Value Chain Trilema & Defense Industrial Base Mergers
Shop Talk
From Patrick Matthews in “Robots Assemble Electric Vehicles”
While the high-tech companies of Silicon Valley are often the big draw for younger generations, the irony is that the automotive industry is just as advanced—perhaps more so if you consider how our smart automation systems are deploying cutting-edge tech, such as AI, into real-world applications. The days of vehicle factories resembling a large ironworks are long gone. They’re now some of the most tech-heavy places on Earth.
These highly automated assembly plants need skilled engineers, programmers and maintenance teams to keep them running, while constantly and quickly adapting to meet the new technologies and features that consumers demand.
Ultimately, if automation is to succeed in our increasingly digital world, it must continue to evolve. Not just in response to a changing product undergoing its biggest reinvention since its inception, but a changing workforce, too.
Assembly Line
Capturing this week's trending industry 4.0 and emerging industrial technology media
What is meant by the Value Chain Trilema?
Whilst we have focused on industrial OEMs for our analysis here, the trilemma is relevant for all sectors. Industrial OEM value chains have unique characteristics that make them difficult to manage: Multiple value chains (ETO/CTO) and corresponding supply models (ATO/MTS/MTO), long lead times often running into years, unclear or delayed demand signals, lack of flexibility and limited sustainability focus. In an increasingly volatile business and geopolitical environment, the impact of these challenges gets compounded, impacting growth, margins, resilience and sustainability.
Deep learning in product design
Digitization has also allowed engineers to give computers a more active role in the engineering process. Generative design and related optimization approaches work by programming a computer to run hundreds or thousands of simulations, tweaking the design between each run until it finds the best solution it can. The resulting geometries can outperform the work of the most experienced human designers.
At its outset, a deep learning surrogates (DLS) process looks a lot like other digital design optimization approaches. The engineering team defines the constraints and desired performance characteristics of the product, and the computer runs multiple conventional simulations on different design options. That’s where the approaches diverge, however.
As those initial simulations are run, they are used to train a neural network, which is set up to take the same inputs and attempts to replicate the outputs of the simulation system. When training is complete, this deep learning model will work just like the conventional simulation, but much, much faster. In real-world projects, deep learning simulation models can run orders of magnitude more quickly than their conventional counterparts.
John Deere Turns To 3D Printing More Efficient Engine Parts
The new thermal diverter valve on the latest versions of John Deere 6R and 6M tractors isn’t just an innovative application of increasingly accessible metal 3D printing technology, it’s the culmination of about two years of R&D. It started with a challenge to ensure John Deere tractors would perform in cold environments. Engineers were tasked with developing a valve that could maintain fuel temperatures without affecting engine performance.
Currently, more than 4,000 valves are being shipped from GKN to the John Deere tractor factory for final assembly at a price per part that is less than forging or milling. Tractors with this 3D-printed part are already in the field, literally. Müller says another benefit of 3D printing this particular part instead of using traditional methods, is added agility in the manufacturing process. Because 3D printing does not require molds or tools, part prototypes were faster and cheaper to create, which accelerated the design process. The design can be tweaked and improved at any time. Plus, when it comes to replacement parts, no standing inventory is necessary. The digital file of this value can be sent to any third-party manufacturer with HP Metal Jet technology and produced relatively locally and quickly.
How a robotic arm could help the US Army lift artillery shells
To fire artillery faster, the US Army is turning to robotic arms. On December 1, Army Futures Command awarded a $1 million contract to Sarcos Technology and Robotics Corporation to test a robot system that can handle and move artillery rounds.
An automated system, using robot arms to fetch and ready artillery rounds, would function somewhat like a killer version of a vending machine arm. The human gunner could select the type of ammunition from internal stores, and then the robotic loader finds it, grabs it, and places it on a lift. Should the robot arm perform as expected in testing, it will eliminate a job that is all repetitive strain. The robot, lifting and loading ammunition, is now an autonomous machine, automating the dull and menial task of reading rounds to fire.
MiR500 at Novo Nordisk
Improved GRU prediction of paper pulp press variables using different pre-processing methods
Predictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector.Hence, this study contributes to examine the most efficient pre-processing approaches for predicting sensory data trends based on Gated Recurrent Unit (GRU) neural networks. To validate the model, the data from two paper pulp presses with several pre-processing methods are utilized for predicting the units’ conditions. The results of validation criteria show that pre-processing data using a LOWESS in combination with the Elimination of discrepant data filter achieves more stable results, the prediction error decreases, and the predicted values are easier to interpret. The model can anticipate future values with MAPE, RMSE and MAE of 1.2, 0.27 and 0.30 respectively. The errors are below the significance level. Moreover, it is identified that the best hyperparameters found for each paper pulp press must be different.
Capital Expenditure
Tracking this week's major mergers, partnerships, and funding events in manufacturing and supply chain
MasterControl Raises $150M Series A Funding Round from Sixth Street Growth at Valuation of $1.3B
MasterControl, a leading provider of quality and manufacturing software solutions for the life sciences, today announced its first ever funding, a $150 million Series A round led by Sixth Street Growth. MasterControl has grown profitably for nearly 30 years, and with this funding is valued at $1.3 billion. The proceeds will be used to accelerate the development of SaaS solutions serving the company’s global life sciences customers, which include Pfizer, Cochlear, Thermo Fisher Scientific and more than 1,100 others worldwide.
MasterControl will use the financing to continue to build and enhance its solutions while also placing more focus on technology that will predict and prevent quality events, and enable true AI assisted manufacturing optimization. Investing in new technologies like AI, machine learning and natural language processing will improve customers’ business outcomes and reduce the time and cost of compliance and red tape, getting life changing products to patients in timeframes that were previously not possible.
SafeAI Raises $38 Million Series B Funding to Accelerate the Deployment of Autonomous Heavy Equipment
SafeAI, a global leader in autonomous solutions for heavy equipment, today announced a $38 million Series B round of funding with key investors including Builders VC, McKinley Management, George Kaiser Family Foundation, and Energy Innovation Capital. Additionally, Moog Inc. joined the round as a strategic investor. SafeAI retrofits construction and mining vehicles with aftermarket hardware and proprietary autonomy software that delivers significant gains in increased worksite productivity, safety and cost savings. The company will use the funding to accelerate their autonomous vehicle technology roadmap, and to scale operations globally to service and deploy their growing customer base.
Chinese driverless port transport startup secures new funding
Senior Automation, a Chinese startup providing driverless solutions for port transport, has raised more than 100 million yuan (about $14 million) in a series A+ funding round, a 36Kr survey found. The new investment in Senior Automation was led by Tsinghua Innovation Ventures and Winreal Investment. The other investors that participated in the latest funding round also include Estar Capital. Senior Automation will use the funds to increase investment in research and development, expand its driverless operations in the area of port transport and develop new commercialization scenarios.
Linse Capital Raises $700M To Back Industrial Technology Companies
Linse Capital, a San Juan, Puerto Rico-based growth equity firm, raised $700m to back industrial technology companies. The capital will be allocated across its flagship fund – Linse Capital Fund I (LCFI) – alongside two co-investment vehicles for portfolio companies Skydio and Verkada. LCFI will invest in a select group of new companies per year, investing between $100m and $400m in each business. The firm aims to become the largest or one of the largest shareholders at exit. Investment areas include four sectors: transportation, energy, logistics and real estate.
South Korea's Hanwha inks $1.5bn deal to buy Daewoo Shipbuilding
Daewoo Shipbuilding, which announced the deal in a regulatory filing Friday, will issue new shares to six Hanwha Group companies through a private placement, giving the group a 49.3% stake to become the largest shareholder. Hanwha Aerospace will invest 1 trillion won and Hanwha Systems, a defense equipment company, will invest 500 billion won. Hanwha, a defense contractor, is looking to expand its scale using Daewoo Shipbuilding’s naval and submarine businesses.
Defense Contractor L3Harris Plans to Buy Aerojet Rocketdyne for $4.7 Billion
Defense firm L3Harris Technologies Inc. said it agreed to buy Aerojet Rocketdyne Holdings Inc. in a $4.7 billion deal that would cement L3Harris’s role as one of six prime defense contractors for the Pentagon. Aerojet is a major maker of engines used in missiles, such as the Javelin deployed in Ukraine. Its products also help power National Aeronautics and Space Administration rockets and U.S. military hypersonic systems designed to deter China’s military expansion. Aerojet was put back up for sale after federal regulators in January sued to block its planned $4.4 billion purchase by Lockheed Martin Corp. on antitrust grounds, sparking a bitter internal board battle.