# Six-Gear Roadmap towards the Smart Factory
## 5 The Six-Gear Roadmap
### Gear 5: AI (Artificial Intelligence)  
This stage is associated with the advanced data analytics and tools used to provide new answers to questions raised from the data analysis stage to add real and unexpected value to the manufacturing industry. It is based on using Big Data, predictive and perspective-based analytics and artificial intelligence-based analytics. AI is the ability of the computer to think and learn itself. Big data, intelligent algorithm and computer systems drive the AI process to enhance machines and people through digital capabilities such as perception, reasoning, learning and autonomous decision-making [88]. In the manufacturing industry, AI is seen as the brain of the Industry 4.0 transformation while other digital technologies provide the muscles to drive the transition from automation to autonomy. Key driving factors to implement AI in manufacturing includes: (i) to improve cost savings; (ii) to transform operations; (iii) to provide better customer service; and (iv) to create better workplace environment [34; 89; 90]. AI also has a potential impact on the economy leading to a stronger and improved economical prospect as a result of a more efficient, sustainable and competitive manufacturing [3]. Applications of AI and the use cases vary in manufacturing. Predictive maintenance of machines and tools is one of the key values created, which has the potential to reduce repairing costs by 12% [90]. AI-based learning algorithms spot trends in the data for early warnings and indications of possible failures and breakdown. This allows maintenance to be scheduled and intervened rather than being dependent on periodic checks, enabling a proactive behavior instead of a reactive behavior, which leads to a more reliable and sustainable production line. An example of an AI predictive maintenance application in the aerospace industry is used by the British engine manufacturers Rolls Royce. The IoT capabilities and advanced analytical tools of Rolls Royce engines in the aircrafts are capable of predicting wear and tear in the engine so that an intervened repair and maintenance awaits as the airplane lands in order to maximize the aircraft availability [91]. Another application of AI in the aircraft industry is the Airbus Skywise platform [92] empowered by machine learning algorithms to enable insights into the data, collected from various sources of the aircraft for maintenance and repair operations. They also provide insights into operational efficiencies optimize the parts usage, reduce aircraft downtime and cut down service costs. A manufacturing application of predictive maintenance is presented in the case study by [PERSON] et al. in [41] where an intelligent spindle monitoring system was developed to monitor and predict the performance of the tool spindle of a CNC machine minimizing maintenance costs and optimizing product quality. AI application in manufacturing also plays crucial role in empowering workforce with a baseline knowledge to improve process efficiency and productivity. AI based algorithms can provide workforce with real-time recommendation(s) to act upon especially for young inexperienced workforce. They can also autonomously act to address raised issues making machines to adjust themselves in order to optimize quality or energy efficiency during production operations [93]. Other use cases of AI in manufacturing include but are not limited to: (i) improvements in quality inspection using images processing and recognition techniques for process optimization and scrap reduction [34; 90]; (ii) in safety monitoring and control using AI-based self-learning models embedded within manufacturing automation systems (e.g., robots). These systems learn from prior experience and human interventions and react to unforeseen situations, resulting in taking pressure off people, reducing human error and improving workplace health and safety [34]; (iii) production scheduling using AI, combined with mathematical optimization tools to plan, schedule and optimize capacity planning [94].