# Artificial Intelligence Software Adoption in Manufacturing Companies
## 2 Literature Review  
### AI in Manufacturing  
While research interest in the use of AI in business is growing, the use of AI in manufacturing is still underrepresented [8]. The existing literature mainly describes task-specific AI implementation in manufacturing companies, often used for quality assurance (quality control) [11], predictive maintenance [12], energy consumption prediction, supply chain management, process optimization, process and product design, and simulation and experimentation [13]. For the purposes of quality control, AI has shown significant advantages in defect detection. One such case is the manufacture of heat exchangers for motor vehicles, where many different parameters affect the tightness of the joints and the entire assembly, which in turn affects the efficiency of the heat exchanger and consequently the failure of the engine. Artificial neural networks have been shown to provide an effective solution with an accuracy of almost 99% in fault detection under real-world conditions [14]. In the context of production equipment and machinery maintenance, predictive maintenance directly contributes to reducing costs due to machine downtime by accurately predicting the remaining lifetime of key components and reducing the frequency and duration of unplanned downtime [15]. While maintenance is important to ensure the longevity of current machinery and equipment, well-organized and scheduled maintenance activities can increase both plant capacity and overall productivity--in some cases by as much as 60% [16]. In addition to well-organized maintenance activities, it is also important for companies to manage their energy consumption and requirements. Recent global events and the goal of achieving net zero emissions worldwide are forcing companies to look for solutions to their energy needs, and AI offers advantages to address these issues [17]. As has already been shown, energy consumption can be improved either by implementing AI to optimize parameter settings [18], or by optimizing material flow to reduce energy consumption, cost and production time per part [19]. When considering material and material flow, it is also crucial to manage sufficient inventory levels, which can depend on various factors in the supply chain. This can either be due to a change in market demand or lead times for certain products and components. By using AI, the entire supply chain can be optimized and improved [20; 21]. As already mentioned, AI can be used to optimize production processes by changing production parameters, but also without changing them. While it is possible to determine the optimal production parameters for specific products, this could lead to unintended consequences or produce results that are not reproducible. If the production processes need to be improved without changing the parameters, this can be done by identifying the causes of defects or failures, predicting production outcomes early to avoid unnecessary actions or adjustments, and diagnosing certain unexpected behaviors in processes to detect problems before they affect the final product [22]. Some production problems can already be avoided in the design phase of the product. Here, AI can help with conceptual design (deriving the product concept based on needs), embodiment design (confirmation of the primary functions) and even detailed design (full, detailed specification of the best design) [23]. In the conceptual phase, AI can help to properly translate the market or customer needs through online reviews [24; 25] or offer alternative concept design options for further review [26]. In the later stages of design, AI can help in selecting the optimal material based on the required properties [27] or by providing the optimal CAD model based on the design requirements [28; 29]. Various AI techniques, such as deep learning, neural networks, expert systems, machine learning and fuzzy logic, are slowly being adopted by small and medium-sized manufacturing companies [30].