3 global manufacturing brands at the forefront of AI and ML
How can machine learning and artificial intelligence change the landscape of manufacturing? According to reports by Deloitte and McKinsey, machine learning improves product quality and has the potential to double cash flow. Let’s take a look at three global manufacturers who are already on board.
If you are a major manufacturer in 2020 and you have employed the likes of Deloitte, McKinsey or PWC, it is safe to assume that they have advised you to invest big in artificial intelligence and machine learning.
According to reports by Deloitte and McKinsey, machine learning improves product quality and has the potential to double cash flow. Let’s take a look at three global manufacturers who are already on board.
Siemens wants to get things right, the first time around
Siemens is the largest industrial manufacturer in Europe, and whether they are putting together planes, trains or automobiles, their goal is to solve production challenges efficiently and sustainably. One of the ways they are able to do this is by using machine learning (ML) to enhance additive manufacturing, otherwise known as AM.
The process involves putting together parts that make objects from 3D model data. The idea is to streamline the manufacturing process into one printing stage. Machine learning plays a crucial part in achieving this goal.
Let’s take a look at the recent creation of the AM Path Optimizer, part of its NX software offering. It’s designed to eliminate overheating during production, an issue that stands in the way of the industrialization of AM. According to Siemens, the path optimizer combines simulation technology and ML to ‘analyze a full job file minutes before execution on the machine’. With this they hope to achieve reduced scrap and increased production yields. In short, they want to minimize trial and error and get it right the first time around.
Although still in the beta stage, the AM Path Optimizer has had some early adopters. TRUMPF, a German industrial machine manufacturing company based in Stuttgart, has been singing its praises, pointing to improved ‘geometrical accuracy, more homogenous surface quality and a significant reduction in the scrap rate expected’.
ConAgra knows what you want to eat tonight
Machine learning and artificial intelligence do not just influence how companies manufacture but also help them decide what they manufacture. American packaged-food company ConAgra is one such company. They are using AI to identify consumer preferences.
The vegan market, for example, is growing rapidly: by 2026 it is projected to be worth just over $24 billion (the vegan cheese market alone will be worth $4 billion). And ConAgra, despite being over a century old, is aware of consumer preferences moving towards healthier options and away from things like processed meat. This awareness comes in part from their AI platform, which analyses data from social media and consumer food purchasing behavior.
This has led the company to produce alternative meat products like veggie burgers and even cauliflower rice. It’s also helped speed up the manufacturing process, so rather than planning for next year, they can design, make, and release a new product in as little as a few weeks.
Bosch wants to create solutions for life
The major appliance manufacturer Bosch is a great believer in AI and has committed substantial resources to making it a central part of its business. In 2016, it launched a $30,000 competition on Kaggle, an online community of data scientists and machine learning practitioners. Competitors were asked to predict internal failures, with the aim of improving Bosch production line performance.
They described the assembly process as much like a souffle, ‘delicious, delicate and a challenge to prepare; if it comes out of the oven sunken, you are going to retrace your steps to see where things went wrong’. In order to identify and predict where its ‘souffles’ go wrong, Bosch records data at every step of the manufacturing process and assembly line.
This is where the Kagglers come in. With access to advanced data analytics and using thousands of tests and measurements for each component on the assembly line, the winners Ash and Beluga were able to so solve internal failures using their own fault detection method.
In 2017, the Bosch Center for AI was founded with the tagline ‘Solutions created for life’. This is part of a broader effort to put AI and machine learning at the heart of the business. What they are working on now is reducing reliance on human expert knowledge base and deploying AI algorithms in safety-critical applications.
More recently, Bosch has been working on preventing increasingly advanced hackers from compromising their cars. According to CTO Michael Bolle: ‘In the area of machine learning and AI, products and machines learn from data, and so the data itself can be part of the attack surface.’
Adapt or die
What Bosch, ConAgra, and Siemens realize is that their business is increasingly reliant on data, and the best way to harness that data is to invest heavily in AI and ML. According to McKinsey, not investing in AI or ML is not really an option, especially if you are a manufacturer with heavy assets: ‘Manufacturers with heavy assets that are unable to read, interpret, and use their own machine-generated data to improve performance by addressing the changing needs of customers and suppliers will quickly lose out to their competitors or be acquired.’