AI represents a paradigm shift for the factory. Today’s factory automates processes and machinery through a rules-based approach, and today’s robotics programming addresses a fixed set of scenarios. In contrast, the factory of the future will use AI support to automate processes and machinery to respond to unfamiliar or unexpected situations by making smart decisions. As a result, technical systems will be more flexible and adaptable. For example, under a rules-based approach, a robot cannot identify and select needed parts from a bin of unsorted parts, because it lacks the detailed programming necessary to cope with the myriad possible orientations of the parts. In contrast, an AI-supported robot can pick desired parts from an unsorted mass, regardless of their orientation.
Various AI use cases involve improving productivity across major areas of operations, both outside and inside the factory walls. Among survey participants, 37% rated production as the area of factory operations in which AI is the most important lever for productivity improvement, while 25% rated quality highest and 12% chose logistics. Consistent with those findings, companies regard the most important AI use cases to be self-optimizing machines, detection of quality defects, and prediction of efficiency losses. Although individual companies will find different use cases especially valuable, producers can achieve full benefits only by applying AI and integrating data pools across functions, and with suppliers and customers.
Outside the Factory. Outside the factory walls, engineering and supply chain management are the most important operational areas in which to apply AI:
Engineering. Producers can use AI to promote R&D efforts that optimize designs, improve responsiveness to customer demand and expectations, and simplify production. AI supports generative product design, in which algorithms explore all possible design solutions on the basis of defined goals and constraints. Through iterative testing and learning, AI algorithms optimize designs and suggest solutions that may appear unconventional to the human mind. Some aerospace companies are using generative design to develop aircraft parts with completely new designs, such as bionic structures that provide the same functionality as conventional designs but weigh significantly less.
Supply Chain Management. Demand forecasting is a key topic for applying AI within supply chain management. By better anticipating changes in demand, companies can efficiently adjust production programs and improve factory utilization. AI supports forecasting of customer demand by analyzing and learning from data related to product launches, media information, and weather conditions. Some companies use machine-learning algorithms to identify demand patterns by consolidating data from warehousing and enterprise resource planning (ERP) systems with customer insights.
Inside the Factory. Inside the factory walls, AI will bring various benefits to production and to such support functions as maintenance, quality, and logistics:
Production. Our study covered the full range of production environments, including continuous processes (such as those for producing chemicals and building materials) and discrete production (such as assembly tasks). In all environments, producers will use AI to reduce cost and increase speed, thereby boosting productivity. They will also use it to improve flexibility and cope with the complexity of production—for example, in manufacturing customer-specific products. AI will enable machines and units to become self-optimized systems that adjust their parameters in real time by continuously analyzing and learning from current and historical data. Already, some steel producers are using AI to enable furnaces to autonomously optimize their settings. AI analyzes the material composition of iron intake and identifies the lowest temperature for stable process conditions, thereby reducing overall energy consumption. In another important use case in production, robots enhanced with intelligent image-recognition capabilities will be able to pick up unsorted parts in undefined locations, such as from a bin or a conveyor belt. First applications are already available in, for example, the automotive industry.
Maintenance. Producers will use AI to reduce equipment breakdowns and increase asset utilization. AI supports predictive maintenance—for example, avoiding breakdowns by replacing worn parts on the basis of their actual condition. AI will continuously analyze and learn from data that machines and units (for example, sensor data and product mix) generate. This technology will especially benefit process industries, where breakdowns lead to lost sales. For example, some oil refineries have implemented machine-learning models that estimate the remaining time before equipment failures. The models consider more than 1,000 variables related to material input, material output, process parameters, and weather conditions.
Quality. Producers can use AI to help detect quality issues as early as possible. Vision systems use image-recognition technology to identify defects and deviations in product features. Because these systems can learn continuously, their performance improves over time. Automotive suppliers have started to use vision systems with machine-learning algorithms to identify parts that have quality problems, including defects not contained in the data set used to train the algorithm. AI can also continuously analyze and learn from data generated by machines and the production environment. For example, AI can compare drilling-machine settings with material properties and behavior to predict the risk that drilling will exceed tolerance levels.
Logistics. Our study focused on in-plant logistics and warehousing, rather than on logistics along the external supply chain. AI will enable autonomous movement and efficient supply of material within the plant, which is essential to managing the growing complexity that comes with making multiple product variants and customer-tailored products. Self-driving vehicles that transport items within the plant and warehouse will use AI to sense obstacles and adjust the vehicles’ course to achieve the optimal route. Producers of health care equipment have begun using self-driving vehicles in their repair centers. Without relying on guidance from magnetic strips or conveyors, the vehicles can stop if they encounter obstacles and then autonomously determine the best route. Machine learning algorithms will use logistics data—such as data on outflow and inflow of material, inventory levels, and turn rates of parts—to enable warehouses to self-optimize their operations. For example, an algorithm could recommend moving low-demand parts to more remote locations and moving high-demand parts to nearby areas for faster access.
Some AI use cases apply to more than one area of operations. For example, virtual agents that are capable of language generation and processing (similar to Apple’s Siri and Amazon’s Alexa) will give operators context-specific information that emanates from IT systems. Some companies are already using pick-by-voice systems to handle picking, packaging, receiving, and replenishment operations. In these applications, a voice system connected to the bill of material in the ERP system directs an operator to the correct bin.
AI systems will suggest solutions to incidents (such as machine breakdowns, quality deviations, and performance losses) on the basis of incident reports (for example, photographs and written reports), which they continuously analyze and learn from. Aircraft manufacturers have implemented a self-learning algorithm that uses incident reports to identify patterns in production problems, and then matches current incidents with similar past incidents and proposes solutions.
Among study participants, expectations that each of the use cases mentioned above will become very important by 2030 ranged from 81% to 88%, but belief that the capability had already been fully implemented in multiple areas of production was quite low (6% to 8%). Exhibit 2 provides an overview of the use cases that survey participants ranked as most important for the factory of the future.