ENERGY AND UTILITIES
One of the major drivers to plan for future is to recognize unseen patterns and trends to anticipate fluctuations. Predictive analytics can assist in finding both new and historical trends, outcome of which can be combined with other correlated factors to create a plan, helping organizations to become proactive to cope-up with market demand and trends. Organizations operating in energy and utilities are required to forecast demand and load respectively. In the case of utilities, it is mandatory to submit an accurate requirement for load forecasts at regular intervals. , A huge chunk of data is generated from oil wells, utility grids, gas grids, smart grids, and other sensors. This terabyte of data from both structured as well as unstructured sources is needed to be analyzed and get real-time insights, hence the industry is in search of advanced analytical tools to get actionable insights. Additionally, considering the transformation in the energy and utility sector a huge possibility of facing unprecedented challenges such as rising cost of operations, changing regulations, environmental concerns, and meeting the changing consumer expectations. To cut the excess costs and manage the resources are some of the prominent factors driving adoption of advanced tools in the respective industries. The deployment of technologies such as Advanced Metering Infrastructure (AMI) and Supervisory Control and Data Acquisition (SCADA) systems is also helping to improve the amount and quality of data that utility sector has on supply and distribution.
COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY
The vendors of predictive analytics software in energy and utilities are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 21 vendors evaluated in the data quality tools market include Alteryx, Inc, Angoss Software Corporation, Dataiku, Domino Data Lab, Exago, Inc., Fair Isaac Corporation (Fico), Good Data, IBM Corporation, Information Builders, Inc., Knime Ag, Microsoft Corporation, Microstrategy Incorporated, NTT Data Corporation, Oracle Corporation, Qliktech, Inc., Rapidminer, Inc, SAP SE, SAS Institute Inc, Sisense, Tableau Software Inc and Teradata Corporation.
Use Cases of Predictive Analytics in Energy and Utilities
Business Planning and forecasting: With predictive analytics, clients can gain visibility into the business planning process and forecast future demands. Predictive analytics help clients identify future demands by analyzing historic data patterns and factors that are expected to affect the demand in the future.
- Reduced asset maintenance costs: There is a growing demand for solution that can provide an accurate prediction of failures, events and outcomes so that clients can avoid unexpected failure costs like expense of field asset in service, damage cost/disposal of damaged utility asset, and other intangible costs. Predictive analytics will help the clients in eliminating the overhead of unplanned asset maintenance and reducing fixed costs.
- Improved customer satisfaction - Adding predictive intelligence competences to the existing systems can help clients to control & avoid asset failures, outages, and penalties. Therefore, planning & prioritizing asset maintenance activities and informing customers before failure strikes will help achieve improved customer satisfaction.
- Improved safety and compliance system - Predictive asset analytics system enables utilities to address possible safety risks and quickly take any appropriate operational action and mitigate safety risks.
- Increasing workforce utilization: Using predictive analytics solutions to handle more number of sites with the same workforce, better route planning and optimization of field crews
- Measuring and optimizing Asset performance and health: Using predictive analytics solution to gather crucial data from field assets in real time and use it to gain visibility into asset health and their condition. Moreover, it will help organization define short-term maintenance and long-term capital replacement strategies.
- Predict customer payment behavior: Predictive analytics help identify customers facing difficulty paying their bills. Being able to predict payment behavior allows organizations to focus on accounts that are likely to fall into the collections process and stop customer churn before it happens.
Case Studies of Predictive Analytics in Energy and Utilities
- Business Planning: Client wanted visibility into the business planning process which included analyzing future demands and to come up with a solution to address these demands. Predictive analytics help the client to identify future demands by analyzing historic data patterns and factors that are expected to affect the demand in the near future
Case Study: SAP SE helps Hunt Consolidated Inc to deploy business planning solution
SAP SE helped US based oil and gas provider, Hunt Consolidated Inc to deploy centralized planning solution using SAP Business Planning. The solution used SAP predictive analytics to predict future demands by analyzing historical consumption trends
- Improved operational efficiency and turnaround time
- Visibility into business planning process
Increasing business performance: The client was looking to predict machine downtime to increase asset performance.
Case Study: Hortonworks helps Noble Energy to increase business performance
Nobel energy is an American oil and natural gas exploration company headquartered in US. The company was looking for a predictive maintenance solution and deployed Hortonworks Data Platform, which used predictive analytics to predict machine downtime.
- Decreased machine downtime
- Predictive Maintenance: Client wanted visibility into asset health to minimize unscheduled downtime. Predictive analytics solutions helped the client to monitor machine health in real time so as to safely predict machine downtime
Case Study: Genpact helps Duke Energy to optimize asset utilization
Genpact helped Duke Energy one of the top provider of wind and solar energy solution implement a predictive maintenance solution using predictive analytics. The company designed Intelligent Process Insights Engine (IPIE), which integrates all the data received from various assets. The data gathered was used for decision making to increase asset uptime and reduce maintenance cost
- Proactive suggestion on repairs and scheduling preventive maintenance
- Real time machine health information
- Business Forecasting: Client wanted one single solution to accurate forecast business requirements for budget forecasting. Client used predictive analytics to predict future demand from historical data sets which helped to accurately predict future outcomes
Case Study: Adaptive Insights helps Dolphin Drilling for budget allocation and forecasting
Adaptive Insights provided automated data collection process to Dolphin Drilling which helped the company to reduce the budget allocation process. The company also provided Dolphin Drilling with scenario planning helping the company to do a comparison of various factors and their effect on the company bottom line
- Allowed the company to make accurate rolling forecast
- Budget allocation process was reduced from days to minutes.
- Predictive Maintenance: Client wanted to reduce asset downtime and take real time decisions to reduce asset wear and tear and avoid any production loss. The company deployed predictive analytics which helped to predict abnormal machine behavior and take preventive action.
Case Study: Microsoft deployed Azure Machine Learning and Azure IoT Edge to help Schneider Electric reduce asset downtime
Microsoft deployed Azure Machine Learning and Azure IoT Edge to help Schneider Electric reduce asset downtime. Microsoft deployed analytics capabilities at the edge device which helped the company to identify and take preventive actions in real time.
- Increased operational efficiency
- Increased agility of maintenance services
- Wireless predictive maintenance: Client was looking for wireless predictive maintenance solution which would be security complaint, environment robust and provide cost effective monitoring of assets.
Case Study: Petasense helps Arizona Public Service to deploy wireless predictive maintenance programs
Arizona Public Service is largest utility company in Arizona and was planning to enter into the California energy market. In order to do so the company wanted to ramp up its production of one its power plant in the region. The company deployed Petasense wireless predictive maintenance solution which not only helped in reducing machine downtime but was also compliant with energy regulations in the region.
- 13 defects were detected in the first 6 months of the installation
- 70% reduction in high-speed amplitudes
- Demand Forecasting: Client was looking for a predictive analytics to help them with forecasting energy requirement in the future.
Case Study: SAS helps Northern Virginia Electric Cooperative (NOVEC) to predict power consumption
NOVEC not being an energy provider, had to estimate future requirement of power by its customer s so that the company can have buy sufficient power so that it can offers competitive pricing to its customers. SAS helped the company to build a model where it extracted data from third party weather forecast and their economic condition
- 50 time series model were build
- The solution provided had 21.7% improvement on comparison with other competing models
- Increasing workforce utilization: Client was looking to handle more number of sites with the same workforce.
Case Study: Seven lakes helps Oasis Petroleum to increase workforce utilization and enhance business performance
Oasis Petroleum is petroleum and natural gas exploration company headquartered in the US. The company equipped is workforce with used Seven Lakes JOYN FDG enabling workers to have optimized preventive maintenance plans
- Automate day to day business process
- Drive business performance by covering more wells at optimized cost
- Increasing Asset performance: Client was looking for a predictive analytics solution capable of fetching, cleaning analyzing and integrating data from field assets. To help the client with the above objective, TCS deployed an analytics-driven energy efficiency management system.
Case Study: TCS helps Anglian Water to maximize asset performance
Anglian Water is a water utility company operating in East of England. The company was looking to implement a telemetry system to integrate data from field assets in real time. TCS implemented an advanced analytics telemetry systems where users can create KPI driven operational target to increase asset performance.
- The analytics system deployed saves £50,000
InfosysCase Study: Infosys helped a multi-national natural resources company to optimize costs for its fleets
Infosys developed a data driven predictive analytics and visualization solution to support decision making and enhance freight operations for a company that manages freight worth more than USD 1 billion annually. The AI driven predictive insights delivered were essential to enhance safety and profitability of the fleets.
- Visibility into –
- Fleet position, route, and speed for optimal operations
- Profitable and unprofitable voyages/contracts and the corresponding root cause(s).
- Safety incidents during voyages
- Performance of internal charter team members
- Streamlined freight operations thereby delivery annual savings of USD 100 million in bunker and freight costs
Predictive Analytics Software in Energy and Utilities Quadrant
Find the best Predictive Analytics Software solution for your business, using ratings and reviews from buyers, analysts, vendors and industry experts
- Product Quality and Reliability
- Support for Custom Data Connectors
- Custom Scripting Language
- Deployment Type
- Hybrid (Deployment type)
- Target Users
- Database Administrators
- Business Analysts
- Data Scientists
- Non Technical Users
- Application Developers
- Support for Languages
- Support for R
- Delivery Mode
- Separate Platform
- As a Service / Connector Free
- Add-on Funtionalities
- Machine Learning / AI
- Streaming / Real-Time
- Mobile Support / Mobile BI
- Product Features and Functionality
- Integration with Big Data Frameworks / Data Stores
- Apache Spark
- Enterprise Features
- Analytics Workflow
- Shared Data Sources
- Server Side Data Processing
- Cloud Hosted Data
- Licensing - Data Volume
- Costs & Units
- Cost - $ per license
- Hybrid (Please specify)
- Core Features
- Visual Analytics Design / Code Free
- Data Investigation
- Statistical Modelling
- Times Series Exploration
- Root Cause Analysis
- Advanced Condition Prediction
- Predictive Grouping
- No. of Third Party Data Providers
- Natural Language Processing (NLP)
- Event Detection
- Breadth and Depth of Product Offering
- Data Management
- Data Preparation (Data Management)
- Interactive Visualisation
- Real Time Dashboarding
- Static Visualisation
- Report Generation
- Data Blending
- Report Automation
- Data Collections
- Customer Data
- Transaction Data
- Geo Spatial
- Location [Pincodes]
- Marketing Data
- Use Cases
- Business Intelligence
- Data Visualisation
- Customer Response Modelling
- Demand Forecasting
- Data Preparation
- Operations Management
- Fraud Detection & Prevention
- Pricing Elasticity Analysis
- Location Intelligence
- Risk Management
- Customer Data Platform
- Sales and Marketing Management
- Network Management
- Workforce Management
- Supply Chain Management
- Web and Social Media Management
- Financial Management
- Root Cause Analysis (Use case)
- Predictive Maintenance and Asset Management
- Event Detection (Use case)
- Services Offered
- Support and Maintenance
- Custom Predictive Algorithms
- Requirement Definition
- Managed Services
- Report Authoring
“Navigate through Journey of Hypotheses"
“Worth the price"
“Decision making made easier with this data analysis program."
The software’s ability to organize and use variable for tool application is what works best for me. Evaluation of the behavior of dependent and independent variables for linear regression analysis makes it easy to compile reports, further enabling easier decision making. It is also extremely user-friendly, with each icon distinctly visible. If I had to pick an area of improvement, I would say it is the quality of its graphics. They do not seem very professional and perhaps they can be updated to seem so.
I believe that this is an ideal software for organizations lookimg to systemize its data and work using dependent as well as independent variables. It works excellently to present inferential statistics to help organizations grow. However, if you’re looking for exceptional graphics, then this might not be the one for you.