FIELD

The field relates generally to information processing systems, and more particularly to techniques for processing resource information in such systems.

BACKGROUND

Commonly, the manufacturing process for computing devices such as laptop and/or desktop computers includes stacking manufactured items after a building stage, organizing the stacked items for software installations, and de-stacking the items for testing and packaging. However, conventional manufacturing management approaches face challenges including, for example, efficiently identifying empty shelf space for stacking manufactured items. Such challenges result in delays in the overall manufacturing cycle time, inefficient use of human labor, and non-optimal usage of manufacturing resources.

SUMMARY

Illustrative embodiments of the disclosure provide techniques for device manufacturing cycle time reduction using machine learning techniques. An exemplary computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment, determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input, and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

Illustrative embodiments can provide significant advantages relative to conventional manufacturing management approaches. For example, challenges associated with delays in manufacturing cycle time, inefficient use of human labor, and non-optimal usage of manufacturing resources are overcome in one or more embodiments through determining manufacturing resource availability in real-time using machine learning models and other machine learning techniques.

These and other illustrative embodiments described herein include, without limitation, methods, apparatus, systems, and computer program products comprising processor-readable storage media.

