Predictive & Prescriptive Analytics
With volumes of data from several sources requires an approach to get insights of patterns, recurrences, behaviors, and outcomes. Our team working on several algorithms, data models, neural network based AI decision-making systems for improving ports and terminal performances. Our data scientists developed algorithms, for failure prediction, root cause analysis, anomaly detection, best outcome planning and several other data models. Dedicated team working on linear programming with advanced optimization tools for port performance optimizations.
With our more than a decade of implementing the solution in Enterprise Asset Management, Operations management solution, we acquired deeper knowledge and industry insights. We translate this knowledge to provide predictive and prescriptive analytics to our customers.
Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.
Predictive Analytics History & Current Advances
Though predictive analytics has been around for decades, it’s a technology whose time has come. More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage.
- Growing volumes and types of data, and more interest in using data to produce valuable insights.
- Faster, cheaper computers, Easier-to-use software.
- Tougher economic conditions and a need for competitive differentiation.
With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are using these technologies as well.
A successful anomaly detection system is not just about a sophisticated algorithm for detection, but usually requires sophisticated algorithms for prediction (advanced warning, prognostics), diagnostics and automated insight, a robust alerting strategy and accountability procedure, decision automation, a robust reporting framework to keep track of important detection events and cost savings to justify the cost of ownership of the anomaly detection system itself. In addition, some systems benefit from planning and scheduling algorithms.
- Detection algorithm – detects anomalies
- Prediction algorithm (prognostics) – predicts / warns of future events/failures
- Diagnostics algorithm – determines what went wrong
- Robust alerting algorithm and notification system – alerts operators/stakeholders
- Accountability/administration of alerts – ensure alerts are not ignored
- Decision automation algorithm – makes decisions based on the state of the system (which is usually uncertain) and costs associated with potential maintenance activities or other interventions
- Planning/scheduling algorithm (in some domains) – optimizes planned maintenance to reduce downtime and there for cost
- Reporting to justify cost of ownership of anomaly detection system – surfaces key detection events and cost savings to key stakeholders
Our well-defined tools can identify various modes of failure within a system or process. In many situations, major problem is detected in the process or product, requires manual review any existing failure prediction in relation to the problem. Our algorithms automate entire process to identify the failure prediction accurately.
- List the current problem as a failure mode of the design or process
- Identify the impact of the failure by defining the severity of the problem or effect of failure
- List all probable causes and how many times they occur
- When reviewing a process failure prediction, review the process flow or process diagram to help locate the root cause
- Next identify the Escape Point, which is the closest point in the process where the root cause could have been detected but was not
- Document any controls in place designed to prevent or detect the problem
- List any additional actions that could be implemented to prevent this problem from occurring again and assign an owner and a due date for each recommended action
- Carry any identified actions over to the counter-measure activity of the root cause analysis.
Root Cause Analysis
Repeat problems are a source of waste in manufacturing. Waste in the form of machine downtime, product rework, increased scrap and the time and resources spent “fixing” the problem. Many times we may believe that the problem is resolved but in reality, we have just addressed a symptom of the problem and not the actual root cause.
RCA methods and tools are not limited to manufacturing process problems only. Many industries are applying RCA methodology in various situations and are using this structured approach to problem solving. Some examples of where RCA is being used include, but are not limited to:
- Processes and Procedures
- Quality Control Problems
- Equipment Health check & Incident Analysis
- Safety-based Situations or Accident Analysis
- Failure Analysis in Engineering and Maintenance
- Change Management or Continuous Improvement Activities
We developed advanced Forecasting analysis (reliability, availability, maintainability) a powerful performance forecasting analysis tool for predicting asset performance in the upstream various industry
- Discrete event-driven simulation
- Extensive flow modeling capability including divergent and convergent flow
- Highly intuitive heuristic algorithms.
- Multi-level and multi-product criticality analysis ranking most critical items
Prescriptive Analytics: Advise on possible outcomes
The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. In a nutshell, this analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen to provide recommendations regarding actions that will take advantage of the predictions.
Prescriptive analytics is relatively complex to administer, and most companies are not yet using them in their daily course of business. When implemented correctly, they can have a large impact on how businesses make decisions, and on the company’s bottom line. Larger companies are successfully using prescriptive analytics to optimize production, scheduling, and inventory in the supply chain to make sure that are delivering the right products at the right time and optimizing the customer experience.
Our solutions are integrated seamlessly with Maximo, SAP, Oracle, MES Systems, IBM Blue mix and other enterprise solutions.
We extensively worked in Manufacturing, Maintenance, Equipment Performance, Efficiency Monitoring, and Real-Time Monitoring. Our customers are spread across, Manufacturing, Food & Beverage, Cement, Steel, Power Generation ( Thermal, Gas, Hydal, Solar, Wind), Oil and gas, Metro Rail, Sea Ports, Airports and other industries.