FSN Analytics

Customer: A global leader automotive OEM, with manufacturing facilities at multiple locations, a widespread distribution channel and after sales services across Asia and Europe in 50 countries.

OBJECTIVES

  • Improved visibility and traceability across the Supply Chain
  • Optimize supply chain for thousands of inventory items.
  • Quickly classify the items to appropriately control each inventory class according to its rating.

SOLUTION

To use Rubiscape (RubiStudio, RubiML, RubiText, RubiSight) to assist for FSN analytics and classify SKUs. To classify these items into Smooth (Fast moving), Intermittent, lumpy and erratic demand (slowing moving) and dead stock(non-moving). To perform this analysis, we can use Average demand interval and Coefficient of variation.

IMPACT

  • Adequate supply of materials - minimizing inventory costs, meeting the customer requirement timely, effectively and efficiently with higher CSAT
  • Eliminates duplication in ordering and better utilization of available stocks
  • Provides a check against the loss of materials and Facilitates cost accounting activities
  • Identifies and locates the inactive & obsolete store items
  • Minimize losses due to deterioration, obsolescence, damage, pilferage.
  • Cost comparison with a consistent & reliable basis for financial statements
  • To help improving demand forecasting accuracy.

Industry: Automotive, Retail, FMCG, Manufacturing

Technology: Rubiscape Enterprise, RubiML, RubiSight, RubiText, RubiStudio, SAP HANA, Qlik, R, Python, D3.JS

Key KPIs: Average Number of Orders, Total Number of Orders, Total Order Quantity, Average Order Quantity

Demand Forecasting

Customer: A global leader automotive OEM, with manufacturing facilities at multiple locations, a widespread distribution and after sales services across Asia and Europe in 50 countries.

OBJECTIVES

  • Automate a process of model building using RubiML for more than 15000+ parts
  • Provide business users tool forecast and manage optimal inventory levels
  • Improve SLA to meet the demand schedules on time and budgets in accordance with sales volume

SOLUTION

The solution comprised of designing and developing specific models, test and deploy them with aim of increasing overall profitability by reducing supply chain costs, better planning and re-usability of spare parts. Using Rubiscape ML Workbench, they could predict the sales volume demand for spare-parts.


IMPACT

  • Better Bin Planning for Inventory Management, reduction in the shortage of the spare-parts
  • Improved after sales service, thus enhanced customer satisfaction
  • More visibility on Supply Chain and control to reduce inventory costs, thereby improved cashflows

Industry: Automotive, Retail, FMCG, Logistics, Energy, F&B

Technology: Rubiscape Enterprise, RubiML, RubiSight, RubiText, RubiStudio, SAP HANA, Qlik, R, Python, D3.JS

Key KPIs: Parts Forecast, Response Time

Fraud Detection

Customer: One of the leading banks in Asia and offers wide range of financial products and solutions to individuals, professionals, private banking clients, and businesses.

OBJECTIVES

  • Enhance and optimise the existing static ML model for fraud detection
  • Make the models flexible and scalable to incorporate new features and data sources
  • Increased accuracy to identify non-obvious fraud patterns, and monitor operations.

SOLUTION

The project involved understanding the changes and complexities of the new data source, business and regulatory requirements. Apply Fraud Analytics Models and automate monitoring features, within their data governance standards.

IMPACT

  • Increased capacity and capability to handle variety of fraud-based data from multiple sources
  • Automated fraud data tasks - cleansing, formatting, parsing
  • Data visualizations that convey exactly how fraudulent activity may occur in the future.
  • Faster pilots for new use cases on sandbox and release in production in just a few weeks.

Industry: Banking, Financial Services, Wealth Management, Insurance

Technology: Rubiscape Enterprise, RubiML, RubiSight, RubiText, RubiStudio, SAP HANA, Qlik, R, Python, D3.JS, HDFS Hive, Impala, RubiSocial

Key KPIs: Customer Loyalty, Defaulters Occurrence, Risk Index

Customer360

Customer: A reputed banking corporation, with a large customer base and diverse business lines, with a broad range of Investment, Superannuation and Retirement income solutions.

OBJECTIVES

  • To get a clear and complete customer view, from internal and external data sources
  • Analyse social media to understand the trends and customer sentiments
  • Empower users to handle large size and complexity of data


SOLUTION

The customer team leverages a unique solution built on Rubiscape for:

  • Comprehensive analysis of news and Social Media feeds to understand market trends
  • Entity extraction, linking and effective use of Text Classification Algorithms to classify news and Social Media feed into various classifications
  • Sentiment detection from the extracted data
  • Frequency and correlation analysis on the textual data

IMPACT

  • News analysis reduces manual work for Analysts and Data Engineers, provides flexibility to manage the classifications, analysis and data visualisations
  • Automated fraud data tasks - cleansing, formatting, parsing
  • Customer 360 view on consumption patterns provide cost-effective plans, delivering tailored experience, thereby reducing customer churn, improving loyalty and customer experience.
  • Identify current and historical trends & patterns, location, channels and devices of the comments to understand popularity, detect sentiments, find out the trend of sentiments over time to translate into measurable action items

Industry: Banking, Financial Services, wealth Management, Insurance

Technology: Rubiscape Enterprise, RubiML, RubiSight, RubiText, RubiStudio, SAP HANA, Qlik, R, Python, D3.JS, HDFS Hive, Impala, RubiSocial

Key KPIs: Sentiment, Link Analysis, Influencing Factors, Followers Index, Audience Engagement, NPS

Asset Performance & Utilization

OBJECTIVES

  • To monitor and track asset utilization (and downtimes)
  • Real-time demand and supply gaps
  • Consumption behaviour pattern.

SOLUTION

The solution was devised to identify potential problems in Energy Grids and Power Distribution equipment in order to improve the life of expensive assets. The solution involved turning vast amounts of data from information assets (Meters, Sensors and SCADA) into actionable insight, foresight and prescriptions for critical decision making – Outage Management, Quality of Service (QoS), Preventive Maintenance activities and more.

IMPACT

  • Real-time insights to detect anomalies and patterns in supply, consumption and leaks
  • Optimized energy distribution
  • Reduced risk of asset failure
  • Optimized asset performance through real-time monitoring
  • Improved operational efficiency through trend spotting

Industry: Energy, Manufacturing, Automotive, Heavy Engineering

Technology: Rubiscape Enterprise, RubiML, RubiSight, RubiStudio, Big Data, SCADA, RubiThings, Power BI

Key KPIs: Asset Performance and Utilization, Historical Parameter Values, Asset Specification

Risk and Compliance

OBJECTIVES

  • Consolidated view of their portfolio to identify key risk parameters due to operational inefficiencies.
  • Consistence experience for the CXO’s decision making process.

SOLUTION

Rubiscape capabilities helped the customer to control their market, credit and regulatory risks; identify new opportunities and use that insight to make decisions with confidence and optimize the portfolio.

IMPACT

  • Minimized the inefficiency of siloed operations by viewing positions from Financial Trading, Plant Operations and Demand Forecasting in one centralized platform.
  • Faster, automated and accurate reporting on operational and regulatory requirements
  • Reduced risk of asset failure
  • Minimized the effects of energy and commodity price volatility.

Industry: Energy, Manufacturing, Automotive, Project Engineering

Technology: Rubiscape Enterprise, RubiML, RubiSight, RubiStudio, Big Data, SCADA, RubiThings, Power BI

Key KPIs: Risk Indicators, Compliance, Portfolio Performance

Stock Market

Customer: The firm is a quantitative Investment Management company trading in global Financial Markets, dedicated to producing exceptional returns for its investors by strictly adhering to the Mathematical and Statistical method.

OBJECTIVES

  • Quantitative modelling to analyse stock market data to maximise returns from portfolios
  • Better exploration, visualisation and better collaboration of the data across the value chain and stake holders in order to draw even better insights.
  • Foresee a possible investment opportunity in the market

SOLUTION

The customer team leverages Rubiscape’s Workbench to:

  • Predict the market trends using Machine Learning
  • Understand the sentiment of the current market
  • Better insights using Rubiscape Analytics & Tableau
  • With help of news and social analysis analyse the market and the prices of the shares in future

IMPACT

  • Portfolio visibility for ensuring higher return
  • Fact based investment decisions reducing the risk levels
  • Better visualisation to know exact details in focus areas and create storyboards for business users
  • Increase the profits by clients and thus improving customer satisfaction..

Industry: Financial Services, Stock & Capital Markets, Wealth Management

Technology: Big Data, Rubiscape ML Workbench, Tableau, Trading App

Key KPIs: Profit, Risk Index

IoT for Fintech

Customer: A leader in financial lending products and services, offering international financing solutions in the business-to-business area. It finances infrastructure, equipment as well as working capital investments, and acts as a manager of financial risks.

OBJECTIVES

  • Collect data from all assets deployed by partners at customer locations for their uptime assurance, availability and optimum utilisation.
  • Add value to vendors and customers by helping partners improve on existing services, and build new opportunities by utilizing the data gathered
  • Purpose-made applications for every customer segment

SOLUTION

This is an IoT Solution built to collect and analyze configuration, usage, and location data from connected equipment and assets to increase parts inventory forecast accuracy, enable proactive demand forecasting, and optimize inventory levels

IMPACT

  • Faster Time to Market and Larger Market Share to build smarter contracts
  • Enhanced Customer Experience with purpose made apps for every customer segment
  • More secure financial lending

Industry: Fintech, Manufacturing, F&B, FMCG, Automotive, Telecom

Technology: Rubiscape Enterprise, RubiStudio, Big Data, SCADA, RubiThings, Java, Sensors, Tableau, XML, JSON

Key KPIs: Inventory Forecast, Utilisation, Availability, Location, CSAT, Top n Analysis

Personalised Marketing

Customer: The customer is one of the leading insurance company in the APAC region. It operates in 35 countries with more than 120,000 employees worldwide. They have adopted InsurTech and Digital transformation program to make business more competitive and sustainable in recent past.

OBJECTIVES

  • Segmentation of customer based on historical customer dataset.
  • Identify the target customer to enforcing of insurance policy .

SOLUTION

Rubiscape AI/ML was used to:

  • Create the Segmentation of Customer using historical dataset.
  • Develop the Prediction Model based on segmentation and derive the score of each person
  • Boost the recommendation of products / schemes and drive personalization so that customers can get personalized experiences.

IMPACT

  • AI models help determine which products and policy options are the best fit for consumer.
  • Determination of individual price based on consumer behaviour and historical data.
  • Aligning better with customer needs and thus having a better conversion rate.

Industry: Insurance, Financial Services, Banking

Technology: RubiML, RubiCast, CRM, Big Data, AI-ML Algorithms, IVR, Geo

Key KPIs: Customer Financial data, Demographic

Customer Analytics

Customer: One of the world leader Insurance company, offering a range of products across the customer’s life cycle, including Children’s Future Plans, Wealth Protection Plans, Retirement and Pension Solutions, Health Plans, traditional Term Plans and Unit Linked Insurance Plans.

OBJECTIVES

  • Aggregate all customer data about interactions, transactions, feedback and agent data.
  • Generate insights-based on end to end customer journey
  • Help with Analytics to design their products and services based on changing customer needs.

SOLUTION

Rubiscape ML workbench provides unique customer experience analytics to help you reduce complexity at an operational level, turning many voices into a singular focus. To build and manage a more easily distinguishable customer database throughout the customer’s lifecycle. The client was able to ensure that the experience of its customers was satisfactory.

IMPACT

  • Using Predictive Modelling, the insurers can identify whether the drivers, by combining their behavioural data with the exogenous factors such as road conditions or safe neighbourhoods.
  • Improved fraud detection through data management and predictive modelling.
  • Match the variables in every claim against the profiles of past claims for better analysis.
  • Improved customer experience with near to real time text analytics to classify, co-relate and action out customer service from Call Centre data, Customer e-mails, Social Media, User Data-logs.

Industry: Insurance, Financial Services, Banking

Technology: RubiML, D3.JS, Python, Qlik, Azure, Social, Web logs, IVR, CRM

Key KPIs: Customer Analytics

Predictive Maintenance

Customer: The customer is an American corporation and manufacturer, retailer, and marketer of non-alcoholic beverage concentrates and syrups and offers more than 350 brands in over 200 countries. They are the best-selling soft drink in most countries and a recognized global brand.

OBJECTIVES

  • Identify factors contributing to failures of the machines/equipment
  • Identify root causes for failures of the machines/equipment
  • Predict the machine/equipment failure in advance to trigger preventive maintenance
  • Identify operating conditions for efficient working of machine/equipment

SOLUTION

  • Analysis of failure types and impact with help of Pareto Chart analysis.
  • Identify anomaly dictation with statistical control chart.
  • Balancing dataset from imbalance dataset of failure rate.
  • Create additional variable using Engineering feature selection and develop the advanced classification model to predict risk score of machines.

IMPACT

  • The Root Cause Analysis helped in identifying minute causes for the failure of each part in the machine. In addition to the identification of root causes, geography wise distributions of such causes added to the efficient planning of maintenance activities thereby reducing costs and improving maintenance planning.
  • The Relationship Association and Correlation analysis helped in diving deeper into the causes to identify if any long term and permanent fixation/ action was required.

Industry: Manufacturing, Project Engineering, Automotive, F&B, Pharma

Technology: Sensor, IoT, RubiML, RubiStudio, D3.JS, Python, RubiSight

Key KPIs:Distribution Chanel Effectiveness, CSAT, Revenue Planning, Cost Control

Quality Assurance

Customer: Customer is a global leader in Manufacturing offering highly engineered industrial products and After Sales Services. They offer wide range of Ride Control products catering to Passenger cars, Utility vehicles, Commercial vehicles and two wheelers for leading OEM brands globally.

OBJECTIVES

  • Analyse production data for identification factors impacting quality of the product
  • Identify efficient operating conditions when there were lowest non-conformities in products

SOLUTION

Rubiscape Platform helped the customer protect their business against these threatening implications by enabling detection of quality issues at an early stage and prediction of their severity, help them address those before they do serious damage. Beyond protection, they use these quality insights to improve product development and production processes.

IMPACT

  • Improve overall product quality, to substantially reduce warranty and liability costs.
  • Spot early indicators of production problems that may impact quality.
  • Jump on indications of trouble and fix the problem before the impact hits.
  • Enabled proactive action to limit the damage of quality issues.

Industry: Manufacturing, Automotive, Retail, Energy, FMCG, Healthcare

Technology: RubiStudio, RubiML Workbench, Python, Machine Logs, ECU, Web, SCADA

Key KPIs: Quality Index, Issue Resolution, OEE, Downtime, MTBF, MTTR

Social Media Sentiment Analysis

Customer: It is a cross media company that focuses on media outlets, events, lead databases, and lead generation. They manage 50 media outlets as well as 300 events annually. Spread worldwide, they are especially prominent in the Europe and US.

OBJECTIVES

  • Collect data from social media
  • Social data analysis using NLP, ML techniques to generate insights like Sentiment Scores, Customer Opinions and NPS
  • Availability of insights on intuitive visual platform.
  • Effectively listen to the customer voice (VoC) and insert the brand message, to improve the customer experience

SOLUTION

The customer leverages the solution to:

  • Monitor & track social comments and sentiment through the tweets with hashtags & handles
  • Use of various textual analysis algorithms to identify frequent words, locations, entities and correlation between such entities.
  • Interactive charts to visualize the output at a glance on textual data.

IMPACT

  • Understand popularity, detect sentiments, find out the trend of sentiments over time, find out correlations between frequently occurring words.
  • Efficiently understand the customer and overall social sentiment, to plan & refine campaigns .
  • Identify current trends, location and devices of the comments to understand popularity, detect sentiments, plot sentiments trend over time to translate into measurable action items.

Industry: Retail, Healthcare, Insurance, Automotive, Banking, Media

Technology: RubiML, RubiStudio, RubiText, R, Tableau, Social, Web, CRM

Key KPIs: Sentiment, Link Analysis, Influencing Factors, Followers Index, Audience Engagement, NP

Target Advertisement

Customer: It is a cross media company that focuses on media outlets, events, lead databases, and lead generation. They manage 50 media outlets as well as 300 events annually. Spread worldwide, they are especially prominent in the Europe and US.

OBJECTIVES

  • Self-service BI/Analytics
  • Data and process automations
  • KPI Dashboard.
  • User Profiling
  • Prediction of CTR/Conversion rate.

SOLUTION

Using advertisement of data to create the KPT dashboard with help of mathematical formulas, it will help to monitor advertising campaign performance. Using historical click stream data to help us predict click through rate or conversion rate.

IMPACT

  • Business users can create their analytical reports in few hours rather than days.
  • The solution has been able to empower existing staff by internalizing the whole process.
  • Machine learning models improve targeting for more relevant ad placement.
  • Improving the advertising campaign performance.

Industry: Retail, Healthcare, Insurance, Automotive, Banking, Media

Technology: RubiStudio, RubiML, Angular JS, Python, CMS, Billing App, Advertisement System, Network files

Key KPIs: Click-through rate (CTR), Return on investment, Conversion rate, Demographics

Pricing Analysis

Customer: With a range of more than 20 individual fashion brands, the company provides fashion clothing and accessories for both women, men, teenagers and children.

OBJECTIVES

  • Identify factors impacting sales of product/brand
  • Do What-If analysis for price against the sales to decide pricing for product
  • Provide insights to marketing teams for personalised services and improved CX

SOLUTION

The customer team leverages Rubiscape’s solution to:

  • Manage multi-brand's competitive pricing through a single platform.
  • Understand at what price the customer will buy the products most.
  • Set prices for each individual brand depending on their target audience.

IMPACT

  • The company was able to attain better margins than before.
  • The competitive pricing helped in making the products more competitive and provided “value for money” satisfaction to the customers.
  • Foreseeing the market trend helped in managing the production line, thus reducing waste and the cost of production.

Industry: Retail, Healthcare, Insurance, Automotive, Banking, Media

Technology: Rubiscape Enterprise, RubiML, Angular JS, Python, Web, ERP, RSS feeds, Informatica

Key KPIs: Customer Experience, Quality of the product, Micro Segmentation, Price Optimization

Competitive Benchmarking

Customer: The company is Asia’s leading supermarket store chain with operations across multiple countries, integrated supply chains and is amongst the top 10 brands globally.

OBJECTIVES

  • Collect customer feedbacks and competitors from CRM, surveys, social media.
  • Analyse customer feedback data using NLP techniques identify customer sentiment, opinions and other insights about products, service and experience.

SOLUTION

    Customer leverages Rubiscape ML Workbench for:
  • Textual analysis and social media analysis of the competitors.
  • Based on buying behaviour and patterns, recommend new products & promotions.
  • Understanding the customer sentiment towards the competitor.

IMPACT

  • Better connect with customers more, as they can track every single conversation of customers with them as well as their competitors.
  • As the customer engagement increased, resulted in footfall growth.
  • Improved store experience.

Industry: Retail, Healthcare, Insurance, Automotive, Banking, Media

Technology: Rubiscape ML Workbench, Angular JS, Python, Social Media Data

Key KPIs: Customer Experience, Churn, Sentiment Analysis

Recommendation Model

Customer: The company is Asia’s leading supermarket store chain with operations across multiple countries, integrated supply chains and is amongst the top 10 brands globally.

OBJECTIVES

To develop the recommendation model using user transactional dataset.

SOLUTION

    Customer leverages Rubiscape ML Workbench for:
  • Identifying product pattern.
  • Market Basket analysis: Using association rules algorithms to get recommendation.

IMPACT

  • Better connect with customers more, as they can track every single conversation of customers with them as well as their competitors.
  • Improve the sales of retail industry.
  • Improved store experience.

Industry: Retail, Telecom, FMCH

Technology: Rubiscape Enterprise, RubiML Workbench, Angular JS, Python

Key KPIs: Seles Index, Customer Profile, Stock Movement

Fleet Services For Telecom MSP

Customer: A global telecommunications company and integrated GSM operator offering 2G, 3G, and 4G LTE and VoLTE mobile services. It is one of the largest mobile network operators.

OBJECTIVES

  • Collect operating data from telecom tower passive infrastructure for continuous monitoring and early identification of service issues
  • Monitoring and tracking of SLAs for Passive Infrastructure
  • Predict failure of equipment to trigger preventive maintenance
  • Identify anomalies/fraud in service from third party vendors

SOLUTION

We built an innovative solution on Rubiscape platform, with mobile native apps (Rubiscape Radio Tool) and the RSU based on Sensor Gateway to enhance customer service pro-activeness capabilities. The system captures data from passive infrastructure (smart as well as non-smart devices) in real time to provide alarms, visibility and action triggers. The solution is capable of monitoring digital services based on complex value chains, and of aggregating information from partners’ systems and converged networks.

IMPACT

  • With the application of an ‘all in one’ concept, as well as the high level of automation in both customer service impact calculation and root cause analysis, the solution allowed to decrease both capital and operational costs.
  • Resolved service quality problems and incidents quickly and efficiently which led to the highest quality of service and customer experience.
  • Network events can be translated automatically and directly into customer impact.
  • SLA Management became quick and efficient

Industry: Telecom, Media, Electronics, Manufacturing

Technology: IoT, Rubiscape Enterprise, RubiML, RubiStudio, Tableau, Android, iOS, AWS S3, Informatica

Key KPIs: Network Up-Time, Service Assurance, Customer Experience, SLA Monitoring

Network Cells Predictions

Customer: A global telecommunications company and integrated GSM operator offering 2G, 3G and 4G LTE and VoLTE mobile services. It is one of the largest mobile network operators.

OBJECTIVES

  • To identify and classify network cells into specific Network Corridors based on their usage patterns
  • Identify anomalies in cell behaviour pattern

SOLUTION

This is an Advanced Analytics / Machine Learning solution to classify the network cells into different provided categories by analyzing the usage pattern based on the KPIs such as ‘Download Volume in GB’ and ‘RRC Attempts’.

IMPACT

  • Automated classification of cells in to transport and no-transport cells reducing manual work.
  • Focused approach towards analysing the cells performance considering the usage patterns.
  • Analysis of Transport Corridor impacting the demand of the network.

Industry: Telecom, Media

Technology: Sensor, Hadoop, AWS, RubiML Workbench, Power BI, SSIS, Geo

Key KPIs: Download volume, RRC Attempts