Business Insider predicts that by next year, manufacturers will spend approximately $267 billion on IoT technology. One of AI’s key roles in that massive investment is its ability to automate and manage the millions of micro-decisions that manufacturers have to make. This enables them to deliver high-quality goods that customers have come to expect. While AI in manufacturing is not a catch-all for every business challenge, there are many specific business functions manufacturers can easily automate for greater cost-efficiency, safety, and accuracy across the manufacturing process.
Here are five examples that show how AI-based decision support systems are automating tasks and helping alleviate the labor shortage in the manufacturing industry:
1. Equipment Maintenance
For factories and industrial operations that are expected to run 24/7, equipment downtime can be a major operational challenge. Whether it’s planned maintenance on a membrane in an industrial beverage plant or an emergency repair on a pipe in an oil well, operators need to be able to manage and react to equipment issues as fast as possible.
IoT sensors can monitor factors that affect equipment conditions across industries such as oil temperature, salinity levels, and vibration levels. IoT sensors can give operators critical insights into wear-and-tear as well as emergency issues, allowing them to shut down equipment to prevent catastrophic failure or take other appropriate actions. The speed of response is an important factor in minimizing the potential losses: unplanned downtime costs manufacturers an estimated $50 billion annually.
2. Quality Control
In an increasingly competitive market, manufacturers cannot afford to waste resources on subpar products. AI algorithms can proactively identify mistakes and abnormalities that can occur at any time along the production process.
There are business tasks that human workers will always be better suited for, but machines can be more appropriate to perform quality control tasks than manual inspectors are.
According to recent Mckinsey statistics, deep-learning-based systems can provide defect detection improvements up to 90% compared to a human inspector.
3. Supply Chain Optimization
AI’s contributions to the manufacturing sector don’t stop at the production line. Algorithms can help companies improve how they deliver their products to their consumers via predictive analytics. Better informed firms are able to shift from a reactionary model to a more profitable and predictive one.
IoT sensors can collect a myriad of data along the industrial supply chain, from transportation and energy consumption to raw material cost fluctuations to weather patterns and other market conditions that can have an impact on a company’s bottom line.
4. Time-Consuming Parts of the Design Process
While automation’s benefits can be clearly observed on the factory floor, AI also is helping streamline and optimize the design process in manufacturing. For small and incremental improvements to a product’s design, AI algorithms are able to explore millions of different tweaks and adjustments to a design to optimize its performance. Factors such as material usage and efficiency, structural strength, and weight can all be assessed and improved upon with AI algorithms.
5. Dangerous Manual Tasks
Safety is an inherent problem when workers and traditional machines are sharing the same space. While rare, accidents are always a possibility when workers are operating near powerful machines with no cognitive awareness.
Cobots are AI-powered collaborative machines designed to safely work alongside skilled laborers in industrial environments. Cobots can assume physical tasks such as heavy lifting and repetitive tasks that require a degree of fine-motor control. At an estimated cost of about $24,000 each, the machines can prove to be a useful and affordable supplement to skilled laborers on a factory floor.
While there is enormous potential in automation for the manufacturing industry, it’s not always an easy task to implement an AI project. Be on the lookout for our upcoming survey report that takes a closer look at the challenges faced by manufacturers and other business leaders when implementing AI initiatives in their businesses.
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