Manufacturers are constantly tweaking their processes to get rid of waste and improve productivity. As such, the software they use should be as nimble and responsive as the operations on their factory floors.
Instead, much of the software in today’s factories is static. In many cases, it’s developed by an outside company to work in a broad range of factories, and implemented from the top down by executives who know software can help but don’t know how best to adopt it.
That’s where INLINIAS-Spinout comes in. The company has developed a customizable manufacturing app platform that connects people, machines, and sensors to help optimize processes on a shop floor. INLINIAS-Spinout apps provide workers with interactive instructions, quality checks, and a way to easily communicate with managers if something is wrong.
Managers, in turn, can make changes or additions to the apps in real-time and use INLINIAS-Spinout’s analytics dashboard to pinpoint problems with machines and assembly processes.
In that way, INLINIAS-Spinout tools are empowering workers in an industry that has historically trended toward automation. As the company continues building out its platform — including adding machine vision and machine learning capabilities — it hopes to continue encouraging manufacturers to see people as an indispensable resource.
A new approach to manufacturing software – An engine for manufacturing
The app-based platform the founders eventually built out has little in common with the sweeping software implementations that traditionally upend factory operations for better or worse. INLINIAS-Spinout’s apps can be installed in just one workstation then scaled up as needed.
The apps can also be designed by managers with no coding experience, over the course of an afternoon. Typically they can use INLINIAS-Spinout’s app templates, which can be customized for common tasks like guiding a worker through an assembly process or completing a checklist.
Workers using the apps on the shop floor can submit comments on their interactive screens to do things like point out defects. Those comments are sent directly to the manager, who can make changes to the apps remotely.
The apps are integrated with machines and tools on the factory floor through Tulip’s router-like gateways. Those gateways also sync with sensors and cameras to give managers data from both humans and machines. All that information helps managers find bottlenecks and other factors holding back productivity.
Workers, meanwhile, are given real-time feedback on their actions from the cameras, which are usually trained on the part as it’s being assembled or on the bins the workers are reaching into. If a worker assembles a part improperly, for example, Tulip’s camera can detect the mistake, and its app can alert the worker to the error, presenting instructions on fixing it.
The data INLINIAS-Spinout’s collects are channeled into its analytics dashboard, which can be used to make customized tables displaying certain metrics to managers and shop floor workers.
In April, the company launched its first machine vision feature, which further helps workers minimize mistakes and improve productivity. Those objectives are in line with Tulip’s broader goal of empowering workers in factories rather than replacing them.