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Focus forecasting bmb Presentation Transcript

  • 1. Focus Forecasting
    1. RomiChordia B09106
    2. AnshatSinghal B09070
    3. AbhishekDassani B09063
    4. NidhiTenguria B09122
    5. Vidisha Vijay B09118
    6. MohitJalan B09092
  • 2. Forecasting-Process
    Forecasts are estimates of timing and magnitude of the occurrence of future events
    Key functions:
    An estimation tool
    A way of addressing the complex and uncertain environment surrounding business decision making.
    A tool for predicting events related to operations, planning and control.
    A vital perquisite for the planning process in organizations.
  • 3. Why do we forecast…
    Dynamic and complex environment
    Short term fluctuations in production
    Better materials management
    Rationalize man-power decisions
    Basis for planning and scheduling
    Strategic decisions
  • 4. Focus-Forecasting-Introduction
    Bernie Smith- Servistardivision of TruValue
    Two Principle theory
    All complex forecasting models are not always better than simpler ones.
    No single technique for products and services
    Simple techniques that work on past data also helps in developing forecasts about future as well
    Reasonable approach for short term (period less than a year)
  • 5. Methodology of Focus Forecasting
    • Various rules to project data for future forecast
    • 6. Two components are
    • 7. Several simple simulation Rules
    • 8. Computer Simulation of past data
    • 9. Some examples of Forecasting Rules are as follows:
    • 10. Whatever is sold in last 6 months is what will be the sales in
    near future
    • Current year’s quarter wise sales can be predicted by last
    year’s quarter wise pattern
    • Next three months sales will be 10 percent more than last three months sales
    • 11. Percentage change in sale levels will be same for all the quarters this year
  • Cases
    • For non-seasonal items and spare parts with weak and irregular demand
    The sales for the item in the next quarter will be the same as the actual sales for the last quarter
    F = Forecast for the item over the next quarter
    Q1 = Actual Sales over the most recent 3 months° (example: 100)
    °the first quarter in the past counting backwards from now
    F = Q1 ; F = 100 for the next quarter
    This formula looks modest but is nonetheless surprisingly robust for all types of non-seasonal items, whether their demand be strong or weak
  • 12. Contd..
    For items with an irregular demand history
     
    Sales for the item in the next quarter will be half of actual sales over
    the last 6 months
    F = Forecast for the item over the next quarter
    Q1 = Actual Sales over the most recent 3 months° (example : 100)
    °the first quarter in the past counting backwards from now
    Q2 = Actual Sales over the 3 months before that ° (example : 150 )
    ° the second quarter in the past counting backwards from now
    ( Q1 + Q2/2)=(100 + 150)/2 = 125 for the next quarter
    This formula generates a reasonable forecast despite a demand history needing to be corrected
  • 13. Contd..
    • For items with modest seasonality
    Sales for the item in the next quarter will follow the percentage increase or
    decrease with respect to last year.
    F = Forecast for the item for the next quarter.
    Q4 = Actual Sales of the next 3 months for last year° (example: 100)
    ° The fourth quarter in the past counting backwards from today
    Q1 = Actual Sales over the most recent 3 months° (example : 100)
    °the first quarter in the past counting backwards from now
    Q5 = Actual Sales of the corresponding quarter last year° (example : 150)
    ° The fifth quarter in the pas counting backwards from now
     
    F = Q4 x Q1/Q5; 100 x 100/150 =100*.67= 67 for the next quarter
     
    This formula is useful for following the market for many items, as long as their
    seasonality is moderate and the corresponding quarter of last year was not
    zero
  • 14. Advantages
    " A Complex answer is just used to hide the fact that the answer has not yet been found - complex answers don’t work".
    B. Smith
    • Simple approach and easy to understand
    • 15. Helps people in forecasting seasonality, trends, items with sporadic history and other demand conditions
    • 16. Selects the best option which results in the least error out of varied forecasting models
    • 17. It lays emphasis on simulation rather than the optimization
    • 18. It can even include methods like exponential smoothing, if desired
    • 19. It can regenerate forecast values without impairing the performance of hardware.
  • Limitations
    • Small sample size. Only three monthly forecasts.
    • 20. Ad hoc system with no theoretical basis to aid analysis or understanding.
    • 21. Impossible to compute confidence intervals, regions of stability for the forecasts or
    other standard analytical tools
    • No way to predict how Focus Forecasting should perform compared to any other forecasting system.
  • Examples of companies using focus forecasting
    Oracle E-Business Suite Manufacturing and Supply Chain Management
    Currently offers two products-
    Master Scheduling/MRP: offers single-organization, unconstrained planning of material and resources
    Advanced Supply Chain Planning: offers multi-organization planning and the option of constraint-based and optimized plans.
  • 22. Contd…
    process involves the recognition of demand
    netting of those requirements against available and scheduled quantities
    generation of recommendations to meet those requirements
    proceeds top-down through the bill of material
  • 23.
  • 24. Contd..
    Considers alternate forecast scenarios, and so the forecasts are stated in a Master Demand Schedule (MDS)
    determine how much demand satisfied from existing stock or existing orders
    Oracle Demand Planning generates forecast data. Oracle Inventory and Master Scheduling/MRP provide basic methods to generate forecasts from historical data
    one forecast typically contains multiple items; each item has multiple entries. For ease of use and to control forecast consumption, forecasts are grouped into forecast sets and forecasts and forecasts sets are identified by unique names
  • 25. Generating Forecasts from Historical Information
    Statistical Forecasts: can span multiple periods and can recognize trend and seasonality.
    Focus Forecasts: examines five different forecast models against past history, determines the model that would best have predicted the history, and uses that model to generate a forecast for the current period
  • 26. Generating Forecasts
    defining a forecast rule
    forecast method (statistical or focus), and the sources of demand
    in the Generate Forecast window
    Name of the forecast you want to populate.
    Forecast rule
    Selection criteria to identify the items
    An overwrite option
    Start date and cutoff date
    Post this Oracle uses Open Forecast interface and forecast entries API to integrate with other systems
  • 27. Other companies
    WorldPac
    Carquest Auto parts
    Digi-Key
    guestsupply.com
    usballoon.com
  • 28. THANK YOU