GEMBO ( www.gembo.co ) has a SaaS Industrial IOT Platform which provides enterprises and users with mission critical data, insights and decision making tools enabling cost reduction and revenue growth through optimization via the use of IoT, Artificial Intelligence and Machine Learning. Farm Operator Intro A large-scale solar farm operator, managing a 50 MW solar farm with significant investments in infrastructure. The solar farm has a current efficiency of 24% and a target of increasing this efficiency over time. The farm operates in an area where electricity prices are approximately $32 per MWh, and it has a large operational budget dedicated to maintaining its equipment. GEM has deployed the Precare cloud, predictive maintenance and solar power prediction Package, on the customers Data, extracted from the solar farm's SCADA system. Problem Due to the complexity of solar equipment and environmental factors, the customer has faced a gradual decline in efficiency, dropping from 26% to 24% over the past few years. As a result, the farm's revenue has been impacted by lower power output, and operational expenses have been steadily increasing. Furthermore, the lack of predictive maintenance (PdM) has led to frequent downtime and unplanned maintenance, further increasing the inefficiencies. The farm owner reached out for a solution that could improve operational efficiency, reduce costs, and enhance overall power production by using predictive analytics to forecast and mitigate potential issues before they occur. Solution After evaluating the farm’s needs and existing system, GEM’s Predictive Maintenance (PdM) and Solar Power Prediction packages were discussed and found as the right fit. This solution was implemented through GEM’s Precare analytics platform, which helps monitor and predict performance, detect faults early, and optimize maintenance schedules. The key components of GEM’s solution included: Real-time Monitoring: Continuous monitoring of solar inverters, panels, and environmental conditions. Predictive Maintenance: Advanced machine learning models to predict the likelihood of system failures and performance degradation. Power Output Forecasting: Predictive analytics to model expected energy production based on historical data and real-time inputs, leading to more accurate forecasting and optimizing grid management. Results After deploying GEM’s predictive maintenance and power optimization system, GEM provided the customer a calculation of a 3 year business analysis Financial Benefits: Revenue Increase: The system’s efficiency would increase by an additional $864,320 in revenue over 3 years due to the increased power output from the higher efficiency (25% vs. 24%). OPEX Reduction: Predictive maintenance would reduce the monthly operational expenses by 3%, leading to OPEX savings of $78,810 over the 3-year period. Net Benefits: The customer will realize a total net benefit of $758,130 after deducting the initial system cost and subscription fees. ROI: The ROI of 658.13% was computed from the net benefits of $758,130 after accounting for system cost investments.

SELF SERVICE DATA STUDIO CASE STUDY  This case study showcases GEMBO, a leader in SaaS Industrial IoT Platforms, enhancing a global manufacturer's operations by deploying its Precare Data Studio BI product across 70 machines. The solution, focused on self-service data management and analytics, resulted in significant manpower savings, improved self-service efficiency, and automated analytics distribution. Unique to GEMBO Precare is its ability to deploy independent sensors, connecting any machine to its cloud, thereby offering predictive maintenance and operational insights without substantial capital expenditure, distinguishing it from other market solutions.