Machina Labs Using Robotics and Artificial Intelligence to Provide Customized, On-Demand Sheet Metal Forming Services Close to Final Manufacture Points

Getting products closer to the final consumer is a supply chain strategy to reduce lead times when an order is placed. Reducing the last mile has been primarily seen within the Fast-Moving Consumer Goods (FMCG) markets, with companies using micro-fulfilment centers and faster last-mile transport methods to deliver orders faster.

Some industries and governments are taking this a step further, aiming to reduce the length of the overall supply chain, rather than just the last mile by bringing manufacturing closer to the customer. This push has been greatly incentivized in recent months through nearshoring and onshoring initiatives from the Biden administration, with strong intentions of boosting supply chain resiliency and efficiency.

Machina Labs is a sheet metal forming manufacturer embodying this trend, using robotics and Artificial Intelligence (AI) to form metal without needing to create expensive dies. Like a craftsman using a hammer, the robotic arms manipulate sheet metal incrementally to produce metal parts on-demand, allowing small orders to be produced economically and close to the customer. Founded in 2019, the company has received investment from Lockheed Martin Ventures and, most recently, Yamaha Motor Ventures; secured key government contracts with both the U.S. Air Force and the National Aeronautics and Space Administration (NASA); and established clients in the automotive, aerospace, defense, heavy machinery, oil & gas, and construction industries.

Currently, metal parts manufacturing relies heavily on creating dies that can mold and form sheet metal on mass. This process is not only expensive, but can make the lead time between part ordering and delivery upward of 50 weeks. While the method can work long term with economies of scale, it does not allow for agility or reactiveness in the design and implementation process. Machina Labs technology removes the need to make expensive dies (which can be up to US$1 million per die design) and allows for a modular, flexible approach to be taken toward metal parts manufacturing.

Once the design for a part has been confirmed by the customer, the robot gets to work manipulating the metal, with the time between design and manufacturing only taking a few hours. Built in AI-driven sensors guide the process, ensuring precision, and data are continuously gathered to develop a digital twin of the created part. While primarily working with steel and aluminum, the robots can also form parts out of materials like titanium at room temperature, expanding the company’s reach into different industries. Each station is referred to as a “work cell” with 11 currently in operation. The company uses both Fanuc and Kuka robots in the work cells.

While currently operating out of its Los Angeles facility, Machina Labs’ model enables deploying its work cells in small facilities near a customer’s final manufacturing site, or even deploying them inside a customer’s facility provided they have the basic infrastructure to support the equipment. Companies can either outsource their manufacturing completely to Machina Labs, use the work cells on a contract basis with a monthly payment, or buy the work cells outright with 3 years of service included. These alternative models enable companies to control and structure their supply chains according to current requirements, facilitating both adaptability and scalability to upstream supply that is more difficult to achieve through traditional part procurement.

The ability to design and manufacture a part within a matter of hours has key benefits to companies working on new product designs, providing ongoing maintenance, or trying to reduce inventory costs. For example, Machina Labs is working with a satellite company that currently orders one part per week, tweaking the design and testing the new part each time before settling on a final design. It is also working with a commercial space transportation and aerospace manufacturer, which is ordering roughly 50 units of a specific part each week that can go straight into the next stage of the building process, avoiding the need to bulk buy and establish storage. Typical inventory management costs can be as high as 20% to 30% of total inventory value, and trying to forecast required units can be a big operational challenge.

Being able to adapt the manufacturing process by uploading a new design to the work cells and reducing the delivery time down to a matter of days aligns strongly with growing Manufacturing-as-a-Service (MaaS) demand, not only allowing for production flexibility, but also providing greater manufacturing access to Small and Medium Enterprises (SMEs).

Machina Labs is positioning itself as a strong provider in the metal fabrication services market that is being characterized by MaaS offerings and the use of automation to leverage profitability. Its competitive advantage lies in the ability to move a very adaptable manufacturing process closer to companies’ final manufacturing points, reducing the supply chain cost and risk, but continued growth will rely on the ability to service higher volumes at an effective price point. The supply resiliency benefit is an easy sell, particularly when securing government agency contracts, but cost will play a much more important role for private company partnerships that are seeking less customized manufacturing.

Assessing this type of part manufacturing in a wider context, it is difficult to ignore companies’ growing interest in localized sourcing. While some have seen nearshoring and onshoring initiatives as political jargon, incentives to source goods closer to home are starting to impact procurement decisions, and many C-suite executives are prioritizing resiliency in the face of recent macroeconomic challenges. Localized inventory placement is seeing a boom to speed up final delivery to consumer, but overall time to market can only be reduced if parts can be received quickly to develop the final products. Taking a product from concept to delivery is ultimately hindered by supply chain constraints; reducing those through this type of solution can greatly increase a producer’s addressable market.

There is also an opportunity with this type of manufacturing to provide greater end-to-end visibility for both companies and the final consumer. On-demand manufacturing with full automation better supports Just-in-Time (JIT) supply models, but partnerships with companies will be stronger if full visibility of the production and delivery process is provided. Scalability to more industries and higher volumes are expected as the technology matures, but providers should be conscious of current delivery capabilities and be completely transparent with the downstream supply chain. Marrying speed with market and volume is a big ask, but continued investment in and leveraging automation will be critical for industries to build more precise and reactive supply chains.