Fundamentals of Operations Management BUS 3 – 140 Forecasting Feb 5, 2008

1. Fundamentals ofOperations ManagementBUS 3 – 140ForecastingFeb 5, 2008

2. Forecasting • A statement about the future value of a variable of interest • Future Sales • Weather • Stock Prices • Other Short term and Long term estimates • Several Methods • Quantitative • History and Patterns • Leading Indicators / Associations (Housing Starts & Furniture) • Qualitative • Judgment • Consensus Used for making informed Decisions and taking Actions based on those decisions

3. Forecasting • Forecasts make a MAJOR IMPACT (Positive or Negative) on: • Revenue • Market Share • Cost • Inventory • Profit

4. Features Common to all Forecasts • Generally assumes that what drove past performance and behavior will drive future performance and behavior • Credit Rating • Insurance Rates • Other • More accurate for groups vs. individuals • Accuracy decreases as time horizon increases Forecasts WILL be wrong – the goal is to predict as closely as possible

5. Three Major Types of Forecasts • Judgmental • Uses subjective, qualitative “judgment” (opinions, surveys, experts, managers, others). Most useful when there is limited data and with New Product Introductions • Time series • Observes what has occurred over previous time periods and assumes that future patterns will follow historical patterns • Associative Models • Establishes cause and effect relationships between independent and dependent variables (rainy days and umbrella sales, pricing and sales volume, attendance at sporting events and food sold, others)

6. Forecasting techniques (Table 3.6) * From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin

7. Timely Accurate Reliable Easy to use Written Meaningful Elements of any Good Forecast * From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin

8. Steps in the Forecasting Process Step 6 Monitor the forecast Step 5 Make the forecast Step 4 Obtain, clean and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast * From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin

9. Forecast Factors (Table 3.5) Forecasts are established with two (2) Units of Measure: 1. Units 2. Dollars Both have significance to the Enterprise * From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin

10. Start with what you KNOW • How many people will attend the next Giants game? • Tickets already sold • Patterns of walk up sales • Visiting team • Weather • School day • Other • How many Sewing Machines will Singer sell this week? • Orders in Backlog • Inventory in Stores • Production capacity • Household Budget • Rent • Car Payment • Bills • Rest of money

11. A Demand Forecast serves many Purposes WHAT is done and WHY? Region Product Line Channel Features Product Customer Revenue Planning Revenue Scenarios Allocation Criteria Commissions & Quotas Scheduling Factory Volumes Materials Planning Balancing Factory Capacity Assessing Direct Cost @ Mixes Analyzing Absorption implications Estimating TAM and Share Pricing Targets Programs & Promotions Margins @ Mixes Message to Analysts Business Need / Benefit

12. How different Functions use Forecast information

13. Forecast accuracy varies over time Over Expected Errors 0 +1 +2 +3 +4 ………………………………………… +n Time in Future (Weeks) Under The further into the future, the harder to predict details with accuracy

14. Detailed Product Forecast Accuracy will vary by Time Horizon Current Week should approach 100% Current Month should be greater than 80% Quarter should be at least 70%

15. Tracking Forecast Accuracy • Level of Aggregation • Item (Mix of individual SKU’s) • Family • Product Line • Channel • Customers • Quantity • Time Buckets • Final consumer sales Absolute values and square roots eliminate the possibility of positive and negative variances canceling each other out – key for Mix tracking; less critical for Revenue tracking Regular tracking and monitoring with enable Demand SENSING, as well as contribute to increased accuracy of future forecasts

16. Inventory Levels in Pipeline Risk of Excess Cost to Manage Need to Forecast Higher Long Lead Time Higher High High Short Lead Time Lower Lower Low Low Relationship of Lead Time, Forecast, Inventory, and Cost

17. Time Series Forecasts (and Behaviors) • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Cycle – wavelike variations of more than one year’s duration • Irregular variations- caused by unusual circumstances • Random variations- caused by chance

18. Graphs help interpret Time Series data (Figure 3.1) Irregularvariation Trend Cycles 90 89 88 Seasonal variations * From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin

19. Relevance of SUPPLY on Forecasts • Historical Sales does not always equal historical Demand • Stockouts • Substitutions • Causal Factors may distort the analysis (pricing, promotions, competitor performance) • Scarcity Behavior • Allocation • Advance buying • Hedging • Hording

20. Guide to selecting Forecasting methods (Table 3.4) * From Stevenson, Operations Management, Ninth Edition, McGraw Hill Irwin

21. Selecting the most useful Forecasting technique(s) • No single technique works in every situation • Two most important factors • Cost • Accuracy • Other factors include the availability of: • Historical data • Computers • Time needed to gather and analyze the data • Forecast horizon

22. Causal Factors • External • Market conditions (e.g. paintings when the Painter passes away) • New competition • Competitors cannot supply • Internal • Pricing • Promotions • Incentives

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24. Other Points to consider • Do not “second guess” the forecast • significant judgment and even debate contribute to the final forecast. Once the forecast is finalized it then becomes the Demand Plan of Record for the enterprise • …… and do not say, “If only we got a better forecast” …… • The forecast should be generated as a team and managed as a team • It is helpful to provide a range of expected Demand • A useful application of Confidence Intervals from Statistics • Product Transitions are very difficult to forecast, but require special attention and monitoring • New Product Introduction • End Of Life Peter Drucker: “The best way to predict the future is to CONTROL it”