Artificial Intelligence in Manufacturing Market worth USD 16.3 billion by 2027

The key factors contributing to the growth of artificial intelligence in the manufacturing market include improving the computing power of AI chipsets and increasing venture capital investments in the manufacturing AI space. However, increasing venture capital investments in the manufacturing AI space is hindering the growth of artificial intelligence in the manufacturing market. Limited availability of a skilled workforce, especially in developing countries possesses a huge challenge to the industry. However, the growing focus on boosting the operational efficiency of manufacturing plants is the biggest opportunity in the market space with Asia Pacific having the highest market share and CAGR for the forecast period.

By using context awareness technology, a control system called Manufacturing Execution System (MES) is designed for managing and monitoring works in a factory, which is based on the dynamic and complex production process in the manufacturing plant. The context-awareness architecture establishes the dynamic interaction properties of the system more thoroughly and guides object specification and abstraction tasks in machine language. This technology provides computing environments with the ability to learn and adopt the context by sensing through different sensors and automatically provides feedback to the user. This service makes the operator attentive, responsive, and predictive. In the manufacturing plant, the production quantity varies on many uncertain factors, including internal and external factors such as random orders, sudden equipment fault, supply modification, and runtime restriction. Context-aware technology finds customized solutions and arranges the machines accordingly for the issues mentioned above. Siemens (Germany) and General Motors (US) use context awareness technology in their plants. Additionally, context-aware information distribution may offer substantial value to industries. It provides task-relevant information or services to users on a manufacturing shop floor, improving decision-making through context-driven recommendations.

Machine learning (ML) enables systems to improve their performance automatically with experience. ML helps develop a computer program/algorithm that can access data and use it to train itself without human intervention. ML is adopted to deal with large volumes of data. The time previously dedicated to analyzing charts and spreadsheets is now being utilized to seek intelligent ways to automate data analysis. ML includes various technologies, such as deep learning, supervised learning, unsupervised learning, and reinforcement learning.

Production planning is a process that combines and transforms various resources used in the production system in a manufacturing plant. The AI system executes the interrelated operational activities involved in the manufacturing processes. The deep learning-based algorithm uses many functions such as Programme Evaluation Review Technique (PERT)/Critical Path Method (CPM) to optimize the production planning under various conditions. The use of AI in production planning leads to the standardization of product and process sequence, dedicated special-purpose machines having higher production capacities and output rates, short production cycle time, perfectly balanced production lines, faster flow of materials, and easy material handling.