Machine Vision Systems Selection

The Machine Vision Systems Selection Guide includes selection criteria, supplier profiles, and strategies to help manufacturing organizations select the correct machine vision solution and supplier for their unique operations and business requirements.

Embrace the Future with Edge Computing for Machine Vision Applications 

While cloud computing has been a game-changer for businesses, concerns surrounding data security and latency issues have led to the emergence of edge computing as a viable alternative. The technology leverages the power of edge devices to perform real-time analysis and decision-making. The advantages of edge computing, including enhanced data security, reduced latency, cost optimization, and offline capabilities, make it an attractive solution for users seeking greater control, improved performance, and reliability.  

Embracing edge computing is essential for organizations as it offers significant advantages over cloud computing in machine vision applications. Its ability to reduce latency, enhance privacy and security, provide flexibility and scalability, and enable offline capabilities make it a compelling choice for organizations seeking to leverage the power of machine vision technology.  

Combining AI and edge computing has unlocked new possibilities for organizations to obtain immediate insights and make informed decisions. By performing computations locally, on the devices where data is generated, edge AI eliminates latency and enables real-time data processing. This has far-reaching implications across various industries, offering improved efficiency, enhanced privacy, and accelerated decision-making.

Machine Vision Selection Strategic Issues

In addition to providing a five-year market forecast, the Machine Vision Systems market research provides detailed quantitative current market data and addresses key strategic issues as follows.

Plan Your Industrial IoT Strategy

Industrial IoT, connecting intelligent physical entities to each other, to internet services, and to applications, is at hand. Industrial IoT’s “digital umbilical cord” allows companies to securely analyze asset performance information; manufacturers are already leveraging this paradigm in their operations. Industrial companies are in a unique position, in that industrial manufacturers are likely to use connected products in their own operations and produce connected products for use by their end customers. The unique demands of this dual use make it important that the entire organization understands the value proposition inherent in intelligent management of connected products; the ability to serve data from ubiquitous connected devices on the plant floor and process sophisticated output from enterprise systems for operational improvement become core enablers for driving the expected savings.

Industry 4.0 provides a vision of a future factory in which smart products collaborate with smart workstations in order to move through production in an effective, efficient, and flexible manner. Each unit produced can be personalized and changes can be accepted throughout production. Individual production assets optimize their resource and energy consumption. Among the expectations for Industry 4.0 are a replacement of the traditional hierarchical structures common in today’s automated production systems and the use of cyber-physical systems (CPS), or IoT technologies in the factory. The impact of these changes will be in plants where products control their own manufacturing process and quality outcome. 

Select the Correct Vision Systems Based on Application

Machine vision systems aid manufacturers in automating production processes, improving product quality, and meeting the stringent quality and safety requirements. With so many types of machine vision systems available today, it is very important to choose the right system based on the application. The uses of machine vision are so diverse that its components can vary from system to system. Careful planning and selection of the optimal sensor, camera, optics, software, frame grabber, lighting, etc., result in high inspection robustness. Users may want to consult with suppliers to determine if any new technologies or solutions could be deployed to enhance their operational efficiencies and optimize performance and uptime.

Machine Vision Selection Table of Contents

Strategic Analysis

  • Executive Overview
  • Market Trends
  • Buyer Strategies
  • Growth Contributors and Inhibitors

Scope of Research

Technology Evaluation and Supplier Selection Criteria

  • Organization
    • Experience
    • Geographic Presence
    • Sales & Support Strategy
    • Strategic Partnerships
  • Product and Technology
    • Equipment Properties
    • Functionality
    • General Requirements
    • Ruggedness
    • Standards Support
  • Services and Support
    • Engineering & Project Management Support
    • Operations & Maintenance Support
    • Training
  • Supplier Business Scope
    • Corporate Presence
    • Market Presence

Leading Supplier Analysis

  • Market Shares of the Leading Suppliers
  • Market Shares by Region
  • Market Shares by Hardware, Software, and Services
  • Market Shares by Hardware Type
    • Vision Sensors
    • Smart Cameras
    • Vision Systems
    • Peripheral Components
  • Market Shares by Application
    • Guidance or Location
    • Surface and Other Inspection
    • Gauging or Measurement
    • Identification/OCR/OCV
  • Market Shares by Intelligence
    • AI
    • Non-AI
  • Market Shares by Industry
  • Market Shares by Machinery Segment
  • Market Shares by Communication Protocol
  • Market Shares by Dimension
  • Market Shares by Customer Type
  • Market Shares by Sales Channel

Leading Supplier Profiles

For More Information

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