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Forecasting

SAS Visual Forecasting Fact Sheet

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Description

Forecasting: Forecasting is the art and science of predicting future events

It may involve taking historical data and projecting them into the future with some sort of mathematical model

It may be subjective or intuitive prediction

Or it may involve a combination of these-that is a mathematical model adjusted by a manager’s good judgment

Types of Forecast: Economic Forecast: Planning indicators valuable in helping organization prepare medium-to-long range forecasts

Technological forecasts: Long term forecasts concerned with the rates of technological progress

Demand forecasts: Projections of a company’s sales for each time period in the planning horizon

Forecasting Time Horizons: 1

Short-range forecast: This forecast has a time span of up to 1 year but is generally less than 3 months

It is used for planning,

job assignments and production

Medium-range forecast: A medium-range forecast generally spans from 3 months to 3 years

It is useful in sales planning,

production planning and budgeting and analyzing various operating plans

Long- range forecast: Generally 3 years more in time spans,

long –range forecasts are used in planning for new products,

Examples: Forecast in Business Organization,

Accounting: New product/process cost estimates,

Finance: Timing and amount of finding/borrowing needs

Human resources: Hiring activities,

Marketing: Pricing and promotion,

MIS: New/revised information system,

Operation: Schedules,

work assignments and workload,

Product or service design: Revision if current features,

design of new products and services

Step in the forecasting process: 1

Determine the purpose of the forecast

How will it be used and when will it be needed

Select the items to be forecasted

Establish a time horizon

The forecast must indicate a time interval,

keeping in mind that accuracy decreases as the time horizons increases

Select the forecasting model

Gather and analyze relevant data

Make the forecast

Monitor the forecast

A forecast has to be monitored to determine whether it is performing in a satisfactory manner

If it is not,

and prepare a revised forecast

Forecasting Approach:  Qualitative or subjective forecasts incorporate such factors as the decision maker’s intuition,

and value system in reaching a forecast

 Quantitative forecasts that employ one or more mathematical models that rely on historical data and/or causal variables to forecast demand

Overview of Qualitative method: 1

Jury of executive opinion: A forecasting technique that takes the opinion of a small group of high-level managers and results in a group estimate of demand

Delphi method: A forecasting technique using a group process that allows experts to make forecasts

There are three different types of participants in the Delphi method: decision makers staff personnel,

Decision makers usually consist of a group of 5 to 10 experts who will be making the actual forecast

Staff personnel assist decision makers by preparing,

collecting and summarizing a series of questionnaires and survey results

The respondents are a group of people,

often located in the different places,

This group provides inputs to the decision makers before the forecast is made

Sales force opinions: The sales or the customer service staff is often a good source of information because of their direct contact with consumers

Sales force composite is a forecasting technique based on salespersons estimates of expected sales

Consumer market survey: A forecasting method that solicits input from customers or potential customers regarding future purchasing plans

Measuring Forecast Error: Forecast error is the difference between the actual value and value that was predicted for a given period

If Ft denotes the forecast in period t,

and At denotes the actual demand in period t,

the forecast error is defined asForecast error= Actual demand – Forecast demand = At

the mean squared error (MSE) and the mean absolute percent error

Mean Absolute Deviation (MAD): MAD is the average absolute forecast error

Mean squared error: The average of the squared differences between the forecasted and observed values

Mean Absolute Percent Error (MAPE): The average of the absolute differences between the forecast and absolute values,

expressed as a percent of actual values

Overview of Quantitative Methods: Time series forecasts: A forecasting technique that uses a series of past data points to make a forecast

Moving averages Naïve approach Exponential smoothing Trend projection

Associative Model: Forecasting technique that was explanatory variables to predict future demand

Linear regression

Moving Average: A moving average forecast uses a number of the most recent actual data values in generating a forecast

The moving average forecast can be computed using the following equation

Ft= MAn

i= an index that corresponds to time period

n= Number of period (data points) in the moving average

AI=Actual value in period t-i

MA= Moving average

Ft= Forecast for time period

Weighted Moving Average: A moving average with most recent demand values adjusted with weights

A weighted average may be expressed mathematically asWMAn =

Where actual value 100 percent

Note: Moving average is useful if we can assume that market demand will stay fairly steady over time

Problem (1

Demand 520 490 550 580 600 420 510 610

a) Develop a three period moving average forecast and a three period weight moving average forecast with weight of 0

b) Calculate MAD for each forecast,

and indicate which would seem to be most accurate

Solution: Step-1: Compute the 3 month moving average using the formula MA=

For many,

the moving average forecast isMA3= Step-2: Compute the 3 month weighted moving average using the formulaWMA3= ∑ For many,

the weighted moving average forecast isWMA3 =

The value for both moving average forecast are shown in the table: Month (1)

Demand(2)

February March April May

WMA3(4)

Absolute for MAD(MA3) [2-3] 60

Absolute for MAD(WMA3) [2-4] 54

September 610

October

Step3: Compute the MAD Value for both forecasts: MAD =

Moving average MAD = Weighted Moving average MAD=

indicating that there is not much difference in accuracy between the two forecast,

the weight moving average is slightly better

Problem (2

It appears that an increasing trend is present

Math (t) 1 2 3 4 5 6 7 8 9 10

Actual Demand(At) 12 17 20 19 24 21 31 28 36

Smoothing constants are assigned the value of and =0

Assume that the initial forecast for month 1 (F1) was 11 units and the trend over the period (T1) was 2 units

Actual Demand

Smoothed forecast (Ft) 11 12

Smoothed Trend (Tt) 2 1

Forecasting including trend(FITt) 13 14

Exponential Smoothing with trend Adjustment: Exponential smoothing is a weighted averaging method based on previous forecast plus a percentage of the forecast error

With trend adjusted exponential smoothing,

estimates for both the average and the trend are smoothed

This procedure requires two smoothing constants,

for the average and for the trend

The computational procedure isStep1: Compute Ft,

the exponential smoothed forecast for period t,

Step 3: Calculate forecast including trend,

Problem (1-2): The monthly sales for Telco batteries,

Monthly: Jan Feb Mar Apr may June July Aug Sep Oct Nov

17 18 20

Plot the monthly sales data on a sheet of a graph paper

Forecast journey sales using each of the following1

A 3month moving average 2

A 3 month weighted moving average using

Exponential smoothing using an

and a September forecast of 18

A trend projection

With the data given,

which method allow you to forecast next march’s sales

? Exponential smoothing: Exponential smoothing is a weighted moving average forecasting technique in which data points are weighted by an exponential function

Smoothing constant is the weighted factor used in an exponential smoothing forecast

=new forecast Previous forecast =smoothing constant =previous periods actual demand

Problem (1-1): The data for sales in 2001 to 2005 are as follows: Year: 2001 2002 2003 2004 2005 2006 Sales: 450 495

563 584

Using exponential smoothing with a weight of 2002 to 2006 if initial forecast for 2001 is 410

Solution:

year 2001 2002 2003 2004 2005 2006

Forecast 410 410+

3(518-443

3(563-466

3(584-495

Problem (2

February March April May June

Income (in$ thousands):

July 72

Forecast for February is $65000 and the initial trend adjustment is 0

The smoothing constants selected are

Actual Income Smoothed forecast( ) $

Forecast

February

Step1: Compute Ft,

the exponential smoothed forecast for period t,

Ft = March =0

5) + (1-0

6) = 53831

8) + (1-0

1) (53831

7) + (1-0

1) (48548

3) + (1-0

1) (44577

March = 0

2)0 =1298

6 April

2(53831

6 = 103

2(48548

65-53831

89) + (1-0

86 = 973

2(44577

15-48548

65) + (1-0

56 = 15

2(40140

47-44577

15) + (1-0

45 = 874

Step 3: Calculate forecast including trend,

February = (65000+0) = 65000 March = (58507+1298

6) = 59805

98) =41015