Autonomous Management Of The Production Line

Beyond the initial stages of crop production, the power of AI truly comes alive when it’s used in the manufacturing and processing arms of the food industry.

The tools of AI offer better visibility, automation, a predictive capacity to operations and finally, complete autonomous management of the food production processes. The Internet of Things (IoT) and computer vision enable end-to-end visibility in the supply chain network, and contribute to quality control, adaptation to rapidly changing market demands, and reducing wastage of resources every step of the way. For instance, it is possible to use AI in sorting and inventory, detection of pests in granaries, or for forecasting shelf life.

We are working towards a future, where the entire production line can be managed autonomously, with little to no human intervention, thereby contributing to a more sustainable, green future.

Our Services Include:

  • Autonomous management of critical production line assets
  • Computer vision for quality testing
  • Forecasting expiration dates for shelf-life
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Case Studies

Smart factory using digital operations, automation and AI

With the recent supply chain crisis, companies have realized the need to accelerate near shore and on shore manufacturing. This acceleration of inbound manufacturing coupled with the shortage of human labor is accelerating the digitization of factories. Our approach of promoting paperless operations, operations visibility, process automation, AI based autonomous process management and computer vision based quality defect reduction allows companies to thrive in this challenging environment.

A demand forecasting platform for a company with worldwide operations

We developed a platform, for a global food ingredients manufacturer, that can unify data from different repositories, both internal and external. Using this data, we developed an AI-based forecasting model that significantly improved the accuracy of both long and short-term demand forecasting for businesses as compared to their legacy forecasting applications.