Machine learning is a path to the future of manufacturing. It is evidenced by the buzz from Silicon Valley techies on machine-as-a-service (MaaS) applications. Large global companies are investing in machine learning technologies to improve all aspects of manufacturing. These include such powerhouses as GE and Siemens. For these companies, it is all about dedicating themselves to reduce labor costs. In addition, it helps to reduce product defects, and unscheduled downtimes, while improving production speeds and workflows.
According to TrendForce, the global AI-driven manufacturing market will exceed $200 billion in 2019. It will escalate to $320 billion in 2020 – a growth rate of 60%. The number of industrial robots operating fully or semi-autonomously will grow to 2.6 million, compared to 1.6 million in 2015. All the signs point to a huge transformation that is sweeping the manufacturing sector.
Machine Learning – Useful Applications
To turn machine learning (ML) into useful applications requires skilled engineers. They must possess a broad-based experience in designing, building and activating sophisticated, high-tech products. Moreover, they must be able to combine this with sufficient knowledge of machine learning technologies and software. The good news is that cutting edge ML technology is readily available from numerous suppliers. From a practical perspective, it is often sufficiently advanced for most applications. Although this is presently not a matter of reinventing the wheel, you still need creative engineers who can use machine learning. They then must be able to design useful applications, preferably with autonomous functionality.
Machine Intelligence and Data
Machine learning is dependent on rich and plentiful data on which these AI-driven machines can feed (learn). In order to work properly, the problems and objectives must be clearly defined to allow the machine to rank, rate, categorize, and predict the right outcomes.
Autonomous machines can be used to detect anomalies and find patterns that lead to breakdowns. These smart machines use past events for correlation to find automatic solutions. They are now heavily utilized in workflow routing based on established patterns. Moreover, they are very efficient in quickly assessing risks, and assigning responsibilities. Also, they can set performance goals and standards, and provide metrics on achieving pre-set goals.
Machine Learning and Cloud Services
Machine learning and AI can be set up as add-ons on cloud services. Also, they can be customized to comply with specific needs. The key to customization is to use individualized data from the customers’ real-time experiences. The data can then be seamlessly expedited to a training engine. As a result, it can then construct a model based on specific experiences.
As defined by deployed assets, employee skills and level of integration in the business process, usage was 42% in the USA. According to ServiceNow, the market is growing rapidly. No company, small or large, can be in the cutting-edge of technology without getting on this bandwagon.
Inadequacy of the Old Models
Unfortunately, many industries are still sticking to their old models and are failing to recognize that they need to upgrade. This involves AI-driven, autonomous robotics, which provide constant improvements in performance. Avoidance hinders efficiency and has serious repercussions on costs for their clients. It is crucial to make appropriate changes to organizational structures to incorporate machine learning.
There are barriers that have to be overcome for successful machine learning integration. These include poor data quality, outdated processes and regulatory red tape. Also, the lack of funding for the new technology and the recruitment of skilled personnel. The companies that can solve these problems quickly become leaders in their particular industries.
There is no doubt that machine learning has a positive effect on the work environment. It provides the means for organizations to reach new heights of productivity and rapid growth.
Follow the Leaders
Many small businesses are following in the footsteps of major companies. They develop and use machine learning tools to design and manufacture their proprietary products as autonomous MaaS systems. This is a trend that has taken the world by storm. In order to compete, small businesses must become keenly aware of advancements in machine learning. In response, they must adopt the new technology and incorporate it into their businesses.