PDF forecasting at scale,facebook profit -SAS Visual Forecasting Fact Sheet - Forecasting
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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

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

- purchasing,
- job scheduling,
- workforce levels,

job assignments and production

production planning and budgeting and analyzing various operating plans

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

- capital expenditures,
- facility expansion,
- research and development

- profit projections

- including recruitment,
- interviewing,
- training and counseling

- gl0bal competition strategies

MIS: New/revised information system,

- internet services

work assignments and workload,

- project management

design of new products and services

Step in the forecasting process: 1

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

- reexamine the method,
- assumption,
- validity of data,
- and so on
- modify as needed,

and prepare a revised forecast

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

- emotions,
- personal experience,

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

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

- and respondents

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,

- distributing,

collecting and summarizing a series of questionnaires and survey results

often located in the different places,

- whose judgment are valued

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

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

and At denotes the actual demand in period t,

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

- - Ft Three commonly used measures for summarizing historical errors are the mean absolute deviation (MAD),

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

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

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

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

Where actual value 100 percent

- the weight of period i,
- between 0 and

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

- 1): A manufacturing has monthly demand for one of its production as follows: Month February March April May June July August September

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

- 20 for the most recent demand values,
- in that order

b) Calculate MAD for each forecast,

and indicate which would seem to be most accurate

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)

February March April May

- 520 490 550 580

- 6 The MAD value for the 3 month moving average is 82
- 0 and the MAD value for the 3 month weighted moving average is 75

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

the weight moving average is slightly better

- 1): A large manufacturer uses exponential smoothing to forecast demand for a piece of pollution control equipment

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

- 1 2 3 4 5 6 7 8 9 10
- 12 17 20 19 24 21 31 28 36

Smoothed forecast (Ft) 11 12

Smoothed Trend (Tt) 2 1

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

estimates for both the average and the trend are smoothed

for the average and for the trend

the exponential smoothed forecast for period t,

- by using equation
- 0 Step 2: Computed the smoothed trend,
- using equation-

- were as follows

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

- 20 21 15 14 13

17 18 20

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

- 5 with the heaviest weights applied to the most recent month

Exponential smoothing using an

and a September forecast of 18

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

- a number between 0 to 1,
- the equation is-

=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

- develops forecasting for

year 2001 2002 2003 2004 2005 2006

- sales 450 495 518 563 584

- 3(450-410)=422 422+
- 3(495-422)=443

3(518-443

3(563-466

3(584-495

- 2): Income at the law firm of Smith and Wesson for the period February to July was as follows: Month:

Income (in$ thousands):

Actual Income Smoothed forecast( ) $

- smoothed

- trend ( )
- including
- trend $

the exponential smoothed forecast for period t,

- by using equation

- 1(70) + (1-0
- 1) (65000+0) = 58507 April

5) + (1-0

- 1) (58507+1298

6) = 53831

8) + (1-0

1) (53831

- 86) = 48548

7) + (1-0

1) (48548

- 56) = 44577

3) + (1-0

1) (44577

- 45) = 40140
- 0 Step 2: Computed the smoothed trend,
- using equation-

March = 0

- 2(58507-65000) + (1-0

2)0 =1298

6 April

2(53831

- 89-58507) + (1-0

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

6) = 59805

- 6 April = (53831
- 86) = 53935
- 75 May = (48548
- 56) = 49522
- 21 June = (44577
- 45) = 44592
- = (40140

98) =41015