Manufacturing is in the midst of a significant digital transformation. Machine learning can be used to deliver the insights needed to improve product quality and production yield.
ML-based solutions can serve daily processes across the entire manufacturing cycle. Designers can expand the range of products with generative design tools, and manufacturers are able to detect many kinds of issues on their production pipeline, like bottlenecks and defects.
Rapid prototyping speeds up the process of new product development. It helps validate the model’s concept design, fit, and function in the initial phases. The prototype can provide a check against the technological parameters and the company’s goals in the final stages.
By using generative design, product designers can expand the product line in ways they never thought about, all the while meeting the required constraints, such as materials and manufacturing limitations.
It takes many steps to make a product: concept creation and design, planning and documenting the manufacturing procedure, ordering supplies, the manufacturing itself - to name a few. Some of the steps are readily automated e.g. CNC work, others require human labour e.g. making CAD design.
AI can be used to reduce human input, streamline and scale the process from the initial idea until the manufacturing of the product.
Manufacturers can gain a better understanding of customers needs by applying data analytics to obtain insights into how customers use products. These customer insights can be used to improve product designs and production processes.
There are numerous manufacturing related forms from which structured data needs to be extracted. Most businesses do this manually.
By automating the process, your company can auto-generate regular reports that are required to inform managers and ensure everyone in the company is aligned. Easily auto-generate reports, analyze their content, and email them to relevant stakeholders.
Machine learning can analyse customers' historical data in real-time so that it can respond to demand fluctuations faster. Predictive models can account for a complex web of factors including consumer buying habits, raw material availability, trade war impacts, weather-related shipping conditions, supplier issues, and unseen disruptions.
Manufacturers can better optimize the number of dispatched vehicles to local warehouses and reduce operational costs since they improve their manpower planning, warehouses can reduce the holding costs, and customers are less likely to experience stockouts.
Being able to predict defects and failures using AI can reduce unplanned downtime on the shop floor, and significantly improve product quality, throughput, and yield.
Sensors in machines create a continuous stream of data on their use and state of maintenance. Predictive maintenance uses data analysis and algorithms to predict the need of maintenance, helping prevent unnecessary downtime.
Voice control gives its users accurate access to information at any stage of the production process, through hands-free, intuitive and efficient interactions.
Workers can connect to other devices in the factory floor and issue key instructions with their voice. This will speed up internal processes, and improve overall productivity.
Customer service plays and important role in manufacturing companies since customers will contact companies for any issue they experience with product quality. Customer service chatbots are capable of handling low-to-medium call center tasks such as issuing support queries, recording complaints, requesting a delivery, amending an order, or tracking a shipment.
Chatbots are also valuable tech to analyze customer experience: by analyzing chat transcripts businesses can better understand their customers and improve the customer journey.
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