Best Artificial Intelligence in Manufacturing in 2020

Summary

The increase in the cost of labor and related supply–demand dynamics along with higher levels of output, better quality, and fewer errors are the major macro drivers for the growth of Artificial Intelligence in manufacturing market. 

Predictive maintenance, package inspection, defect detection, barcode reading, product and component assembly, machine safety, tracking and tracing, are the major applications for which computer vision technology is deployed in a manufacturing plant. Volvo’s Early Warning Systems collects data from its vehicles and applies Teradata ’s Unified summary Architecture to predict car failures and improve diagnostics and service.

Best Artificial Intelligence in Manufacturing

Artificial Intelligence (AI) in manufacturing is defined as a simulation of human intelligence used to communicate with the machines, extract the data from the field, analyze the extracted data, and perform the required task. 

From material movement to machinery inspection and self-diagnostics, which are usually operated by human labor or robot with the help of human intelligence, the AI-based system would perform in lesser time, cost, and human intervention. 

360Quadrants recognizes the below-listed companies as the best artificial intelligence in manufacturing.

Artificial Intelligence in Manufacturing 2020: 

1. Intel Corporation 

2. Nvidia Corporation 

3. IBM Corporation 

4. Google 

5. General Electric 

6. Microsoft 

7. Siemens AG 

8. Progress DataRPM 

9. General Vision 

10. Sight Machine 

Artificial Intelligence in Manufacturing 

Market Overview 

AI is a set of computational technologies that make a computer “ think” intelligently. Since the inception of AI, developments have been patchy and unpredictable; however, significant recent developments have proved a boon to this sector. 

The manufacturing industry is witnessing a new wave of technological revolution, which is boosting the idea for implementation of AI in factories/plants. AI-based solutions are adopted in manufacturing facilities to improve productivity by maximizing asset utilization, minimizing downtime, and improving machine efficiency. Moreover, Artificial Intelligence in manufacturing is expected to enhance productivity through quality control by detecting defects and help in the predictive maintenance of factory machinery. 

Extensive usage of big data, Industrial Internet of Things (IIoT), smart factory, and robotics are among the major factors driving the growth of this market. The increase in the cost of labor and related supply–demand dynamics along with higher levels of output, better quality, and fewer errors are the major macro drivers for the growth of Artificial Intelligence in manufacturing market. Therefore, manufacturers and technology providers are spending heavily on research and development (R&D) to fulfill this market demand . 

Artificial Intelligence in manufacturing market marks a revolutionary convergence of different stakeholders such as hardware providers, software and technology companies, and manufacturers. Artificial Intelligence in manufacturing market is expected to be valued at USD 1.03 billion in two thousand and eighteen and is likely to reach USD 17.22 billion by 2025, at a CAGR of 49.5% . 

COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY 

20 companies offering top artificial intelligence in manufacturing were analyzed, shortlisted and categorized on a quadrant under Visionary Leaders, Innovators, Dynamic Differentiators, and Emerging Companies to identified best facial recognition solution providers. 

The competitive leadership mapping (Quadrant) showcased below provides information for twenty top artificial intelligence in manufacturing. Vendor evaluations are based on two broad categories: product offering and business strategy. Each category carries various criteria, based on which vendors have been evaluated. The evaluation criteria considered under product offerings include the breadth of offering, delivery (based on industries that the vendors cater to, deployment models, and subscriptions), features/functionality, delivery, product quality and reliability, and product differentiation. The evaluation criteria considered under business strategy include geographic footprint (on the basis of geographic presence), channel strategy and fit, vision alignment, and effectiveness of growth (on the basis of innovations, partnerships, collaborations, and acquisitions).

VISIONARY LEADERS

Visionary leaders are the leading market players in terms of new developments such as product launches, innovative technologies, and the adoption of growth strategies. These players have a broad product offering that caters to most of the regions globally. Visionary leaders primarily focus on acquiring the leading market position through their strong financial capabilities and their well-established brand equity. 

DYNAMIC DIFFERENTIATORS 

Dynamic Differentiators are established players with very strong business strategies. However, they have a weaker product portfolio compared to the visionary leaders. They generally focus only on a specific type of technology related to the product. 

INNOVATORS 

Innovators in the competitive leadership mapping are vendors that have demonstrated substantial product innovations as compared to their competitors. The companies have focused on product portfolios. However, they do not have very strong growth strategies for their overall business, when compared with the visionary leaders. 

EMERGING COMPANIES 

Emerging companies have niche product and service offerings. Their business strategies are not as strong as that of the established vendors. The emerging vendors include the new entrants in the market, emerging in terms of product portfolio and geographic reach, and require time to gain significant traction in the market. 

Who uses Artificial Intelligence in Manufacturing? 

Automobile Industry

Artificial Intelligence in manufacturing market for the automobile industry was the largest in 2017. To reduce costs and improve the quality of vehicle production, automobile makers have started using computer vision and machine learning technology. In many passenger vehicles, four clips known as roof mole clips, which secure weather-proofing strips above each of the four doors are incorrectly assembled resulting in the interruption of vehicle sequencing and expensive hand reworking. This leads to increased downtime, vehicle recall, and loss of revenue. Moreover, painting the vehicle, inspecting the combustion engine, and electrical wearing are big issues. Manual system or robotics cannot zero down these issues. 

AI-based computer vision technology is installed on the production line to inspect the clips on the driver ’s side. The AI-based technology detects the clip’s presence, orientation, and movements in paint variations, and combustion engine input output ratio. During the comparison, an extensive amount of deep learning algorithm is used. In the final assembly line, the computer vision technology inspects each of the two front and two rear end clips, along with the painting, nuts, and electric wearing. The deep learning algorithm helps to ensure accurate results. 

Energy and Power 

Globally, the increasing demand for energy and power encourages companies operating in the energy and power industry to adopt AI-based solutions that can help them enhance production output with minimum maintenance and reduced downtime. Maintenance and inspection are the major issues along with material movement in a thermal plant as the material needs to travel a long distance inside the plant with heavy weights. The equipment such as turbine, conveyer belt, grids, and voltage transformer are costly. Moreover, there are issues related to fuel mix, ambient temperature, air quality, moisture, load, weather forecast models, and market pricing in the power industry. Using Artificial Intelligence in manufacturing-based technologies, these issues can be solved accurately as well as predict the possible issues that may arise in the future. 

Pharmaceutical Companies

The pharmaceutical companies are using Artificial Intelligence in manufacturing for quality control, material movement, and production planning. In quality control, the computer vision technology analyses the images, ingredients, and separates the outlier object, if found. This quality control method is followed by material movement and production planning. In this material movement procedure, the AI-based system checks whether the right material is being packaged. Moreover, the AI system also places the material in the storage room once the process is completed. 

Heavy Metals

Heavy metals and machine manufacturing comprise the production of various kinds of machinery used in construction, infrastructure, or manufacturing applications. A few important processes for manufacturing these devices are stamping, bending, forming, and machining, which is required to shape individual pieces of metals. Processes such as welding, and assembling are used to join separate parts. Maintenance cost and repairing cost of machines directly affect the total cost of production. 

The implementation of Artificial Intelligence in manufacturing in the heavy metals and machine manufacturing industry can help manufacturers analyse machine conditions in advance to avoid unplanned downtime and wastage. Machine breakdown and unplanned downtime delay the production process, which causes a huge loss to industrial equipment manufacturers. 

Semiconductors and Electronics

Artificial Intelligence in manufacturing is used for production planning, quality control, and material movement. The implementation of AI-based solutions is expected to benefit manufacturers in terms of optimizing the production cost, technology implementation, and integration of components. The manufacturing of electronics equipment is a complex process and requires manufacturing-related data on a real-time basis. These data help in planning and maintaining the production process efficiently. Thus, AI solutions help the companies operating in the semiconductor and electronics equipment industry to analyse the data collected at all points to aid improved decision-making for the manufacturing process. Which technologies are used in Artificial Intelligence in Manufacturing? Machine Learning - Machine learning enables systems to automatically improve their performance with experience. ML aims to develop a computer program/algorithm that can access data and use it to train itself with no human intervention. ML is expected to account for the largest share of Artificial Intelligence in manufacturing market. This is attributed to the enormous availability of data, also called big data, and increasing adoption of ML by factories to improve productivity, reduce machine downtime, and reduce operational cost. 

Natural language processing (NLP) 

Natural language processing (NLP), a form of automatic speech recognition, is among the emerging AI technologies. NLP is developed for making real-time translation and developing systems that can interact through dialogues. NLP consists of text analytics, speech analytics, IVR, pattern and image recognition, auto coding, classification and categorization, and optical character recognition OCR. 

In NLP, a computer uses a statistical model that converts natural language into a programming language file 

Vision Technology

Computer vision technology is concerned with the physical structure of 3D objects attached to an intelligent computing system. Computer vision analyses the information of different geometric shapes, volumes, and patterns, and provides visual feedback to the user, which is further used to draw the inference. The fundamental objective of computer vision technology is to interpret the picture obtained through a high-resolution camera. These systems are employed in robots or satellite systems. Predictive maintenance, package inspection, defect detection, barcode reading, product and component assembly, machine safety, tracking and tracing, are the major applications for which computer vision technology is deployed in a manufacturing plant. 

Context-Aware Computing

Context-aware processing or context-aware computing has been an integral part of a computer system. The development of more sophisticated hard and soft sensors has accelerated the growth of context-aware processing. Context-aware processing is featured in most smartphones nowadays, with some core applications being reminders of calendar events, personalized user interface, and tagging pictures with time and identity. Increased processing power, innovative sensing capabilities, and improved connectivity have resulted in the growth of context-aware processing systems. A few core applications include heat, light and sound sensing, and machine monitoring. What drives the Artificial Intelligence in Manufacturing market? Increasingly large and complex data set. Evolving industrial IoT and automation. Improving computing power and declining hardware costs. Increasing venture capital investments. 

Quality Assurance

Siemens needed a faster solution from the quality assurance process of 5,000 turbine blades without compromising on accuracy or safety. A manual procedure would require up to 6 hours for inspection. To counter this, Siemens Gamesa partnered with Fujitsu to develop an AI solution that would automatically detect flaws using AI and DL technologies. The cordially built solutions reduced nondestructive testing (NDT) scanning by 80%. 

Breakdown Predictions

Volvo (Sweden) works with Teradata (US) to carry out predictive, machine learning-driven analytics. Volvo’s Early Warning Systems collects data from its vehicles and applies Teradata ’s Unified summary Architecture to predict car failures and improve diagnostics and service. With over 80% of Volvo’s cars being connected, Volvo analyzes over 500,000 hazardous incidents every week. Originally, Volvo (Sweden) took 15 years of usage and diagnostics data for diagnostics and design. 

Predictive Maintenance

Rolls-Royce (UK) chose Microsoft Azure platform to build its data analytics system. Rolls-Royce will be able to analyze rich data and perform data modelling to detect operational anomalies with the help of Microsoft Cortana Intelligence Suite. Microsoft Cortana Intelligence capabilities help the company derive meaningful insights from large data. Cortana Intelligence allows the company to look at wider sets of operating data and spot correlations that can improve a flight schedule or a maintenance plan and help reduce disruption in operations.