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BI in FMCG
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BI in FMCG

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BI in FMCG BI in FMCG Presentation Transcript

  • BI in FMCG/Retail Industry Presented By- Team: Phoenix Abhirup Das (09BM8002) Shweta Jain (09BM8031)
  • Contents
    • Brief Overview of Retail/FMCG Industry
      • Major Changes
      • Need
      • Key Performance Indicators (KPIs) & Scorecard
    • BI System Framework
    • Data Modeling: Retail Scenario
    • Data Warehousing
    • BI for Retail
      • Key Business Challenges
      • Application
    • Top Trends in Retail/FMCG Industry
    • Recommendations
    • Increasing Competition
    • Increasing Customer Expectations
    • Retail-FMCG space dynamics
    • Impact on strategy & operations
      • Increasing Flexibility
        • Outsourced Contract Manufacturing
        • Demand driven supply chain
      • Improving Customer service
        • Retail outlet segmentation
        • Customized Retail service
        • Supply Chain segmentation
      • Greater Organizational Alignment
        • EPM, Divisional Scorecards
        • Integrated Sales & operations planning
      • Greater Customer focus
        • Demand analysis
        • Product characteristics
    Major Changes in Retail/FMCG Industry
  • Needs of Retail/FMCG Industry
    • Organization Alignment & Strategy Management
    • Financial Analytics for greater ROCE
    • Customer Analytics
      • Trade Marketing Analytics
      • Workforce Analytics
    • Supply Chain Analytics
    • Operations Analytics
    QRP CR VMI CPFR Level of Collaboration among SC partners Sophistication of need for BI
  • Retail KPI’s Reliability Responsiveness Performance Attributes Flexibility Costs Asset Management L1 Metric Perfect order fulfillment Order fulfillment cycle time Capacity change COGS and SCMC C2C Cycle and ROA (adopted from SCC ) SCOR Defined KPI’s Popular KPI’s Sales compared to budget/target Sales compared to last year (or any other period) Sales per Square Foot Wage Cost Recovery Average Sale per Customer/Transaction Units per Customer/Transaction Conversion rate Sales per Hour (for store or associate) – selling hours only Sales per Hour (for store or associate) – total labor hours Time Spent in the Store
  • BI System Framework Sales Force & CRM Excels BI Layer (Business logic, number crunching, metric/report generation ) Data Warehouse (Data Storage) Source Systems SAP ETL Layer Data Extraction, Cleansing, Staging, Transformation & Loading PLM HRMS Visual Audits Outlet Sales, Invoices, Purchase Orders Inventory, Transport Orders, Salary Advice, re-distribution Costs Business Plan, Forecast, A/R, A/P, Audit, Production Plans, Market Research, Production Jobs
  • Data Modeling: Retail Scenario
    • Brief Description of the Business
    • Smart-Mart is a leading grocery retail chain
    • Each stores has departments like grocery, bakery, dairy, healthcare, cosmetics etc
    • 60,000 individual products(SKUs)
    • Bar Codes (UPCs) imprinted on packages
    • Hand-held scanners feed data directly to POS
    • Management goal is efficient ordering , stocking and selling of products while maximizing profit
    • Decisions to do with pricing and promotion
  • Data Modeling: Retail Scenario Dimensional Design Steps: 1. Select the Business Process : “POS retail sales ” process to be analyzed: what products are selling on what stores on what date under what promotion scheme? 2. Decide Granularity : Individual line item on a POS transaction 3. Choose the Dimensions Retail sales star-schema 4. Identify the Facts Date Dimension Date Key (PK) Date Attributes TBD Promotion Dimension Promotion Key (PK) Promotion Attributes Store Dimension Store Key (PK) Store Attributes Product Dimension Product Key (PK) Product Attributes POS Retail Sales Transaction Fact Date Key (FK) Product Key (FK) Promotion Key (FK) Store Key (FK) POS Transaction Number Facts Additive facts Non-Additive facts sales quantity Unit price sales dollar amount percentage cost dollar amount Ratio gross profit gross margin
  • Data Modeling: Retail Scenario Dimensional Table Attributes: 1. Product Dimension Product dimension table Product dimension table detail Merchandise Hierarchy Other Attributes Sample Report drill down Product Dimension Product Key (PK) SKU Number (Natural Key) Product Description Brand Description Category Description Department Description Nutritional Value Storage Type Shelf Life Type Product key Product Description Brand Category Department Nutritional Value Storage type Shelf Life 001 Marie Gold Sunfeast Biscuit Bakery Fat-rich Normal 3 m 002 Marie Light (Baked) Priya Biscuit Bakery Protein based Dry-Cool 3m 003 Modern daily-fresh Modern Bread Confectionary Carbo Dry-Cool 7d 004 Choco Ice Cream 1 L Amul Frozen Desserts Frozen Foods Carbo High-cool 3m Department Sales Dollar Amount Sales Quantity Bakery 64,000 8,000 Confectionary 40,000 7,000 Department Brand Sales Dollar Amount Sales Quantity Bakery Sunfeast 34,000 4,000 Bakery Priya 30,000 4,000 Confectionary Modern 40,000 7,000
  • Data Modeling: Retail Scenario 2. Date Dimension Sample Report Dimensional Table Attributes: Date Dimension Date Key (DK) Date Full Date Description Day of Week Calendar Month Calendar Year Fiscal Year- Month Holiday Indicator Weekday Indicator Date Key Date Full Date Description Day of Week Calendar Month Calendar Year Fiscal Year- Month Holiday Indicator Weekday Indicator 1 1/1/2002 1-Jan-02 Tuesday January 2002 F2002-01 Holiday Weekday 2 1/2/2002 2-Jan-02 Wednesday January 2002 F2002-01 Non-Holiday Weekday 3 1/3/2002 3-Jan-02 Thursday January 2002 F2002-01 Non-Holiday Weekday 4 1/4/2002 4-Jan-02 Friday January 2002 F2002-01 Non-Holiday Weekday
  • 3. Store Dimension 4. Promotion Dimension Dimensional Table Attributes: Store Dimension Store Key (PK) Store Name Store Number (Natural Key) Store Street Address Store City Store County Store State Store Zip Code Store Manager Store District Store Region Floor Plan Type Photo Processing Type Financial Service Type Selling Square Footage Total Square Footage First Open Date Last Remodel Date Promotion Dimension Promotion Key (PK) Promotion Name Price Reduction Type Promotion Media Type Ad Type Display Type Coupon Type Ad Media Name Display Provider Promotion Cost Promotion Begin Date Promotion End Date
    • Data warehouses are capable of analyzing huge volumes of data, such as POS data
    • Until you combine market information with data management capabilities, it’s just data.
    • Identifies patterns in sales and in shoppers’ behaviors
    • Helps in planning sales promotions, pricing strategies and merchandising decisions
    • Steps to structure the data warehousing capabilities
    Increasing Customer Loyalty with Data Warehousing
  • Business Intelligence for Retail
    • Why?
      • To align business with client needs
      • To differentiate from competitors
      • To optimize product mix and space utilization
    • Retail Data Sets
      • Traditional retail information (i.e. POS, GMROI)
      • Market data (i.e. market share, competitor pricing)
      • Promotional data (i.e. special pricing offers)
      • Client data (i.e. loyalty scheme)
    • Input to Decision Support Systems
      • Reporting capabilities for key performance metrics
      • Performing complex analysis (e.g. promotion, pricing strategy, product mix)
      • Developing statistical models that predict client needs and behaviors
    • Putting Decision Support to Work
  • Key Business Challenges
    • Store site selection
    • Understanding customer buying behaviors and preferences
    • Product assortment
    • Inventory management and logistics
    • Product pricing, including clearances and promotions
    • Vendor management
  • BI Application
    • Questions that can be addressed
    • What items should be included in the inventory
    • What pricing and promotional strategy is the most effective
    • What the demand will be for a select assortment of merchandise
    • What impact an incremental price change will have on demand
    • Which floor plan will sell more designer apparels
    • Which customers will respond to a direct mail or exchange offer
    • Where to place retail outlets
    • How many of each size or color of an item to put in each store
    • When and how much to discount
    • Few industry standard MIS Reports
    • Vendor Performance Analysis
    • Inventory Control (Inventory levels, safety stock, lot size, and lead time analysis)
    • Product Movement and the Supply Chain
    • Demand Forecasting
    • Incentive Reporting
    • Royalty Program
    • Minimized Lost Sales and Improved Inventory Turns
    • Full Lifecycle View of Products
    • Market Basket Analysis
    • Supplier Vendor Performance Index
  • Top Trends in FMCG & Retail Sector IT Budget Outlook by CIOs from the FMCG & Retail sectors Technologies which companies will invest in to save operational costs using IT Top challenges faced by CIOs / CTOs Source: Survey - FMCG - Infrastructure Agenda 2010
  • Recommendations
    • Competition among retailers accelerates, profit margins will become thinner
    • To have a secure, dynamically scalable infrastructure to efficiently address the ever-changing business demands
    • BI and analytics will play a key role
    • Implement technologies that enhance the customer experience 
    • IT initiatives will help the business drive market share—rather than just supporting the business
    • Using BI tool will make decision making easier and efficient
  • Q & A