How 1 Amazing AI/ML Strategy Can Propel Aerospace Manufacturing

At what stage would you expect the aviation industry to ensure 100% safety and dependability of every airplane flying out? Not at the tarmac! The assurance must start at the manufacturing level itself. Undoubtedly, it is a mission-critical industry, heavily relying on accurate Quality Gate checks before the aircraft roll out.

The “2022 Aerospace Industry Outlook” published by Deloitte reported the current macroeconomic trends, indicating the demand for small and medium-sized aircraft is growing again. Consequently, aircraft manufacturers need to shift toward digital and operational efficiencies that could accelerate time to market and reduce cycle times. [1]

The Importance of a Scalable and Robust AI/ML Strategy

What company would want to risk losing its competitive thrust? A global leader in the aerospace manufacturing sector was struggling with a terrible lag in its aircraft delivery rate.

Multiple factories were involved in the production of the vehicle structure. The aircraft assembly utilized 100+ workstations to handle 3,000,000+ parts. The final assembly line was also a complex system of a series of workstations designated to complete crucial tasks.

Quality Gates that were established after every few stations to assess the critical parameters detected several internal time and quality perturbations such as the late start of assembly, quality deviations need for reworks, etc. Also, there were external perturbations such as delays in supplier deliverables, crucial deliverables with open work items, etc. An early warning alerts system was also completely missing, which worsened the situation further.

Aside from the lack of visibility on the upstream processes and the pending work in progress (WIP), the failure to pass through quality gate checks affected their output performance.

Consider the enormous impact on business due to such anomalies in the production process!

Yes, AI/ML can give aerospace manufacturing wings!

Our Bodhee® Predictive Quality AI App facilitated integrated data analytics, providing foresight on bottlenecks, predicted success rate at quality gates, and identified failure patterns in critical downstream parameters. Predictive alerts and accurate recommendations for process correction were given by our AI app when the aircraft were only in the initial stages of assembly.

Our revolutionary and much-acclaimed Digital Twin technology enabled the accurate simulation of the entire complex flight engineering and intricate manufacturing processes. The virtual representation of the physical world provided the necessary levels of visibility for precision manufacturing.

The user-friendly and interactive real-time data visualization made the generated production models easily interpretable by production planners. End-to-end visibility of the manufacturing of flight vehicles allows AI-powered data analytics to provide actionable insights, which can minimize the scope for errors in the production line to slip through the Quality Gates.

The advanced ML algorithms utilized the training data and the LIVE production data to provide actionable insights for the operators to make informed timely decisions, which improved process control.

The value derived within just 8-12 weeks of having incorporated our Bodhee® Predictive Quality AI App and ML in their production process proved to be transformative, to say the least. The work closure rate improved by 20%, and there was a commendable quality improvement of 15%. [2]

That was just a high-level retelling of our data-intensive, scalable, and robust AI/ ML technique, which catalyzed the streamlining of their manufacturing, lifting it out of the doldrums!


[1] Deloitte. (2021). 2022 Aerospace and Defense Industry Outlook. Deloitte United States.

[2] Neewee. (n.d.). Case studies Archive – Neewee. Neewee.

Brunton, S. L., Nathan Kutz, J., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A. W., Hogan, T., & McDonald, D. (2021). Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning. AIAA Journal, 1–26.

Top 5 AI Predictions for Manufacturing in 2022 (And How to Prepare Today)

The year that has gone by—2021 was an eye-opener at many levels. Both process industries and discrete manufacturers have woken up to the crucial benefits of AI implementation. It is now a proven fact that leveraging Big Data and AI can facilitate the detection of patterns, accurate interpretations, and real-time communication of actionable insights to catalyze efficient workflows, faster decision-making, and optimized production.  

In 2022, Big Data and AI apps will undoubtedly impact how manufacturers do business, but what impressive strides will the powerful technologies take in the upcoming New Year? 

Top 5 AI Predictions for Manufacturing from experts: 

  1. Monetization of manufacturing data will be at the crux of agile manufacturing. 

Organizations that maximize their investments in data now will be the early birds to catch the business benefits and rule the roost ahead of their competitors. Stronger data teams and AI-enabled integrated data analytics will streamline processes for optimal efficiency in operations.   

Aside from that, data-drivennetworking between organizations will become the norm as AI apps will enable safe interoperability and data exchange. Ethical acquisition and exploitation of data, not only within but also across organizations, will establish data alliances like never before.  

The monetization of manufacturing data is the process that provides a measurable economic advantage by using (aggregated and transformed) manufacturing data to exploit data-driven knowledge spillover effects. AI apps can help shed inhibitions around data privacy. Strategies for monetizing manufacturing data can be manifold, offering an added economic value of more than $100 billion is predicted. [1] 

  1. A more holistic approach by factories toward AI adoption will prove to be a veritable game-changer. 

Factories dipping their toes and testing the waters will take the plunge. Instead of working in phases with incorporating AI applications in their processes, the push will be toward a system-wide change. Realized intelligent factories using integrated data from all the assets—operational and human—in the manufacturing network will become prevalent features. The gap between AI leaders and the companies following in step will get mitigated with accelerated digitalization. In fact, we can expect organizations to work with a blueprint, clearly prioritizing AI initiatives for business value and definitive manufacturing goals. 

  1. Green AI adoption for sustainability initiatives will be the mark of a responsible manufacturer. 

With the help of AI, companies will make a conscious effort of switching to environmentally sustainable manufacturing. The shift from a manufacturer-centric approach to eco-centric decision making will turn the industry on its head, allowing more responsible players to get into the front row.  

AI applications can offer tremendous new possibilities to help manufacturers adopt green manufacturing methods and reduce their carbon footprint. AI can optimize and ensure responsible energy consumption in manufacturing facilities and reduce wastage of natural resources. Integrating data analytics and AI in the manufacturing processes can facilitate the designing of products for sustainability by taking into account the environmental impact at every step of the product lifecycle.  

  1. Reinforcement learning will bring a radical transformation to the manufacturing scenario. 

Reinforcement learning (RL), an unsupervised machine learning algorithm, will deliver a transformative advancement in 2022.  

AI applications will get more intelligent with the simulation-based dynamic programming method. RL algorithms converge toward optimal process control strategies with data-driven solutions to problems in the production domains, enabling an autonomous manufacturing system. The key will be having suitable data sets to train algorithms, which requires a culture of data sharing within companies. Then machine learning can step out of the lab, where the training is slow and expensive, and get into production environments, where things get real. 

As it trains on the shop floor, instead of working with pre-programmed training modules, the complex real-time evaluation helps the AI applications to determine which actions are suitable in the long term. Reinforcement learning can thus bring a turning point in the world of manufacturing.  

Read about RL in detail HERE

  1. The Internet of Behavior (IoB) will enhance customer-centricity 

AI applications can help organizations collect and analyze behavioral data of employees engaged on the shop floor. The collection and usage of that data to influence future behavior is called the Internet of Behavior (IoB). As organizations improve the quality of data captured, they can also combine data from various sources and leverage IoB to improve the process workflow or influence interaction with customers, understanding their needs, overcoming challenges, and deriving customer-specific solutions. 

As per Gartner, IoB will tightly link customer experience and employee experience to transform the business outcome. It can help differentiate a business from competitors, creating a sustainable advantage. This trend enables organizations to capitalize on COVID-19 disruptors, including remote work, mobile, virtual, and distributed customers. [2] 

That is how we see things shaping up!  

As the world transitions from the pandemic to a state of normality, 36 % of manufacturers say they are currently engaged in AI projects, and 23 % more are planning to use AI in the coming months to unlock $13 trillion in value that industry experts anticipate from industrial sectors.  Manufacturers learned a lot during the pandemic and are now looking for the best way to become more productive, safer, and more agile by leveraging the petabytes of data insights harvested from connected factories. [3] 

We hope this article gives you a great insight into where the industry is heading in 2022. 


[1] Trauth, D. (2020, July 27). Monetization of manufacturing data. Senseering. 

[2] Panetta, K. (2020, October 19). Gartner Top Strategic Technology Trends for 2021. 

[3] IOT World Today. (2021, November 9). AI led Digital Transformation of Manufacturing: Time is NOW. IoT World Today. 

The Lazy Manufacturer’s Way of De-risking Christmas.

As the year-end approaches, we can imagine, there is no place busier than Santa’s Workshop at the North Pole. Under the diligent guidance of our dear old Santa Claus, his legendary team of efficient elves make marvelous toys and presents for all the children around the globe.  

In contrast to that singular legendary workshop, some lazy manufacturers put off production until just a couple of months before Christmas when they see they really need to get to it. And before you laugh it off, allow us to light some candles for you. Lazy Initialization can prove to be a SMART performance optimization strategy. Pay close attention to how you could avoid WIP or wastage, improve throughput, and still manage to meet the increasing demand during the festive season. 

You can easily sense there is overall fatigue from the pandemic and the crowds of people are out on a buying spree to de-stress. These are clear indicators of the increased demand this winter for product manufacturers. No manufacturer would want to miss the peak sales period! However, you would think the lazy manufacturer who has started his production a little later in the day runs the risk of not delivering in time for the Christmas fervor.  

Well, that notion may be true. To meet consumer demands, reducing production time and increased efficiency in inventory management becomes crucial. Stock-out due to underestimation, or the delay in raw material procurement, could disturb the product assembly and prevent the fulfillment of customers’ orders. That could, in fact, translate into a manufacturing disaster. Artificial Intelligence (AI) Applications have emerged as the solutions to such tricky situations.  

“Any sufficiently advanced technology is indistinguishable from magic.” 

– Arthur C. Clarke 

Like the elves on the shelves and their Elfinmagic, AI apps can also control the production processes. One of the mythical spells is called Elementumkinesis —the power to manipulate all elements. Our real-world AI solutionsdo not have such tricky names.

We simply call it the Bodhee® Integrated Micro Scheduling AI App. It improves process control by synchronized and LIVE end-to-end production scheduling. It gives a direct business benefit of optimized manufacturing with improved capacity utilization of 5-7%.  

Together with Machine Learning algorithms, our AI app tracks and prevents unexpected delays or unforeseen stoppages in work with high accuracy. Thus, it considerably reduces the risk of impact on your production schedules and improves work order closure rate by 15-20%.   

Our Reinforcement Learning algorithms help streamline your manufacturing with data-driven optimum inventory management. It catalyzes line efficiency and labor productivity by preventing downstream bottlenecks and thus helps prevention of delays in batch cycle-time by 3-5%.  

To wrap things up…. 

 Year-end festivities and Christmas present a great opportunity to leverage AI applications for handling demand and ensuring business growth. The adoption of AI in manufacturing allows companies to optimize production and efficiency. Quicker production can increase sales and customer satisfaction.  

In the past, Christmas was a time where businesses saw a lull in production and had to lay off employees. Now, Christmas can be a time for joy and celebration for manufacturers, too! With the implementation of AI, a manufacturing system can increase production, in tandem with the growing demand, without hiring more staff or investing in new equipment. Some also successfully bag their annual manufacturing goals and golden ROI during the peak period.  

Much like children, adults also revel in the joy of Christmas through gifting. Intelligent, agile manufacturing can enhance the spirit and be the greatest gift to all shoppers during Christmas. 


Foglia, E. (2018, November 8). “Any sufficiently advanced technology is indistinguishable from magic.” CCCB LAB.