A financial services company was able to integrate and curate multiple data sources and then add Kassandra’s predictive intelligence capabilities to support strategic and tactical decision making. Within weeks of implementation, the company experienced a 50% reduction in reporting time due to the automation of forecasting and reporting. It also resulted in a 15% drop in headcount utilization as it enabled users to easily build datasets on their own, which was 5x faster, and without any technical expertise required. With Kassandra’s automated machine learning tool, we were able to add plug & play insights without the need for a dedicated resource, allowing teams to focus on value-added work.

These datasets proved useful for business intelligence reporting as well using Kassandra’s deep probabilistic forecasting capabilities.

An organization that owns and operates a large cattle herd across multiple cities was able to address prevailing complexities involved in the planning stage of biological supply chains. Using Kassandra’s forecasting capabilities, we were able to leverage its existing ERP systems in a more structured way, which enabled the organization to identify potential deficits and opportunities to make long-term predictions on the products that were ready to sell.

Post implementation, the organization was able to forecast production for any given future week, on-demand, compared with at least half a day previously to generate a single week’s production advice. This helped the organization to reduce planning time per month from 2 FTE days to 0 and increase its forecast window by 400% to one month (from 1 week previously).

An organization involved in the agriculture space was able to completely replace its manual biological supply chain processes to record and maintain data, enabling on-demand projection views by global teams. We used Kassandra’s automated machine learning capabilities to populate insights into reporting views, thereby increasing forecast fulfillment by 220% to $5.3 million each week.

The Trabeya team (comprising Data Scientists, Engineers, and Business Analysts) built a modular system that complemented its existing ERP process to provide a dynamic view of available resources and production; this was done via an enterprise forecast tool to predict quality, quantity and availability of agriculture produce. The connections and creation of AI engines in the cloud removed any additional load as well as custom solutions that pose processing limitations.