Realizing the Promise of Autonomous & Distributed Industry
Assembly Line
Capturing this week's trending industry 4.0 and emerging industrial technology media
The industrial metaverse: A game-changer for operational technology
By combining its AI-based autonomous drone-control solution and advanced machine-learning capabilities with machine vision tools, Nokia Bell Labs has created a technology that can track the growth of millions of plants. “We have developed a completely autonomous drone solution with multiple drones flying through this farm,” says Klein. That allows the farm to monitor details such as the height and color of its plants, spot poor growth areas, and predict the production yield.
“We actually built a complete digital twin of the farm that gives the growers a real-time picture of the entire production throughout the farm,” says Klein. With data analysis, the farm can optimize its water, energy, and nutrient consumption; speed up troubleshooting; improve accuracy in yield forecast; and maintain a consistently high quality.
Covariant Robotic Depalletization | Mixed-SKU Pallets
3D Printing Helps Realize the Promise of Distributed Manufacturing
Additive manufacturing offers a solution to the challenges of distributed manufacturing by enabling local and highly flexible production of small quantities. For many use cases, additive manufacturing systems and processes are now technologically ready for small-series production. Applying 3D printing in distributed manufacturing will be most beneficial for producing high-value parts, such as those used in the aerospace and medical-technology industries, or low-volume replacement parts. These are among the transformative technology applications that constitute Industry 4.0.
In 2022, BCG undertook a study, in collaboration with RWTH Aachen University and the ACAM Aachen Center for Additive Manufacturing, to capture insights into how the application of 3D printing in distributed manufacturing adds value and what the prerequisites are for successful use cases. The study included interviews with a panel of approximately 15 leading experts in business and academia, from a variety of countries.
Why digital sourcing platform Fictiv stays in China when others are leaving
Despite challenges around COVID restrictions and geopolitics, “the China manufacturing base is not going away,” said Fictiv’s founder and CEO Dave Evans in an interview with TechCrunch. “Thirty years ago, Shenzhen was a fishing village, and now it’s the center of the world for manufacturing. It’s going to take a while for other ecosystems to really catch up,” he said, adding that Apple and its contract manufacturer Foxconn have offered a strong playbook for a generation of factory owners in the country.
“Because it’s so hard to access China in the last years, the value we have in combining software, technology and all the AI that we built with boots on the ground right next to our manufacturing partners has built a really compelling offering for all customers because they can’t fly to China,” said the CEO. The firm has built a global network of 250 vetted manufacturing partners, a third of which are in China, where production capacity is often larger. The rest of its suppliers are from India and the U.S. To date, Fictiv has produced some 20 million parts for thousands of customers. It runs a team of just over 300 employees around the world.
How a high-rise being built in Detroit can change the way cities are built
The future is now: Unlocking the promise of AI in industrials
Many executives remain unsure where to apply AI solutions to capture real bottom-line impact. The result has been slow rates of adoption, with many companies taking a wait-and-see approach rather than diving in.
Rather than endlessly contemplate possible applications, executives should set an overall direction and road map and then narrow their focus to areas in which AI can solve specific business problems and create tangible value. As a first step, industrial leaders could gain a better understanding of AI technology and how it can be used to solve specific business problems. They will then be better positioned to begin experimenting with new applications.
Manufacturing needs MVDA: An introduction to modern, scalable multivariate data analysis
In most settings, a qualitative/semi-quantitative process understanding exists. Through extensive experimentation and knowledge transfer, subject-matter experts (SMEs) know a generally acceptable range for distinct process parameters which is used to define the safe operating bounds of a process. In special cases, using bivariate analysis, SMEs understand how a small number of variables (no more than five) will interact to influence outputs.
Quantitative process understanding can be achieved through a holistic analysis of all process data gathered throughout the product lifecycle, from process design and development, through qualification and engineering runs, and routine manufacturing. Data comes from time series process sensors, laboratory logbooks, batch production records, raw material COAs, and lab databases containing results of offline analysis. As a process SME, the first reaction to a dataset this complex is that any analysis should be left to those with a deep understanding of machine learning and all the other big data buzzwords. However, this is the ideal opportunity for multivariate data analysis (MVDA).
How Does Advance Concrete Use a Robot for Welding
Monarch Tractor Launches First Commercially Available Electric, ‘Driver Optional’ Smart Tractor
Local startup Monarch Tractor has announced the first of six Founder Series MK-V tractors are rolling off the production line at its headquarters. Constellation Brands, a leading wine and spirits producer and beer importer, will be the first customer given keys at a launch event today.
The debut caps a two-year development sprint since Monarch, founded in 2018, hatched plans to deliver its smart tractor, complete with the energy-efficient NVIDIA Jetson edge AI platform. The tractor combines electrification, automation, and data analysis to help farmers reduce their carbon footprint, improve field safety, streamline farming operations, and increase their bottom lines.
DuPont + Augury: Driving Innovation with Predictive Maintenance
Capital Expenditure
Tracking this week's major mergers, partnerships, and funding events in manufacturing and supply chain
Industrial autonomous driving: Chinese venture capitalists' newest fad
Given that the volume of venture capital investment in startups participating in autonomous driving of commercial vehicles has slowed this year, one may expect VCs to slow down funding rounds for all kinds of autonomous driving-related companies. However, this has not been the case.
Several China-based industrial autonomous driving startups have also secured sizable rounds, including Holomatic (禾多科技), which secured USD100 million in a series C round from the venture capital arm of Guangzhou Automobile Group, and J-Elephant (捷象灵越), a developer of autonomous forklifts that sealed hundreds of millions of yuan in a Pre-A round from Sequoia Capital, Sinovation Ventures, among others.
Flow Engineering raises $8.5m to develop engineering collaboration platform for ‘new age’ hardware engineers
Flow Engineering, the first collaboration platform for hardware engineering teams designing complex systems, is today announcing $8.5m in seed funding. Flow is Github for hardware engineers, a single source of truth helping teams design complex systems and get products to market 30-50% faster. EQT Ventures led the round with participation from some of Europe’s top tech investors, including Backed VC, David Hegelson (Unity), Charlie Songhust (ex-Microsoft), Kyle Parrish (Figma) and Matt Clifford (EF). Today’s funding will help Flow realise its mission of supporting hardware engineering teams solve some of humanity’s greatest problems.
Ineos forms JV with Sinopec for Chinese petrochemicals complex
UK-based chemical and energy group Ineos has signed an agreement to acquire a 50% stake in the Tianjin Nangang Ethylene Project in China from Sinopec. The joint venture (JV ) agreement will allow Sinopec to leverage Ineos’s technological knowledge and operational expertise, and expand Ineos’ presence in China.