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Linkages

Linkages. How much we are going to sell is obviously important to marketing Forecasts help us to plan investments - or to determine if an investment is a good idea Forecasts tell us if we will have to hire new people and or train our existing people in new skills

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Linkages

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  1. Linkages • How much we are going to sell is obviously important to marketing • Forecasts help us to plan investments - or to determine if an investment is a good idea • Forecasts tell us if we will have to hire new people and or train our existing people in new skills • Technological forecasts might indicate the need to change our MIS function

  2. Forecasts as part of planning • How much demand we are going to have leads to a number of other questions • large demand for standard products: line flow • demand for custom products: jumbled flow • demand leads to capacity • demand indicates when we schedule work • etc. • In other words a forecast is one of the first things we need when planning – for the long term and the short term.

  3. Why do forecasts matter ? • People: If we do a bad job forecasting demand we may not have the right number (or type) of people on hand. • Capacity: If we under forecast we will not be able to make enough stuff (lost sales) over forecasting will result in expensive wasted capacity. • Supply chain: Our suppliers are also dependent on our forecasts: • What if we have them build stuff based on an erroneously high forecast?

  4. Characteristics of forecasts • Short term: Less than a year • quantitative • can be very accurate • dis-aggregated • Long term: More than a year • often very qualitative • much harder to be accurate • generally aggregated

  5. Types of forecasts • Economic: What is happening in the world, country, state, and locality. Aggregated across companies and usually industries. • ISM index • The federal reserve • Technological: changes in technology that may change products and / or processes • BW survey of research labs • Demand: Sales of our company’s products - often driven (partially) by economic and technological.

  6. Quantitative verses Qualitative • When numbers do not exist and or are inaccurate we can use qualitative methods (long term forecasts especially) • delphi methods • market research • the “gut” • Most people want to use numbers • why? • is this always best? • See readings on methods people do choose- and what would be best.

  7. Forecasting demand • There are 5 components of demand: • Average demand – not in book • Trends • Cyclicality • Seasonality • Random factors • What should we be able to forecast ?

  8. Trend Sales of Dallas Cowboys Paraphernalia Volume Year 1999 2000 2001 2002 2003 - projected

  9. Seasonality Beverage sales at the 6 pac shop MON TUS WEN FRI SAT SUN TUR MON

  10. Seasonality 2 Umbrella sales Summer Winter Summer Winter Spring Fall Spring Fall

  11. Cyclicality The business cycle • Where are we in the business cycle? • to forecast the end of a period of growth what signs would you look for? • What do you think Greenspan looks for?

  12. Determining the quality of a forecast Forecast error = demand - forecast negative errors indicate ? Mean Absolute Deviation (MAD) Mean percentage deviation (MAPE)

  13. Determining the quality of a forecast 2 • Why don’t we use the average deviation? • What does the MAPE tell us that the MAD does not ? • can we compare the MADS for two different products ? • can we use MAD to compare the same forecasting method in a variety of situations ? • We also want to examine Bias

  14. MAD / MAPE example

  15. A quick aside • The forecasting tools we are going to use are generally basic and fairly simple. • See the articles I placed on the web- this is what people use • Regression is “to fancy” for many managers • Our goal is to find the method that best fits our pattern of demand- no one right tool

  16. Actual forecasting tools • The simplest method: the naive forecast • this period’s demand = last period’s demand • when is this acceptable ? • Time series methods: future demand is predicted from past (historical) demand. • moving averages • simple and weighted • exponential smoothing

  17. Moving averages • A simple tool to predict demand when it is safe to assume that over time demand is fairly stable (change is slow). • A 3 period moving average: • A five period moving average:

  18. Moving average example

  19. Weighted moving averages • Moving averages work fine when the world is fairly stable - but what if our world is changing ? • Weighted moving averages (WMA) - place more weight on recent events (why) . • WMA = (Σ (weight period n) (demand in period n)) / Σ weights • Determining weights is an art - generally do not weight most recent period more than 50%.

  20. AWMA example:Weights 5,3,2

  21. Exponential smoothing • Exponential smoothing is a very popular (and simple) form of the weighted moving average. • Basic form: • What happens as the smoothing constant increases ?

  22. Exponential smoothing: examples • smoothing constant = .2 period demand forecast 1 25 21 2 24 21+.2(4) = 21.8 3 21.8+.2(2.2) = 22.24 • smoothing constant = .5 period demand forecast 1 25 21 2 24 21+.5(4) = 23 3 23+.5(1) = 23.5

  23. Seasonality • Because seasonality is a pattern we can predict it using indices. • For example: yearly demand = 800 units indices: spring = .85 summer = 1.46 fall = .76 winter = .93 • F spring = 200 * .85 = 170 • F summer = 200 * 1.46 = 292 • f fall = 200 * .76 = 152 • f winter = 200 * .93 = 186

  24. Determining Indices

  25. More indices stuff • The sum of the indices should = the number of seasons. • Formula for the index : average demand specific season average demand all seasons

  26. Regression Models • The basic regression model • F = constant + b1X1 + b2X2 • b1 is a constant • X1 is an independent variable • You can of course use only 1 independent variable in your model- or more than 2 (sometimes many more than 2)

  27. Some obvious uses of regression • We can use regression to forecast when we have a trend in the data. • If the trend is the major source of change in the data might be able to use a simple regression model where time is our only independent variable • Ft = constant + b (time period) • We might also make the season an independent variable • We can obviously include just about anything else in the model that makes sense • demand for ice cream - might include temperature • demand for MBA classes - unemployment rate

  28. Other issues • Double exponential smoothing • Fancy way to try and cope with trends – works well when there is a one time change – not as well when the trend is always going up / down • Focused forecasting • Economist

  29. Book problems you should be able to do • 4.2, 4.4, 4.6, 4.8, 4.10, 4.26, 4.28

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