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Effective Forecasting Strategies: Key Considerations for Success

Explore essential factors in successful forecasting: decision environment, loss function, forecast objective, horizon, information set, and methodology complexity. Learn how to minimize errors, choose accurate forecasting methods, and optimize production decisions. Understand various loss functions, forecast statements, and information sets to enhance your forecasting accuracy and decision-making process.

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Effective Forecasting Strategies: Key Considerations for Success

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  1. Chapter 2 Basic Considerations for Successful Forecasting

  2. Six Considerations • Decision Environment - Loss Function • Forecast Objective • Forecast Statement • Forecast Horizon • Information Set • Complexity of Methodology

  3. Decision Environment • The Loss function summarizes the cost of a forecast error! • The forecast error is actual minus the forecast:

  4. Examples of Loss functions • BAKERY, Inc. is selling pies for $15 each, our cost is $10. We must choose production for tomorrow. Any pie not sold is wasted. Our forecast is used to determine the amount of pies to bake.

  5. What is the Loss function? • Key Facts: • If we produce too few pies(e>0), it costs us $5 per pie in lost sales • If we produce too many pies(e<0), it costs us $10 per pie because the pie is wasted. • Verbally, the loss is bigger if we overestimate demand than if we underestimate demand • BE CONSERVATIVE FOR THIS PROJECT!!

  6. Using Math, The Loss Function is:

  7. Common Loss Functions • L(e)=e2 • L(e)=|e| • L(e)=0 if our forecast of the change in y is correct, 1 if we miss the direction! • Many statistical estimators are based on the first loss function

  8. Forecast Objective • Event Outcome • Will LSU men go to NCAA? • Timing Forecasts • When will the economy go into recession? • When will sales be highest? • Time Series • How much will we sell next year?

  9. Forecast Statement • Point Forecast • Our sales will be 32 million next year • Interval Forecast • There is a 95% chance sales will lie between 28 and 36 million next year. • Density Forecast • Our forecast of sales is normal distributed with a mean of 32 million and s.d. 2 million

  10. Forecast Horizon • How far into the future do you want to forecast and at what time interval • Sales next month only • Monthly sales for the next two years • Annual sales over a ten year period

  11. Information Set • What Information will be used for the forecast? • Often determined by the budget for the forecast. • Should I use only lagged sales to forecast? • Will the boss buy other data for my forecast? • Can I spend two months gathering data?

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