Adeyl Khan, Faculty, BBA, NSU Car buyer- Models & Option Does the dealer know! Basic Managerial function- Planning

Adeyl Khan, Faculty, BBA, NSU

Chapter 3 Forecasting

Car buyer- Models & OptionDoes the dealer know!

Basic Managerial function- Planning


Forecast

A statement about the future value of a variable of interest such as demand.Forecasting is used to make informed decisions.

Long-range (Plan system) Short-range (Plan use of system)

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Uses of Forecasts

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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

Example!


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

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Timely Reliable

Accurate

Meaningful

Written Easy to use

Elements of a Good Forecast


Steps in the Forecasting ProcessStep 1

Determine purpose of

forecast

Step 2 Establish a

time horizon

Step 3 Select a forecasting

technique

Step 4 Obtain, clean and

analyze data

Step 5 Make the forecast

Step 6 Monitor the

forecast

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Types of ForecastJudgmentalTime seriesAssociative models

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QuantitativeQualitative


1. Judgmental ForecastsExecutive opinionsSales force opinionsConsumer surveysOutside opinionDelphi method

Opinions of managers and staff (Experts) Anonymous, encourage honesty,

Questionnaire sequence Achieves a consensus forecast

Other usage of this method

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Subjective/Qualitative inputs


2. Time Series ForecastsUses historical dataTrend - long-term movement in dataSeasonality - short-term regular variations

in data Specific dates, days, times

Cycle – wavelike variations of more than one year’s duration Economic, political, GDP …

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Irregular variations - caused by unusual circumstances

• Atypical- remove from analysis

Random variations - caused by chance

• Include?


Forecast Variations

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Trend

Irregularvariation

Seasonal variations

908988

Figure 3.1

Cycles


2. Time Series Forecasts Naive Forecasts

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Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell....

The forecast for any period equals the previous period’s actual value.


Naïve Forecasts

Simple to useVirtually no costQuick and easy to prepareData analysis is nonexistentEasily understandableCannot provide high accuracyCan be a standard for accuracy

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Uses for Naïve Forecasts

Stable time series data F(t) = A(t-1) | Forecast(Feb) = Actual (Feb -1=Jan)

Seasonal variations F(t) = A(t-n) | Forecast(July) = Actual (July -4=Mar)

Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2))

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Techniques for Averaging

Moving averageWeighted moving averageExponential smoothing

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2. Time Series Forecasts Moving AveragesMoving average – A technique that

averages a number of recent actual values, updated as new values become available.

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

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Ft = MAn= nAt-n + … At-2 + At-1

Ft = WMAn= n

wnAt-n + … wn-1At-2 + w1At-1


Simple Moving Average

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35

40

45

50

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

Actual

MA3

MA5

Ft = MAn= n

At-n + … At-2 + At-1


Ft = MAn= n

At-n + … At-2 + At-1

Ft = MA3= 3

At-3 + At-2 + At-1

Ft = MA5= 5

At-5 + At-4 + At-3 + At-2 + At-1


Exponential Smoothing

Premise--The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting.

Weighted averaging method based on previous forecast plus a percentage of the forecast errorA-F is the error term, α is the % feedback

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Ft = Ft-1 + (At-1 - Ft-1)


Example 3 - Exponential Smoothing

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Period Actual Alpha = 0.1 Error Alpha = 0.4 Error1 422 40 42 -2.00 42 -23 43 41.8 1.20 41.2 1.84 40 41.92 -1.92 41.92 -1.925 41 41.73 -0.73 41.15 -0.156 39 41.66 -2.66 41.09 -2.097 46 41.39 4.61 40.25 5.758 44 41.85 2.15 42.55 1.459 45 42.07 2.93 43.13 1.87

Ft = Ft-1 + (At-1 - Ft-1)


Picking a Smoothing Constant

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Actual


Time series forecastingCommon Nonlinear Trends

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Parabolic

Exponential

Growth

Figure 3.5


Linear Trend Equation

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

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Ft = a + bt

0 1 2 3 4 5 t

Ft

Adeyl Khan, Faculty, BBA, NSU22


Calculating a and b

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b = n (ty) - t y

n t2 - ( t)2

a = y - b t

n


Linear Trend Equation Example

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tWeek

t2 ySales

ty

1 1 150 1502 4 157 3143 9 162 4864 16 166 6645 25 177 885

S t = 15 S t2 = 55 S y = 812 S ty = 2499(S t)2 =

225


Linear Trend Calculation

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y = 143.5 + 6.3t

a = 812 - 6.3(15)

5 =

b = 5 (2499) - 15(812)

5(55) - 225 =

12495-12180

275 -225 = 6.3

143.5


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.

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Types of Forecasts3. Associative ForecastingPredictor variables - used to predict values

of variable interestRegression - technique for fitting a line to a

set of pointsLeast squares line - minimizes sum of

squared deviations around the line

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Linear Model Seems Reasonable

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A straight line is fitted to a set of sample points.

0

10

20

30

40

50

0 5 10 15 20 25

X Y7 152 106 134 15

14 2515 2716 2412 2014 2720 4415 347 17

Computedrelationship


Linear Regression Assumptions

Variations around the line are randomDeviations around the line normally distributedPredictions are being made only within the range of observed valuesFor best results:

Always plot the data to verify linearity Check for data being time-dependent Small correlation may imply that other variables are important

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Forecast AccuracyError - 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

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MAD

• Easy to compute• Weights errors linearly

MSE

• Squares error• More weight to large errors

MAPE

• Puts errors in perspective


MAD, MSE, and MAPE

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MAD = | Actual – forecast |

n

MSE = (Actual – forecast ) 2

n - 1

MAPE = n

( | Actual – forecast | / Actual )*100


Example 10

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Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*1001 217 215 2 2 4 0.922 213 216 -3 3 9 1.413 216 215 1 1 1 0.464 210 214 -4 4 16 1.905 213 211 2 2 4 0.946 219 214 5 5 25 2.287 216 217 -1 1 1 0.468 212 216 -4 4 16 1.89

-2 22 76 10.26

MAD= 2.75MSE= 10.86

MAPE= 1.28


Controlling the ForecastControl 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 3-33

Sources of Forecast errors

• Model may be inadequate

• Irregular variations

• Incorrect use of forecasting technique


Tracking SignalRatio of cumulative error to MAD

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

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Tracking signal =å(Actual - forecast)

MAD


Choosing a Forecasting Technique

No single technique works in every situationTwo most important factors

Cost Accuracy

Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon

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Operations Strategy

Forecasts are the basis for many decisionsWork to improve short-term forecastsAccurate short-term forecasts improve

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

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Supply Chain Forecasts

Sharing forecasts with supply can Improve forecast quality in the supply chain Lower costs Shorter lead times

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Adeyl Khan, Faculty, BBA, NSU38


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.

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Learning Objectives

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.

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Adeyl Khan, Faculty, BBA, NSU3-41

Exponential Smoothing


Linear Trend Equation


Simple Linear Regression

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Adeyl Khan, Faculty, BBA, NSU Car buyer- Models & Option Does the dealer know! Basic Managerial function- Planning