General Motion Control

The General Motion Control (GMC) market research study delivers current market analysis plus a five year market and technology forecast.  It is available in multiple editions including worldwide, all regions, and most major countries.   

General Motion Control Systems Support Sustainability and Connected Machinery 

New ARC Advisory Group research on the General Motion Control (GMC) market projects solid growth as industrial operations seek automation solutions that support energy conservation and sustainability initiatives. Semiconductor and electronic devices, electric cars, packaging solutions, and materials that are essential for energy-efficient solutions and to support substantiality will require a new generation of machinery that will use GMC systems to produce these products. The general motion control market remains fragmented, with a broad and geographically diverse range of suppliers, and a plethora of possible applications affecting dozens of industries.

General Motion Control Strategic Issues

In addition to providing a five-year market forecast, the General Motion Control market research report provides detailed quantitative current market data and addresses key strategic issues as follows.

Leverage Machine Learning and Artificial Intelligence    

Another class of industrial analytics gets most of the attention today. This class is machine learning, a subset of AI, and it uses algorithms to replicate human capability for recognition. Natural language processing and chatbots are examples of AI and are often combined with machine learning for practical application. One of the biggest points of confusion in the market today for end users is the propensity for machine learning providers to classify and market their solutions as “artificial intelligence” or “artificial intelligence and machine learning.” Neither is wholly accurate.

In manufacturing operations, machine learning is often used for predictive analytics that combine unsupervised learning for pattern discovery and supervised methods for identification. As it ingests large amounts of data, machine learning can identify discriminative patterns and the probability of a behavior occurring. One of the benefits of machine learning is that techniques can adapt to incorporate new behaviors and data sets without being explicitly told what to look for. This is critical for asset-intensive environments where similar machines can experience vastly different operating environments.

Machine learning can provide a host of benefits across a variety of discrete manufacturing applications. For example, one manufacturer reduced scrap by 40 percent using machine learning to detect previously unidentifiable production inefficiencies. In another example, this one involving motorcycle production, visual inspection videos and images were processed using machine learning trained in the cloud and then deployed locally. Within two weeks, the analytics could predict conditions leading to product quality issues, such as dents and seat wrinkles, with 90 percent accuracy. This method enabled the manufacturer to identify and avoid quality issues well in advance of traditional trend and threshold analysis.

Utilize Connected Machines and Production Lines    

As more machines and entire production lines are connected, maintenance and operational data will be utilized to determine how the manufacturing operations are performing. Machine builders and manufacturers will both have access to production data. Machine builders will use the information to redefine how the machine operates by redeploying software on the machine. Manufacturers will leverage the information to improve production processes and product design. Both machine builders and manufacturers will benefit by using the data to improve upon the next generation of manufactured product or machine design. This is now being referred to as closed loop manufacturing.

Machine builders will assume a greater role in maintenance services for machinery. This will lead into the age of the software-defined machine. The concept was introduced 50 years ago with the first Apollo missions where spacecraft computing systems were being reprogrammed throughout the mission to handle different segments of the flight plan. Today, Tesla has taken this concept to the next level. The Tesla vehicles are connected; data from these machines is collected and analyzed. The vehicle software is reloaded to improve the performance of the vehicle based on the way it is being used. Closed loop manufacturing will enable industrial machinery builders to provide ongoing maintenance services to their customers. Manufacturers will experience even greater benefits as closed loop manufacturing will enable them to make adjustments in manufacturing processes as well as improve product design.

The model-based enterprise will require numerous standards that allow information to flow seamlessly from the product design, manufacturing production equipment, and inspection. There is a significant amount of interest from manufacturers in developing standards for the model-based enterprise as many see this as essential to smart manufacturing.