Use prescriptive insights to contextualize AI predictive analytics and determine next best actions
Artificial intelligence doesn’t just help you predict when your assets and processes are trending to failure. It provides prescriptive insights that help you understand the underlying reason for the problem and determine which corrective actions you should take to resolve it most efficiently.
What are prescriptive insights?
Prescriptive insights are actionable recommendations derived from analytics processes to achieve desired outcomes in response to expected events. They provide the answer to the question: “how can we make X happen instead of Y?”
Prescriptive insights are commonly used to help decision-makers contextualize real-time data vs. predicted behavior on their assets, for faster root cause analysis of problems and providing support to identify corrective actions.
Prescriptive insights in manufacturing
Prescriptive insights in oil and gas
Prescriptive insights in renewables
Improving energy output of renewable power plants is key to reducing the cost of energy. Prescriptive insights improve operational outcomes by enabling analysts and technicians to spend less time processing and managing data and more time working on solutions.
How does artificial intelligence enable prescriptive insights?
Employing machine learning to develop predictive models, natural language processing (NLP) to automate workflows of unstructured natural language data, and other AI techniques, SparkCognition’s predictive and prescriptive analytics combine to deliver real-world, high-value outcomes—like preventing asset failures, improving shipping logistics, detecting fraud, and optimizing renewable energy platform production.
Gain next best action recommendations to improve throughput
- Get recommendations for next best actions to avert accidents, repair and service components, and optimize processes.
- Run complete system health checks.
- Create alerts customized for your key operational metrics.
Reduce research time spent on root cause analysis
- Empower your maintenance staff to proactively undertake root cause analysis.
- Eliminate maintenance guesswork and reduce the time to resolution.
- Prevent unnecessary repairs.
- Increase throughput and uptime by reducing the time required to optimize performance.
- Reduce costs of replacement parts and spare inventory by reducing unnecessary maintenance.
Capture tribal knowledge and upskill your workforce
- Automate workflows to extract, organize, and interpret tribal knowledge from unstructured sources.
- Enable machine learning models to use millions of potential associations.
- Make prescriptive insights available to SMEs and maintenance personnel about what caused a particular event and why.
AI-powered prescriptive insights across industries
AI-powered prescriptive insights in manufacturing
AI-powered prescriptive insights in offshore oil and gas
Prescriptive insights aid root cause analysis at an extremely granular level, so you can stop issues from recurring and identify new opportunities to improve plateaued utilization.
AI-powered prescriptive insights in renewables
Discover how SparkCognition’s natural language processing solution enables you to identify non-obvious hazards in the workplace. Proactively mitigate health and safety threats, ensure the safety and health of your workforce, and reduce lost work time and risk of potential litigation.
Increasing the benefit of predictive maintenance with prescriptive insights
How SparkCognition delivers prescriptive insights to identify non-productive time on oil platforms
Non-productive time consumes as much as 20-25% of all platform operating time each year. For offshore platforms, this can add up to billions of dollars in lost revenue.
SparkCognition’s customer, a major upstream E&P operator, wanted to better categorize and analyze platform activities to track and eliminate non-productive time and invisible lost time.
Step 1: Leveraging natural language processing
Our natural language processing platform, SparkCognitionTM Deep NLP successfully categorized platform activity, labeling each activity with a code and sub-code for ease of analysis.
Step 2: Providing prescriptive insights
Deep NLP provided new insights into the most frequently occurring activities on the platform with high accuracy, such as identifying the 46 most frequently occurring code and sub-code combinations.
Step 3: Determining events and causes
Using SparkCognition’s prescriptive insights, the E&P operator effectively pinpointed non-productive time and invisible lost time, as well as their causes, automating a process that would have required significant human labor and time.