I'd like to learn how US based manufacturing companies are utilizing Artificial Intelligence and Machine Learning technologies to create value or reduce costs. What kinds of technologies are used and who are the major technology providers?

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I'd like to learn how US based manufacturing companies are utilizing Artificial Intelligence and Machine Learning technologies to create value or reduce costs. What kinds of technologies are used and who are the major technology providers?

Artificial Intelligence and Machine Learning are revolutionizing manufacturing in various industries from oil and software to pharmaceuticals and aircraft. Many US industry leaders are continually investing millions of dollars into developing AIs that can streamline production, reduce costs, improve safety, and expand the customer experience. General Electric, Boeing, Ford, Microsoft, P&G, Johnson and Johnson, Pepsi and Exxon Mobil are all examples companies that have made use of AI and ML in their manufacturing or other processes. We have provided an overview of each company and its use of AI/ML below.


As requested, we included a source for the most important keywords regarding AI and ML. This AI glossary includes 28 of the most important and most used phrases within the AI and ML sphere.

We also included two articles for further reading and general information:
1. 10 Ways Machine Learning Is Revolutionizing Manufacturing — Forbes article listing various ways of how AI and ML will impact manufacturing and production.
2. AI in manufacturing beneficial, but adoption slow — an article detailing the seemingly slow but steady adoption of AI in various industries.

We also recommend these lists for further reading as they provide even more great examples of AI and ML adoption in manufacturing and production from around the globe. (article 1, article 2, article 3)

1. Boeing

The company has an article on their website about how they use AI and ML to improve and innovate in various segments of production.
They have created "machine-learning algorithms that can identify patterns in data, and make recommendations accurately within just a few minutes."
Benefits for the Design process
The algorithm can "complete tasks much quicker with higher first-time quality." The article also notes that "simulating the design-to-build process through augmented reality, reinforced by predictive analytics, could expose a new way of implementing customer-driven design changes by understanding the quality, cost, supplier choices and available inventory implications before building the product."
Benefits for the Supply Chain
Using the algorithms, "accurate demand forecasting could drive significant improvement across the entire supply chain affecting cost, productivity, and quality."
A few examples of such improvements are:
— using analytics, natural language, and other methods to create a flexible pricing model.
— using analytics to dynamically adjust inventory levels, usually a major challenge in management.
Benefits In the Factory
According to the Boeing article, "complex jobs can be automated to improve productivity, quality, and safety while helping to meet delivery schedules."
"To automate and leverage AI, data from sensors on machines can be connected with traditional data such as design, inventory and safety records, to optimize tasks. Instead of simply identifying a task to be automated, a deep learning model can analyze all the data, determine patterns and recommend the best task for automation."
Benefits for the Service in general
Since Boeing aircraft are intended for years of heavy use, using preemptive analytics to identify issues and hazards improves customer satisfaction and the general trustworthiness of the company. Detecting and preventing problems before they occur builds confidence and paves the way for higher customer satisfaction. "By deciphering usage patterns such as flight conditions, location, temperature, altitude, wind speed, and direction, we could predict with confidence when a part needs maintenance, repair or replacement."

2. Ford

In a bid to further develop their position in the self-driving automobile market, Ford will invest $1 billion in a company called Argo AI.
While no technical details are mentioned in the press release for the investment, Argo AI will provide their "robotics experience and startup speed on artificial intelligence software."
As Argo AI's focus is on self-driving cars, the company will be tasked with developing the virtual driver system for Ford's fully autonomous vehicles, planned for release in 2021.

3. P&G

P&G created "The Olay Skin Advisor," a phone or tablet app that uses AI to analyze a person's skin and recommends products based on the results.
"The Olay Skin Advisor marks the first application of deep learning in the beauty industry, according to P&G, and seeks to arm women with the knowledge they need to better navigate the often-confusing beauty aisle."
We assume the intended benefit of this application — besides the increased profits through more accomplished purchases — would be a better understanding of customer needs and a more customized shopping experience, which results in a better brand image for the company.
"Olay’s research shows that browsing the shelf is the No. 1 purchase influencer for women, yet one-third of women do not find what they are looking for. We saw an opportunity to help women understand their skin better than ever, before they even step foot in the store."

4. Pepsi

Pepsi used the computer vision and neural network solution by Block Six Analytics to measure the effectiveness of their ad visibility during sports broadcasts.
Block Six Analytics founder Adam Grossman explains their process like this: “With our neural network, we taught our system to learn the Pepsi, Lipton and Tostitos logos by providing thousands of images of each brand. [...] The neural network learns the important features of each logo, such as shape, size, and color, to identify the logos in different contexts.” The company "then applies its corporate asset valuation model to determine Pepsi’s value from a return on investment (expected revenue generated) and return on objectives (brand and marketing goals) perspective." They then layer and combine these metrics and give an evaluation within 48 hours of the broadcast, enabling Pepsi to adapt their advertising practices dynamically.
According to Justin Toman (PepsiCo Sports Marketing Senior Director), “this technology is going to become more and more important to allow sponsors like (Pepsi) and others to make more real-time decisions to optimize value within a season and potentially even within a game at some point,”

5. Exxon Mobile

The oil company is collaborating with MIT to create AI robots that can explore the oceans.
"An estimated 60 percent of oil underneath the earth’s surface in North America is due to natural seeps. Robots with the ability to navigate these oceanic regions and detect oil seeps can contribute to protecting the ecosystem and serve as indicators for robust energy resources."
The Tech Emergence article highlighting ExxonMobile also mentions Shell, China Petroleum, Gazprom, and Total as key drivers of AI development in the oil industry.

6. Microsoft

We found a detailed report on the use of AI and ML in several companies. The report contains a case study of how Microsoft places enormous emphasis on the importance of ML in their product development: "Through machine learning, Microsoft has significantly improved the products for which it has become known: the Bing search engine, Skype internet phone service, MS Office software suite, and more. Machine learning has also become important for improving Microsoft’s business processes in finance (for example, customer credit checking), IT (for example, detecting computer security threats), and other areas."
"One of Microsoft’s biggest AI initiatives over the last eight years has been its Bing search engine.' The quality of the ranking results that are produced by Bing depends entirely on the machine learning models behind it,' explains [Joseph] Sirosh. These models help Bing figure out the best content to summon in online searches. 'Machine learning is totally built into the fabric of the product, and is one of its biggest differentiators.'"
Bing has evolved through the years and held a 20% share of the search engine market in 2015, an 11% increase from 2009.
Note: during our research, the Tata Consultancy Services website hosting the study experienced technical difficulties. A cached version of the study is still accessible here if the problem persists.

7. General Electric

General Electric is mentioned in a larger Tech Emergence article about how various companies are investing heavily in AI and ML:
"In 2015 GE launched its Brilliant Manufacturing Suite for customers, which it had been field testing in its own factories. The system takes a holistic approach of tracking and processing everything in the manufacturing process to find possible issues before they emerge and to detect inefficiencies. Their first “Brilliant Factory” was built that year in Pune, India with a $200 million investment. GE claims it improved equipment effectiveness at this facility by 18 percent."
"The goal [...] is to link design, engineering, manufacturing, supply chain, distribution and services into one globally scalable, intelligent system. It is powered by Predix, their industrial internet of things platform. In the manufacturing space, Predix can use sensors to automatically capture every step of the process and monitor each piece of complex equipment."
Other companies mentioned in the article include Siemens, Fanuc, and KUKA.

8. Johnson & Johnson

According to a Tech Emergence article, Johnson & Johnson teamed up with IBM to utilize their Watson AI. "The collaboration would initially concentrate on virtual patient coaching for individuals undergoing joint replacements and spine surgeries and rehabilitation support to improve patient outcomes. Coaching would be accessible through a mobile app designed to monitor and guide patient behavior throughout the pre- and post-operative experience."
Johnson & Johnson has had multiple attempts at using AI in their products and services. One was the SEDASYS System, which was introduced as the “first computer-assisted personalized sedation (CAPS) system.” The system was ultimately a failure and was pulled from production — even though initial results were promising.
Another AI-related project is the company's collaboration with Google to create an AI-assisted surgical robot. "Verb Surgical aims to leverage AI to help surgeons interpret what they see during surgery." No updates are available as of 2017 on this project.
Other companies included in the Tech Emergence article are Genentech, Pfizer, Novartis, and Bayer.


To sum it up, Artificial Intelligence and Machine Learning are revolutionizing manufacturing in various industries from oil, to pharmaceuticals and aircraft. Many industry leaders are continually investing millions of dollars into developing AIs that can streamline production, reduce costs, improve safety, and expand the customer experience.