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CHAPTER. 12. Forecasting. Management Mathematics-76. AHNAF ABBAS. CHAPTER. 12. Objectives . How to Classify Forecasts How to Calculate Moving Averages How to Perform Exponential Smoothing. Management Mathematics-76. AHNAF ABBAS. FORECAST :

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  1. CHAPTER 12 Forecasting Management Mathematics-76. AHNAF ABBAS

  2. CHAPTER 12 Objectives • How to Classify Forecasts • How to Calculate Moving Averages • How to Perform Exponential Smoothing. Management Mathematics-76. AHNAF ABBAS

  3. FORECAST: • A statement about the future value of a variable of interest such as demand. • Forecasts affect decisions and activities throughout an organization • Accounting, finance • Human resources • Marketing • MIS • Operations • Product / service design

  4. Uses of Forecasts

  5. I see that you willget an A this semester. Basic Assumptions • Assumes causal systempast ==> future Past patterns (behavior) will continue into future. • Forecasts rarely perfect because of randomness • Forecast accuracy decreases as time horizon increases JOSH I

  6. Timely Accurate Reliable Easy to use Written Meaningful Elements of a Good Forecast

  7. “The forecast” Step 6 Monitor the forecast Step 5 Prepare the forecast Step 4 Gather and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast Steps in the Forecasting Process

  8. Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... Naive Forecasts The forecast for any period equals the previous period’s actual value.

  9. Naïve Forecasts • Simple to use • Virtually no cost • Quick and easy to prepare • Data analysis is nonexistent • Easily understandable • Cannot provide high accuracy

  10. Types of Forecasts Types By lead time The timebetween when the forecast is made and the future point to which it refers. • Long-term: more than 10 years. • Medium-term: up to 5 years. • short-term: months to a year.

  11. Types of Forecasts • Univariate :Using past patterns e.g. Time series which uses historical data assuming the future will be like the past. • Multivariate- uses explanatory variables to predict the future, i.e. Past relationships between multiple variables. • QualitativeJudgmental - uses subjective inputs

  12. Judgmental Forecasts (Qualitative) • Executive opinions • Sales force opinions • Consumer surveys • Outside opinion • Delphi method • Opinions of managers and staff

  13. Time Series Forecasts • A time series is a continuous set of observations that are ordered in equally spaced intervals(e.g one per month). • Basic concept of univariate forecasting: Future values = f( Past values ) e.g. Two months average sales : Forecast for June = (April sales + May sales ) / 2 Univariate methods includes smoothing (averages) and exponential smoothing.

  14. Multivariate Forecasts • Known as Causal methods: make projections of the future by modeling the relationship between a series and other series. e.g. A forecast for furniture sales may be based on a relationship between economic indicators such as housing starts, personal income ,No. of new marriages etc… : Future values = f( Past values, Values of other variables ) e.g. June demand = 50 + 0.2 MS + 1xAPI +0.5NH Multivariate methods include multiple regression and econometric.

  15. We’ll guess same as last month

  16. We’ll guess same as last month plus a little more for a possible trend

  17. This is easy, who needs forecasting

  18. Continue with our successful method: guess the same as last month plus a little more for a possible trend

  19. Definitely looks like a trend

  20. Trend might be a tad steeper than I thought

  21. Opps

  22. Momentary deviation, trend will continue

  23. See, I told you this was easy!

  24. Trend will continue

  25. Opps, another momentary fluctuation:

  26. Trend should continue

  27. Oh oh!

  28. Sales has leveled off: Lets average last few points

  29. Oh oh, maybe things are going down hill

  30. Let’s be conservative and Assume a negative trend

  31. Thank goodness, we are still basically level

  32. We’ll guess same as last month

  33. This stuff is easy

  34. We have for sure leveled off

  35. Big trouble!!! Chief forecaster Joshi and CEO Joshi1 fired!

  36. New chief forecaster points out the obvious trend

  37. Remarkable turnaround in sales. New CEO Joshi2 given credit

  38. Still looks like a trend to me

  39. Maybe not!

  40. Level except for anomaly

  41. Have things turned around?

  42. I’ll hedge my bets

  43. Things have turned around. Perhaps Joshi2 truly is a genius

  44. Trend up!

  45. Not bad!

  46. Revise trend a tad

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