Boeing’s AI looking in the right places?

Components of AI Aviation Safety JDA Aviation Technology Solutions

ARTIFICIAL INTELLIGENCE in aviation has benefitted from the availability of massive data bases and MOST IMPORTANTLY the power of today’s meta computers to Analyze All Of The Information, To Identify Reliable Trend Lines And To Help The Safety Experts Design Measures That Are Likely To Lessen The Likelihood Of Reoccurrences!!!

Below is an excellent article that both touts the Boeing initiative to use AI “to PREDICT TO PREVENT” and then the author makes a convincing case that their efforts have “seemingly fallen short, culminating in a catastrophic year for Boeing in 2024.”

This report lays out the company’s millions invested in AI and outlining the four major campaigns that the Boeing’s Vishwa Uddanwadiker named in 2022 as Chief Aerospace Safety Officer[1] (CASO) and more recently moved(promoted)  to Chief AI Officer[2]. This history does not include an explanation for the disappointing results.

Without greater access to the inner working the SMS driven internal processes, one has to postulate why this dissonance between expectations and performance. The Boeing Press Release on Predict to prevent: Aerospace safety analytics where he lists his team’s accomplishments:

  • A deep study of LANDING PERFORMANCE CALCULATIONS and runway overruns
  • A closer look at system engineering models that describe NORMALIZATION OF DEVIATION and interrogating how it shows up in the data
  • Comparison of several predicted HAZARD PROBABILITIES and observed event frequencies based on fault trees and operational data
  • Natural language processing applied to large quantities of TEXT FOUND IN COSPS and potential safety issues and potential safety issue mining in public datasets.

What does each of these safety studies examine? The problems associated with the operation of the customers’ aircraft, NONE assesses these internal concerns:

  • what internal problems have contributed to the design of its planes,
  • the QC of their contractors,
  • the work cards and supervision of the hangar assembly line,
  • an analysis of the existing measures of safety performance; SMS was voluntarily implemented several years ago and the data collected failed to predict the flaws that were involved in the company’s unfortunate recent problems.
    • Looking at those obvious problem might FLAG more relevant indicia of the sources of the human sources of these errors.
  • the SMS process itself between its inception to today; what and how it was examining of the SAFETY CULTURE did not appear to identify the inception points of this complex business—a reevaluation of the past might provide guidance of a better understanding of what went wrong or was missed.

It’s not the size of your data library but the rigor and thoughtfulness of the team’s analyses. Hopefully Mr. Uddanwadiker has already focused his organizations on these spheres.

Artificial Intelligence at Boeing – Two Use Cases

Boeing, founded in 1916, is a global aerospace company and one of the largest aircraft manufacturers in the world. Headquartered in Arlington, Virginia, the company operates in commercial airplanes, defense, space, and security sectors. 

In 2022, Boeing reported revenues of $66.6 billion, a significant increase from $62.3 billion in 2021. However, the company faced a net loss of $5.1 billion in 2022. As of 2023, the company’s revenue was $77.794 billion, and the net loss was $2.222 billion. 

These figures represent an increase in revenue but a continued net loss for Boeing in the 2023 fiscal year.

While specific overall AI investment figures are not publicly available, Boeing has made strategic moves in this area. These include a $50 million commitment to AEI HorizonX’s second venture fund in 2022, which partly focuses on AI-related startups, and a 2017 investment in SparkCognition, an AI company

Boeing’s AI initiatives span various sectors, including autonomous flight technologies, urban air mobility solutions, and advanced AI for unmanned systems, demonstrating the company’s commitment to maintaining its position at the forefront of aerospace innovation.

[SINCE THE FOCUS IS SAFETY, THE SECTION IMMEDIATELY BELOW HAS BEEN DELETED AND THE QUOTATION RESUMES AT THE SAFETY SECTION]

This article explores two compelling use cases that illustrate how Boeing’s AI initiatives are actively supporting its strategic business objectives:

  • Managing spending with generative AI (GenAI): Implementing GenAI-powered solutions to automate sourcing, analyze spend patterns, and facilitate competitive bidding to reduce costs and improve decision-making, particularly for low-value, high-volume purchases.
  • Addressing safety risks with predictive data models: Leveraging data integration, advanced modeling, and machine learning to identify potential hazards before they become critical issues.

[BEGIN DELETION]



[RESUME QUOTATION]

Addressing Safety Risks with Predictive Data Models

In the wake of a series of high-profile safety incidents in 2018-2019, Boeing intensified its focus on preventing accidents and improving overall safety. According to a 2022 press release, the company’s Chief Aerospace Safety Officer[3] (CASO) is now spearheading a new initiative with a mission to “DRIVE AEROSPACE SAFETY TO PREVENT ACCIDENTS, INJURY OR LOSS OF LIFE.”

Boeing’s new safety strategy centers around the concept of PREDICT TO PREVENT.” This approach involves:

  • Data Integration: Collecting and analyzing vast amounts of data from various sources across the aerospace industry.
  • Advanced Modeling: Utilizing system engineering and ACCIDENT CAUSATION MODELS to identify potential hazards.
  • Machine Learning: Employing AI and machine learning algorithms to detect patterns and anomalies in the data.
  • Cross-functional Collaboration: Bringing together experts from different disciplines to interpret and act on the insights generated.

The new Boeing Safety Intelligence Solution is built on four pillars:

  • Compliance
  • Conformance
  • Fleet Safety
  • SMS Performance

Screenshot from Boeing Predict to Prevent (Source: Boeing)

“The first three-run parallel to Design, Build and Operate. The fourth is about how well the SMS is functioning in the real world,” said Vishwa Uddanwadiker, safety analytics lead in Boeing’s Chief Aerospace Safety Office (CASO), in the previously mentioned interview published by Boeing on its website. 

As reported by Bloomberg in one of its articles, Boeing’s safety management system collects and monitors data from an array of internal and external sources, like design and manufacturing data, audit findings, and even reports that repair stations file to flag failed and malfunctioning parts to the Federal Aviation Administration. 

In the same article, Uddanwadiker mentioned that earlier this year, the company began using a machine-learning algorithm that it developed jointly with the FAA to scan and mine data from the so-called “SERVICE DIFFICULTY REPORTS” for worrisome risks emerging within the global fleet. They’re written accounts of parts breakdowns that are filed by the maintenance shops and are not easily categorized.

The article also states that the safety team at Boeing tracks 20 key performance measures on a weekly basis that are closely correlated to safety risks in designing, building, or operating its aircraft. 

Despite Boeing’s efforts to overhaul its safety management systems through the “PREDICT TO PREVENT” initiative launched in 2022, the subsequent years have seen a SERIES OF SAFETY INCIDENTS that STARKLY HIGHLIGHT THE ONGOING CHALLENGES in the company’s safety practices. What was intended to be a transformative approach—leveraging AI and machine learning to detect potential risks—has seemingly fallen short, culminating in a catastrophic year for Boeing in 2024. 

While Boeing initially integrated advanced data models and cross-functional collaboration to anticipate and mitigate safety hazards, the surge in incidents, as reported by Business Insider, has cast serious doubt on the effectiveness of these measures. 

The company has endured enormous criticism for how it handled its safety failures. As major news outlets have extensively covered, the disconnect between Boeing’s proclaimed safety reforms and the harsh reality of its ongoing safety woes has left the company grappling with a severe loss of public trust and scrutiny over its management decisions. 

A number of company executives have been dismissed – including CEO Dave Calhoun’s decision to leave the company after 2024 – in an effort to hold Boeing leadership accountable. However, it’s noteworthy that Vishwa Uddanwadiker, the safety analytics lead overseeing this initiative, was moved to the role of Chief AI Officer in March 2024. Much of the public criticism of Boeing has focused on company culture rather than specific safety protocols or data gathering initiatives, and Uddanwadiker’s continued tenure with the company appears to reflect that. 

In terms of the applicability of the technology at hand to drive safety in aerospace initiatives, more generally, documentation from NASA’s System Wide Safety Project mentions that machine learning helps in aviation safety by enhancing data analysis capabilities and improving anomaly detection. It also states that by integrating data from multiple sources and providing real-time monitoring, AI and ML offer a more comprehensive and up-to-date understanding of airline operations.


[1] Boeing, a global leader in aerospace technology, has announced the appointment of Vishwajeet Uddanwadiker as its Chief AI Officer. Uddanwadiker brings over two decades of experience in IT leadership, data science, and aerospace safety analytics to his new role, where he will spearhead Boeing’s AI initiatives to drive innovation and
Read more at: https://aimresearch.co/leadership-moves/vishwajeet-uddanwadiker-assumes-role-as-chief-ai-officer-at-boeing

[2] There is no explicit designation by these Boeing releases that Mr. Uddanwadiker is the Boeing FAR Part 5 ACCOUNTABLE EXECUTIVE.

[3] Boeing, a global leader in aerospace technology, has announced the appointment of Vishwajeet Uddanwadiker as its Chief AI Officer. Uddanwadiker brings over two decades of experience in IT leadership, data science, and aerospace safety analytics to his new role, where he will spearhead Boeing’s AI initiatives to drive innovation and
Read more at: https://aimresearch.co/leadership-moves/vishwajeet-uddanwadiker-assumes-role-as-chief-ai-officer-at-boeing


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