# Forecasting Fundamentals

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MAN 3520 Fall 2012 CP2 Forecasting: Page 1 of 44

FORECASTING FUNDAMENTALS

Forecast: A prediction, projection, or estimate of some future activity, event, or occurrence. Types of Forecasts - Economic forecasts o Predict a variety of economic indicators, like money supply, inflation rates, interest rates, etc. - Technological forecasts o Predict rates of technological progress and innovation. - Demand forecasts o Predict the future demand for a companys products or services.

Since virtually all the operations management decisions (in both the strategic category and the tactical category) require as input a good estimate of future demand, this is the type of forecasting that is emphasized in our textbook and in this course.

MAN 3520 Fall 2012 CP2 Forecasting: Page 2 of 44

TYPES OF FORECASTING METHODS

Qualitative methods: These types of forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences and are subjective in nature. They do not rely on any rigorous mathematical computations.

Quantitative methods: These types of forecasting methods are based on mathematical (quantitative) models, and are objective in nature. They rely heavily on mathematical computations.

MAN 3520 Fall 2012 CP2 Forecasting: Page 3 of 44

QUALITATIVE FORECASTING METHODS

Qualitative Methods

Executive Opinion Approach in which a group of managers meet and collectively develop a forecast

Market Survey Approach that uses interviews and surveys to judge preferences of customer and to assess demand

Sales Force Composite Approach in which each salesperson estimates sales in his or her region

Delphi Method Approach in which consensus agreement is reached among a group of experts

MAN 3520 Fall 2012 CP2 Forecasting: Page 4 of 44

QUANTITATIVE FORECASTING METHODS

Quantitative Methods

Time-Series Models Time series models look at past patterns of data and attempt to predict the future based upon the underlying patterns contained within those data.

Associative Models Associative models (often called causal models) assume that the variable being forecasted is related to other variables in the environment. They try to project based upon those associations.

MAN 3520 Fall 2012 CP2 Forecasting: Page 5 of 44

TIME SERIES MODELS

Model Nave

Description Uses last periods actual value as a forecast

Simple Mean (Average)

Uses an average of all past data as a forecast Uses an average of a specified number of the most recent observations, with each observation receiving the same emphasis (weight) Uses an average of a specified number of the most recent observations, with each observation receiving a different emphasis (weight) A weighted average procedure with weights declining exponentially as data become older Technique that uses the least squares method to fit a straight line to the data A mechanism for adjusting the forecast to accommodate any seasonal patterns inherent in the data

Simple Moving Average

Weighted Moving Average

Exponential Smoothing

Trend Projection

Seasonal Indexes

MAN 3520 Fall 2012 CP2 Forecasting: Page 6 of 44

DECOMPOSITION OF A TIME SERIES

Patterns that may be present in a time series Trend: Data exhibit a steady growth or decline over time. Seasonality: Data exhibit upward and downward swings in a short to intermediate time frame (most notably during a year). Cycles: Data exhibit upward and downward swings in over a very long time frame. Random variations: Erratic and unpredictable variation in the data over time with no discernable pattern.

MAN 3520 Fall 2012 CP2 Forecasting: Page 7 of 44

ILLUSTRATION OF TIME SERIES DECOMPOSITION

Hypothetical Pattern of Historical Demand Demand

Time

MAN 3520 Fall 2012 CP2 Forecasting: Page 8 of 44

TREND COMPONENT IN HISTORICAL DEMANDDemand

Time

MAN 3520 Fall 2012 CP2 Forecasting: Page 9 of 44

SEASONAL COMPONENT IN HISTORICAL DEMANDDemand

Year 1

Year 2

Year 3

Time

MAN 3520 Fall 2012 CP2 Forecasting: Page 10 of 44

CYCLE COMPONENT IN HISTORICAL DEMANDDemand

Many years or decades

Time

MAN 3520 Fall 2012 CP2 Forecasting: Page 11 of 44

RANDOM COMPONENT IN HISTORICAL DEMANDDemand

Time

MAN 3520 Fall 2012 CP2 Forecasting: Page 12 of 44

DATA SET TO DEMONSTRATE FORECASTING METHODSThe following data set represents a set of hypothetical demands that have occurred over several consecutive years. The data have been collected on a quarterly basis, and these quarterly values have been amalgamated into yearly totals. For various illustrations that follow, we may make slightly different assumptions about starting points to get the process started for different models. In most cases we will assume that each year a forecast has been made for the subsequent year. Then, after a year has transpired we will have observed what the actual demand turned out to be (and we will surely see differences between what we had forecasted and what actually occurred, for, after all, the forecasts are merely educated guesses). Finally, to keep the numbers at a manageable size, several zeros have been dropped off the numbers (i.e., these numbers represent demands in thousands of units).

Year 1 2 3 4 5 6

Quarter 1 62 73 79 83 89 94

Quarter 2 94 110 118 124 135 139

Quarter 3 113 130 140 146 161 162

Quarter 4 41 52 58 62 65 70

Total Annual Demand 310 365 395 415 450 465

MAN 3520 Fall 2012 CP2 Forecasting: Page 13 of 44

ILLUSTRATION OF THE NAVE METHODNave method: The forecast for next period (period t+1) will be equal to this period's actual demand (At).

In this illustration we assume that each year (beginning with year 2) we made a forecast, then waited to see what demand unfolded during the year. We then made a forecast for the subsequent year, and so on right through to the forecast for year 7. Actual Demand (At) 310 365 395 415 450 465

Year 1 2 3 4 5 6 7

Forecast (Ft) -310 365 395 415 450 465

Notes There was no prior demand data on which to base a forecast for period 1 From this point forward, these forecasts were made on a year-by-year basis.

MAN 3520 Fall 2012 CP2 Forecasting: Page 14 of 44

MEAN (SIMPLE AVERAGE) METHODMean (simple average) method: The forecast for next period (period t+1) will be equal to the average of all past historical demands.

In this illustration we assume that a simple average method is being used. We will also assume that, in the absence of data at startup, we made a guess for the year 1 forecast (300). At the end of year 1 we could start using this forecasting method. In this illustration we assume that each year (beginning with year 2) we made a forecast, then waited to see what demand unfolded during the year. We then made a forecast for the subsequent year, and so on right through to the forecast for year 7. Actual Demand (At) 310

Year 1

Forecast (Ft) 300

Notes This forecast was a guess at the beginning. From this point forward, these forecasts were made on a year-by-year basis using a simple average approach.

2

365

310.000

3

395

337.500

4

415

356.667

5

450

371.250

6

465

387.000

7

400.000

MAN 3520 Fall 2012 CP2 Forecasting: Page 15 of 44

SIMPLE MOVING AVERAGE METHODSimple moving average method: The forecast for next period (period t+1) will be equal to the average of a specified number of the most recent observations, with each observation receiving the same emphasis (weight).

In this illustration we assume that a 2-year simple moving average is being used. We will also assume that, in the absence of data at startup, we made a guess for the year 1 forecast (300). Then, after year 1 elapsed, we made a forecast for year 2 using a nave method (310). Beyond that point we had sufficient data to let our 2-year simple moving average forecasts unfold throughout the years. Actual Demand (At) 310

Year 1

Forecast (Ft) 300

Notes This forecast was a guess at the beginning. This forecast was made using a nave approach. From this point forward, these forecasts were made on a year-by-year basis using a 2-yr moving average approach.

2

365

310

3

395

337.500

4

415

380.000

5

450

405.000

6

465

432.500

7

457.500

MAN 3520 Fall 2012 CP2 Forecasting: Page 16 of 44

ANOTHER SIMPLE MOVING AVERAGE ILLUSTRATIONIn this illustration we assume that a 3-year simple moving average is being used. We will also assume that, in the absence of data at startup, we made a guess for the year 1 forecast (300). Then, after year 1 elapsed, we used a nave method to make a forecast for year 2 (310) and year 3 (365). Beyond that point we had sufficient data to let our 3-year simple moving average forecasts unfold throughout the years. Actual Demand (At) 310

Year 1

Forecast (Ft) 300

Notes This forecast was a guess at the beginning. This forecast was made using a nave approach. This forecast was made using a nave approach. From this point forward, these forecasts were made on a year-by-year basis using a 3-yr moving average approach.

2

365

310

3

395

365

4

415

356.667

5

450

391.667

6

465

420.000

7

433.333

MAN 3520 Fall 2012 CP2 Forecasting: Pag

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