Advanced manufacturers have enjoyed a post-COVID bounce in orders as the world attempts to return to normal, especially in the aerospace industry. Airbus raised its 20-year delivery forecast, for example, and overall commercial aircraft orders are now at their highest level since 2015, according to industry reports.
It’s not just aerospace. Many manufacturing sectors—from automotive to industrial robotics—are experiencing skyrocketing demand. What has changed in the post-COVID world is the ability to produce products. Problems include severe supply chain disruptions and an ongoing labor shortage. For example, in July 2020, Airforce Technology magazine reported that the aerospace industry had about 50,000 unfilled jobs globally, with technology position outflows twice as large as inflows.
To address these challenges and quickly ramp up production, factories must adopt a determined strategy to immediately introduce smart software to automate and optimize their factories. These AI-based “digital assistants” can maximize workforce and machine utilization, as well as streamline operations while lowering production costs. Automation technology, including software, will be a particularly strong antidote to the labor shortage.
Such software solutions are driven by a powerful combination of advanced AI-based solutions and Industrial Internet of Things (IIoT) capabilities, which allow forward-thinking manufacturers to create smart, hyper-automated and highly optimized production lines.
The IIoT has been discussed at great length, but it is only one part of the factory automation software story related to the collection of valuable factory floor data. The more important question is what you do with that data. The best manufacturing optimization software solutions have three distinct but interconnected operations:
The IIoT is about connectivity. It means connecting things such as raw materials, components and machinery in a factory through the deployment of smart sensors.
These are now ubiquitous and cheap, yet highly advanced. They collect and transmit vast amounts of data, operating constantly in real time.
Deploying a network of smart sensors across every element of the production line is a crucial first step on the manufacturing optimization journey. Factories should collect data to understand the status and progress of each manufacturing step along the process.
Using the IIoT to collect and store vast amounts of data in real time is a challenge, but an even bigger challenge is figuring out what to do with all the data. Vast collections of data are too complex for humans to usefully analyze by themselves, but with advanced AI capabilities it’s possible to rapidly generate detailed analyses, resulting in a huge range of predictions, as well as actionable insights and recommendations.
The optimize and automate stage is the point where factory managers implement improvements based on data analysis. AI-based digital assistants will optimize a production line, ensuring maximum production efficiency and reducing waste by using raw material, tools and resources in a highly efficient manner. Digital assistants bring automation and prevent errors that lead to delays or quality issues resulting in rework and scrap.
These tools also bring flexibility because they can respond almost immediately to unexpected events on the factory floor. If an autoclave goes down, a digital assistant can instantly recreate the most optimal production plan, shifting work onto any other autoclave and allowing planners and managers to reschedule manufacturing tasks.
Another important part of the optimize and automate stage is the digital thread, which is a comprehensive, searchable record of the production process, from raw material to end product. The digital thread brings major benefits to any manufacturing operation. By delivering a holistic view of an asset across its entire lifecycle, it allows high levels of quality control.
If a raw material batch used in manufacturing is later found to be faulty, for instance, the digital thread can allow a manufacturer to isolate only those kits or products that were impacted, eliminating the need for a larger quality incident or wider product recalls. It also ensures that highly regulated industries—such as aerospace manufacturing, where there is effectively a “zero tolerance for errors” mentality—are always audit ready.
By using this data-driven approach, manufacturers can make informed decisions, streamline workflows, and proactively address potential bottlenecks or quality issues. Plataine’s technology empowers aerospace and defense manufacturers to achieve increased throughput, ensuring that resources are utilized efficiently and cost effectively. As a result, production timelines are shortened, overall productivity is significantly enhanced and waste is minimized, leading to a more sustainable and competitive industry landscape.
With more than 90 years of experience designing and producing aircraft and aerostructures, Middle River Aerostructure Systems (MRAS) provides products and support to engine makers, airplane manufacturers and aircraft operators. MRAS implemented Plataine’s AI-based manufacturing system, helping the company optimize its operation processes, including inventory management and the monitoring of time-sensitive materials.
As a result, all the data regarding each material item is automatically logged, enabling full visibility and traceability into the production process, including each asset’s location, expiration date, ETL and length remaining. This enables streamlined job allocation, while the pick lists ensure that composite materials are used within their shelf life, and material waste and costs are minimized.
MRAS experienced the following benefits:
AI and the IIoT will create a new normal in manufacturing as higher levels of efficiency become standard. Meanwhile, manufacturing optimization software can go a long way to alleviating problems caused by the labor shortage and the supply chain crisis. For all these reasons, the best time to implement an AI-based manufacturing optimization solution is now.
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