Artificial intelligence (AI) applications, ranging from virtual assistants to advanced robotics, are on the verge of disrupting end-to-end value chains in manufacturing as demand shifts dramatically.
Due to the magnitude of change, many manufacturers will be forced to adopt new plant designs, reshape their manufacturing footprints, and devise new supply chain models. AI advancements will allow the industry to take advantage of the rapid growth in data volume to optimize processes in real-time. They can reduce inventory costs with better supply and demand planning, improve engineering efficiency, prevent faults, increase safety by automating risky activities, and increase revenue with better sales lead identification and price optimization.
On the other hand, many businesses are unprepared for the future because they lack visibility into the nature of changes such as new plant models, the implications for how they do business, and what they’ll need to manage the transition to a more collaborative working model.
Intelligent manufacturing is a “smart” approach to manufacturing in which machines connected via the internet assemble parts and adapt to new processes with minimal human intervention. Artificial intelligence has become a top priority for governments and corporations worldwide as they prepare for a reorganization of global industrial value chains driven by AI. This post explains how artificial intelligence could transform manufacturing.
Engineering and R&D: Quicker turnaround and fewer iterations
Engineers and researchers are currently confronted with numerous challenges, ranging from rapid growth in demand in emerging markets to market fragmentation caused by consumers’ desire for customization. At the same time, budget constraints force engineering teams to increase their productivity and efficiency, even as the number of designs that can be considered and optimized against process capabilities is limited, limiting product performance predictability. By eliminating waste in the design process, AI-powered technologies can help deliver more efficient designs than previously possible. AI enables shorter process cycle times and a greater focus on real-time negotiations and other interactions, allowing faster innovation to reach the market.
Predictive analytics with machine learning is a powerful tool for semiconductor manufacturers to reduce their time to solve design problems. Manufacturers can use machine learning to consolidate large amounts of data, improving the “design for manufacturability” process from start to finish. When making trade-offs, such as balancing safety and cost weighting, AI will also enforce value-based decisions more explicitly. Manufacturers can use AI to integrate production and customer feedback in real-time to improve product design. AI-based tools could help suppliers be more accountable throughout the supply chain. Meanwhile, deep learning and network theory will aid engineering development teams in real-time optimization of their composition, working methods, and key performance indicators.
Better grip on costs and supplies
Keeping manufacturers well-supplied with parts is a difficult task. Hundreds of thousands of different parts must be sourced from tens of thousands of suppliers worldwide. AI technologies can provide transparency on supplier machine availability, performance, and downtime when manufacturers’ systems are digitally linked with their suppliers’ systems. They can also assist in balancing the supply chain and real-time inventory optimization.
In the future, machine learning algorithms will automate and optimize supplier research and analysis; e-auctions will be supplemented with virtual agents, which will automatically program in-person interactions when needed. According to estimates, full automation could result in a 39 percent reduction in IT staff.
Improvements in the effectiveness of review processes
Program reviews can sometimes miss emerging issues or fail to prioritize critical decisions and tasks, resulting in millions of dollars in delays. The process is hampered by faulty communications between the marketing and manufacturing functions, mostly manual.
Manufacturers can use deep learning to optimize the key performance indicators of program reviews in real-time. Deep learning networks are already being used to make real-time predictions, but they are trained using historical data in batches (not updated with real-time data streams). Tailoring a model in real-time necessitates using deep learning on real-time data. This will enable better forecasting, detection, and prevention of material and staffing bottlenecks and energy consumption optimization. Virtual agents could notify program leadership and team members when problems arise and suggest solutions. Natural language development will be a game-changer because it will allow virtual agents to interact with team members and assist them in solving problems.
Ability to cut costs, reduce waste, and speed time to market
Inefficiencies in manufacturing and assembly cost businesses billions of dollars each year. Existing fault detection and classification tools can be wildly inaccurate, resulting in costly and unnecessary assembly line disruptions. An aerospace company used artificial intelligence to solve these issues and saved €350 million. Using advanced analytics to review data from every assembly process step and then rewriting standard operating procedures based on the results yielded nearly 60% of the savings. The rest came from reducing warehouse costs and inventory levels by utilizing machine learning algorithms, collaborative robots, and self-driving vehicles.
Factory managers can use deep learning to update and improve the accuracy of standard-operating-procedure predictions, especially during ramp-up, while also keeping an eye on component availability and risk management. AI tools can also improve asset reliability, with machine learning improving the predictive accuracy of defaults or production interruptions.
Virtual agents will deliver instructions and information on tablets or other interactive personal communications devices to reduce assembly errors and flatten the learning curve for new operators. Assembly lines will be heavily automated and optimized, with control and scheduling linked directly to real-time dispatching systems.
Improving the accuracy of forecasting
Improving the accuracy of MRO work forecasting and concentrating sales efforts on the most promising leads can significantly impact a company’s EBIT. One company in the aerospace industry, for example, reported a profit increase of around €300 million as a result of using machine learning to forecast ten years of repair events for over 17,000 commercial aircraft and develop a deal scoring tool to advise on “what good looks like” when pricing MRO work. Advanced analytics and artificial intelligence (AI) tools also optimize processes around unplanned maintenance events, allowing the manufacturer to respond to disruptions more effectively and increase uptime.
Predictive analytics will be shifted to cognitive assessments thanks to AI tools. Manufacturers’ sales and services will be automatically optimized as the algorithms discover new rules. Machine learning could optimize global supply networks for spare parts to proactively stock critical and non-critical parts, limiting aircraft downtime and lowering inventory costs. Virtual agents and deep learning technology will improve the training provided to maintenance operators and pilots, and machine learning could optimize global supply networks for spare parts to proactively stock critical and non-critical parts, limiting aircraft downtime and lowering inventory costs.