The main aim of the system is a reduction in production outages, prevent unexpected equipment failure and reduce the monetary costs. This work describes the analytical platform for generating early warning systems (EWS) which predicts impending failures, condition-based monitoring, root cause analysis of failures and characteristics patterns, equipment management, monitoring, and visualization in an easy manner. The system predicts frequent failures ahead of the time to reduce downtime and increase the productivity of the progressive cavity pump.

Challenges – Handling big data, dealing with garbage data, handling asynchronous data, addressing missing values, false positive rate.

Our solution comprises the set of modules to address above mentioned challenges using advanced data processing and machine learning techniques. Accurate prediction of failure events well advanced in time requires more powerful and robust ML algorithms. This system is an end-to-end predictive platform that analyzes sensors using advanced AI/ML algorithms and delivers the probability of failure of specific PC pump for next 7 days. Based on the probability of failure, the operator can act on it. The system also provides valuable insights, e.g. which pumps are pumps under control and out of control for a long time.

This system predicts 7 days early failure detection . 55% reduction in frequency of pump breakdowns. 35% less downtime of pumps as compared to formal system . 65% reduction in maintenance costs due to less failure occurrences.

A product for asset management, operations & maintenance in water treatment

Challenges – Data Lake building from scratch, Handle multiple variations, customizations in product, quality & validation of data.

In the water treatment industry operation and maintenance plays a very critical role, currently all data collection, events and reporting done manually without any digital application.

Product features and business values

Centralized Data Storage

• Ensures data accuracy and reliability.
• Facilitates organized and secure storage of critical data.
• Provides a solid foundation for future Data Science projects.
• Enhances insights and decision-making for improved plant performance.

Asset Health Monitoring System

• Operated in optimal and efficient way.
• Reduces chemical and energy consumption.
• Increases cost-effectiveness of water treatment processes.

Alarms and Early Warning

• Reduces unplanned plant downtime.
• Enables proactive compliance management.
• Minimizes the risk of costly fines and disruptions.

Plant Optimization

• Maximizes resource recovery.
• Improves efficiency and reduces operational costs.
• Promotes a sustainable approach to water treatment.

Inventory Management

• Ensures availability of chemicals when needed.
• Reduces plant downtime due to shortages.
• Optimizes inventory levels and forecasting for cost savings.

Supply Chain Demand Forecasting

The client, a prominent player in any industry, was interested in finding methods to create dependable market predictions and improve customer service levels. Consequently, they aimed to achieve precise and reliable forecasts for the product’s demand and ensure the appropriate supply to meet that demand. Additionally, they sought to optimize production timing and streamline delivery processes to minimize inventory expenses and address supply chain irregularities. The rapid shifts in customer demand resulting from the global pandemic pose significant challenges for demand forecasting.

Challenges that come with demand forecasting

Machine learning algorithms require a large volume of high-quality data to produce accurate forecasts. However, data may be incomplete, noisy, or biased, which can affect the accuracy of the forecast, or it can lead to inaccurate predictions. In the case of intermittent, erratic, and lumpy data patterns, reaching an accurate forecast is difficult compared to a smooth pattern. Forecasting requires expertise in data analysis, statistical modelling, and domain-specific knowledge related to the forecast industry or sector. A lack of expertise in these areas can lead to inaccurate predictions.

Our solution involves multiple AI/ML models to perform the data processing, feature extraction, and model building on the data. This system generates precise forecasts related to demand, inventory requirements, and sales for next 18 weeks by applying through the analysis of extensive data sets and the detection of patterns and correlations,

Business values –

Improved financial planning, better inventory management, improved supply chain planning, enhanced labor planning, reduced waste.

Predictive lead generation

Client is one of the leading FinTech company in India selling a variety of own- and third-party financial products through phone based, digital and feet on street channels. Having huge number of leads per week while some of leads will buy and most of them will not buy. And depending on the customers type, every resource must deal with a lot of customers. To have call with all lead not resource efficient.

This system is a data driven approach to insure only leads who are likely to buy are engaged with resources. Predictive lead scoring is a system that involves the use of ML algorithms to score leads instead of arbitrarily decided frameworks. Algorithm considers the already existed data and use it to determine which of your leads are sales qualified.

Business Benefits to Client-

Higher lead conversion ratio, Higher sales with lower marketing spend, Higher sales team and channel efficiency, More accurate sales prediction -Identify shortfalls early and take proactive action, Ease of sale increases – Time saving for salesperson and customer