code: SR112024A18661

Japan predictive maintenance market size reached USD 774.7 Million in 2024. Looking forward, IMARC Group expects the market to reach USD 7,400.7 Million by 2033, exhibiting a growth rate (CAGR) of 28.5% during 2025-2033. The market is being propelled by several significant factors, including the expanding use of machine-to-machine (M2M) communication, greater investments in prolonging the operational lifespan of aging industrial equipment, and the increased incorporation of remote monitoring for conducting advanced inspections.

Predictive maintenance is a methodology that relies on the use of condition-monitoring tools and systems for real-time equipment performance monitoring. This approach incorporates technologies like the Internet of Things (IoT), artificial intelligence (AI), and integrated systems to connect various assets and share and analyze critical data. It encompasses components such as predictive maintenance sensors, industrial controls, and business software like Enterprise Asset Management (EAM) and Enterprise Resource Planning (ERP) systems. The core function of predictive maintenance is to employ condition monitoring equipment to assess and analyze asset performance. It gathers diverse data points, including temperature, vibrations, and conductivity, enabling engineers to anticipate equipment or asset failures and plan for proactive repairs or replacements. Predictive maintenance offers advantages such as cost reduction, extended equipment lifespan, and enhanced productivity. Furthermore, its demand is on the rise due to its contribution to safety compliance and the ability to take preemptive corrective actions.

Japan Predictive Maintenance Market Trends:

The predictive maintenance market in Japan is experiencing substantial growth, driven by the country's technological prowess and its commitment to optimizing industrial operations. Japanese industries have been quick to adopt predictive maintenance strategies that leverage advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and integrated systems. These technologies are used to monitor and analyze critical equipment data in real-time, allowing for the early detection of potential failures or maintenance needs. Japan's extensive manufacturing sector, including automotive and electronics industries, has recognized the value of predictive maintenance in reducing downtime, lowering maintenance costs, and ensuring the efficient operation of machinery and production lines. Additionally, the integration of predictive maintenance with safety compliance measures has made it a crucial component of industrial processes. As Japan continues to prioritize innovation and efficiency in its industrial landscape, the predictive maintenance market is expected to witness further expansion and adoption across various sectors.

Japan Predictive Maintenance Market Segmentation:

Component Insights:

Solutions
Services

Deployment Insights:

On-premise
Cloud

End User Insights:

Energy and Utilities
Transportation
Manufacturing
Healthcare
Others

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Key Questions Answered in This Report:

How has the Japan predictive maintenance market performed so far and how will it perform in the coming years?
What has been the impact of COVID-19 on the Japan predictive maintenance market?
What is the breakup of the Japan predictive maintenance market on the basis of component?
What is the breakup of the Japan predictive maintenance market on the basis of deployment?
What is the breakup of the Japan predictive maintenance market on the basis of end user?
What are the various stages in the value chain of the Japan predictive maintenance market?
What are the key driving factors and challenges in the Japan predictive maintenance?
What is the structure of the Japan predictive maintenance market and who are the key players?
What is the degree of competition in the Japan predictive maintenance market?

1 Preface

2 Scope and Methodology

2.1 Objectives of the Study
2.2 Stakeholders
2.3 Data Sources
- 2.3.1 Primary Sources
- 2.3.2 Secondary Sources
2.4 Market Estimation
- 2.4.1 Bottom-Up Approach
- 2.4.2 Top-Down Approach
2.5 Forecasting Methodology

3 Executive Summary

4 Japan Predictive Maintenance Market - Introduction

4.1 Overview
4.2 Market Dynamics
4.3 Industry Trends
4.4 Competitive Intelligence

5 Japan Predictive Maintenance Market Landscape

5.1 Historical and Current Market Trends (2019-2024)
5.2 Market Forecast (2025-2033)

6 Japan Predictive Maintenance Market - Breakup by Component

6.1 Solutions
- 6.1.1 Overview
- 6.1.2 Historical and Current Market Trends (2019-2024)
- 6.1.3 Market Forecast (2025-2033)
6.2 Services
- 6.2.1 Overview
- 6.2.2 Historical and Current Market Trends (2019-2024)
- 6.2.3 Market Forecast (2025-2033)

7 Japan Predictive Maintenance Market - Breakup by Deployment

7.1 On-premise
- 7.1.1 Overview
- 7.1.2 Historical and Current Market Trends (2019-2024)
- 7.1.3 Market Forecast (2025-2033)
7.2 Cloud
- 7.2.1 Overview
- 7.2.2 Historical and Current Market Trends (2019-2024)
- 7.2.3 Market Forecast (2025-2033)

8 Japan Predictive Maintenance Market - Breakup by End User

8.1 Energy and Utilities
- 8.1.1 Overview
- 8.1.2 Historical and Current Market Trends (2019-2024)
- 8.1.3 Market Forecast (2025-2033)
8.2 Transportation
- 8.2.1 Overview
- 8.2.2 Historical and Current Market Trends (2019-2024)
- 8.2.3 Market Forecast (2025-2033)
8.3 Manufacturing
- 8.3.1 Overview
- 8.3.2 Historical and Current Market Trends (2019-2024)
- 8.3.3 Market Forecast (2025-2033)
8.4 Healthcare
- 8.4.1 Overview
- 8.4.2 Historical and Current Market Trends (2019-2024)
- 8.4.3 Market Forecast (2025-2033)
8.5 Others
- 8.5.1 Historical and Current Market Trends (2019-2024)
- 8.5.2 Market Forecast (2025-2033)

9 Japan Predictive Maintenance Market - Competitive Landscape

9.1 Overview
9.2 Market Structure
9.3 Market Player Positioning
9.4 Top Winning Strategies
9.5 Competitive Dashboard
9.6 Company Evaluation Quadrant

10 Profiles of Key Players

10.1 Company A
- 10.1.1 Business Overview
- 10.1.2 Product Portfolio
- 10.1.3 Business Strategies
- 10.1.4 SWOT Analysis
- 10.1.5 Major News and Events
10.2 Company B
- 10.2.1 Business Overview
- 10.2.2 Product Portfolio
- 10.2.3 Business Strategies
- 10.2.4 SWOT Analysis
- 10.2.5 Major News and Events
10.3 Company C
- 10.3.1 Business Overview
- 10.3.2 Product Portfolio
- 10.3.3 Business Strategies
- 10.3.4 SWOT Analysis
- 10.3.5 Major News and Events
10.4 Company D
- 10.4.1 Business Overview
- 10.4.2 Product Portfolio
- 10.4.3 Business Strategies
- 10.4.4 SWOT Analysis
- 10.4.5 Major News and Events
10.5 Company E
- 10.5.1 Business Overview
- 10.5.2 Product Portfolio
- 10.5.3 Business Strategies
- 10.5.4 SWOT Analysis
- 10.5.5 Major News and Events

11 Japan Predictive Maintenance Market - Industry Analysis

11.1 Drivers, Restraints, and Opportunities
- 11.1.1 Overview
- 11.1.2 Drivers
- 11.1.3 Restraints
- 11.1.4 Opportunities
11.2 Porters Five Forces Analysis
- 11.2.1 Overview
- 11.2.2 Bargaining Power of Buyers
- 11.2.3 Bargaining Power of Suppliers
- 11.2.4 Degree of Competition
- 11.2.5 Threat of New Entrants
- 11.2.6 Threat of Substitutes
11.3 Value Chain Analysis