LATHRUP VILLAGE—Industry 4.0 is the catchall term for recent advances in computing power, data analysis, sensor technology, wireless data speed, and even battery technology—which combine to make it possible to use sensors and software to improve safety, quality, and productivity in manufacturing.

Wednesday, well over 100 people gathered at The Mint conference center to hear the latest news on these new technologies, in a “Manu-Tech Pitch Event” convened by the Michigan Economic Development Corp. and the Centrepolis Accelerator at Lawrence Technological University.

Officials with the MEDC and its Pure Michigan Business Connect program talked up their efforts to spread I4.0 technologies among Michigan manufacturers, including grants to implement I4.0 technologies ranging from a simple 3D printer to a huge data analysis project. Already, $2.2 million has been awarded to 95 companies in 40 of Michigan’s 83 counties.

Panel discussions zeroed in on I4.0 uses and benefits, including using machine vision and artificial intelligence to do a better job of detecting defects in manufactured goods. Speakers from companies like Lear, Magna, and GM also emphasized that they’re interested in investing in novel I4.0 technologies and startups.

Panelists also noted the impact that I4.0 technologies can have on a company’s carbon footprint. One panelist said that just by using “virtual twin” technology to lay out factory machines for maximum efficiency, a production center was able to cut its energy use by 30 percent.

And yes, robots are coming for jobs—but only the lousy ones. Panelists emphasized that robots are taking over jobs that are the so-called three D’s—dull, dirty, and dangerous.

Speaking of investing in I4.0 technologies, the event also featured a pitch competition for thousands in cash prizes. Five companies competed in two groups–early stage companies, those whose businesses are mostly still just an idea, and growth stage companies, established companies looking to get to the next stage

Winning in the growth company competition and a $10,000 investment was Gildform, Detroit-based developers of a platform giving jewelry artists on-demand design technology, production services, and fulfillment of orders. The platform even offers financing to jewelry designers through a partnership with a fintech firm. The runner-up winner of a $5,000 investment was Elm Park Labs, a Royal Oak company developing software for virtual reality, augmented reality, and mixed reality for use in training on everything from operations to safety to service to maintenance.

Winning in the early stage competition and a $10,000 investment was LightSpeed Concepts, an Albion-based company adapting 3D printing technology to the creation of sand molds used in the metalcasting industry. The company’s proprietary BlueNano sand binder makes the company’s offerings twice as fast as competitors at half the cost. The runner-up, winning a $5,000 investment, was Ulendo, a University of Michigan spinout that is developing software that makes 3D printers twice as fast by compensating for the vibration created by the printer.

Gildform also won the People’s Choice Award, a $5,000 prize awarded in a vote of attendees and sponsored by the patent law firm Ward Law.

Other companies presenting were:

Early stage: Amplio, an Indiana-based company developing a one-click procurement for businesses using advanced algorithms analyzing price and availability; Khenda, a company founded in Turkey that uses analysis of cell phone videos of manufacturing processes to conduct advanced time studies; and Savormetrics, a Canadian company that uses sensors and artificial intelligence to improve quality control of perishable groceries.

Growth stage: Capriol, a West Bloomfield Township-developer of mobile, autonomous robots for machine tending tasks in factories and warehouses; Kiuey, Detroit-based developers of a cloud-based system to streamline and automate the production part approval process in manufacturing; and DT4o, Farmington Hills-based developers of an artificial intelligence and machine learning-driven platform that turns machine monitoring data into predictive maintenance improvements, energy efficiency, and improved quality.

This story was written by Matt Roush.