DSquare Solutions
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DSquare Solutions

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An overview of the capabilities of DSquare Solutions

An overview of the capabilities of DSquare Solutions

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  • good one Anand. well created.. like teh content, carrier slide..
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  • This definition of RM brings into focus ALL the key components available to the Hotel Revenue Manager to increase profitabilityPriceCustomer SegmentationRoom TypeSales ChannelHotels have reported increasing Revenues (Top Line) by 3%-6% through implementing a RM solution. Some cases have reported increases as high as 17%
  • A intuitive interface allows the Revenue Manager to analyze any future occupancy date and view optimized recommendations for setting the “BAR” for future dates based on expected occupancy

DSquare Solutions DSquare Solutions Presentation Transcript

  • DSQUARE SOLUTIONS Capability Overview
  • THE VISION Superior Decisions Inferior DataTo help our customers traverse the path from Data to Decisions quickly,effectively and efficiently
  • THE APPROACHIdea• Frame the Decision need Analysis • Manage the necessary data, models and analysis Decision • Support Decision making through insight generation Execution • Monitor and track decision execution and effectiveness Value • Measure and report value Full Lifecycle support for a truly Analytical decision making process
  • THE FOUNDATIONS Content Carrier • Relevant and • Appropriate Reliable Information business application to deliver the insights at the right point Consumption Cost • Present the insights • All of this at a cost in a form that is that needs no easily consumed by justification! the business
  • OUR CAPABILITIES Data Infrastructure Modeling and Management Optimization We also build and deliver Our Structured Information mathematical models custom Framework (SIF) is a fast tailored to the customer implementation data needs that cover areas like management solution that has statistical predictive models, been deployed at varying optimization models (LP, MIP) scales (Large Enterprise to using Open source tools like Localized Business Unit) COIN-OR, R
  • STRUCTURED INFORMATION FRAMEWORKSource Data Useable Data Relevant & Actionable Business Value through (Uncertain (Analytical Data analysis better decisions Hygiene) Store) Data Staging – Tested, Filtered, Strategic Modeling cleansed Metrics Definition and Collaborative Analytical Data Store Tracking Decision (Commercial Database) Support and Analysis Exceptions and Alerts Data Integrity Operational Dashboard, Data Dashboards quality metrics
  • RESOURCE MANAGEMENT – ALLOCATION AND OPTIMIZATIONSituation / Problem Statement Solution • Web based Resource Management and • Customer has multiple Allocation simultaneous projects with • Captures Resource Cost, Skills and varying deliverables and Competencies skill requirements • Project Requirements – Timelines, Skill requirements and target margins • Globally distributed work • Global Allocation Optimizer identifies force, with varying cost and “Best” resource to assign to each project based on competencies • Skill / Competency matching • Manage allocation of • Margin maximization resources to projects to • Operational constraints optimize on margins and • MIP Formulation solved using open source solver delivery SLA • Identifies Skill and competency gaps for further Training and Development
  • CUSTOMER ATTRITION AND LOYALTYSituation / Problem Statement Solution • B2C customer facing a • Statistical model “Predicts” Customer Attrition individual customers challenge propensity to attrite and • How to identify potential identifies critical factors that customer attrition and precipitate attrition initiate preventive • Identifies “High-Risk” measures customers with clear callouts of the reason for the attrition risk • Define a “Play Book” that triggers action from the provider for each High-Risk customer based on the identified factors
  • ENGAGEMENT MODEL
  • ENGAGEMENT MODELS TO SUIT EVERY BUSINESS NEEDConsultative Engagement• Identify problems, opportunities and challenges• Time and Material based costing Services Delivery • Build custom solutions and models to solve critical problems • Fixed price engagements, Pay-Per-Use Embedded expertise – Retainer model • Invest, Build and manage customer specific expertise and knowledge • Annual subscription model BOT • Transfer expertise and personnel to customer for continued in-house support • To be discussed 
  • DELIVERY MODEL GEARED TO UNLOCKINGVALUE SaaS Ramp up Only pay for Rapid ROI delivery consumed what you use Realization model solutions Hosted on Cost- Consulting Extremely Pay-Per-Use Effective and Analytics fast delivery or Cloud offerings can cycles Subscriptioninfrastructure be added „a ensures models – No upfront la carte‟ to rapid ROI available costs the solution realization involved
  • THE PEOPLE• Anand Srinivasan • Anand is the founder and CEO and brings several years of experience managing the R&D and modeling teams at Sabre and Dell. • He has vast and rich expertise in solving large optimization problems for Fleet and Crew optimization, Revenue Management etc. • He has also worked with the Supply Chain team at Dell building models for manufacturing optimization, logistics, lean manufacturing, supply network optimization, inventory staging and management and pricing for demand shaping• Anand Srikumar • Anand Srikumar brings several years of experience in the Banking and Financial Services industry with GE and Standard Chartered Bank • He brings expertise in Consumer Credit scoring, Portfolio Risk Management and Financial Modeling
  • Optimization and Advanced Analytical solutions from DSquareCASE STUDIES
  • HOTEL REVENUE MANAGEMENT
  • WHAT IS REVENUE MANAGEMENT AND WHY IS IT RELEVANT?• The practice of selling the right room, for the right price, to the right customer, through the right channel• Industry benchmark standards have established that Implementing a Revenue Management solution can increase Room Rent Revenues by 3% to 6% with minimal increase in operational costs • Revenue increase of 17% have been reported in some cases
  • REVENUE MANAGEMENT COMPONENTS ANDALGORITHMS Unconstrained Demand Forecasting • Observed Historical Bookings is a “end state” of bookings after some bookings have been rejected due to capacity constraints • Statistical models Forecast the “Unconstrained” (Without capacity restrictions) demand for various price points. • “How many people wanted to buy?” from “How many people bought” Optimization of Inventory controls • Optimization algorithm to identify the “Best” price point to sell rooms at • A Dynamic programming formulation that uses the unconstrained forecast for a given occupancy day and works backward to determine the optimal selling price on any given day leading up to the occupancy • Can be re-optimized periodically to reset prices based on actual observed demand
  • RM SOLUTION – SYSTEM SAMPLE Forecasted Occupancy for RM price recommendations various Price Points (Baseline, for future dates Low Fare, and High Fare) Target Occupancy Date Today Estimated Benefits of recommended controls
  • Consumer MarketsMARKETING PLAN OPTIMIZATION
  • THE PERSONAL COMPUTER MARKET• Consumers are flooded with choice • Brands, Models, Configurations, Form Factors, Special Offers • Guaranteed to find a model (configuration) that meets the need of all but the most esoteric consumer requirement• Brand Equity has been significantly diluted • Informed consumers look to other brands for the right configuration/price• Extremely critical to hit the right configuration in the market at the right price point Only possible approach to find the “Sweet Spot” is using Choice Models
  • CONSTRUCT Group Customers by Segment • Laptop, Tablet etc. Choice Set visible to the Segment • Available makes, models, configurations Significant Product Attributes • Memory, Screen Size, HD Capacity, Price etc. Estimate importance of each attribute • Statistical Techniques Predict probability (Market Share) of each possible configuration introduced into the market Optimize promotional activity and Marcomm spend to drive customers to high margin sales
  • MODEL PREDICTED AND ACTUAL SHARES COMPARISON50 Feb 12 (Calibration) 60 Mar 12 (Prediction)40 50 3.4% 40 16.3%30 Error 3020 2010 10 0 0 Acer Compaq Dell HP Lenovo Sony Toshiba Acer Compaq Dell HP Lenovo Sony Toshiba Forecasted Actual Forecasted Actual50 40 May 12 (Prediction) Apr 12 (Prediction)40 30 12.8% 42.6%30 Error Error 2020 1010 0 0 Acer Compaq Dell HP Lenovo Sony Toshiba Acer Compaq Dell HP Lenovo Sony Toshiba Forecasted Actual Forecasted ActualApr Data shows large deviation from Prediction – Possibly due to heave promotional activity by Dell
  • HOW TO USE THE MODEL• Profitability Enhancement • Clearly identified attributes that impact sales • Can choose high margin components between “nodes of indifference” • E.g. Screen Resolution• Sales Target Setting • The Model predicts “Fair Share” based on product attributes. Any share capture above that can be attributed to Sales and Marketing Activity• MarCom ROI • Can quantify the uplift generated in sales in a given month due the MarCom Spend. • Apr Marcom/Promotions resulted in +20% market share for Dell• Competitive Response • Evaluate the impact of new product launch or price move by competition and respond appropriately