How to solve the AI of manufacturing enterprises（artificial intelligence）

In the past ten years, the design, development and implementation of artificial intelligence has expanded to many fields. Manufacturing companies are committed to understanding the commercial potential of AI and finding suitable AI talents.
　　 More and more countries are beginning to recognize the opportunities brought by artificial intelligence and start to formulate national-level artificial intelligence strategies. Finland launched the artificial intelligence program in 2017 and was one of the first countries to launch the program. The AI ​​plan identified a small number of companies as pioneers in the implementation of artificial intelligence; most companies are in the early stages of using data and artificial intelligence in their operations.Addressing the AI ​​skills gap
　　 One way to solve the AI ​​skills gap is to increase resources for education in numbers, mathematics, and technology. Take Finland as an example. The current education system does not pay enough attention to the application of AI in different fields. Academic and training programs cannot keep up with the rapid pace of AI innovation. AI education should start as early as possible and carry out this education at every stage. Officials from academia, companies, and the public sector must work together to ensure that comprehensive AI courses are provided. Large-scale online open courses (MOOC) provide a new path to provide basic AI education for the public, which is a very good method. However, a deeper understanding usually requires tailored educational modules.

Compared with many other industries, the manufacturing industry currently lags behind in AI and machine learning (ML) applications. Adopting new technologies, especially in the process industry, requires lengthy planning, which is time consuming. Manufacturing companies have a long history of optimizing production, and the investment life cycle may last for decades, so changes cannot be made quickly. In addition, safety and environmental regulations also require strict supervision.
According to the industry forecast of the PwC AI Impact Index, by 2023, the operating profit margin of some industry sectors (the percentage of how much money is left in each euro after deducting the cost of goods and operating expenses) may increase by 60% to 100 %. There may be differences in the "AI promotion curve" of different industries, which are mainly affected by two factors: 1) the speed at which the industry adopts different AI applications; 2) the development of AI solutions that can solve specific industry problems.

Benefits and challenges of AI manufacturing
　　In the manufacturing industry, short-term gains are expected to come mainly from process automation and productivity-based solutions. The medium-term benefits come from the huge potential of intelligent automation, which can automate more complex processes

The productivity increase brought by AI and ML depends not only on the introduction of the technology itself. There is also a need to change the organization of work and expand the knowledge of employees.
　　Research shows that the biggest obstacle to the adoption of AI and ML is the skill gap. Most of the time, the survey will point to the technologies needed to develop AI and ML solutions. However, the biggest skills gap between AI and ML is spread across the entire organization.
　　 The final report of the Finnish AI Program pointed out that Finland provides high-quality education for those who aim to become AI professionals (information technology, mathematics), but there are gaps in AI applications. In these areas, the effect of AI will be the fastest. The working group stated that to achieve the ambitious AI goals, the most important thing is to ensure the provision of diversified education, to invest in new education methods, and to develop new talent attraction plans.
　　 Continuous education for employees is a challenge. Different operations and mechanisms can solve these problems. A key factor is to increase managers’ awareness and understanding of AI opportunities to ensure that there is sufficient investment in new and more flexible educational methods.

Requirements for employee AI skills
　　 Employee ability requirements are affected by changes in job demand in the job market. In the task of developing and applying AI, the demand for new talents is growing rapidly. Ordinary educational approaches cannot solve this demand. New operating methods and mechanisms are needed to help effectively improve the AI ​​skills of existing employees.
　　 Generally speaking, most of the abilities of employees are acquired through on-the-job learning, so companies have more responsibility for the development of employees’ abilities. Companies should actively seek opportunities to educate and train employees internally or in cooperation with other organizations.
　　 There are many educational methods, but there are few on-site learning in the industry 4.0 environment. Companies need appropriate performance evaluation strategies and employee training, as well as self-regulation, reflection, collaboration, and blended learning to reduce the risk of excluding employees from the Industry 4.0 environment. Enterprises without proper training will affect their production efficiency, product diversity and quality.

Companies need to equip existing professionals with AI skills to use their knowledge in an AI-driven environment. A 2018 study on "future working environment" and "learning family" supports this argument. The study pointed out that training employees in AI and ML skills may be an effective way to fill the skills gap.
　　 The success of employee training will depend on their flexibility, problem-solving ability and willingness to participate in lifelong learning; otherwise, employees may not be able to keep up with the changing needs of the workplace and work procedures. This challenge can also explain why many companies are reluctant to invest in cyber-physical systems (CPS) that usually include AI. Enterprise-level skills management and public education reform are important factors for the introduction of CPS.