Market Scenario:

Predictive maintenance is regular monitoring of the actual mechanical condition, operating efficiency, and other indicators of the operating condition of machines and process systems will provide the data required to ensure the maximum interval between repairs and minimize the number and cost of unscheduled outages created by the failures. Predictive maintenance actually means to improve productivity, product quality, and overall effectiveness of manufacturing and production plants. Predictive maintenance is a condition-driven preventive maintenance program. Predictive maintenance uses direct monitoring of the mechanical condition, system efficiency, and other indicators to determine the actual mean-time-to-failure or the loss of efficiency for each machine and system in the plant. The main reasons for implementing predictive maintenance would be it can be used as- a maintenance tool, a plant optimizing tool and a reliability improvement tool.

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Predictive maintenance major benefits are, it reduces time required for maintenance and reduces the costs of maintenance. Recently there has been a rise in demand for transforming maintenance and reducing asset downtime, these are the key market drivers for predictive maintenance market. The rising dependence on big data and emerging concepts such as the Internet of Things (IoT) and the rising focus of organizations on reducing the operational cost is further expected to influence the growth of predictive maintenance market during the forecast period. The study indicates that lack of training for operators and lack of trust in predictive maintenance technology is a major threat for the predictive maintenance market.

Predictive maintenance has the ability to specifically predict asset failure, enabling enterprises to take the asset out of production just before it is to fail, thus it ensures that the production is not hampered in any way due to the failure of the asset. This ability of predictive maintenance is the major driving force for the predictive maintenance market.

The global Predictive Maintenance market is expected to grow at USD ~6334 Million by 2022, at 27% of CAGR between 2016 and 2022.

Key Players

The prominent players in the Predictive Maintenance Market are – IBM (U.S.), SAP SE (Germany), Software AG (Germany), General Electric (U.S.), Robert Bosch (Germany), Rockwell Automation (U.S.), PTC (U.S.), Warwick Analytics (U.K.), RapidMiner (U.S.), Schneider Electric SE (France), eMaint Enterprises, LLC (U.S.) and SKF (Sweden) among others.

Segments:

Predictive Maintenance by Component:

By Solution

By Service

Support and Maintenance
System integration
Consulting
Predictive Maintenance by Techniques:

Vibration monitoring
Oil analysis
Visual inspection
Shock pulse
Ultrasonic leak detectors
Electrical insulation
Performance testing
Wear and dimensional measurements
Signature analysis, time and frequency domain
Nondestructive testing
Predictive Maintenance by Deployment Type:

Cloud
On premise
Predictive Maintenance by End-user:

Manufacturing
Aerospace & defense
Healthcare
Automotive
Transportation
Government
Predictive Maintenance Global Market- Regional Analysis

The regional analysis of Predictive Maintenance market is being studied for region such as Asia pacific, North America, Europe and Rest of the World. The study indicates that North America region would dominate the Predictive Maintenance market by the forecast period owing to the presence of a large number of solution and service vendors. The study indicates that Asia-Pacific countries like China, Japan, South Korea, India and others are highly investing to increase the efficiency of production assets. The study reveals that Asia-Pacific region would show a positive growth in predictive maintenance market by the forecast period.

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Intended Audience

Predictive maintenance vendors
Resellers and distributors
Technology investors
System Integrators
Government Organizations
Research/Consultancy firms
TABLE OF CONTENTS

1 MARKET INTRODUCTION

1.1 INTRODUCTION

1.2 SCOPE OF STUDY

1.2.1 RESEARCH OBJECTIVE

1.2.2 ASSUMPTIONS

1.2.3 LIMITATIONS

1.3 MARKET STRUCTURE

2 RESEARCH METHODOLOGY

2.1 RESEARCH NETWORK SOLUTION

2.2 PRIMARY RESEARCH

2.3 SECONDARY RESEARCH

2.4 FORECAST MODEL

2.4.1 MARKET DATA COLLECTION, ANALYSIS & FORECAST

2.4.2 MARKET SIZE ESTIMATION

3 MARKET DYNAMICS

3.1 INTRODUCTION

3.2 MARKET DRIVERS

3.3 MARKET CHALLENGES

3.4 MARKET OPPORTUNITIES

3.5 MARKET RESTRAINTS

4 EXECUTIVE SUMMARY

MARKET FACTOR ANALYSIS
5.1 PORTER’S FIVE FORCES ANALYSIS

5.2 SUPPLY CHAIN ANALYSIS

6 PREDECTIVE MAINTENANCE MARKET, BY SEGMENTS

6.1 INTRODUCTION

6.2 MARKET STATISTICS

6.2.1 BY COMPONENT

6.2.1.1 BY SOLUTION

6.2.1.2 BY SERVICE

6.2.2 BY TECHNIQUE

6.2.2.1 VIBRATION MONITORING

6.2.2.2 ELECTRICAL INSULATION

Continued…

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