亚太预测性维护市场 – 行业趋势和 2029 年预测

亚太预测性维护市场分析和规模

目前，多家公司正在推出下一代端到端云平台。新兴技术的应用不断增加，有助于获得有价值的决策见解，从而促进了行业增长。各种垂直终端用户越来越多地寻求节省成本和减少停机时间，这推动了市场增长。

Data Bridge Market Research 分析称，2021 年预测性维护市场价值为 17.3 亿美元，预计到 2029 年将达到 75.9 亿美元，预测期内复合年增长率为 20.3%。除了市场价值、增长率、细分市场、地理覆盖范围、市场参与者和市场情景等市场洞察外，Data Bridge Market Research 团队策划的市场报告还包括深入的专家分析、进出口分析、定价分析、生产消费分析和 pestle 分析。

亚太预测性维护市场范围和细分

市场定义

A predictive maintenance software system is used to monitor any instrumentation or machine's performance and condition while in operation. The software system monitors the instrumentation using advanced techniques, allowing the machinery to be maintained on a regular basis before any failure occurs. The predictive maintenance software system has found use in a variety of fields, including the detection of three-phase power imbalances caused by harmonic distortion, the identification of distinct motor electrical phenomenon spikes, and the detection of heating caused by dangerous bearings.

Predictive Maintenance Market Dynamics

• DriversExpansion of small and medium sized industries

One of the major factors driving market growth is the increasing number of small and medium-sized businesses around the world. In other words, an increase in the number of banking, financial services, and insurance (BFSI), government and public sector, healthcare and life sciences, manufacturing, retail and e-commerce, telecommunications, and information technology (IT) industries is directly influencing market growth.

• Advancements in futuristic technologies

Constant advancements in big data, machine-to-machine (M2M) communication, and artificial intelligence have opened up new avenues for the dissemination of information derived from artificial means. IoT bias generates massive data from various sources, such as detectors, cameras, and other connected bias. However, the data has no value unless converted into actionable, contextual information. Big data and data visualization methods allow pharmacists to gain new perspectives through batch processing and offline analysis. Real-time data analysis and decision-making are frequently performed manually; however, to make it scalable, it is preferable to be performed automatically.

Opportunities

• Advanced machine language operations

Across nearly every perpendicular, advanced asset operation is becoming less desirable. As a result of connected bias, result providers equipped with AI and ML can collect and transform a vast quantum of client-related data into meaningful perceptivity. AI can also be combined with IoT bias to optimize various aspects of service delivery, such as predictive conservation and quality assessment, without the need for human intervention.

Restraints

• Lack of skilled workers

Trained workers must manage the most recent software systems to implement AI-based IoT technologies and skill sets. As a result, workers must be trained on how to operate new and upgraded systems. Furthermore, diligence are dynamic in their embrace of new technologies. However, they face a shortage of largely professed pool and complete workers. As the majority of global merchants organize prophetic conservation systems, the demand for a broadly professed pool grows. Companies must develop grit in areas such as cybersecurity, networking, and operations.

This predictive maintenance market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the predictive maintenance market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

COVID-19 Impact on Predictive Maintenance Market

COVID - 19 has fundamentally altered the dynamics of business operations. Though the COVID - 19 outbreak has shed light on flaws in business models across verticals, it has also provided several opportunities for businesses to digitalize and expand across borders as the abandonment and integration of technologies such as AI, analytics, IoT, and blockchain has increased during the lockdown period. During the first and alternate diggings of 2020, the retail and manufacturing sectors saw significant drops in business performance. Nonetheless, with the availability of vaccines and significant control of the epidemic, these sectors are expected to see increased investment throughout the cast period as prophetic conservation results rise in elevation across various business functions.

Recent Development

• In May 2021 SAS Institute launched its SAS Viya platform to help lay the groundwork for data and logical success by incorporating new data operation results into its critical, pall native SASViya platform.

Asia-Pacific Predictive Maintenance Market Scope

The predictive maintenance market is segmented on the basis of components, deployment mode, organization size, vertical, and stakeholder. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.

Components

• Solution
• Services

Deployment Mode

• Cloud
• On-Premise

Organization Size

• Large Enterprises
• Small and Medium-Sized Enterprises

Vertical

• Manufacturing
• Energy and Utilities
• Transportation
• Government
• Healthcare
• Aerospace
• Defense
• Others

Stakeholder

• MRO
• OEM/ODM
• Technology Integrators

Predictive Maintenance Market Regional Analysis/Insights

The predictive maintenance market is analyzed and market size insights and trends are provided by country, component, deployment mode, organization size, vertical, and stakeholder as referenced above.

The countries covered in the predictive maintenance market report are Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, and Rest of Asia-Pacific.

中国是主导地区，因为智能传感器和可通过基于云的分析系统进行通信的车载电子设备的使用越来越多，产品供应商能够提前评估设备的工作状况和服务要求。

报告的国家部分还提供了影响市场当前和未来趋势的各个市场影响因素和市场监管变化。下游和上游价值链分析、技术趋势和波特五力分析、案例研究等数据点是用于预测各个国家市场情景的一些指标。此外，在提供国家数据的预测分析时，还考虑了亚太品牌的存在和可用性以及它们因来自本地和国内品牌的激烈或稀缺竞争而面临的挑战、国内关税和贸易路线的影响。   

竞争格局和预测性维护市场份额分析

预测性维护市场竞争格局按竞争对手提供详细信息。详细信息包括公司概况、公司财务状况、产生的收入、市场潜力、研发投资、新市场计划、亚太地区业务、生产基地和设施、生产能力、公司优势和劣势、产品发布、产品宽度和广度、应用主导地位。以上提供的数据点仅与公司对预测性维护市场的关注有关。

预测性维护市场的一些主要参与者包括：

• 微软 (美国)
• IBM 公司（美国）
• SAP SE（德国）
• SAS AG（德国）
• TIBCO Software Inc.（美国）
• 惠普企业发展有限公司 (美国)
• Altair Engineering Inc.（美国）
• Splunk Inc.（美国）
• 甲骨文 (美国)
• Google LLC（美国）
• 亚马逊网络服务公司（美国）
• 通用电气（美国）
• 施耐德电气（法国）
• 日立有限公司（日本）
• PTC（美国）
• RapidMiner, Inc.（美国）
• 卓越运营 (OPEX) 集团有限公司（英国）
• 澳洲野狗（澳大利亚）
• Factory5（俄罗斯）

SKU-56993