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市场调查报告书
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1856804

车队预测性维护分析市场预测至2032年：按部署类型、车队类型、组件、应用和区域分類的全球分析

Predictive Maintenance Analytics For Fleets Market Forecasts to 2032 - Global Analysis By Deployment Type, Fleet Type, Component, Application and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球车队预测性维护分析市场预计到 2025 年将达到 73 亿美元，到 2032 年将达到 394 亿美元，预测期内复合年增长率为 27.1%。

车队预测性维护分析是指利用技术解决方案监控车辆、机械和车队运营，并在故障发生前预测维修需求。这些系统利用物联网感测器、远端资讯处理和人工智慧主导的分析技术，预测零件磨损、优化维护计划、减少停机时间并提高安全性。车队营运商、物流公司和商业运输服务提供者利用预测性维护来降低营运成本、延长车辆使用寿命并提高效率。市场支持在车队密集型产业中采用数据主导的决策、资产管理和主动维护策略。

美国卡车运输协会表示，数据驱动的维护计划对于最大限度地减少计划外车辆停机时间至关重要，而计划外车辆停机时间是物流运营中最大的成本驱动因素。

物联网车辆感测器的应用日益广泛

推动市场发展的关键因素之一是物联网感测器在商用车队中的日益普及。这些感知器即时持续监测引擎健康状况、轮胎气压和煞车片磨损等关键零件。大量高频、精细的数据为预测演算法提供了必要的资源。透过分析这些讯息，车队管理人员可以从定期维护转向基于状态的维护方法，从而避免代价高昂的故障，并透过将感测器数据直接转化为可执行的洞察，优化车辆运作。

数据互通性和准确性问题

资料互通性和准确性方面的挑战是限制因素。车队通常由不同製造商的车辆组成，每家製造商都有其专有的资料格式和远端资讯处理系统。这导致资料流彼此孤立且不一致，难以进行统一的聚合和分析。此外，感测器故障和校准漂移会导致数据不准确，从而造成误报和预测失误。确保从不同来源获取干净、统一且可靠的数据仍然是有效部署面临的重大技术和操作难题。

与物流技术平台伙伴关係

与成熟的物流和货运管理平台建立策略伙伴关係关係蕴藏着巨大的市场机会。透过将预测性维护分析直接整合到这些广泛使用的运输管理系统 (TMS) 和车辆营运中心，供应商可以提供无缝衔接的增值服务。这种嵌入式方法降低了车队营运商的采用门槛，并透过在现有工作流程中提供预测性洞察来增强其价值提案，从而加速透过现有分销管道的市场渗透。

来自通用人工智慧提供者的竞争压力

市场面临来自大型通用云端人工智慧和分析平台的威胁，这些平台提供通用机器学习工具。这些科技巨头可以利用其庞大的基础设施、品牌知名度和规模经济优势来制定具有竞争力的价格。这些供应商有可能将分析层商品化，迫使专注于预测性维护的供应商不断展现其卓越的专业技术、针对特定车型的演算法调优以及与汽车OEM数据的深度集成，以证明自身价值并保持竞争优势。

新冠疫情的影响：

疫情初期扰乱了车辆运营，并延缓了对新技术的投资。然而，它最终却成为了催化剂，严重衝击了供应链，凸显了营运韧性的迫切需求。这场危机加速了车队营运的数位转型，管理者们寻求数据驱动的工具来优化其缩减后的资产规模的效率和可靠性。对降低成本和最大化车辆运转率的日益重视，提升了预测性维护分析的长期提案。

预计在预测期内，云端基础的解决方案细分市场将成为最大的细分市场。

由于其卓越的扩充性、较低的前期成本和易于部署，预计在预测期内，云端基础的解决方案将占据最大的市场份额。云端平台使各种规模的车队都能获得强大的分析功能，而无需在本地IT基础设施上进行大量投资。云端平台支援无缝的远端监控、来自分散式车辆的即时数据处理，以及轻鬆整合空中下载 (OTA) 更新以改善演算法。这种灵活性和营运支出模式使云端成为迄今为止最便捷的部署选择。

预计在预测期内，轻型商用车细分市场将以最高的复合年增长率成长。

受电子商务和最后一公里配送服务的爆炸性成长推动，轻型商用车车队预计将在预测期内呈现最高的成长率。这些车队面临巨大的压力，必须尽可能减少车辆停机时间，以满足紧迫的交货期限。对于许多中小型业者而言，预测性维护正从一种奢侈品转变为一种必需品，因为它能够直接保障其创收能力，防止配送车辆发生意外故障，从而避免物流中断和客户满意度下降。

占比最大的地区：

亚太地区预计将在预测期内占据最大的市场份额，这主要得益于其庞大的製造业和物流产业，尤其是中国、日本和韩国。快速的工业化进程、蓬勃发展的电子商务以及政府大力支持智慧交通和工业4.0的倡议是关键驱动因素。该地区庞大的商用车辆数量以及提高物流效率的迫切需求，为预测性维护解决方案的广泛应用创造了有利条件，从而优化车队营运。

复合年增长率最高的地区：

在预测期内，北美预计将呈现最高的复合年增长率，这主要得益于其先进的技术基础设施、主要远端资讯处理供应商的集中以及强大的数据主导车队管理文化。严格的监管合规要求和高昂的人事费用使得非计划性停机造成的损失极为巨大。在这种环境下，车队营运商积极寻求预测性解决方案，以期透过提升资产可靠性、安全性和降低总体拥有成本来获得竞争优势，从而推动了高阶分析技术的快速普及。

免费客製化服务：

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  • 区域细分
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
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    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究资讯来源
    • 初级研究资讯来源
    • 次级研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 新兴市场
  • 新冠疫情的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球车队预测性维护分析市场依部署类型划分

  • 云端基础的解决方案
  • 边缘分析平台
  • 混合部署模式
  • 本地部署系统

6. 全球车队预测性维修分析市场（依车队类型划分）

  • 轻型商用车
  • 大型车队
  • 公共运输
  • 建筑和采矿车队
  • 物流和最后一公里车辆

7. 全球车队预测性维护分析市场（按组件划分）

  • 软体平台
  • 感测器硬体
  • 服务

8. 全球车队预测性维护分析市场（按应用领域划分）

  • 引擎和传动系统监测
  • 轮胎和煞车系统分析
  • 电池健康监测
  • 远端资讯处理和使用优化
  • 保固和备件预测

9. 全球车队预测性维护分析市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十章：重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十一章 企业概况

  • Samsara Inc.
  • Geotab Inc.
  • Omnitracs LLC
  • Verizon Communications Inc.
  • Fleet Complete
  • Trimble Inc.
  • Teletrac Navman
  • Fleetcor Technologies, Inc.
  • Michelin Group
  • Bridgestone Corporation
  • Continental AG
  • ZF Friedrichshafen AG
  • Aion-Tech Solutions Ltd.
  • Siemens AG
  • Honeywell International Inc.
  • Rockwell Automation, Inc.
Product Code: SMRC31676

According to Stratistics MRC, the Global Predictive Maintenance Analytics For Fleets Market is accounted for $7.3 billion in 2025 and is expected to reach $39.4 billion by 2032 growing at a CAGR of 27.1% during the forecast period. Predictive Maintenance Analytics for Fleets refers to technology solutions that monitor vehicles, machinery, and fleet operations to anticipate maintenance needs before failures occur. Using IoT sensors, telematics, and AI-driven analytics, these systems predict component wear, optimize service schedules, reduce downtime, and improve safety. Fleet operators, logistics companies, and commercial transport providers use predictive maintenance to lower operational costs, extend vehicle lifespans, and enhance efficiency. The market supports data-driven decision-making, asset management, and proactive maintenance strategies across fleet-intensive industries.

According to the American Trucking Associations, data-driven maintenance scheduling is critical for minimizing unplanned vehicle downtime, which is a top cost driver for logistics operations.

Market Dynamics:

Driver:

Growing adoption of IoT fleet sensors

The primary market driver is the proliferating integration of IoT sensors across commercial vehicle fleets. These sensors continuously monitor critical components like engine health, tire pressure, and brake wear in real-time. This massive influx of high-frequency, granular data provides the essential fuel for predictive algorithms. By analyzing this information, fleet managers can move beyond scheduled maintenance to a condition-based approach, directly translating sensor data into actionable insights that prevent costly breakdowns and optimize vehicle uptime.

Restraint:

Data interoperability and accuracy issues

A significant restraint is the challenge of data interoperability and accuracy. Fleets often comprise vehicles from different manufacturers, each with proprietary data formats and telematics systems. This creates siloed and inconsistent data streams that are difficult to aggregate and analyze cohesively. Furthermore, sensor malfunctions or calibration drift can lead to inaccurate data, resulting in false alerts or missed predictions. Ensuring clean, unified, and reliable data from diverse sources remains a major technical and operational hurdle for effective deployment.

Opportunity:

Partnerships with logistics tech platforms

A substantial market opportunity lies in forming strategic partnerships with established logistics and freight management platforms. By integrating predictive maintenance analytics directly into these widely-used Transportation Management Systems (TMS) and fleet operation hubs, providers can offer a seamless, value-added service. This embedded approach lowers the adoption barrier for fleet operators, providing them with predictive insights within their existing workflow, thereby enhancing the value proposition and accelerating market penetration through established distribution channels.

Threat:

Competitive pressure from generic AI providers

The market faces a threat from large, generic cloud AI and analytics platforms that offer broad-purpose machine learning tools. These tech giants can leverage their extensive infrastructure, brand recognition, and economies ofscale to offer competitive pricing. They pose a risk of commoditizing the analytics layer, forcing specialized predictive maintenance vendors to continuously demonstrate superior domain expertise, fleet-specific algorithm tuning, and deeper integration with automotive OEM data to justify their value and maintain a competitive edge.

Covid-19 Impact:

The pandemic initially caused fleet operational disruptions and delayed investment in new technologies. However, it ultimately acted as a catalyst by severely stressing supply chains and highlighting the critical need for operational resilience. The crisis accelerated the digital transformation of fleet operations, as managers sought data-driven tools to optimize the efficiency and reliability of a reduced asset base. This heightened focus on cost-saving and maximizing vehicle utilization boosted the long-term value proposition of predictive maintenance analytics.

The cloud-based solutions segment is expected to be the largest during the forecast period

The cloud-based solutions segment is expected to account for the largest market share during the forecast period, owing to its superior scalability, lower upfront cost, and ease of deployment. Cloud platforms allow fleets of all sizes to access powerful analytics without significant investment in on-premise IT infrastructure. They enable seamless remote monitoring, real-time data processing from dispersed vehicles, and effortless integration of over-the-air (OTA) updates for algorithm improvements. This flexibility and operational expenditure model make cloud the dominant and most accessible deployment choice.

The light commercial fleets segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the light commercial fleets segment is predicted to witness the highest growth rate, reinforced by the explosive growth of e-commerce and last-mile delivery services. These fleets face intense pressure to minimize vehicle downtime to meet tight delivery windows. For many small-to-midsized operators, predictive maintenance transforms from a luxury to a necessity, as it directly protects their revenue-generating capacity by preventing unexpected delivery van failures that disrupt logistics and customer satisfaction.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, ascribed to its massive manufacturing and logistics sector, particularly in China, Japan, and South Korea. Rapid industrialization, booming e-commerce, and extensive government initiatives supporting smart transportation and Industry 4.0 are key drivers. The region's vast number of commercial vehicles and the pressing need to improve logistics efficiency create a fertile ground for the widespread adoption of predictive maintenance solutions to optimize fleet operations.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with its advanced technological infrastructure, high concentration of leading telematics providers, and a strong culture of data-driven fleet management. Strict regulatory compliance requirements and high labor costs make unplanned downtime exceptionally expensive. This environment encourages rapid adoption of advanced analytics, with fleet operators actively seeking predictive solutions to gain a competitive advantage through superior asset reliability, safety, and total cost of ownership reduction.

Key players in the market

Some of the key players in Predictive Maintenance Analytics For Fleets Market include Samsara Inc., Geotab Inc., Omnitracs LLC, Verizon Communications Inc., Fleet Complete, Trimble Inc., Teletrac Navman, Fleetcor Technologies, Inc., Michelin Group, Bridgestone Corporation, Continental AG, ZF Friedrichshafen AG, Aion-Tech Solutions Ltd., Siemens AG, Honeywell International Inc., and Rockwell Automation, Inc.

Key Developments:

In September 2025, Samsara Inc. launched its new "Asset Health Predictions" module, which uses AI to analyze real-time sensor data from connected vehicles, providing fleet managers with a 14-day forecast of potential component failures for brakes, starters, and alternators.

In August 2025, Geotab Inc. introduced its "Fleet Resilience Analytics" platform, leveraging its extensive data lake to benchmark individual vehicle health against aggregated fleet data, identifying outlier vehicles at high risk of breakdown and recommending pre-emptive maintenance.

In July 2025, Verizon Connect announced a strategic integration with "ZF Friedrichshafen AG", creating a closed-loop system where Verizon's telematics data automatically triggers service alerts and orders genuine ZF parts for commercial vehicles equipped with its advanced chassis components.

Deployment Types Covered:

  • Cloud-Based Solutions
  • Edge Analytics Platforms
  • Hybrid Deployment Models
  • On-Premise Systems

Fleet Types Covered:

  • Light Commercial Fleets
  • Heavy-Duty Fleets
  • Public Transport Fleets
  • Construction & Mining Fleets
  • Logistics & Last-Mile Fleets

Components Covered:

  • Software Platforms
  • Sensors & Hardware
  • Services

Applications Covered:

  • Engine & Drivetrain Monitoring
  • Tire & Brake System Analysis
  • Battery Health Monitoring
  • Telematics & Usage Optimization
  • Warranty & Spare Part Forecasting

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 Emerging Markets
  • 3.8 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Predictive Maintenance Analytics For Fleets Market, By Deployment Type

  • 5.1 Introduction
  • 5.2 Cloud-Based Solutions
  • 5.3 Edge Analytics Platforms
  • 5.4 Hybrid Deployment Models
  • 5.5 On-Premise Systems

6 Global Predictive Maintenance Analytics For Fleets Market, By Fleet Type

  • 6.1 Introduction
  • 6.2 Light Commercial Fleets
  • 6.3 Heavy-Duty Fleets
  • 6.4 Public Transport Fleets
  • 6.5 Construction & Mining Fleets
  • 6.6 Logistics & Last-Mile Fleets

7 Global Predictive Maintenance Analytics For Fleets Market, By Component

  • 7.1 Introduction
  • 7.2 Software Platforms
  • 7.3 Sensors & Hardware
  • 7.4 Services

8 Global Predictive Maintenance Analytics For Fleets Market, By Application

  • 8.1 Introduction
  • 8.2 Engine & Drivetrain Monitoring
  • 8.3 Tire & Brake System Analysis
  • 8.4 Battery Health Monitoring
  • 8.5 Telematics & Usage Optimization
  • 8.6 Warranty & Spare Part Forecasting

9 Global Predictive Maintenance Analytics For Fleets Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 Samsara Inc.
  • 11.2 Geotab Inc.
  • 11.3 Omnitracs LLC
  • 11.4 Verizon Communications Inc.
  • 11.5 Fleet Complete
  • 11.6 Trimble Inc.
  • 11.7 Teletrac Navman
  • 11.8 Fleetcor Technologies, Inc.
  • 11.9 Michelin Group
  • 11.10 Bridgestone Corporation
  • 11.11 Continental AG
  • 11.12 ZF Friedrichshafen AG
  • 11.13 Aion-Tech Solutions Ltd.
  • 11.14 Siemens AG
  • 11.15 Honeywell International Inc.
  • 11.16 Rockwell Automation, Inc.

List of Tables

  • Table 1 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Deployment Type (2024-2032) ($MN)
  • Table 3 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Cloud-Based Solutions (2024-2032) ($MN)
  • Table 4 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Edge Analytics Platforms (2024-2032) ($MN)
  • Table 5 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Hybrid Deployment Models (2024-2032) ($MN)
  • Table 6 Global Predictive Maintenance Analytics For Fleets Market Outlook, By On-Premise Systems (2024-2032) ($MN)
  • Table 7 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Fleet Type (2024-2032) ($MN)
  • Table 8 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Light Commercial Fleets (2024-2032) ($MN)
  • Table 9 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Heavy-Duty Fleets (2024-2032) ($MN)
  • Table 10 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Public Transport Fleets (2024-2032) ($MN)
  • Table 11 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Construction & Mining Fleets (2024-2032) ($MN)
  • Table 12 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Logistics & Last-Mile Fleets (2024-2032) ($MN)
  • Table 13 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Component (2024-2032) ($MN)
  • Table 14 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Software Platforms (2024-2032) ($MN)
  • Table 15 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Sensors & Hardware (2024-2032) ($MN)
  • Table 16 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Services (2024-2032) ($MN)
  • Table 17 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Application (2024-2032) ($MN)
  • Table 18 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Engine & Drivetrain Monitoring (2024-2032) ($MN)
  • Table 19 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Tire & Brake System Analysis (2024-2032) ($MN)
  • Table 20 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Battery Health Monitoring (2024-2032) ($MN)
  • Table 21 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Telematics & Usage Optimization (2024-2032) ($MN)
  • Table 22 Global Predictive Maintenance Analytics For Fleets Market Outlook, By Warranty & Spare Part Forecasting (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.