Data Sciences

Data Sciences

Predictive & Prescriptive Analytics

With volumes of data coming from several sources, one requires an approach to get an insight about the data patterns, recurrences, behaviors and outcomes. Our team works on several algorithms, data models and neural network based AI decision making systems for improving ports and terminal performances. Our data scientists have developed algorithms, for failure prediction, root cause analysis, anomaly detection, best outcome planning and several other data models. We have a dedicated team working on linear programming with advanced optimization tools for port performance optimizations.

With our more than a decade of implementing solution in Enterprise Asset Management and Operations Management solution, we have acquired deeper knowledge and industry insights based on these experiences. We translate this knowledge to provide predictive and prescriptive analytics to our customer.

Predictive Analytics

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 providing a 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 to gain competitive advantage. The need for predictive analytics has grown over the years due to:

  • Growing volumes and types of data, and more interest in using data to produce valuable insights.
  • Faster, cheaper computers, and 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.

Anomaly Detection

A successful anomaly detection system is not just about a sophisticated algorithm for detection. It 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 detections system itself. In addition, some systems benefit from planning and scheduling algorithms.

It includes:

  • 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 – ensures 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 therefor cost.
  • Reporting to justify cost of ownership of anomaly detection system – surfaces key detection events and cost savings to key stakeholders.

Failure prediction

Our well-defined tools can identify various modes of failure within a system or process. In many situations, for a major problem that is detected in the process or product, one requires manual review any existing failure prediction in relation to the problem. Our algorithms automate entire process to identify the failure prediction accurately. This includes:

  • Listing the current problem as a failure mode of the design or process.
  • Identifying the impact of the failure by defining the severity of the problem or effect of failure.
  • Listing of all probable causes and how many times they occur.
  • While reviewing a process failure prediction, review the process flow or process diagram to help locate the root cause.
  • Identifying the Escape Point, which is the closest point in the process where the root cause could have been detected but was not.
  • Documenting any controls in place designed to prevent or detect the problem.
  • Listing 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.
  • Carrying any identified actions over to the counter-measure activity of the root cause analysis.

Root Cause Analysis (RCA)

Recurring 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 think 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 the 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 are:

  • Processes and Procedures.
  • Quality Control problems.
  • Equipment Health check and Incident Analysis.
  • Safety-based situations or Accident Analysis.
  • Failure Analysis in Engineering and Maintenance.
  • Change Management or Continuous Improvement Activities.

Performance Forecasting

We have developed an advanced forecasting analysis (reliability, availability, maintainability), a powerful performance forecasting analysis tool, for predicting the asset performance in the upstream various industry. The analysis tool allows for:

  • 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

This relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to guide them towards a solution. In a nut-shell, these analytics are all about providing advice. Prescriptive analytics attempts to quantify the effect of future decisions in order to decide 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, providing recommendations regarding actions that will take advantage of the predictions.

Prescriptive analytics are 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 and 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 Expertise:

Our solutions are integrated seamlessly with IBM Maximo, SAP, Oracle, MES Systems, IBM Blue mix and other enterprise solutions.

We extensively are work 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.


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