The 'app' designed for energy providing companies allows a company to profile its customers based on their predicted consumption pattern for advanced and efficient energy consumption planning. It covers the entire customer journey from onboarding to consumption in order to forecast and monitor patterns.

The ‘app’ considers knowledge of the sun’s path, the atmospheric conditions and the characteristics of a solar energy plant which utilizes the sun’s energy to create solar power that is subsequently transformed to electric power. It thus predicts the solar power generation and forecasts the load for the next 24 hours.

This ‘app’ relies on identifying key differentiators that divide customers into different groups of individuals who are similar in specific ways relevant to marketing.

The ‘app’ aims at calculating short term energy demand to predict the day-wise electrical energy usage till the next billing date and corresponding potential revenue at the overall business level. The ‘app’ helps energy producers negotiate contracts with financial entities or utilities that distribute generated energy.

The ‘app’ helps identify significant factors in mining processes that cause productivity loss and low throughput. It provides direction to influence the significant drivers to improve productivity. This helps in diagnosis and analysis of constraints and deviations, improved operational performance and efficient utilization of mining machines.

Situational Awareness, the ‘app’, for water utility industry has been developed to help a company to improve its customer service, operational effectiveness and environmental performance. It provides a single screen view to recognize, visualize and act on events by drawing intelligent conclusions for the incident on how they relate to each other.

It involves determining the level of leakage within a defined area of the water network. Water flow is periodically analyzed in order to monitor the levels of leakage [specially night leakages] and high consumption locations within a defined area of the water network.

Wipro Treatment Plant optimization ‘app’ offers a holistic 360 view of operations. It enables efficiency gains across sites, predicts and prevents water and air pollution incidents, enhances field productivity, optimizes site processes and chemicals usage, predicts faults within the treatment plant by leveraging suitable Artificial Intelligence (AI) predictive models, big data exploratory and analytics platform to correlate IT and OT data.

Helps save high-valued critical assets from unprecedented, catastrophic damage by detecting occurrence of predefined events (anomaly) from real-time sensor feeds to multiple sources.

Understands different characteristics of customers and classifies them into actionable number of customer archetypes. In addition, identifies drivers that influence overall customer satisfaction.

Facilitates anomaly detection, fraud prediction, and predictions on the profitability of energy trading transactions.

Analyzes risks associated with mines and plants related to assets, competency and environmental impact.

Measures individual asset health scores for all sites and accurately predicts the timeline for recommended actions to prevent unexpected failure or unscheduled downtime of assets.

The ‘app’ helps predict the expected time till failure in drilling assets. Also, helps identify the possible reasons for failure thereby increasing the run-life while reducing cost.

The ‘app’ helps identify machines that have high probability of failure along with the path to failure. This helps to significantly reduce the processing lead time, optimize cost of service and increase productivity.

The ‘app’ aims at proactively maintaining and preventing unplanned downtime of electrical conductors. It helps ascertain the exact root causes for conductor failure and increase operational efficiency.

The ‘app’ quantifies the sales impact of various marketing activities and helps simulate marketing scenarios for ‘what-if’ analysis. This helps the marketing manager re-allocate the marketing budget proportionately to evaluate the direct impact on sales/value.

The ‘app’ provides insights on work order capacity; understand the work performance and job costing for water utilities. Also helps in job forecasting to provide insights on skills needed, resource planning and workforce composition.

The ‘app’ helps understand the customer response to the services offered by a utilities provider and assesses the customers churning out as well as new customers rolling in. The solution helps to better plan and utilize resources to prevent churn, onboard new customers and consistently increase profitability.

The solution gives a better understanding of the customer behavior. This helps to better plan and utilize resources to prevent churn, onboard new customers and continue to increase profitability.

Allows the company to successfully manage the workforce by forecasting the call volumes on a particular day. Explores change in call volumes during the days or weeks in order to meet demand without under-staffing or over-staffing, change in call volumes.

Saves high-valued critical assets from unprecedented and catastrophic damage. Also helps in understanding of causes behind catastrophic events based on myriad data sources.

The rig state of the drilling machine is identified based on applying logic on machine sensor data. Stream Analytics Job consumes data in real-time to provide instant insights.

The app uses statistical models for forecasting the demand based on historical unit sales. It also accounts for external factors such as crude oil price and number of oil rigs in production to qualify as control variables.

Predicts which customer will experience a bill shock by receiving consumption data from digital meters/ billing cycles and cumulating the current consumption patterns for each customer over a year. Helps customers control their costs and manage their spend on water better.

Based on Artificial Neural Networks, the solution predicts the probability of occurrence of sewer flooding incidents in various geographic regions based on influential factors.

The objective of the app is to minimize odor related incidents by real time analysis of data from various sensors, establish linkage of the spikes in sensor data with incidents and complaints, and depict the information visually to facilitate timely mitigation.

Prediction of effluents concentration based on historical sensor data enables a workflow where the potential breach can be identified and remedial action can be taken proactively.