Another year has passed, and if we thought that 2021 was full of drama, 2022 reached new heights. So here we are again, reflecting on 2022 and looking forward to the trends we expect to see in 2023. Now with Covid-19 behind us we are feeling optimistic, in spite of today’s challenges.
In 2022, the Aerospace composites industry faced huge challenges, with demand nearing 2019 levels, as demonstrated in the chart below. September 2022 saw domestic traffic levels back to 81% of pre-pandemic (2019) levels. Challenges from the spike in demand were exacerbated by labor shortages and growing supply chain challenges.
While 2022 didn’t introduce these challenges for the first time, supply chain struggles and talent acquisition have seen issues for some time, they were worsened by the quick post-pandemic recovery and the Russian-Ukrainian war that started in February 2022.
A demonstration of August 2022 demand for European commercial flights, compared to August 2019:
As reported by Deloitte, the aerospace and defense (A&D) industry has shown signs of a strong rebound in 2022. However, supply chain and talent issues continue to limit the industry’s growth.
To address today’s challenges, manufacturers are investing in a few key areas:
- Aircraft and engine design to improve fuel-efficiency
- Lower operating costs
- Lower and towards zero-emissions commercial aircraft for the future.
Our 2022 trends
Below we are listing key investment areas related to Industry 4.0 trends that are expected to rise in 2023, but let’s first briefly review the 2022 trends we named a year ago:
We forecast growing investment in extreme-automation for the purpose of rework minimization, throughput increase, and an overall reduction in composites manufacturing operational costs.
We discussed predictive analytics and maintenance, designed to minimize downtime costs and build more accurate production plans that can adapt to changes in demand.
We wrote about decision intelligence, helping managers make optimal decisions under pressure and overcome talent shortages on the production floor.
And we listed a few other trends as well.
Looking to the future: 2023 Aerospace composites manufacturing optimization trends
The market is expected to register fluctuating growth trends in the long term, while inflation, supply chain, and labor-shortage concerns will continue.
Today, Industry 4.0 implementations are focused on helping manufacturers meet the growing demand, increase productivity, and overcome supply chain challenges, all while dealing with workforce shortages.
A major part of this mission includes implementing new technologies to automate repetitive tasks and optimize production processes (resulting in increased quality, minimized rework, reduced material waste, increased throughput, and optimized production planning based on accurate demand forecasting).
These are the main Industry 4.0 trends we forecast to be in the spotlight for the Aerospace composites industry in 2023:
1. Working around supply chain challenges using material optimization
The Aerospace supply chain with multiple tiers of suppliers is extremely complex. According to Deloitte, an average American commercial aerospace company has more than 12,000 tier-2 suppliers (!!).
Supply chain challenges are compounded when it comes to dealing with time-sensitive materials (such as composites).
Therefore, Aerospace-parts manufacturers are already increasing their implementation of Industry 4.0 tech to optimize inventory management throughout the production floor – from optimal planning of new inventory orders (while considering production planning and today’s supply-chain delivery timelines), to maximizing inventory utilization and minimizing waste.
What does this look like in practice? One example features optimizing time-sensitive composite materials by blending AI engines with IIoT sensors connected to legacy software such as ERP or MES. The result: Production managers receive alerts and recommendations on the maximized inventory utilization in order to meet production planning requirements, e.g. which rolls to unfreeze, rolls to cut first, which composite materials are about to expire, and more.
This also includes smart cutting and kitting automated processes, significantly more sophisticated than traditional ones, to reduce material waste.
Below is an example that shows the traditional way of cutting material into different parts, with each cut separately.
Here, is the same process using smart kitting and cutting based on the actual production plan. All parts are mixed and cut from the same sheet, resulting in almost 20% material savings:
Additionally, in the coming year we will likely see an acceleration in the shift from global to local sourcing, including raw materials, in order to avoid concentration risk. Manufacturers are also likely to build stronger relationships with suppliers from countries with free trade agreements.
Lastly, Aerospace manufacturers are expected to also focus on supply chain visibility and control, as well as tighter collaboration between different parties.
To learn more about the benefits of combining AI with IIoT, check out our article on the subject: The Dramatic Impact of AI Combined with IIoT on Reducing Manufacturing Operational Costs
2. Expanding extreme automation capabilities, to maximize the utilization of existing workforces
Extreme automation (also known as hyper-automation) aims to push automation as close as possible to the hypothetical limit – 100% autonomy of the manufacturing production floor, where operators and machines are running fully automated processes. 100% seems unreachable, but the industry is working to automate more and more functions and processes.
The benefits of extreme automation are clear: from eliminating production delays and streamlining processes to reducing errors and rework to freeing the existing workforce from repetitive tasks and allowing the staff to increase throughput.
The infographic below shows data from Plataine’s customer base. It demonstrates the impact of incorporating extreme automation in terms of cost reduction and production increase:
2. Doubling down on Predictive analytics and adaptive AI – lessons learned from the pandemic
Leave automation aside for a minute, one of the most powerful AI applications for the shop floor is predictive analytics.
“Critical challenges like demand forecasting require a robust prediction system based on operation data analysis and without this, manufacturers can never plan for the future.” Source
Adaptive AI solutions aim to help manufacturers adapt quickly to changes in real-world circumstances that were previously unforeseen. They use real-time feedback to change their learning dynamically and adjust. This makes them suitable for operations where rapid changes in the external environment require an optimized response.
Related here is predictive (and hence preventive) maintenance.
George J. Newton writes for Business View Magazine, “Ninety-eight percent of businesses report that a single hour of downtime costs them over $100,000. That’s a lot of money to be wasting, which is why adopting preventative maintenance strategies is essential to keep costs down, especially today.”
According to TWJ, “Unplanned downtime costs industrial manufacturers an estimated $50 billion annually. Equipment failure is the cause of 42 percent of this unplanned downtime. Unplanned outages result in excessive maintenance, repair, and equipment replacement”
Essentially, analyzing data generated from IIoT sensors combined with other legacy systems, AI and ML algorithms can predict how likely a machine, a tool, or any other piece of equipment will need maintenance or fail. Once the likelihood surpasses a predetermined threshold, the system automatically triggers an alert in real-time, offering concrete recommendations on how to deal with the new situation (e.g. direct the work order to a different workstation, change the shift schedule, etc.).
Specific use cases in the Aerospace Composites industry:
- Defrosting (thawing) and cutting time-sensitive materials based on accurate demand predictions and real-time changes
- Scheduling ahead during the shift to fit the planned production time frame and respond to changes
- Coordinating the autoclave to maximize utilization and avoid running them half empty
4. Automated Decision Intelligence (DI) to help managers avoid human errors and increase throughput, given the labor shortage
If you want to really help the factory floor manager, don’t flood them with more data, give them the actions you recommend to take…
Think of “decision intelligence” like a responsive GPS system. If an accident occurs on the highway, it doesn’t overwhelm the driver with information on possible alternatives. It calculates the best route given the new circumstances. That is the difference between ‘data presented in reports/dashboards’ and ‘decision intelligence.’
The biggest challenge is not collecting or storing data. Instead, it’s analyzing contextual data and turning it into meaningful insights that improve business operations. That’s the real role of AI.
Similar to predictions, decision intelligence (DI) is built on historical learning and pattern identification. These analytics supplant the impulsive and biased decisions that people make.
Given the industry faces a major shortage of experienced employees, digital assistance that includes decision intelligence and active recommendations ensure that new employees can successfully manage production tasks at scale. We all know new workers typically require massive training processes and may make painful mistakes during the learning curve. Decision intelligence can flatten the learning curve and considerably reduce the time it takes to onboard new staff.
In today’s hectic manufacturing landscape, full of unpredictable environmental changes and fluctuating demands, making the right decisions is becoming too difficult for the human mind without technological assistance. This is especially true when decisions need to be made at scale in an instant. A lesson learned from the pandemic is the industry should deepen investments in solutions that offer intelligence layers on top of analytical ones.
5. Accelerating factory floor visibility and nearing 100% traceability
Visibility helps manufacturing businesses identify inefficiencies in order to reduce operational costs and compensate for Covid-19 losses.
Manufacturing aerospace composites poses unique visibility challenges. Starting with regulations that ensure the highest level of safety and the highest quality standards. Since many materials are time-sensitive, we need to track and manage them as they move on the factory floor from one station to another, from receiving to the freezer to the autoclave and beyond.
Additionally, it’s crucial to know how much inventory is on hand and to predict when to order more materials or if we will be left with expired composites.
Manufacturers that track time-sensitive material the old-fashioned way (using paper or Excel) miss out on real-time visibility. This results in material loss, quality defects, and rework.
That’s why the industry is moving towards using sensors in an IIoT network alongside AI to accurately monitor inventory and make granular calculations. Traceability and visibility are fundamental to reducing operational costs and increasing quality.
6. Investing in sustainability to meet new standards and further reduce costs
With climate change rightfully driving greater policy and regulatory attention, as well as growing activism, Aerospace Composites manufacturers need to continue investing in sustainability throughout 2023.
New regulations and public opinion are forcing the industry to respond more proactively than ever before.
Thinking about time-sensitive materials, and sustainability depending on the entire production process, progress requires optimization actions that span way beyond the type of materials used.
Industry 4.0 tech plays a key role here as well. The mix between data analysis and predictive capabilities helps manufacturers diminish energy loads and reduce material waste – key elements of sustainability. It includes maximizing material use and minimizing waste; increasing quality to lower defects and rework that results in increased energy consumption; optimizing the layout and curing to save energy; supporting recycling of expired materials and more.
For instance, being able to predict the Autoclave utilization rate in each curing cycle, identifying parts that share the same curing recipe, and coordinating (or scheduling) the layup operations and material cutting in accordance. This results in maximizing both the Autoclave energy and throughput while minimizing waste.
The research quoted above shows Industry 4.0 digitization enables 7%–10% throughput improvement, 35%–45% reduction in engineering hours/unit, 25%–40% reduction in labor, and 15%–20% improvement in asset efficiency. With numbers like that it is easy to see why it is expected to exponentially increase in 2023.
In 2023 The Aerospace manufacturing industry will continue to increase its investment in solutions that add advanced capabilities on top of existing legacy systems such as MES or ERP.
Such solutions leverage IIoT and AI engines to produce close to 100% traceability and visibility, automate composite material cutting, improve scheduling decisions, inventory management decisions, and more. Automated alerts and recommendations will be essential to help production managers deal with the amount of data generated and how it should be considered as part of the decision-making processes.
To keep up with the competition and to recover from the pandemic financial losses, manufacturers will have to onboard tech that can significantly improve their production efficiency, cut operational costs, enable extreme automation, and better utilize their inventory and workforce. This will ensure companies come out of this unbelievable crisis stronger than before.
While we cannot accurately predict the future, we can see what way the wind is blowing and adjust our sails accordingly. As it currently looks, we have good reason to be hopeful.