Where Are the Industry 4.0 Third-Party APIs?
Exposing and leveraging third-party APIs is critical to the success of Industry 4.0 just like Web 2.0 companies used APIs to create new business models and gigantic businesses.
Application programming interfaces (APIs) are a big business and a core subject to one of the biggest intellectual property lawsuits in Silicon Valley. Yet, they are rarely mentioned on the factory floor. APIs typically take a set of inputs, perform an operation, and deliver a set of outputs. These inputs and outputs are usually very well defined. The best APIs carry out very specific tasks and are optimized for performance. Lastly, APIs are often chained together to accomplish much bigger goals and objectives. If this sounds like a manufacturing line, it’s because in essence, it is! APIs make the creation of digital goods much easier just like machinery enables manufacturing lines to create physical goods efficiently.
Cobbling together Third-party APIs enable companies to scale up easily, test out new ideas with minimal integration, and create new revenue streams. For example, a new eCommerce can start with minimal capital by incorporating APIs from Stripe, Shopify, Cloudflare and others to accept payments, create a online store, and host web content in fewer lines of code than this newsletter. If a new payments provider comes along, Stripe can be swapped out by changing the seven lines of code with some other payments API provider. As the company gain customers and collects data on emerging trends that data could be monetized by creating an API for others to use. The key to third-party APIs is that they are published, easily accessible, and often free to use so that the community can try then as they see fit without much friction.
On the factory floor, APIs are embedded in every robot, control system, sensor, and data system. However, these APIs are often locked down by the OEMs that create them. They are not easily accessible and prohibitively expensive. Often times, the API is a complete afterthought as what only matters is that the machine or subsystem is able to perform its task consistently and with high accuracy. Even today, automakers are struggling to integrate new sensors into their vehicles to support autonomy due to API issues. If Industry 4.0 is to reach its potential, third-party APIs must make their way to the factory floor.
I’m betting the winning Industry 4.0 companies will be the ones that provide competitive hardware with large libraries of open and accessible APIs spanning digital twins, additive manufacturing, robotics, control systems, and finished goods. These companies will be more adaptable to changing demand signals, launch new products faster, and create new revenue streams through exposing data generated by both their products and operations via APIs. If you know of any leading Industry 4.0 API-first companies, please send them my way.
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Artificial Intelligence: Driving Digital Innovation and Industry 4.0
Date: February 15, 2021
Author: @ralph_ohr
Intelligent AI solutions can analyze high volumes of data generated by a factory to identify trends and patterns which can then be used to make manufacturing processes more efficient and reduce their energy consumption. Employing Digital Twin-enabled representations of a product and the associated process, AI is able to recognize whether the workpiece being manufactured meets quality requirements. This is how plants are constantly adapting to new circumstances and undergoing optimization with no need for operator input. New technologies are emerging in this application area, such as Reinforcement Learning – a topic that has not been deployed on a broad scale up to now. It can be used to automatically ascertain correlations between production parameters, product quality and process performance by learning through ‘trial-and-error’ – and thereby dynamically tuning the parameter values to optimize the overall process.
Read more at Siemens Ingenuity
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Date: February 19, 2021
Author: Qiyao Wang
For many industrial and commercial operations, maintenance accounts for a large part of operating costs. For instance, maintenance costs range from 15% to 60% of the total production costs in manufacturing plants. In the airline industry, the 2014 global spend on maintenance, repair, and overhaul accounted for around 9% of the total operational costs, and this spend is estimated to reach 90 billion dollars in 2024. Even with maintenance cost being such a substantial part of the overall cost, maintenance managers have little visibility into whether maintenance expenditure is money well spent or not, i.e., whether the maintenance is effective or not. In this blog, I’d like to talk about a mathematical formulation of the maintenance effectiveness evaluation problem and a systematic way of solving it.
Read more at Hitachi Industrial AI blog
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Date: February 19, 2021
Author: Isaac Arnsdorf and Ryan Gabrielson
The Trump administration deployed the Defense Production Act last year to give vaccine manufacturers priority in accessing crucial production supplies before anyone else could buy them. And the Biden administration used it to help Pfizer obtain specialized needles that can squeeze a sixth dose from the company’s vials, as well as for two critical manufacturing components: filling pumps and tangential flow filtration units. The pumps help supply the lipid nanoparticles that hold and protect the mRNA — the vaccines’ active ingredient, so to speak — and also fill vials with finished vaccine. The filtration units remove unneeded solutions and other materials used in the manufacturing process.
These highly precise pieces of equipment are not typically available on demand, said Matthew Johnson, senior director of product management at Duke University’s Human Vaccine Institute, who works on developing mRNA vaccines, but not for COVID-19. “Right now, there is so much growth in biopharmaceuticals, plus the pinch of the pandemic,” he said. “Many equipment suppliers are sold out of production, and even products scheduled to be made, in some cases, sold out for a year or so looking forward.”
Read more at ProPublica
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Date: February 24, 2021
AWS announced the general availability of Amazon Lookout for Vision, a new service that analyzes images using computer vision and sophisticated machine learning capabilities to spot product or process defects and anomalies in manufactured products. By employing a machine learning technique called “few-shot learning,” Amazon Lookout for Vision is able to train a model for a customer using as few as 30 baseline images. Customers can get started quickly using Amazon Lookout for Vision to detect manufacturing and production defects (e.g. cracks, dents, incorrect color, irregular shape, etc.) in their products and prevent those costly errors from progressing down the operational line and from ever reaching customers. Together with Amazon Lookout for Equipment, Amazon Monitron, and AWS Panorama, Amazon Lookout for Vision provides industrial and manufacturing customers with the most comprehensive suite of cloud-to-edge industrial machine learning services available. With Amazon Lookout for Vision, there is no up-front commitment or minimum fee, and customers pay by the hour for their actual usage to train the model and detect anomalies or defects using the service.
Read more at Business Wire
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Date: February 24, 2021
Author: Çağlayan Arkan
What makes the Microsoft Cloud for Manufacturing unique is our commitment to industry-specific standards and communities, such as the Open Manufacturing Platform, the OPC Foundation, and the Digital Twins Consortium, as well as the co-innovation with our rich ecosystem of partners.
Read more at Microsoft Cloud Blogs
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