For Casting Report Me 2nd

Learning Objectives • List the elements of a good forecast. • Outline the steps in the forecasting process. • Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each. • Compare and contrast qualitative and quantitative approaches to forecasting • Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems. • Describe two measures of forecast accuracy. • Describe two ways of evaluating and controlling forecasts. • Identify the major factors to consider when choosing a forecasting technique. 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

Uses of Forecasts Accounting

Cost/profit estimates

Finance

Cash flow and funding

Human Resources

Hiring/recruiting/training

Marketing

Pricing, promotion, strategy

MIS

IT/IS systems, services

Operations

Schedules, MRP, workloads

Product/service design

New products and services

Features of Forecasts

• Assumes causal system past ==> future • Forecasts rarely perfect because of randomness • Forecasts more accurate for groups vs. individuals • Forecast accuracy decreases as time horizon increases Elements of a Good Forecast

Timely

Reliable

ul

f g n

M

e

i n a

Accurate

Written E

y s a

to

e s u

Steps in the Forecasting Process

“The forecast”

Step 6 Monitor the forecast Step 5 Make the Stepforecast 4 Obtain, clean and analyze Step 3data Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast Types of Forecasts • Judgmental - uses subjective inputs •

Time series - uses historical data assuming the future will be like the past

• Associative models - uses explanatory variables to predict the future Judgmental Forecasts • Executive opinions • Sales force opinions • Consumer surveys • Outside opinion • Delphi method – Opinions of managers and staff – Achieves a consensus forecast Time Series Forecasts • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Cycle – wavelike variations of more than one year’s duration • Irregular variations - caused by unusual circumstances • Random variations - caused by chance

Important Variables

A(t) • F(t) • t • t+1 • t–1 • n

Actual figure for period t Forecast figure for period t Time period t Time period t + 1 (next period) Time period t – 1 (previous period) Time period n

•

Techniques for Averaging •

Moving average

•

Weighted moving average

•

Exponential smoothing

•

Moving Averages Moving average – A technique that averages a number of recent actual values, updated as new values become available.

A +…A t-n

F = MA = t

t-2

+A

t-1

n

n

Weighted moving average – More recent values in a series are given more weight in computing the forecast.

w A +…w F = WMA = t

n

n t-n

A

n-1 t-2

n Simple Moving Average

+w A

1 t-1

47 45 43 41 39 37 35 1

2

3

4

5

6

7

8

9

10 11 12

At-n + … At-2 + At-1

Ft = MAn=

n

Exponential Smoothing

Ft = Ft-1 + α(At-1 - Ft-1) Period

Actual 1 2 3 4 5 6 7 8 9 10 11 12

Alpha = 0.1 Error 42 40 43 40 41 39 46 44 45 38 40

42 41.8 41.92 41.73 41.66 41.39 41.85 42.07 42.36 41.92 41.73

Alpha = 0.4 Error -2.00 1.20 -1.92 -0.73 -2.66 4.61 2.15 2.93 -4.36 -1.92

42 41.2 41.92 41.15 41.09 40.25 42.55 43.13 43.88 41.53 40.92

-2 1.8 -1.92 -0.15 -2.09 5.75 1.45 1.87 -5.88 -1.53

Picking a Smoothing Constant Ft

Ft = a + bt 0 1 2 3 4 5

• • • •

Ft = Forecast for period t t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line

Calculating a and b

t

b =

n ∑ (ty) - ∑ t ∑ y n∑ t 2 - ( ∑ t) 2

a =

∑ y - b∑ t n

Linear Trend Equation Example t Week 1 2 3 4 5 Σ t = 15 (Σ t)2 = 225

b =

a =

Σ t 2 = 55

5 (2499) - 15(812) 5(55) - 225

y Sales 150 157 162 166 177

t2 1 4 9 16 25

=

ty 150 314 486 664 885

Σ y = 812 Σ ty = 2499

12495-12180 275 -225

= 6.3

812 - 6.3(15) = 143.5 5

      Techniques for Seasonality

•

Seasonal variations – Regularly repeating movements in series values that can be tied to recurring events.

•

Seasonal relative – Percentage of average or trend

•

Centered moving average – A moving average positioned at the center of the data that were used to compute it.

Associative Forecasting • • •

Predictor variables - used to predict values of variable interest Regression - technique for fitting a line to a set of points Least squares line - minimizes sum of squared deviations around the line

Linear Model Seems Reasonable X 7 2 6 4 14 15 16 12 14 20 15 7

Y 15 10 13 15 25 27 24 20 27 44 34 17

Computed relationship 50 40 30 20 10 0 0

5

10

15

Linear Regression Assumptions •

Variations around the line are random

20

25

• • •

Deviations around the line normally distributed Predictions are being made only within the range of observed values For best results: – Always plot the data to verify linearity – Check for data being time-dependent – Small correlation may imply that other variables are important

•

The importance of appropriate evaluation criteria – Different criteria may lead to totally different conclusions. – Evaluation criteria must be designed to reveal the problems in the model. Error - difference between actual value and predicted value Mean Absolute Deviation (MAD) – Average absolute error Mean Squared Error (MSE) – Average of squared error Mean Absolute Percent Error (MAPE) – Average absolute percent error

Forecast Accuracy • • • •

MAD, MSE and MAPE •

MAD – Easy to compute – Weights errors linearly

•

MSE – Squares error – More weight to large errors

•

MAPE – Puts errors in perspective

=

MAD MSE

∑

Actual

−

forecast

n =

∑ ( Actual

− forecast)

2

n -1

MAPE =

∑( Actual

− forecast

/ Actual*100)

n Period 1 2 3 4 5 6 7 8

MAD= MSE= MAPE=

Actual 217 213 216 210 213 219 216 212

2.75 10.86 1.28

Forecast 215 216 215 214 211 214 217 216

(A-F) 2 -3 1 -4 2 5 -1 -4 -2

|A-F| 2 3 1 4 2 5 1 4 22

(A-F)^2 (|A-F|/Actual)*100 4 0.92 9 1.41 1 0.46 16 1.90 4 0.94 25 2.28 1 0.46 16 1.89 76 10.26

Controlling the Forecast • •

Control chart – A visual tool for monitoring forecast errors – Used to detect non-randomness in errors Forecasting errors are in control if – All errors are within the control limits – No patterns, such as trends or cycles, are present

Sources of Forecast errors •

Model may be inadequate

•

Irregular variations

•

Incorrect use of forecasting technique

Tracking Signal •

Tracking signal – Ratio of cumulative error to MAD

Tracking signal =

∑(Actual-forecast) MAD

Bias – Persistent tendency for forecasts to be Greater or less than actual values.

Choosing a Forecasting Technique • • •

No single technique works in every situation Two most important factors – Cost – Accuracy Other factors include the availability of: – Historical data – Computers – Time needed to gather and analyze the data – Forecast horizon

Operations Strategy • • •

Forecasts are the basis for many decisions Work to improve short-term forecasts Accurate short-term forecasts improve – Profits

– – – –

Lower inventory levels Reduce inventory shortages Improve customer service levels Enhance forecasting credibility