adding value to information

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  • 1. Adding Value to Information via Analytics.Perspective from BA&MS Research and Projects May 2008

2. Outline

  • Historical perspective. When can analytics enhance value of information?
  • Using analytics to utilize information.
    • Supply chain
    • Workforce management
    • Carbon management
  • Using analytics to extract information.
    • Collaborative filtering, Netflix challenge
    • ASCOT
    • BANTER
  • Using analytics to collect information.
    • Prediction markets
    • Peer-to-peer services
    • Personal benchmarking

3. Information / Analytic services start up when a new sector of economic activity begins to take-off Information / Analytic Service Starting Points 2000 1990 1980 1970 1960 1950 1940 1930 1920 1900 IMS Health Brand Pharmaceutical market begins to take off R.L. Polk meets with Alfred Sloan to discuss information needs in growing auto market Polk Auto Registry Database A.C. Nielsen Network TV advertising opens up Early Mover position in an emerging market is critical Getty Images Digital Photography takes over Navteq GPS becomes commercially usable Stock market crash of 1907 Moodys aQuantive Internet advertising begins to grow Morningstar Take-off in individual mutual fund investing Fair-Isaac Consumer credit goes mass market 4. Outline

  • Historical perspective. When can analytics enhance value of information?
  • Using analytics to utilize information.
    • Supply chain
    • Workforce management
    • Carbon management
  • Using analytics to extract information.
    • Collaborative filtering, Netflix challenge
    • ASCOT
    • BANTER
  • Using analytics to collect information.
    • Prediction markets
    • Peer-to-peer services
    • Personal benchmarking

5. Utilizing Information

  • We consider situations where information is already available
    • From ERP or other business process automation tools
      • Historical data
      • Some enterprise generated view of the future
    • May be combined with purchased data from information services
    • Most examples now are within an enterprise or an enterprise driven value net
  • We focus on the case where analytics are applied to the information with the goal of optimizing the use of resources
  • Examples:
    • Supply Chain
    • Workforce management
    • Carbon management

6. Supply Chain Collaboration: IBM Buy Analysis Tool ( i BAT) Improve Inventory Cost in IBM's Extended Supply Chain Business Problem Solution Business Value

    • A significant percentage of IBMs hardware sales in high-velocity servers are sold through major channel partners such as Arrow, Ingram, and Tech Data.
    • Lack of alignment between procurement, manufacturing, and channel sales resulted in significant price protection and sales incentive costs for IBM and high inventory-related costs for our channel partners
    • Web-based collaboration platformfor IBMs channel replenishment planningthat c ombines innovative forecasting and inventory analytics with up-to-date visibility of channel sales and inventory data
    • Optimized buy recommendations for channel partnersbased on statistical forecasting techniques and risk-optimized inventory replenishmentmodels
    • Proactive r eview system that initiates demand shaping based on supply and demand imbalances
    • Standard SOA-based solution design which can easily be adapted to specific ERP environments
    • Patent-pending methodology
    • Cornerstone of IBM Server Groups Business Partner Transformation Initiative
    • Fully deployed with IBMslargest channel partners across the United States, Canada and Europe
    • Solution enables business partners to carry 15-25% less inventory without negatively impacting their delivery performance
    • Lower channel inventory resulted in lower price protection expenses for IBM, improved cash flow, and higher operating margins

7. Available to Sell (ATS) Find saleable product recommendations to consume excess inventory Business Problem Solution Business Value

    • With shrinking product lifecycles, component supply overages can quickly lead to obsolescence requiring costly inventory writeoffs.One way to avoid this costs is to find products to build and sell that would consume the excess supply.
    • In a complex product environment such as IBM Servers, product build-out typically requires additional procurement of non-excess parts to square with the excess supplies.With part commonality across many possible product configurations, this leads to an enormous number of potential build-out strategies to choose from.Additional factors such as part substitution, re-work costs, and marketing constraints make this a difficult optimization problem.
    • ATS Engine uses IBMs Watson Implosion Technology to find optimal sales recommendation portfolio given: excess part supplies, bill of material, procurement and value-add costs, product demand upper bounds, and product pricing.
    • Pegging module assigns excess consumption additional costs to each product in the sales recommendation allowing users to pick which build-outs to execute and promote in market.
    • What-if capability enables users to cost a targeted build-out plan, supporting end-of-life processes.
    • ATS Engine and Process fully deployed in IBMs Systems Technology Group since 2002.
    • Solution drove build-outs and sales recommendations which consumed $200 million worth of excess inventory in 2002.
    • Ongoing usage of the tool keeps excess supply from becoming obsolete.
    • System is integrated with IBMs Central Planning Engine with Web-based, on-demand availability within IBM STG.

8. Application Areas in Workforce Management Many opportunities to improve workforce management through utilization of information JAN APR JUL DEC DEMAND FORECASTING CAPACITY PLANNING STRATEGIC PLANNING TRAINING AND LEARNING SKILL&ENGAGEMENT ANALYTICS MATCHING & SCHEDULING ? x Now Target 9. Workforce challenges -The DATA is distributed in many enterprise applications

  • There is no single Enterprise Resource Planning tool for labor management
  • Supply (given in terms of roles or skills)
    • Traditional HR systems contain information about the current job
      • Structured: Position code, salary, location, shift, etc
      • Unstructured: Education, IBM courses, dept history, awards
    • New Job Role/Skill Set with job taxonomy and skill list
      • Full Text Resumes
  • Demand (given in terms of engagements or contracts)
    • Past and Current Contracts (and history of deal closure)
    • New opportunities: Sales Opportunity Database
  • Missing link
    • Bill of resources = set of skills required to deliver an engagement
    • But billing database includes detail (by individual) on employees participation in engagements
    • And additional sources include contractor/engagement data

10. Business Consulting Examples Can range from one month, one skill set.. .to more than 10 months, 16K hours, and wide range of job roles/skill sets Weekly variations appear to be driven by calendar effects, vacation schedules, and resource availability Supply Chain-PLM Engagements 11.

  • Several different sources of dataHigh level account information, such as
      • Client name
      • Account description
      • Offering information
      • Billing (Fixed price, best estimate)
    • Ledger information
      • Project cost, revenue
    • Labor claiming information
      • Hours claimed per week by each employee on a

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